Deliverable D2.1 - INTEGRAL

Integrated ICT-platform based Distributed Control in electricity grids with a large share of Distributed Energy Resources and Renewable Energy Sources

HIGH-LEVEL SPECIFICATION OF THE FUNCTIONALITIES FOR NOVEL ELECTRICITY DISTRIBUTION GRID CONTROL

Deliverable D2.1 Luong Le Thanh, Raphael Caire, Bertrand Raison, Seddik Bacha, François Blache, Rene Kamphuis, Cor Warmer, Nikos Hatziargyriou, Aris Dimeas, Frits Bliek, Hugo Niesing, Felip Miralles

Identifier:

……

Date:

17-09-2008

Class:

Deliverable

Responsible Partners:

IDEA

Annexes: Distribution:

PU

Overview:

This project is funded by the European Commission Under the 6th Framework Programme (Project FP6-038576)

D2.1 High level specification of the functionality………………………….

Project FP6-038576

INTEGRAL: Integrated ICT-platform for Distributed Control in Electricity Grids

The INTEGRAL consortium consist of: ECN Principal Contractor & Coordinator NTUA/ICCS Principal Contractor IDEA Principal Contractor Blekinge University of Technology Principal Contractor Gasunie Engineering&Technology Principal Contractor WattPic Intelligent Principal Contractor EnerSearch AB Principal Contractor Grenoble InP Principal Contractor ICT Principal Contractor

The Netherlands Greece France Sweden The Netherlands Spain Sweden France The Netherlands

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Control Versions: Version Draft 00

Date 1 March 2008

Draft 01

10 April 2008

Author Description of Changes Raphael Caire, Bertrand Raison, Le Thanh Luong (IDEA) Preliminary structure for deliverable François Blache, Daniel Roye, Seddik Bacha (Grenoble InP) Cor Warmer, René ICT description Kamphuis (ECN) enhancements Market-based coordination

Draft 02

Draft 03

Draft 04

Draft 05

Draft 06

Deliverable

12 April 2008

Raphael Caire, Bertrand Raison, Le Thanh Luong (IDEA) François Blache, Daniel Roye, Seddik Bacha (Grenoble InP) 22 May 2008 Raphael Caire, Le Thanh Luong (IDEA) Nikos Hatziargyriou (NTUA) from 15 April 2008 11 June 2008 Raphael Caire, Le Thanh Luong (IDEA), Rene Kamphuis (ECN), Frits Bliek (GET), Nikos Hatziargyriou (NTUA) 20 June 2008 Raphael Caire, Bertrand Raison, Le Thanh Luong (IDEA), Seddik Bacha (INPG) 26 August 2008 Raphael Caire, Bertrand Raison, Le Thanh Luong (IDEA), Seddik Bacha (INPG), Rene Kamphuis (ECN), Frits Bliek (GET), Aris Dimeas (NTUA), Hugo Niesing (Wattpic), Felip Miralles (CRIC) 17 September Raphael Caire, Le 2008 Thanh Luong (IDEA), Aris Dimeas (NTUA), Hugo Niesing (Wattpic), Felip Miralles (CRIC)

Demo C : 4.2

Insert the contribution of the partners and review Insert the contribution of the partners and review

I. Pictures IV.1.

Last review for Demo A and Demo C

Last review for Demo B

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Table of Contents I II

Introduction ........................................................................................................................7 A framework for novel electricity distribution networks ......................................................9 II.1 Active networks - ICT coordination between different actors ......................................9 II.2 External market interface ..........................................................................................13 II.3 Four power system business model context views and scenarios............................16 III Improvement of distribution network efficiency with ICTs.............................................20 III.1 Normal operation ...................................................................................................20 III.1.1 Intelligent Load Management .............................................................................21 III.1.2 Intelligent DG Management ................................................................................23 III.1.3 Distributed Energy Management Systems (DEMS)............................................26 III.1.4 Network reconfiguration for grid losses ..............................................................27 III.1.5 Coordinated voltage control and Optimal Power Flow........................................28 III.1.6 Conclusions ........................................................................................................29 III.2 Critical operation ....................................................................................................30 III.2.1 Ancillary Services ...............................................................................................30 III.2.2 Intelligent load shedding .....................................................................................31 III.2.3 Intentional Islanding capability............................................................................31 III.3 Emergency operation.............................................................................................36 III.3.1 Protection systems coordination.........................................................................36 III.3.2 Increase of automation devices to fast service restoration.................................36 III.4 Conclusions ...........................................................................................................37 IV High level INTEGRAL functions ...................................................................................38 IV.1 Demonstration A ....................................................................................................38 IV.1.1 High Level description of Demonstration A .....................................................38 IV.1.2 PowerMatcher coordination.............................................................................41 IV.1.3 Event based markets.......................................................................................42 IV.1.4 The dual market view ......................................................................................43 IV.1.5 Agent based coordination in dual markets ......................................................45 IV.1.6 Supporting Functionality ..................................................................................46 IV.2 Demonstration B ....................................................................................................47 IV.2.1 High Level description of Demonstration B .....................................................47 IV.2.2 Network configuration for Demonstration B.....................................................48 IV.2.3 Priority settings Demonstration B site..............................................................50 IV.2.4 Control and Communication element – ZigBee...............................................51 IV.2.5 External power network commutation .............................................................54 IV.2.6 External power network ...................................................................................55 IV.2.7 Test Software control system ..........................................................................55 IV.2.8 Central control system – Intelligent PC control system ...................................57 IV.3 Demonstration C ....................................................................................................58 IV.3.1 High Level description of Demonstration C .....................................................58 IV.3.2 Fault distance computation .............................................................................60 IV.3.3 Fault Passage Indicators (FIs).........................................................................62 IV.3.4 FI - based fault location ...................................................................................64 IV.3.5 Service restoration ..........................................................................................66 V Conclusions .....................................................................................................................70

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List of Figures Figure II-1. Need for ICT coordination between actors in future distribution networks 10 Figure II-2 Context diagram of intelligent Grid applications on low voltage levels 12 Figure II-3 FENIX view on the evolution of grid control components 15 Figure II-4. Possible roles and connections between grid coordination 16 Figure II-5. Timeframes of commercial trading on the energy commodity markets 18 Figure II-6. Revenue stream view in power delivery 18 Figure II-7. Prediction view of power delivery 19 Figure III-1. Distribution system architecture examples 20 Figure III-2. Network losses statistics of French Power Systems for 2006 21 Figure III-3. Simple adaptive consumption algorithm [BOEDA-2007] 23 Figure III-4. Operation of the cap based coordination method, including steps for resource agent (RA) future planning (1), submission of plans to the information repository (2), sum and cap setting (3), and retrieval of these from the information repository (4). Figure from [PLATT-2007]. 25 Figure III-5. General algorithm for loss reduction using network reconfiguration 28 Figure III-6. Communication system for coordinated voltage control [RICHARDOT-2006] 29 Figure III-7. Frequency Control via droop 32 Figure III-8. Fault location computation 36 Figure IV-1. Residential configuration for demonstration A 40 Figure IV-2. Overall configuration of residential area for demonstration A 41 Figure IV-3. Microeconomics and control theory unified in a multi-agent system 41 Figure IV-4. The PowerMatcher architecture; coming from a hierarchy based mechanism, growing towards a more organic, network of networks. 42 Figure IV-5. Aggregation of Households for dual control goals: Technical Aggregation for ancillary services delivery to DSOs / TSOs and Commercial Aggregation for integration into energy markets. 44 Figure IV-6. Cross-section Aggregation Market 45 Figure IV-7. Micro-network scheme. Components of generation and consumption 49 Figure IV-8. EM250 System-on-Chip overview 52 Figure IV-9. Zig Bee nodes based on EM250 developed by CRIC 53 Figure IV-10. Electrical Power Supply network of the Demo B site at Mas Roig 54 Figure IV-11. Test of Software control systems for Demo B 57 Figure IV-12. Network configuration for Demo C in France 59 Figure IV-13. Network Modeling for the Demo C in France 60 Figure IV-14 . Equivalent schema and analytical equations for fault distance computation with the presence of Distributed Generation (1-feeder substation; 2-DG; t- transformer; ddirect; i-inverse; o-zero sequence) 61 Figure IV-15. Constitution diagram of FIs 62 Figure IV-16. Circulation of Single phase to earth fault current with the presence of DER 63 Figure IV-17. Influence of DER on the signaling of FIs 64 Figure IV-18. Fault distance computation 65 Figure IV-19. Fault path determination in combination with FIs 66 Figure IV-20. Exact fault location 66 Figure IV-21. Isolation of fault affected zone by opening the feeder circuit breaker 67 Figure IV-22. Reduction of fault affected zone by opening the remote switches and closing circuit breaker 67

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Figure IV-23. Reduction maximum of fault affected zone by opening the manual switches and closing the tie remote control switches 68 Figure IV-24. Service restoration for sane portion by connection to the nearby feeders 69

List of Tables Table II-1. Inventory of net effects in several transition cases [Key-2004] Table III-1. Usual voltage levels in French networks Table IV-1. Renewable Energy Sources' parameters Table IV-2. Priority settings of consumption for Demo B Table IV-3. Converters/Inverters associated with RE Generators of Demo B Table IV-4. EM250 Features

11 20 49 51 51 53

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This document is the first contribution to Work Package 2 of the FP6 INTEGRAL STREP-project.

I

Introduction

Due to the increasing complexity of electricity grid interconnected with a large amount of dispersed generators (presented in the CRISP project), the conventional operation modes and devices of the distribution network are not capable to ensure the management of a large amount of information and high-level functionalities (Advanced Distribution Automation-ADA) [GOODMAN-2005] [SUTHERLAND-2006] for future electricity distribution networks. Indeed, these functionalities require comprehensive information management support and coordination between power system devices (circuit breakers, switches, fault indicators - FIs, on-load tap changers, capacitor banks, sensors for instance), customers devices (controllable loads, Distributed Generators - DGs - with modulation abilities for instance) and also the information and communication systems for the control of different tasks during the various operating states. These difficulties should be overcome by intelligently using advanced Information and Communication Technologies (ICT). Furthermore future electricity supply and demand with a large share of distributed generation may lead to new market models in which these generators, in combination with flexible loads, are controlled based on the current, real-time, market value, either at high-end markets or at the local level. These markets may be set up by commercial parties or by network operation. In the FENIX project the commercial and technical virtual power plant concepts have been worked out (see www.fenix.org). In the CRISP project, the potential of services for imbalance reduction of a portfolio containing intermittent supply based on wind power has been demonstrated. In order to enable these markets, intelligent ICT plays a key role. ICT systems are actually responsible of measuring, transmitting and analyzing the parameters or variables that the Electric Power System (EPS) needs to manage and to control in the different operating states. In the future networks, ICT systems must be set as a tool to enhance EPS coordination and then increase the whole EPS robustness. Especially, in the distribution network, ICT systems might enable the operation of the network to take the best advantage of the local resources such as controllable loads and decentralized generators based (or not) on renewable resources. Otherwise, to increase DG owner incomes and to be able to participate to power markets, DGs need ICT to coordinate all of them and reach the critical amount of energy to be market participant [KAMPHUIS-2004]. This infrastructure is then mandatory for real development of un-supported DG. Indeed, even if some EU countries have chosen the subvention way to initiate new DG technologies, this situation may not last for ever. ICT thus has functionalities tightly-coupled to the physical operation of the grid (the EPS), but also ICT-applications may form a loosely coupled layer connecting devices to fulfill an optimization target not directly coupled to the operation of the physical grid (market operation). The transmission system is already provided with a wide and complex data-acquisition and control system. It has been developed gradually along the last decades. Up to now, the monitoring and fast control of the distribution network (MV and LV) is less pronounced, except for the cases where the need for availability of the energy involved and the need of power quality supply are high (as in major cities such as New York or Paris for instance).

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Similarly no ICT infrastructure exists nowadays that enable small producers and consumers at household or small offices level to participate in market-based services. The lack of specific functions for information and communication systems in the control and protection of the distribution networks jeopardizes the achievement of the required goals. The challenge of the project is to design a common ICT system which will be able to play an essential role for the efficiency operation of the distribution network in the future, in physical terms, but also in a commercially viable way. The INTEGRAL project aims to build and demonstrate an industry-quality reference solution for Decentralized Energy Resources (DERs) aggregation-level control and coordination, based on commonly available ICT components, standards, and platforms. To achieve this Integrated ICT-platform based Distributed Control (IIDC) solution, the project will take the following steps: • Define Integrated Distributed Control as a unified and overarching concept for coordination and control, not just of individual DER devices, but at the level of largescale DER/RES aggregations. • Show how this can be realized by common industrial, cost-effective and standardized, state-of-the-art ICT platform solutions. • Demonstrate its practical validity via three field demonstrations covering the full range of different operating conditions including: o normal operating conditions of DER/RES aggregations, showing their potential to reduce grid power imbalances, optimize local power and energy management, minimize cost etc. o critical operating conditions of DER/RES aggregations, showing stability when grid-integrated. o emergency operating conditions, showing self-healing capabilities of DER/RES aggregations. This deliverable will be outlined as follows: Section 2 focuses on the active network as a means to enable novel distribution networks through a more or less loosely coupled ICTlayer. Section 3 describes the abilities of ICT integrated into the distribution network to enhance the local and/or global energy efficiency plan. Section 4 presents the functions that could be included in the different demo sites of the INTEGRAL project.

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II A framework for novel electricity distribution networks The Integral project has as its main aim to create the conditions for a real seamless integration instead of a connection of small distributed generation and consumption units in the electricity grid.

II.1 Active networks - ICT coordination between different actors A new kind of networks named active network is foreseen as a possible evolution of the current passive distribution networks and may be technically and economically the best way to initially facilitate DG in a deregulated market. The active networks have been specifically conjectured as facilitators for increased penetration of DG and are based on recognition that new ICT technology and strategies can be used to actively manage the network. Currently, the mapping between the real-world behavior of appliances and their primary processes is not directly reflected in the value electricity has within these processes. It is a well-known saying in the electricity world that 80 percent of generation profits are made with 20 percent of the generation capacity. The articulation of the demand and the flexibility of generation has to be accounted for to use these resources effectively. Otherwise, the rising of fuel prices, the geopolitical factors in energy supply and the recent drastic climate changes have encouraged politicians of every country to exploit the local, cleaner and cheaper energy as much as possible. The interest of Distributed Energy Resources (DER) has been proven in these recent years. They help electrical utility to respond to the various technical, economical and ecological challenges of our modern societies. Thus, one of the highest priorities of the electrical engineering research in this century will focus on the efficient distribution network operation in presence of DER. However, the DERs are generally not easy to be dispatched and their production are mainly related to environmental constraints (so-called intermittent characteristic as for wind or solar) or process related conditions (e.g. heat needs of a building in relation to Combined Heat and Power - CHP - generation or heat pumps). Adding DER to a distribution system causes impacts in the network, such as power flow redirection, voltage profile variation, short-circuit fault level modification, power losses evolution, among others (see [HADJSAID-1999], [CORTINAS-1999], [FRAISSE-2001], [MIAO-2001], [CAIRE-2002]). Unlike traditional dynamic unit commitment for the transmission systems based principally on the large power plants to respond to the variation of demand, the efficiency of distribution networks should rely also on the strict combination of three distributed resources: distributed generators, distribution grid and consumers having controllable load and source. Improving efficiency for the distributed generations relies mainly on technologies enhancement including internal combustion engines, wind turbine, photovoltaic, biomasses, fuel cells, among others. Furthermore, the market efficiency of some renewable and nonrenewable distributed generators could be greatly enhanced with combined production (for instance cogeneration of electricity and usable waste heat is incorporated into the energy conversion processes). For the distributed consumers, it is important to apply the most efficient technologies such as: more efficient lighting systems, more efficient motors and controls, heat pumps for water and building heating, absorption cooling, and so on. However, the developments of technology often parallel the management and the operation improvement. Developing the optimal scenarios of scheduling distributed generators and controllable loads, as well as minimizing the demand charge and in order to reduce the operating losses and ensure system security is a major research interest. Page 9 of 75


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In the technical aspect, the system operators should define specification rules so that all system resources can contribute to energy efficiency in order to establish intelligent and flexible distribution networks. Thus, intelligent ICT will be the most important coordinated key to ensure a smart distribution network for different operating states. Figure II-1 presents the common intelligent distribution systems including coordination between the distributed resources supported by an ICT system from an EPS point of view (SCADA/DMS).

Figure II-1. Need for ICT coordination between actors in future distribution networks

Such a system would also help dealing with various distributed resources through their specific intelligent functionality so that the objective of efficient operation could be achieved. In economic aspect, within the wholesale market these parameters are already used extensively. For smaller consumers, in accounting models generation and demand are clustered and averaged in profiles and financial calculation is averaged over longer periods. Future grids have to have a larger degree of exposure to the whole market-based mechanism used for coordination as customer onsite generation becomes more important. This means, that there will be more data-acquisition and control connected to electricity generating and distributing appliances. Small, autonomous grid variants exist in the form of Microgrids. In research environments, these grids can be operated using a grid stability based operating strategy [MicroGrids2006]. On the other hand, top-down operation of the grid using high-level control and services is also commonplace. Between these extremes, a number of operational problems have to be solved. PES-Entities in the grid may cluster physically within a geographically constrained area with intelligent distribution control as the binding element, but also other remote entities with, for instance, similar commercial objectives in market portfolios. Examples of the first are inverters/gateways at the home level or small business segment level that optimize local electricity usage (E-Box [KAMPHUIS-2003]) within a real-time price context. Larger capacity Page 10 of 75


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inverters, with a larger number of appliances connected may also pursue part of the objectives in maintaining attributes of power on a local level. Apart from the physical grid, other virtual entities may exist, that pursue a joint optimization objective. Using grid applications as a service may satisfy a market financial optimization target. This report focuses on the possible applications, that form the starting point for the information analysis to arrive at a number of applications and key ICT-components in future power grids in three different operational circumstances foreseen. Currently, there is a large number of studies and initiatives to assess future functions of intelligent power systems and to construct a hybrid, fundamentally different architecture for an ICT-network enabling the power grid to flexibly accommodate novel devices and clusters of devices. Most noticeable in this respect is the Intelligrid [CEIDS-2004], a program in the United States executed by EPRI. The scope of Intelligrid is very broad and not directly focused on the intake and flexible accommodation of DG-RES. A project with a comparable theme is Gridwise [Gridwise-2006] A technical summary of architectural issues has been given recently as a final report on architectures within the CRISP project [CRISP-D1.7]. Other important relevant initiatives on the European level are the EU_DEEP-project [EUDEEP-2006], the SULTELNET-project [SUSTELNET-2006] and the activities performed on a European coordinated scale in the IRED-cluster [IRED-2006]. Working package 2 in the Integral-project is concerned with the definition of ICT enabled functions of future electricity dispersed grids with a large proportion of small distributed generation/renewable energy resources. As described in various articles in literature e.g. [FlexibelWP1-2006] there are a number of implications for the grid when making a transition from centrally controlled to dispersed with merely some central coordination. When compared to hierarchically operated electricity grids with power centrally generated at high voltage levels on a large scale delivering electricity to consumers on lower Voltage levels in the network, dispersed electricity grids offer a number of challenges for technological research. In the EU long-term vision on future grids [SmartGrids-2006] waterfall grid models will be more and more replaced by grids with electricity produced via installations or clusters of installations 'bubbling' upwards. ICT is considered to be an essential enabler for this new development. Grids with components, connected by modern communication technology, Smart Grids, equipped with facilities for more intelligent coordination, are expected to play an ever more pronounced role in such a transition. % of G e ne rat ion G rid Pe ne tra tio n S ce na rios

DE R Im pa c t a nd its Role in t he G rid Inte rc onne c tion a nd Inte gra tion O bje ct ive s Rule s /S t an dard O pe rat ing P roce du res Ma in Co nce rns w ith- res pe c t-t o s ys te m dy na m ic grid im pa ct s

≤ 2% ≤ 10% ≤ 25% I. L o w-n u mb er s an d II. Mo d er ate -le ve l of III. Hi gh -l eve l o f D E R DE R wi th re la tive ly wi th ca p aci ty o f gr id l eve l o f D E R with r el ati vely sti ff g rid so ft g rid co nn e cti on le ss th an th e loa d co n n ectio n d em a nd V e ry lo w, n ot No n cr itica l, ca n Cr itica l to p o we r si gn ific an t to g rid affe ct dis tr ib uti on d el ive ry a nd me e ti ng o p er ati on vo ltag e n e a r DG d em a nd N on in ter fer en ce , Ma n ag e a n y lo cal E n ga g e DE R fo r g o od citize n an d di strib u ti on im pa cts sys te m o p er atio n s an d co m pa tib le co n tr o l IE E E 1 5 47 -2 0 03 Mo d ifie d 1 54 7 , a d d Ne w r ul es i ncl ud e cu r re nt p ra ctice ne two rk a n d o pe r atio n a n d g ri d r ad ia l fe ed e rs pe n e tr atio n lim its su p po rt re q ui re me nt - Vo lta ge a nd - In te rfe re with - A va il ab ili ty cu r re nt trip lim its, re g ul atio n , - R eg u la tion p rov id ed - Re spo n se to - Re co ve ry time s, Ra m pi ng re sp on se fa u lts - Isla n din g - In ter ac ti on s of - Syn ch ro n iza ti on - Co o rd in atio n . m ach in e con tro ls … … Tra ns it ions O n-

1 0 0% IV. DE R o p er ate s pa rt tim e a s an isla n d o r mi cro gr id Pr im ar y po we r sou r ce fo r stan d al on e op er atio n Re ly o n DE R fo r stab ili ty a n d re gu la tio n Sta n da lo n e ru le s th a t ar e syste m de p en d en t - A vai la bi lity - L oa d fo llo wi ng - V ol ta g e co n trol - No rm al an d re se rve c ap ac ity a nd O ff -G rid… …

Table II-1. Inventory of net effects in several transition cases [Key-2004]

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When applying standard system development methods supported by ICT to model such smart grids, as a first step, applications have to be identified and, then, an inventory can be made of requirements and information streams for these applications. Defining the architecture for a more intelligent power grid also has a broader scope than standard ICTdevelopment. Within working package 2 of the Integral-project, focus is on the interaction between power network applications and PES-components working together on a number of different timescales to obtain simultaneously an energy balance and a power balance within capacity constraints. Important aspect in this approach also is that several ICT-layers play a role in such applications and the number of stakeholders gradually increases while going from the harsh constraints of real-time short response time automated, direct grid control, to demand response applications and control of a large number of renewable energy systems, each with different users and stakeholders. This is represented in the figure below. Apart from direct control to the level of PES-components, other ICT-interfaces will emerge. SCADA/IEC-based systems PES-components

Real-Time Control

Market

Coordination User Interface

WEBServices Planning

Monitoring

Primary Process Interface

PES-interface

User Context

User generation /consumption process Figure II-2. Context diagram of intelligent Grid applications on low voltage levels

The perception of how power system applications are to be made differs when viewed from a PES-perspective as compared to an ICT perspective. From the ICT-modeling point of view the definition of the control dimension, the time dimension, the data dimensions and the market dimension form the major cornerstones. The control and time dimension have to do with reaction on events in the power system; the data and market dimension may point to the actor/entity most likely to react on events in the system. From the functionality partitioning and implementation point of view, the definition of the PES-nodes and the PES-connected processors and the definition of the communication path have to be considered. The secret of ICT-modeling is an as accurate and as close as possible mapping of the primary processes and behavior in a software system. A main new point to be treated in this context is the degree of exposure of end-user primary processes to markets. This has a link not only to the supply side, but also to the demand side and the implementation of time-dependent

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energy efficiency measures. Energy efficiency measures may have a time-dependent tradeoff as well. As an example 'application' of a future power system, the delivery of 'spinning reserve' may serve. Having spinning reserve is an essential pre-requisite for proper operation of the power system in reaction to events occurring within small time spans. Handling will be done differently by actors if a considerable part of the power system has dispersed generation capacity consisting of a large number of small units. In large systems, operated in top-down fashion, the mechanical rotational inertia of large generators is the main provider of this service. In highly distributed generation systems, this service will be more-or-less absent. It has to be provided by small power electronics systems with short-term storage via, for example, super-capacitors. For delivery of this service, timing requires response times in the order of milliseconds.

II.2 External market interface In the current electricity distribution infrastructure, within a liberalized market context, ICT is applied for automatic metering of generation and consumption of customers above a certain connection power. Metering has to be accountable and serves settlement of contracts. To minimize trading risks the electricity sector will offer more time-of-use en real-time pricing contracts to ever lower segments in the market. Transport tariffs will be more associated to the grid level, where transport has taken place. The same is expected to take place for taxing of electricity or giving tax credits to certain classes of renewable generation. Current electricity systems are operated and accounted for without inherent knowledge and remuneration of prices paid in the several markets used to get an overall nationwide balance between demand and supply on several timescales. Future electricity grids have to be operated market aware and context aware. On one hand, this means that market information will have to be available in several places in the network; on the other end, market information will be generated at all nodes in the network. Optimally, using the available supply and demand capacity then can be derived from the current snapshot of the situation on a number of markets. Most operation of the electricity grid does not change abruptly. Neither does it change from day-to-day. Therefore some persistent information has to be present. With respect to persistent information one might think of meteorological data and operational data of primary processes involved in power generation and consumption. Furthermore, in deriving an operational strategy, experiences have to be stored and provisions have to be made to facilitate learn from them. History information should enables and enhances context awareness Compared to the current grid architecture a larger amount of data-acquisition and processing hardware will be necessary. The communicational demand for the different concepts should vary from the possibility to send and receive a simple signal, over one way communication of more complex data, to full bi-directional communications [CRISP-D1.7]. The communicational needs are lowest for the implementation of some concepts that have already been in use for peak load reduction, such as broadcasting a set of tap water heaters to shut down for a predefined period of one or two hours. A somewhat higher demand on communications comes with the implementation of full electronic markets with end user (consumer and DG) bidding. Data exchange mechanisms should adhere to common communication models and standards for the physical and logical network. It is expected, that the IP-protocol provides an important cornerstone for the exchange of messages and that information frameworks such as .NET or Java Enterprise Edition framework will provide cornerstones for the implementation model. The timescale for phenomena in power grids

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ranges from micro-seconds to planning periods over decades. Focus in the WP3-activities will be to utilize the dedicated fast PES-hardware (ms-scale) in combination with the ICTnodes operating on local electronic markets (up to one day-ahead). Reacting on events and response times for messages following computerized communication pathways necessitate a cascade of storage options for several storage periods and time scales on the grid. In this respect, traditional top-down operated grids have a large top ‘storage’, compensating for deviations in a number of properties of the electricity delivered like balance, Voltage, reactive power and so on. Novel grid architectures should support a diversity of storage options from the time dimension as well as the size and physical location dimension. Key element for enhancing the stability of the grid should be to increase the flexibility of real-time power grid operation by continuously optimizing the amount of stored energy on a number of timescales. Just like the internet, the electricity grid will be interactive for both power generation sources and consumers (loads). In 2030, energy service companies will allow everyone to have access to the provision of electricity supply services such as the demand management capabilities and demand response facilities. Enabled by smart metering, electronic control technologies, modern communications means and the increased awareness of customers, local electricity supply management will play a key part in establishing new services that will create value for the parties involved. In this context, metering services will represent the gateway for access to the grid of the future and will have a critical consequence on power demand evolution. For this reason, electronic meters, automated meter management systems and telecommunications, together with other communications systems that use electricity supply networks as their delivery infrastructure” will serve as enabling technologies. Information and Communication Technology (ICT) and business process integration will be valuable tools in the real time management of the value chain across suppliers, active networks, meters, customers and corporate systems [SmartGrids-2006]. Another way of accounting, settlement and reconciliation for actors in the value chain will arise. A traditional split-up in energy unit based commodity price, capacity based distribution price and taxes will undergo changes. To reflect the flexibility of device types, differentiation in time and flexibility of operation has to be rewarded. Five levels of accounting have to be discriminated: • The internal market prices for the consumption/production of the commodity. In the PowerMatcher control algorithms, bid-curves and prices are the only information exchanged to establish concerted operation of devices. Per unit prices in bid curves only tell the status of the internal process, taking into account a utility of the electricity consuming process in the appliance. • The transport related market price. In certain circumstances, electricity transport constraint exists. Processes able to react on these changes flexibly by decreasing or increasing their load can gain extra profits. • The capacity related market price. Recent experiences in weak distribution grids in exceptional meteorological hot-weather circumstances showed large problems, which could have been avoided with using intelligent demand response. In future grids, the possibility of temporary capacity shortages is also existent, but the possibility to deal with them is abundant. • The external market prices. These include APX-like and balance market prices. • The contracts with end-users. These may differ from customer category and type of usage. Apart from a metering interface a control interface is necessary in order to give the grid information about the electricity consuming or producing process. Direct control of suppliers and demanders will not be desirable, but an influence on the control strategy may well be necessary in most cases. One might think of knowledge of maxima and minima in capacity

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and power, historic performance data and so on. Furthermore because of energy compensation, a transport path related market price may be imagined. To give an idea of market implications, as another example, settlement of market transactions may be considered. At this moment the TSO is involved in this process; in future power markets it could be envisaged, that power markets are also massively dispersed and not the TSO takes care of this process but an entity more close in the operational range of a traditional distribution company. Automation of this process, then, could be mean handling a large number of micro transactions not in scope of current legacy billing systems. .

Figure II-3 FENIX view on the evolution of grid control components

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II.3 Four power system business model context views and scenarios CommercialPhysical Manage Transport

WideArea Grid

Trader Portfolio

Balance Supply Demand

LocalArea Grid

Manage Portfolio Manage Distribution VPP Commercial VPP Technical

Coordinate Commercial Cluster

Grid Level

Coordinate Technical Cluster Device Cluster Device 2 Device Optimization_xy

Device_y

Device_x

In-home Energy Management_x

In-home Energy Management_y

Figure II-4. Possible roles and connections between grid coordination

Buying/selling, transporting/distributing of electricity as well as applying taxing schemes to electricity are the primary activities in the electricity sector. In non-liberalized settings, utility companies perform all three activities. With increasing unbundling, separate actors may perform parts of these activities. Depending upon the geographical location and the market design, the granularity for transport and distribution differs. The US has more localized transport areas, while Western European countries have larger aggregated control areas. The principal actors, from a grid operation perspective, are the traders/brokers, the transmission and distribution grid operators and the owners of production and consumption devices. The various activities are depicted in Figure II-4. Using the Garson-Sarson approach for information modeling, the rectangles denote entities, the rounded ones refer to processes. Arrows indicate information streams. The vertical axis gives an indication of the hierarchical level in the grid. The electricity producing and consuming entities are devices (loads and

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generators). It can be seen, that installations on the demand and supply side may play a role in the grid in a number of manners: •

•

•

•

•

•

Balance Supply Demand and Manage Transport pertain to balancing, contingencyand transport management on the higher voltage levels. Done by a TSO or ISO. In most countries this is done on the basis of the operation of Programmes of Programme Responsible Parties on a one day ahead basis. These programmes are checked for line- and transport capacities (load flow control). This mechanism reflects the current optimization of nationwide supply and demand matching in liberalized countries. In order to minimize the burden of imbalance, PRPs are given incentives to schedule their demand and supply one day ahead as accurately as possible from demand and supply predictions. Manage Portfolio. A portfolio is in the hands of a programme responsible party. Generally, consumption profiles of classes of small customers in portfolios are derived from historic data and are stratified based on a particular meteorological day and day type. The same mechanism also holds for reconciliation. Imbalance costs are evenly spread to small customers according to their averaged profile, not on the basis of their actually measured usage of electricity in time. In the same way as for consumption profiles, the production profiles of small customers could be stratified in the same way to yield accurate predictions to be used in planning the portfolio. During day-to-day operation, for larger loads, the realizations are monitored in real-time by programme responsible parties to allow for slight strategy changes in the control strategy of the portfolio. Coordinate Commercial Cluster. A number of different generators and/or consumers together are managed as a whole virtual power plant with certain characteristics and dynamics given by the constituent loads. This entity can be part of a portfolio to optimize. The operation of a portfolio within a commercial VPP-context is not constrained to a certain area. To the operator of a portfolio, the ‘plant’ acts as any other plant in the portfolio, possibly even with greater versatility. The load curve of operation of the VPP could have a similar shape as physical counterparts in the portfolio. Coordinate Technical Cluster. A cluster of devices is managed to generate a virtual power plant to assist network operation. This is a version of the virtual power plant that is confined to a certain network topology in a geographical area. The ‘plant’ operates in the hands of a network operator and is like a voltage regulator at a feeder or equipment to deliver ancillary services. A technical VPP might be used to mitigate distribution bottlenecks or react to contingencies by acting in a micro-grid configuration in case connected parts of the grid are in a erroneous state. A typical VPP is geographically bound. Device2Device Optimization. Confined area coordination of supply and demand. In this case one could imagine owner confederation of an apartment complex that manages equipment in individual apartments to keep a contracted profile within limits. Coordinated (synchronous or asynchronous) operation of devices here reduces network losses and avoids investment cost for the high voltage transport infrastructure. Part of the distribution grid acts as a local energy exchange network. In-home Energy Management. This business model implies in-home electricity usage strategy management [KAMPHUIS-2003]. As an example, the amount of imported electricity is managed in view of external price developments. Steering individual devices to synchronicity between demand and supply is one of the control objectives.

Operation of devices may be part of one or more activities in the grid. Which activity leads to financial gains is strongly dependent upon the tariff structure, the current status of the grid Page 17 of 75


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and the flexibility in demand and supply which may be linked to e.g. (micro-) climate conditions.

Figure II-5. Timeframes of commercial trading on the energy commodity markets

Figure II-5 illustrates possible trading arrangements for exchange of power. Depending upon the predicted future demand and supply, market parties may agree long-term contracts that may be hedged by futures. Typically a strip of power (MW) delivery via a device during a certain period is traded. In order to fine-tune their projected portfolio for the next day, a trader may buy/sell additional power on the day-ahead market.

Figure II-6. Revenue stream view in power delivery

On an intra-day market, a trader may buy and sell to update their portfolio based on smaller time frames ahead up to even real-time measured realizations. Finally, the imbalance market and the regulation market allow the TSO to achieve real-time balance using primary and secondary reserves. In the contracts, generated device power is predominantly traded.

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Demand in the form of demand response, however, may also be present in the transactions as negative production reserve. Having the ability to switch-off loads, then, is rewarded, instead of having ‘spinning’ generation capacity. All transactions have to fit within the highvoltage transport constraints and the geographically defined distribution constraints. The revenue streams are contained in Figure II-6. A number of these streams are dependent upon the real-time situation of the grid (italicized); others are fixed in time. Prosumers are traditional utility customers, that also have small or large (ESCO1’s) DG-RES production facilities.

Figure II-7. Prediction view of power delivery

Finally, a prediction view of the same system is contained in Figure II-7. Programme responsible parties send in their programme to the TSO. Prediction plays an important role on several time frames. Having the ability to predict production or generation accurately combined with an ability to adapt delivery/consumption on a short term, increases value in the power system control setting. As described above, power delivery can be considered to be a complex multivariate optimization problem, in which the application of ICT can aid at a number of hierarchical levels and timeframes. The important question for the Dutch field test is, how to exploit the added value of small consumer micro-CHP power generation. The power could be delivered to a neighbouring heat-pump, be part of a long term contract for a fleet of thousands of micro-CHPs delivering power in even strips to hedge APX-risks and also could aid a distribution company in postponing or mitigating investments in the LV-grid. At which level individual power consuming and generating devices can be aggregated and share data structures depends upon their role in each of the views described. A classification of load types is necessary for this.

1

Energy Service COmpanies

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III Improvement of distribution network efficiency with ICTs III.1 Normal operation Distribution networks are the final part of the power delivery infrastructure which takes the electricity from the highly meshed, high-voltage transmission networks and delivers it to the end consumers. The largest part of end users with some small industrial facilities and offices and almost all the commercial stores are connected into this network via power transformers. The distribution infrastructure is extensive. The urban constructions are very often underground whereas the rural constructions are usually overhead. There may be a mixed structure with new underground construction for suburban regions. The choice of voltage level and frequency is the result of a technical-economic and historic consideration. Thus, every country owns different voltage levels and frequency levels. In France, the voltage levels of the public distribution network are given as the Table III-1 [CRISP-D1.1] with a range of variations of [Un-10% / Un+6%] for the Low Voltage level and [fn-1% / fn+1%] for the frequency. HTB

> 63 kV

HTA

5.5 kV, 10kV, 15 kV, 20 kV, 33 kV

BTA

400 V Table III-1. Usual voltage levels in French networks

Distribution networks are usually "looped systems operated in radial". It is because of the protection scheme for a radial network is relatively simple and economic. That means there are two types of switches: normally closed switches which connect line sections (sectionalizing switch) and normally open switches on the tie-lines which connect two primary feeders or two substation or loop-type laterals, see Figure III-1. However, to ensure power supply reliability, some meshed networks were, sometimes, established. Looped structure

Looped system operated in radial

B HT

/ HT

A

NO switch

DG

NC switch

LOAD

Circuit breaker

Figure III-1. Distribution system architecture examples

Network losses reduction and enhanced performances of the network are essential tasks for the system operator (if he is financially responsible for them, which is not the case in Spain for instance). In effect, losses stem from both of electricity transmission and distribution

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network. These are usually divided into two categories: technical losses (related to network configuration, operation mode and characteristic of equipments) and economic losses (related to the measurement of electricity flows). In Europe and North America, average network losses are around 7% whereas this rate is about 8.8% for the world. The difference of technical network losses between the European countries depends on their size or population density, that play an essential role on network length, as well as the operation, maintenance conditions. Likewise, the proportion of transmission or distribution in the whole power network decides also the level of losses since the losses situated more important within distribution than transmission network. For instance, in France, in 2006, the energy losses of the whole network went up 31,374 GWh/year (i.e. 6.56% of interne energy consummation of the country) in which distribution network losses were 19,947 GWh, this correspond to 64% of the total losses [RTE-2006]. Distribution network losses keep obviously a large amount of energy losses of power systems. Thus, losses reduction of distribution network losses is a key element to improve the energy efficiency of the whole power system. In countries with a liberalized electricity infrastructure distribution companies must buy the losses in their grids in the same way as commercial parties have to do. On the other hand, distribution load is versatile during the time, in different zones and/or different feeders depending of customers type (commercial, industrial or household). It also presents different characteristics according to their corresponding distribution line materials and gauges. In distribution networks, many kinds of methods have been proposed to reduce the network losses; four of them are listed thereby: • intelligent load management (load curtailment/shifting), • intelligent DG management, • network reconfiguration, • coordinated voltage control.

Losses Power Energy

6.E+05

31347

5.E+05

1.E+05

11427

123528

2.E+05

354855

3.E+05

478383

19947

4.E+05

0.E+00 Transmission (GWh)

Distribution (GWh)

Total (GWh)

Figure III-2. Network losses statistics of French Power Systems for 2006

III.1.1 Intelligent Load Management The time variance of supply from distributed resources, in particularly from renewable energy resources generally does not match the load profile (without using coordinated storage devices). It may lead to congestion or energy surpluses which cause the decrease in efficiency and threaten the system security. Some recent researches in this context [ASHOK-

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2001], [BOEDA-2007] and [KUPZOG-2007] show that the benefit of an information and communication infrastructure supporting load management was certain. In fact, ICT systems have a wide range of application in electricity systems, from customer databases to automated spot markets. The communication among the infrastructure’s stakeholders is fundamental to cope with growing and versatile flows and congestions. To optimize the power flow inside distribution network, operators may rely also on distribution load. The concepts of Load Management (LM) or Demand Side Management (DSM) appear generally as interchangeably used terms to deal with this objective. DSM addresses the implementation and evaluation of methods to influence the amount and timing of energy use. Generally speaking, short term DSM (DR, Demand Response) and long term DSM (energy efficiency measures) can be distinguished. While long term DSM refers usually to a tool to optimize the annual unit commitment and optimization of energy loads without "real time" intervention in the process, short term DSM concentrates on the optimization of the actual load chart (Load Shaving for instance). The objective is to coordinate interruptible loads in order to reduce congestion on the power network while taking into account the “pay back” constraints. A DSM system contains some main functions like Load Shedding (or Load Curtailment) and Load Shifting. Load Shedding is often motivated by grid stabilization (security to prevent blackouts) and is interfering with customer interests. Indeed, load reduction in critical grid situation is not considering the user process functionalities. It is often applied in emergency situation. For the long term planning, loads may be sorted by priority levels. Afterward, zones or types of load which have the lower priority level will be shedder firstly. Load Shifting is not reducing energy consumption, but it is able to reduce peak loads by shifting consumption to off peak times. As a short term method, it allows to improve the balance in load charts, with minor decrease in the functionalities for end users. Thus, a load classification is required to determine what the most appropriate loads for a specific control type are. Based on the controllability, there are "critical load" which cannot be controlled and "controllable load" which can be interrupted fully or partially for a determined or undetermined duration. This load type can be used for load shifting. The controls of load shifts, distributed storage and curtailments of interruptible loads are the major tools of modern DSM systems where advanced ICT features with intelligent algorithms are integrated. With this introduction, coordination between the different resources is needed in order to achieve the optimal efficiency. Based on the information about the specific processing, properties and energy storage capacity, operators can schedule again energy consumption of some loads. The commitment time of the interruptible loads can be scheduled by intelligent algorithms to obtain the best load curve. A simple algorithm for load shifting is depicted in Figure III-3 [BOEDA-2007]. Assuming that necessary requirements for communication, metering and control will be available, loads are gathered and sorted at each time step. Then, the resulting list is compared to the available production for this time step. Depending on the difference between consumption and production: if production is lower than consumption, control signals are sent to controllable loads, if production is higher than consumption, excess production is lost, stored or even could be fed back into the network. After this load control or storage decision, load data are updated, and gathered again for the next step. Otherwise, CIGRE has adopted the Demand Side Integration (DSI) term to include all the initiatives trying to influence the electricity consumption [BENOIT-2007]. The following initiatives have been considered following these practices: Indirect initiatives that encourage consumption efficiency and demand reduction, e.g., financing of energy efficient lighting, devices. Initiatives based on sending real time price signals to customers. Electricity price must be different at different times of the day. Indirect

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load control initiatives will force or encourage customers to reduce their consumption during few periods, e.g., interruptible contracts. Customers must execute the reduction themselves. Direct load control programs where DSO, TSO or programmed operators disconnect part of the customer's load. Direct communication is then needed. Examples can be found in the US where EnerNoc has erected a dispatch center for contracted consumer loads. Initiatives or market structures that allow the participation of the customers offering load reductions in exchange for certain price.

SCADA/DMS

Load data acquisition at t

Load sort according to Use, Controllability, Power available control time t=t+1 Comparison with available production at t

Load control decision Storage decision

Alimented pool updated

Figure III-3. Simple adaptive consumption algorithm [BOEDA-2007]

Real-time local power markets allow competitive and/or coordinated operation of controllable loads and distributed generation. The PowerMatcher concept, developed in the CRISP project, is a main advocate of this ultimate customer site integration model [KOK-2006].

III.1.2 Intelligent DG Management Coordination of devices at the local level can no longer be exercised by traditional central control techniques. A trend towards decentralized control is visible in current research and applications, and agent-based technology is considered an essential building block [SMARTGRIDS-2006]. Agent technology has been applied in intelligent management of demand and supply in various ways. PowerMatcher concept The PowerMatcher concept [KOK-2006] implements real-time local power markets that allow competitive and/or coordinated operation of controllable loads and distributed generation. It is developed in the CRISP project as a main advocate of ultimate customer site integration. The concept implements a market based coordination mechanism for coordination of supply and demand of electricity in networks with a high share of distributed generation. It can be used in order to facilitate efficient use of distributed energy resources (DER) in different ways.

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• • • • •

Integration of intermittent Renewable Energy Sources (RES) such as wind and solar power in the grid by coordination of controllable loads based on the availability of wind and solar power. Aggregation of a large fleet of distributed generation, including controllable loads, into so-called virtual power plants and thus reap benefits previously only available to large power plants. Contribution to ancillary services at the distribution level by addressing distributed generation and controllable loads as (part of) the reserve capacity for balancing services. Congestion management by rescheduling distributed generation and controllable loads based on line or node capacity. Reduction of peak demand on the substation by shifting distributed generation towards periods with peak load and controllable loads away from peak periods Thus it contributes to the peak shaving functionality in that it reduces the need for peak shaving.

The PowerMatcher concept is built on so-called agents, pieces of software that represent local installations and are goal-oriented with respect to the local process. Based on the local state of the process these device agents place bids on a local market in the form of demand or supply functions. Similarly business agents can place bids on the same market in order to influence the behavior of the local installations. These business agents may control business-oriented goals, such as (virtual) power plant operation on an external market or avoiding peak loads or congestion within the grid. A market agent is responsible for the local market organization and the outcome of the market. This outcome is a price at a moment in time / period of time, from which each agent can derive its allocation of power from its original bid function. On the market prices will rise during periods in which demand should be discouraged (and supply encouraged) and prices will fall vice versa. Peer to peer algorithms It can be discussed whether the PowerMatcher concept behaves as a client-server architecture or as a peer to peer architecture. A market platform behaves as a server entity towards its underlying agents. However, the architecture strongly resembles a Napster or even a KaZaA architecture that are said to be peer to peer networks. Administration in these systems is done by one (Napster) or a number of interconnected (KaZaA) platforms. The only real peer to peer action in Napster and KaZaA systems is the exchange of files. In the PowerMatcher concept the administrative operation on the market platform resembles the KaZaA approach. KaZaA super nodes may be compared to PowerMatcher market platforms. However commodities are exchanged in stead of files, and the power network is used in stead of a computer network for delivering 'packages of electricity'. [HAUSHEER-2007] calls this type of network a centralized (Napster) or hybrid (KaZaA) peer to peer architecture. Note that the peer to peer qualities become even clearer in an event-based market concept, in which the market agent assumes the role of a broker. Negotiation algorithms Negotiation algorithms are characterized by an iterative process of negotiations in which agents try to optimize their utilities. In order for negotiation strategies to work each agent has to be willing to accept a compromise solution. In [WEDDE-2006] the DEZENT system is described in which agents representing partially autonomous producers and consumers perform distributed automated negotiations in order to optimally make use of local supply. They also stress the need for flexible and scalable concepts that do not rely on centralized decisions. Such concepts make the control system fault tolerant in that they do not depend on one single node to be operational for the whole system to function.

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Cap based coordination

Figure III-4. Operation of the cap based coordination method, including steps for resource agent (RA) future planning (1), submission of plans to the information repository (2), sum and cap setting (3), and retrieval of these from the information repository (4). Figure from [PLATT-2007].

CSIRO has developed an agent based coordination system for coordination of demand that is based on adaptation of demand due to demand caps [PLATT-2007]. Resource agents representing consuming devices send their energy demand plans to an information repository or bulletin board. A summing agent aggregates all plans to get the total predicted power demand for a particular time interval. For the same interval a broker agent determines a demand cap figure, which indicates a desired total power consumption for all agents, and is based on information such as prices from electricity market brokers, or status information from network operators. The total sum of plans, together with the demand cap, are communicated to the resource agents that can modify their planned power consumption in order to minimize the consumption in intervals with a total sum of demand greater than the demand cap. The local process is always respected, but flexible demand will be shifted to other intervals. The modified power consumption plans are again placed on the information repository and the process is iterated until the cap is met or until a maximum number of iterations is reached indicating the demand cap cannot be met. Demand response, critical peak pricing The previous algorithms all are based on distributed intelligence and have the potential of being robust with respect to the scale of the system under control. Scalability will become a main issue in future electricity networks with a large share of small producers, such as microCHP and PV. Distributed algorithms also enable active participation of all resources in the system. More traditional load control concepts have been emerging during the last years as new ways of load management. Demand Response (DR) is defined as the adjustment of electricity consumption in response to an external signal. Critical Peak Pricing (CPP) programs are aiming at compensating customers for reducing or shifting their electric energy usage (when requested) away from periods with peak load.2 Both DR and CPP are initiated 2

Dual tariff systems can be seen as a precursor of CPP and have been in use for a long time in order to shift demand to night hours.

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by the utility and depend on voluntary participation of end users. More compulsory measures may have to be taken in case of critical circumstances, leading to load shaving or load shedding programs. The article [WARMER-2007] shows that distributed control concepts have a number of advantages over DR and CPP. By having a decentralized initiation they create active customers having a share in market power based on local autonomy. Distributed control leads to dynamic responses, thereby being able to act proactively in case of critical circumstances. Also it creates a determined outcome that can be fixed in ‘real-time’ contracts. And the concepts are based on transparency with respect to demand and supply, bringing distributed generation at an even level with large power plants.

III.1.3 Distributed Energy Management Systems (DEMS) In future high-DG-RES grids a number of control applications will not only be exerted by traditional players in the sector but also by players like energy service companies. Apart from the commercial applications mentioned earlier in the market context, grid related application types/services also might include • • • •

•

• • •

Low level inverter spinning reserve service providing. Increasingly, small clusters of consumers and producer are exposed to the grid using a dedicated inverter. Management of Energy balance on lower Voltage grid levels. Having an energy balance diminishes currents from higher grid levels and in this way transport costs. Management of power (energy per unit of time). An flat power profile and powerduration curve enable lower costs in wiring between nodes in the network Flexible demand response by operation of clusters of different types of DG-RES. E.g. Domestic fuel cells (see http://www.cerespower.com/ planning a rollout of SOFC fuel cells in Britain) arranged in a commercial cluster might try operating on market peak price levels. As these systems generate a significant amount of kWh/e per kWh/th they could lead to peak electricity generation at peak heat demands. Enable granular demand response in a number of grid situations as defined in the project-definition. Utilizing the specific device characteristics characterization specific actions can be exerted like using the collection of all refrigerator loads to relieve or mitigate distribution peak loads. Management of capacity (maximum energy per unit of time). The capacity picture comes up, when investments in transport capacity are to be made. Management of Power Quality, where specific load types can give a different contribution. Reaction of customers to price dependent tariffs. An example of a system currently in operation in the US is operated by http://www.thewattspot.com/ , where users can choose three different operating profiles (normal, cheap, green).

All these services operate on different timeframes and, more importantly, with different timing requirements. Delivering micro spinning reserve may occur on the ms-scale for spinning reserve like applications up to market applications delivering contracted capacity for timeframes of weeks. As the intricate interaction between the PES-hardware and the ICTinfrastructure is the major challenge for future power systems to happen. In the deliverable the challenge is to get a right distribution of ICT-components across the PES-equipment layer with real-time hard control, the strategic, operational layer with soft control and the WEB-service layer each with their own constraints.

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III.1.4 Network reconfiguration for grid losses Line losses from the line current flowing through the resistance of the conductors are the largest cause of losses on the distribution system. Like any resistive losses, line losses are a function of the current squared multiplied by the resistance. Mathematically, the total real power losses (PLoss) for a one phase system can be expressed as it follows: n

n

i =1

i =1

PLoss = ∑ ri I 2 = ∑ ri

Pi 2 + Qi2 Vi

2

Equation III-1

where ri, Pi, Qi, Vi, are respectively the resistance, real power, reactive power and voltage of branch i, and n is the total number of branches in the system. There are many ways to reduce losses in the traditional distribution network, for instance: increasing voltage level with on-load tap changer (OLTC) on the HV/MV substation transformer if loads have constant power behaviors or increasing power factor with distributed capacitor banks. Meanwhile, another method that can achieve multi objective for operation of distribution network including losses reduction is Distribution Network Reconfiguration. This can be achieved by balancing loads (exchange between feeders). Distribution Network (DN) reconfiguration is a process that consists in changing the status of the network switches in order to re-supply the non-energized areas after a fault occurrence (see section III.3), or to optimize given criteria (such as grid losses) in normal operation. In a more constrained energy environment, the electric distribution operators are more and more interested in the minimization of active energy losses and consequently in the network reconfiguration process enhancement for this purpose. The reconfiguration for loss minimization in open loop radial distribution systems is basically a complex combinatorial optimization problem, since the normal open sectionalizing switched must be determined appropriately among a huge population. A state of the art for optimization algorithms of loss minimum reconfiguration of distribution network updated is contained in [ENACHEANU-2008]. Figure III-5 shows a general algorithm for losses reduction with network reconfiguration. Assuming that efficient ICT systems will be available in the future, all states of switches will be monitored and controlled. Network state could be sent in local intelligent centers where an optimization computation with the objective of losses reduction should be performed. The switch states resulting from optimizations can be used to construct the new network configuration. The optimal reconfiguration is achieved when the loss reduction objective and electro-technical constraints are fulfilled.

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SCADA/DMS

PMU

RTU

Optimization: Mathematical Programming Heuristic Algorithms Intelligent Algorithms

Select Desirable Switching Options with Optimization Solution

Perform Reconfiguration using Switching options

No

Losses reduction? Yes

Optimal Network Reconfiguration Figure III-5. General algorithm for loss reduction using network reconfiguration

The minimum losses or load balancing computation nowadays have to take into account the existence of distributed generators (DGs) in the distribution network. The dispersed facilities can reduce the loss of distribution system with an appropriate allocation. However, when DG is disconnected form a distribution network by fault, violation of operation constraints, such as line current capacity and voltage drop, may occur. From a reliability point of view, robust system configuration for suddenly disconnecting DG from the distribution network can be determined. In [YASUHIRO-2004], the authors propose an algorithm to determine loss minimum configuration of a distribution system with DGs while maintaining system reliability.

III.1.5 Coordinated Voltage Control and Optimal Power Flow The increased amount of dispersed generators in the distribution network may change the voltage profile on the grid due to the modification of active and reactive power flows in the network impedances. In normal conditions, for a safe power supply, the voltage has to be maintained between upper and lower admissible values on every nodes of the distribution grid. With a high penetration level of DG unit on the distribution network able to modulate their reactive power injection, they have a great potential to contribute to voltage profile control. However, voltage regulation has to take into account the additional losses due to reactive power flow in the conductors. Therefore, coordinated voltage control is an important issue for DNO. A coordinated voltage control in distribution network with the presence of DG have been carried out in [RICHARDOT-2006]. This method takes advantage of distributed generation reactive power injection to maintain voltage to its set-point value at the substation and some other specific nodes called pilot buses. Coordinated voltage control adjusts the voltage of several pilot buses located in the controlled area. The initial set-point values are computed every hour with an Optimal Power Flow. They are then recomputed to do the junction every

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10 seconds by minimizing a constrained multi-objective function in which power losses can be taken into account. All these constraints can be real time set using the multi-agent approach. The multi-agent approach focuses on the interaction and cooperation of autonomous agent groups. It means that a lot of measurement information at the pilot buses, at substation and at DG will be exchanged and distributed by the execution and coordination agents in order to estimate the set-point values.

SCADA/DMS

OPF Pilot Buses Set-Point Voltage Optimization

Vpref

D-RCT DER VOLTAGE SET-POINT VALUE OPTIMIZATION

Viref 3 Optimization Objectives :

Voltage Control

Vi

Pilot Buses Voltage Control Pilot buses Optimization

Reactive Power Control

DER i

DER Voltage Control

Vi Vpilots Pilot Buses

Qi

Distribution network

Figure III-6. Communication system for coordinated voltage control [RICHARDOT-2006]

To achieve these requirements, the ICT system is a trade-off between needs and capabilities (or cost). ICT system such as fast SCADA system seems to be suitable to respond to the need of VPP with decentralized intelligence in the substations. The set-point parameters will result from the optimization computation in order to achieve the most performance of network operation including the loss minimization.

III.1.6 Conclusions The different issues mentioned above demonstrate the possibility of integrated ICT systems and decentralized intelligence into the distribution network operation in order to improve the global efficiency in steady state conditions. Each system contains their specific characteristic and functionality to achieve the different operation tasks. However, this solution is not yet expected by the DNO or TSO because of the complexity in management and the cost constraints. Thus, a common architecture for ICT-based services could be an ideal solution for the distribution network in the future.

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III.2 Critical operation Critical operation concerns the case where the upstream MV network is stressed either due to overloading conditions or some disturbance. The "emergency operation" concerns the case when a serious disturbance in the MV network, at the extreme case a blackout, leads to Microgrids automatic isolation. This is the most important perhaps feature of Microgrids, i.e. their capability to island and continue supply at least the critical loads. In the extreme case, it is considered as the area where the system is close to collapse. Some small variations in the network parameters can give information of the network health. For instance, a system collapse (or total blackout) due to voltage instability is normally preceded by a slow power system voltage level reduction. Some of these disturbances are caused because of the long distances between the network power sources and the load consumption areas. Generator angle instability is another reason leading the system to collapse. In those cases, DG can provide support to the EPS through the reduction in distance and response time between the load generation and consumption. LV microgrid cells are important to support the MV grid in critical operation when possible, and provide voltage support, i.e. it can keep reactive power at the in feed equal to zero or even inject reactive power. Frequency support is also possible. If this is not possible, the LV Microgrid must be able to perform islanding and reconnect on demand (emergency situation). In all these cases fast communication between level 1 cell agents and microgrid cell agents is required. Furthermore close communication between the different elements of the Microgrid is also required in order to take the appropriate decisions. The actual situation of each of the elements must be known by the decision agents and communication must be safe and must be available in a short time response. The main support functions that Microgrids can provide to the EPS in critical operation are: • To provide ancillary services to the HV grid • Intelligent load shedding (depending both on voltage and frequency control) • Intentional islanding capability.

III.2.1 Ancillary Services To provide the network with ancillary services, specifically tertiary reserve, is an issue of interest both in normal and in critical situations. In normal, because it can help to reduce the transmissions lines losses and in critical situations because take over the tasks from the central power generation. Here we are only focusing on the critical situation. When the network is close to voltage collapse, a low down of the network voltage level reduction can be observed for several minutes. This situation is normally caused by an unsustainable difference between the generated power and the load consumption. To cope with this critical situation common practices are to act on the AVR of the power generators or to modify the settings of the OLTCs. These actions need to be done semiautomatically and in a short time period. LV cells including generating elements can react to these emergencies by providing the network with reactive power close to the load demand. Voltage and frequency control in LV Microgrids is discussed in section III.2.3

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III.2.2 Intelligent load shedding Intelligent load shedding is an effective way to deal with problems related to the excess of active power consumption. On the previous section, we have discussed about the actions to perform when the network is close to voltage collapse. In parallel to the injection to the network of reactive power, a reduction in the consumption of reactive power may be required. Thus, loads which are classified as to present a high absorption of reactive power are suitable for shedding and preventing voltage collapse. Under frequency is also a situation that can be caused by an excess in the active power consumption. Thus the main action to perform here is load shedding. To implement this, a classification of the loads regarding priority and availability to be shed is required. Intelligent load shedding has to deal with the coordination of responses of local production and local controlled loads. Actions can be executed in areas, production and consumption and time response here is very important. Within this technique, the most challenging issue is to have communication within all the Microgrid elements in order to know in a timely manner their actual status and their availability to be shed.

III.2.3 Intentional Islanding capability When failures occur in the MV or HV system, the distribution network may break into isolated Microgrids, each of which must be supplied by itself. With an intelligent distributed approach where the micro controllers (both the DG Microsource Controllers (MC) and the Load Controllers (LC)) will act, in a very fast way, as independent agents and making an efficient use of the local resources, it will be possible to maintain system operation in an islanding condition.

Frequency control In isolated operation mode frequency control is a challenging problem. The frequency response of larger systems is based on rotating masses and these are regarded as essential for the inherent stability of these systems. In contrast, Microgrids are inherently inverter dominated grids without or very little directly connected rotating masses, like flywheel energy storage coupled through inverter. Since micro-turbines and fuel cells have slow response to control signals and are inertia-less, isolated operation is technically demanding raising loadtracking problems. The converter control systems must be adapted to provide the response previously obtained from directly connected rotating masses. The frequency control strategy should exploit, in a cooperative way, the capabilities of the micro sources to change their active power, through frequency control droops, the response of the storage devices and load shedding.

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f f0

∆f

-1

0 frequency droop

-1%

1

P PN

Figure III-7. Frequency Control via droop

The conventional power system employs conventional frequency droops, as shown in Figure 2. As a result, the communication system of the generator just has to handle the set value f0, the slope of the droop and alert messages. In principle, this concept can be also adopted by the MCs of the Distributed energy resources, in order to provide load sharing capabilities. However, this will not be straightforward to implement in Microgrids due to the impact of active power on voltage profile. In interconnected mode, frequency control is not a problem, when the much larger distribution grid maintains the frequency for power imbalances inside the Microgrid. The main challenge comes during the transition from interconnected to island operation, when the interconnection is interrupted due to fault. Large interconnected systems can handle loss of their interconnection or outage of even their largest unit by maintaining enough spinning reserve of adequate quantity and quality (speed of reaction). The loss of the interconnection providing a large or even the largest percentage of power inevitably requires either very large amount of storage or effective load curtailment strategies. The provision of adequate spinning reserve is either impossible or clearly cost-ineffective. With loss of the grid due to voltage drops, faults, blackouts etc. the Microgrid transfers to island operation. This will require an immediate change in the output power control of the micro-generators as they change from a dispatched power mode to one controlling frequency of the islanded section of network. The frequency control strategy will exploit, in a cooperative way, the capabilities of the micro sources to change their active power, through frequency control droops, the response of the storage devices and load shedding (by steps).

Voltage Control Appropriate voltage regulation is necessary for local reliability and stability. Without effective local voltage control, systems with high penetration of distributed energy resources are likely to experience voltage and/or reactive power excursions and oscillations. Voltage control requires that there are not large circulating reactive currents between sources. Since the voltage control is inherently a local problem, voltage regulation faces the same problems in both modes of operation, i.e. isolated or interconnected. In the interconnected mode, it is conceivable to consider that distributed generators can provide ancillary services in the form of local voltage support. The capability of modern power electronic interfaces offers solutions to the provision of reactive power locally by the adoption of a voltage vs. reactive current droop controller, similar to the droop controller for frequency control (Figure III-7). These droop parameters may need to be adapted to system operating conditions, either by receiving a signal from the MGCC or through the recognition of a given pattern of system

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operation (associated for instance to the fact of being a week day, with sun, etc.). In this case we will have a passive adaptive agent approach. Similar to frequency control however, the main difficulty comes from the close coupling of P&Q effects, so that voltage regulation based on reactive power injection alone is impossible, unless excessive amounts are available. Active power injection can critically affect voltage magnitudes as well, and this should be considered in scheduling the system operation. The high cost of active power compared to reactive power complicates further the issue of voltage control.

Black Start functions If a system disturbance provokes a general black out such that the Microgrid was not able to separate and continue in islanding mode, and if the MV system is unable to restore operation in a specified time, a first step in system recovery will be a local Black Start. Two types of Black Start functions are needed: • •

Local Black Start of the MicroGrid after a general system black out; Grid reconnection during Black Start.

The strategy to be followed is a matter for investigation and involves the cooperation of the various system controllers both central and local, using predefined rules and exploiting autonomous agent concepts. The restoration process for any power system is a very complicated process. The related restoration tasks are usually carried out manually, according to predefined guidelines. They have to be completed fast in a real time basis under extreme stressed conditions. In a Microgrid, the whole procedure is much more simple because there are not many loads, switches and large, difficult to control, generation units. In addition, the power electronic interfaces of the distributed resources and loads offer considerable flexibility. Thus, the idea of creating a totally automatic system for restoration seems quite realistic. A special feature of the Microgrid central controller concerns re-connection during Black Start, helping in this way the upstream DMS system that is managing the MV distribution network. During faults on the main grid the Microgrid may be disconnected from the main Utility and will continue to operate with as much connected DG, as possible. During reconnection the issue of out-of phases reclosing needs to be carefully considered. The development of local controllers in close co-ordination with the Microgrid Central Controller functions need to be developed and evaluated from the dynamic operation point of view through studies to be performed in the simulation platform. These Black Start functions contribute to assure an important advantage for power system operation in terms of reliability as a result from the presence of a very large amount of dispersed generation.

Black Start of a Medium Sized Isolated Network In the following the general procedure used in island networks when it becomes necessary to face a general blackout is described. This procedure reflects the reality of the field as it is not only theoretical: it has already been tested, in particular after a blackout in 2001 in a French island. The general requirements of the procedure are: • For the restoration of the network, under the responsibility of the control centre, it is essential to have an independent power source at its disposal. This is the reason why it is equipped with a Standby Power Supply to cope with the case of a generalized failure.

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•

No specific communication infrastructure was used in this case: only telephones, mobiles and radios. The steps followed during the restoration procedure can be summarized as follows: • Removing all loads existing in the network in order to limit the load to feed when the network is actually switched on; • BS of a gas turbine unit and energization of the high voltage network (63 kV) • Start-up of other gas turbines • Restoration of the network, witch includes: • Switching on the MV (20 kV) feeder which supplies the control centre to supply it from the main network; • Connecting the available generators to the network; • Switching on the MV feeders one by one relating to the load coupled to the network. An emergency procedure can be also used according to the generators available in the network. Sometimes, it is possible to appeal to a private generator. In this case, a particular command is issued which contains the limits of the responsibilities and access conditions to private and public installations. In all cases, operations are under the responsibility of the utility.

Microgrid Black Start The restoration procedure in a MG has some similarities with the approach adopted on a medium sized power system, namely: the need for several sources with BS capabilities and standby power supply and a monitoring and control scheme embedded in the Microgrid Central Controller. Blackstart functionalities can be based on a set of rules identified in advance and embedded in the Central Controller, or a highly decentralized, Multi Agent System (MAS) approach can be followed.

Sequence of Actions for Microgrid Black Start During normal operation the Microgrid Central Controller (MGCC) periodically receives information from Load Controlers (LC) and Microsource Controllers (MC) about consumption levels and electric production and stores this information in a database. It also contains information on the technical characteristics of the different MS. Microgrid black start procedure involves a set of rules and conditions to be checked during the restoration stage, which are identified in advance and embedded in MGCC software. These rules and conditions define a sequence of control actions carried out during the restoration procedure. The implementation of a blackstart procedure requires the availability of some MS with BS capabilities, which involves an autonomous local power supply in order to feed local auxiliary control systems and launch generation. Beyond this essential condition it is also required availability for: • Bidirectional communication between the MGCC and MC / LC. • Updated information, obtained before disturbance on the status of load/generation in the MG and about availability of MS to BS. • Automatic load disconnection after system collapse. • MV/LV distribution transformer disconnection from the MV network, before starting the BS procedure. • LV network area separation. After a general blackout, the MGCC will try service restoration in the LV area supplied by MS, based on the information stored in a database on the last MG load scenario, by performing the following sequence of actions:

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• •

•

• • • •

•

Disconnection of all loads in order to avoid large frequency and voltage deviations when energizing the network. The MG should also be sectionalized around each MS with BS capability in order to allow it to feed its own (protected) loads. Building the LV network. The inverter associated with the storage device will be responsible for LV and Distribution Transformer (DT) energization. In order to follow the grounding LV protection guidelines, presented in Work Package E, the MG should keep the earth reference, available in the earth connection of the neutral of the DT. Therefore, when building the LV network it is necessary to energize the DT as soon as possible. When energizing the DT by the LV side, a large inrush current is experienced. To overcome this problem, transformer energization should be performed using a ramp-wise voltage wave form. Small islands synchronization. MS already in operation in stand alone mode should be synchronized with the LV network. The synchronization conditions (phase sequence, frequency and voltage differences) should be verified by local MC in order to avoid large transient currents. Connection of controllable loads to the LV network is performed if the MS running in the LV network have the capacity to supply these loads taking into account the available energy storage. Connection of non-controllable MS or MS without BS capability, like PV and wind generators. Other MS without BS capability can be connected to the grid and even absorb power to restart. Load increase. In order to feed as much load as possible, depending on production capability, other loads can now be connected. Change the control scheme of the inverters: after service restoration on the MG, the control schemes of the DG inverters are changed from VSI to PQ control. This is required because batteries that are assumed to be installed in the DC link of these MS are not suitable to respond to frequent load variations, since charge and discharge cycles reduce significantly their life-cycle. On the other hand, flywheel life is almost independent of the depth of discharge. Flywheel storage systems can operate equally well on frequent shallow discharges and on very deep discharges. MG synchronization with the MV network when it becomes available. The synchronization conditions should be verified again.

MicroGrid Black Start based on Multi Agent Systems The general idea is that the agents will execute all the necessary actions without human interference. Furthermore some actions are decided in advance, if possible. This approach has two main advantages. The first advantage is that the computational demand during the critical event is limited and this is very important if we consider that the time limits are very strict and the processors that will be used in a future MG are not powerful supercomputers, as in large centralized power systems. The second advantage lies in the fact that during the blackout there are several communication problems. Therefore the data exchange should be even more limited. The ideal situation is the one where no communication would be needed (all the actions decided in advance). The main actions are in chronological order: • Identification that the system is in blackout state. • Identification of which production units have start up capabilities as well as the controllable loads • Making all the necessary switching actions to prepare the network for start up. • Launch the BS units. • Start the loads. Page 35 of 75


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III.3 Emergency operation III.3.1 Protection systems coordination Improvement in the utility reliability and power quality can be done by minimizing the effect of faults and interruption of supply on customers. One of the ways is to enhance the coordination between the protection systems and the switches. Fault indicators (FI) and overcurrent protective relays are the main devices used for fault detection and isolation in distribution networks. Power restoration is done actually manually by the operator (remotely for some switches and manually on field for others). When a permanent fault occurs, operator can know its occurrence from the information delivered by FIs. Then, orders to the breakers and switches will be sent for localizing, isolating the faulty section and re-energizing the sane sections of the network. The faulted element could be found on faulty section just determined and will have to be repaired. Finally, and after the maintenance, the faulty section can be re-supplied. In theory, if we know the voltages and currents during a fault at a given measurement point, we can use them to estimate the distance to the fault. The equation is very simple, just Ohm's law (measurement at steady state of the fault):

d= Z1td

Source

Z1d

V I .Z line

Equation III-2

Zdcomm.x

I1dm Z2td

V1dm

Zdcomm(l-.x)

Z2d Rf

V1d

DG

V2d

Zl

V2dm

k =i  Zf k −1 = + + Zf Zf ( 1 ).Zdcomm.x  i i −1  Z DGk = k 1   Z1td + Z1d Zf 0 = Z 2td + Z 2d

∑

Figure III-8. Fault location computation In [PENKOV-2006], it is presented fault distance calculation algorithms for distribution network in the presence of distributed generators (Figure III-8). This method based on the symmetrical component computation. The data needed are the impedances of the machines transformers, conductors and the fault current of sources, as well as the network topology. The fault current from DGs could be determined by on site measurements or by estimation from DG models. Then, the faulty section is cross-determined both from this computed distance and from the indication from FIs systems. If needed, directional FIs can be used [PHAM CONG-2006]. In the INTEGRAL project, the idea is to communicate with FIs and fault distance calculation devices to quickly locate the faulty section of the grid. These fault treatment processes need protection systems which are coordinated by ICT infrastructure.

III.3.2 Increase of automation devices to fast service restoration Outages and faults are inevitable in power systems. The general practice for alleviating outages or faults is isolation of the faulted part of the power system. In the process of Page 36 of 75


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isolation, some un-faulted areas loose power. Restoring power, as soon as possible, to these out-of-service areas is essential. That process is called restoration. Restoration entails a fast and efficient switch operation scheme that isolates the faulted area and restores the remaining parts of the system. The system should automatically reconfigure to maintain this, which needs a good understanding of the system dynamics and distributed control. Also, with the recent initiatives to enhance the reconfigurability and continuity of supply for distribution systems, much effort is being concentrated on distribution automation. With high penetration level, DGs could be able to re-energizing some local loads. Depending on their size, location and blackstart capabilities, DG units may be requested to contribute to network restoration after a partial or complete system collapses. In the [PHAM-2005], the authors proposed a restoration process with both participation of local DG units and transmission system. This process is to re-energize by islanding in transmission network in parallel with distribution network cell formed only by the availability of DG source (with blackstart abilities). The power system could be restored in upstream coordinating with downstream to pick up more loads earlier and to reduce the collapsed time. This process has been called deep build together. However, the capability of DG units (with black start abilities) interconnected to distribution network is very often limited as small or medium size (from few tens of kW to few MW). They cannot safely pick up the load greater than its static and dynamic characteristics. Thus, the loads in the distribution level must be first divided in sub area and, when reenergized, observable with sufficient accuracy. Furthermore, the automatic restoration process relies basically on "Remote Controlled Switch" (RCS). Therefore, more RCS and ICT components to measure and remotely control the EPS will be required. For planners and DNO, this process is obviously an optimization problem. The major research focuses basically on "Maximizing restored load in the system" with the various technical and economical constraints: • Restoration time, • Black- start capacity of DG units (active power limit, reactive power limit, start up time, etc), • Grid normal operation constraints (line thermal limit, Voltage limit, Stability limit, etc), • Protection ability for small areas (if a fault occur in islanded area, itt must be cleared), • Network reconfiguration constraints (switches status, cell level management, etc), • Minimum of ICT components or automation devices (number of RCS, placement of RCS, etc) [PHAM-2006].

III.4 Conclusions Advanced ICT systems in the near future will allow coordination between a large number of DG/RES nodes, the load nodes with the distribution network operators. An integrated ICT concept could contribute to the following objectives: - Stabilize the network en reduce peak loads. - Prevent dangerous situations - Minimize effects of network failures during critical operations. - Black Start of isolated networks. Different operators and market parties have different incentives and priorities in their business goals. Each of these market parties has different interest and objectives in each grid level. It is the challenge for Integral to develop a non-ambiguous view on an automated coordination system that meets the requirements for each market role at each grid level. Therefore, these systems standardized systems with clear predefined functions are

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necessary to ensure that after implementation, the stability and control of the network is actually improved This necessity aims to enhance the computation abilities, to increase the coordination between local communications and global communications. Including some conclusions from coordination of supply and demand and allowing for the necessary sizing and configuration possibilities. Within the INTEGRAL framework, the geographical and electrical criterion will be proposed to take the ICTs dimension feasible. The results of CRISP and MICROGRID projects will be pursued." The introduction of advanced ICT systems is able to enhance the EPS operation. The new ICT networks could represent valuable tools in the system for utilities to acquire information, communicate it for further utilization in order to improve the coordination in the EPS and then to prevent and avoid dangerous situation for human and materials. Therefore, each facility necessitates a dedicated structure and function of ICT systems. Different operators may have different incentives and priorities to invest in different parts of their network. Thus, these systems are necessary to be sized and configured in order to fulfill these requirements and achieve the global efficiency of power systems.

IV High level INTEGRAL functions IV.1 Demonstration A This section describes: • High Level description of Demonstration A. • The targets and goals of Demonstration A. • The coordination concept, e.g. the PowerMatcher, that will be applied. • Overview of the context in which the PowerMatcher concept is working and from which a number of scenarios will be derived that will be part of the implementation of the demonstration. • Overview of the functionality required for Demonstration A.

IV.1.1 High Level description of Demonstration A Demo A, titled as “Normal Operations” is aiming to demonstrate the day-to-day operations of future energy networks in which a large number of DER/DES devices are implemented in the households. Consumers are expecting undisturbed performance of these devices without any impact on their lifestyle or comfort. At best it can be expected that they are willing to schedule the washing of their laundry within a certain timeslot if this would result in lower energy bills or it would contribute to CO2 reduction by washing on (locally produced) green energy. Demo A is expected to demonstrate such an undisturbed situation where a large number of DER/DES devices are introduced into the living area of prosumers. Within a few years from now it is expected that µCHP systems will be introduced in large scale in the North West European market. This will increase the energy efficiency and reduce the CO2 footprint. Due to the fact that the electricity and heat demand is not synchronous within a household it can be expected that the next generation of µCHP systems will deliver 30 – 40% of the electricity back to the local electricity network.

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Based on results from previous field test 20 – 30% of the electricity produced by a µCHP systems can be delivered in a flexible way by using heat storage. This flexibility can be used to optimize the energy balance on several levels in the network as de-scribed in the following paragraphs. One of them is to compensate for the intrinsic (unpredictable) variation in electricity production of PV-solar panels and Windmills and stabilize the electricity system. In this way the introduction of µCHP systems can support the introduction of renewable energy sources in a transition to a sustainable society. Demo A will primarily use three techniques to maximize the flexibility in energy production and consumption, which will be used to minimize energy imbalances and decrease peak loads: i. Energy Storage, both heat and electricity ii. Load Management (load planning & shifting) iii. Smart Comfort Each of these techniques is described in more detail below in the context of demonstration A: Energy Storage a. Heat Storage The heat and electricity will be produced simultaneously by a µCHP system but normally the heat and electricity demand within households will not follow the same pat-tern. With the application of a heat storage the production and heat demand gets de-coupled and therefore the heat and electricity production becomes to a great extend independent from each other. This allows to regard the µCHP from a VPP perspective primarily as a generator for electricity with a mixture of uncontrolled production, planned production and controllable production. Intelligence is needed to: • Ensure that the heat demand is always met within the household, • The production of the electricity can be coordinated. This will be based on the PowerMatcher concept as described in the following paragraphs. Uncontrolled production happens when the filling of the heat storage drops below its minimum and heat is required within the household. Most µCHP systems however do have a secondary burner to deliver the peak loads in the heat demand and should be ignited in such a case preventing uncontrolled production of electricity. Based on historical consumption and the expected heat demand for the upcoming period the heat production should be planned according to the minimum required level. The remaining part can be used for flexible production of electricity and the resulting heat can be stored in the boiler. b. Electric Storage The intention of the usage of Electric Storage within Demo A is two fold: • Energy Storage The batteries should be used to optimize the energy costs within a house-hold. At first sight one should expect the batteries to load when there is over production within a household, but since the PowerMatcher intends to do a costs optimization they should load at moments that electricity is cheap or unload when electricity prices are high. Of coarse price differences for internally generated power and externally bought electricity should be taken into account which can be in most cases be expected to be in favour of the internally produced electricity since no taxes or transport costs have to be taken into account but does not have to be true in all cases. • Grid Plug-in Hybrid or All Electric Mobility Integration of hybrid or all electric cars or scooters can contribute to further stabilizing the network by incorporating their electrical storage in the smart grid concept. However the Page 39 of 75


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optimization strategy cannot be copied directly from a regular energy storage system. Since both the expected arrival and departure times have to be taken into account as well as the expected mile-age. Usage of the PowerMatcher technology should ensure it finds an acceptable balance between minimizing the electricity costs and the comfort to ensure a sufficient radius of action. c. Load Management In order to be able to minimize the electricity costs and optimize the benefits of the locally produced electricity one should be able to shift and/or plan electrical loads within a household therefore two type of devices are considered to incorporate within the field test: • Electric refrigerators, freezers, washing machines and clothes dryers. • Hot fill washing machines and clothes dryers. • The incorporation of the electric devices is straight forward. They should have the capability to run when electricity prices are low. • The hot fill devices have the advantage that the heat is produced in a very efficient way by the µCHP and therefore canceling out the inefficiency of centrally produced electricity. This results in significantly lower washing costs. As a side effect more electricity is produced by the µCHP, which if these hot fill devices can be scheduled result in more flexible power available. • Smart Comfort • Further flexibility in the electricity production can be achieved by the introduction of smart comfort in the form of a “flexible thermostat”. Such a thermostat has lifestyle based steering profiles that deliver a high bandwidth around the set-point during the night or when the occupants are not at home. A day-time and in the evening hours when the house is occupied comfort is required and the thermostat should steer as close to the set-point as possible. This delivers more freedom to the µCHP to fill the boiler. d. Configuration of DEMO A In order to incorporate a mixture of available customer off the shelf products and newly developed techniques in a pre-market phase the VPP of Demo A will connect three locations: Residential Area, GET Laboratories and the ECN Laboratories. In the residential area 30 – 60 households will be configured as depicted below:

Figure IV-1. Residential configuration for demonstration A

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The overall configuration can be depicted as follows:

Figure IV-2. Overall configuration of residential area for demonstration A

IV.1.2 PowerMatcher coordination In the earlier EU-funded CRISP project the PowerMatcher concept has been developed and tested. The PowerMatcher is based on a merge between control theory and microeconomic theory: on one hand commodities (i.e. electricity) is being sold on a market at a negotiated price, and on the other hand the same commodity is used to control a process or a desired output. Agents are used in order to create an intelligent distributed control model for marketbased coordination of electricity demand and supply. (Figure IV-3) Based on the state of a process the agent determines the input needs and translates these into a bid function. A large deviation from the desired output is reflected in high price bids for electricity demand and vice versa. Thus the flexibility of the underlying processes leads to flexible use of electricity. The total system acts as a collection of local independent controllers that behave in accordance with conventional control engineering theory.

Figure IV-3. Microeconomics and control theory unified in a multi-agent system

In the PowerMatcher concept each device that controls a process is represented by a control agent, which tries to operate the process associated with the device in an economically optimal way. The electricity consumed or produced by the device is bought, respectively sold, by the device agent on an electronic exchange market [KOK-2006]. The electronic

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market is implemented in a distributed manner via a network structure in which so-called PowerMatchers, as depicted in Figure IV-4, coordinate demand and supply of a cluster of devices directly below it. The PowerMatcher in the root of the tree performs the price-forming process; those at intermediate levels aggregate the demand functions of the devices below them. A PowerMatcher cannot tell whether the instances below it are device agents or intermediate PowerMatchers, since the communication interfaces of these are equal. This ensures a standardized interface for all types of devices.

Figure IV-4. The PowerMatcher architecture; coming from a hierarchy based mechanism, growing towards a more organic, network of networks.

A number of different architectures may be derived from the above general concept, in which intermediate matchers can have local responsibilities such as preserving network constraints, leading to different price-forming scenarios such as local marginal pricing (LMP). Also at each level in the network business agents may input their goals at PowerMatcher nodes in the form of standardized bid function. Thus a DNO may trigger demand (and supply) response actions in a PowerMatcher market. The essential difference with traditional demand response is that the device agents are operated autonomously, yet reaching the desired result.

IV.1.3 Event based markets The root PowerMatcher in Figure IV-4 has one or more associated market mechanism definitions, which define the characteristics of the markets, such as the time slot length, the time horizon, and a definition of the execution event (e.g. “every 5 minutes”, “every day at twelve o’ clock”). In previous versions of the PowerMatcher at each execution event the root PowerMatcher did send a request to all directly connected agents to deliver their bids. The device bids are aggregated at the intermediate matchers and passed on upwards. The root PowerMatcher determines the equilibrium price, which is communicated back to the devices. From the market price and their own bid function each device agent can determine the power allocated to the device. This root-based event mechanism has been replaced (or extended) by a mechanism in which agents can start (partial) market actions based on process events. In such an event based market agents can bid, or adjust their bid, at every moment their situation changes and directly receive a new contract after revaluation of the market. Also those agents that are influenced by the market revaluation are informed of their contract changes. Other agents Page 42 of 75


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are not bothered. The event based design increases responsiveness of devices to events from their environment. Also the communication overhead may be reduced, especially for devices at rest (space heating during the night). And not all communication will be focused on fixed moments in time, but will spread out. Event based markets may also give a means to handle ramping up and ramping down effects of devices. During ramping up a device may place new a bid on the market every few minutes, each time with a higher power bid, until maximum power is reached. This may lead to communication overhead (several bids are needed for one on/off action of a device), however an enhanced bid protocol may be used that takes into account the ramping up/down cycle of a device. Event based markets and periodic markets can be applied in a hybrid way. The periodicity of the latter can be reduced if event based bids are allowed in the market. Also no special mechanism for detection of lost agents is needed since the periodic market will find out. The event based market concept also closely resembles the Napster or Kazaa networks, since only agents will be approached that are involved in a transaction. Thus market agents more and more take the role of broker agents.

IV.1.4 The dual market view In the previous paragraph two field tests have been mentioned, one with a commercial control goal of reducing imbalance in a trade portfolio, and the other with a technical control goal, reducing distribution level peak loads. Both field tests have been successful in the contribution to their respective goals. Although commercial operation based on an open market model serves as a base model for European electricity supply, one cannot disregard the technical constraints due to grid operation. This is already functioning at the transmission level: in most European countries a power exchange market is used to establish a market price for the next day; this commercial allocation is followed by a technical approval due to network limitations in order to ensure security of supply. In the distribution system no similar model exists today. This paragraph provides a high level market-based view of this dual control goal for the distribution level that fits within the agent-based approach of the PowerMatcher. It is based on earlier work in the FENIX project [FENIX-2007], but elaborates on this vision in that it emphasizes the independence of commercial and technical virtual power plant operation and the coordination between both. A “prosumer site” is an end-user premise, domestic, commercial or industrial, which has distributed generation and/or responsive loads installed on it. Figure IV-5. gives a structural concept for both the technical interaction model and the business trading model for intelligent grid control. There are three main stakeholders: • A prosumer Household (owner) who aims at lowering energy costs (increasing DG revenues and lowering electricity demand costs). Increasing the DG benefits is achieved by actively delivering services to the other two stakeholders on the CAA (see below). • The Commercial aggregator (Balance Responsible/Energy Supplier and buyer of the locally generated electricity) who aims at optimizing their complete portfolio. Whereas they will on the one hand try to flatten out their total demand curve by scheduling a proper price scenario for the VPP and optimize their spark spread. On the other hand they will use the VPP to minimize their imbalance costs. The commercial aggregator operates the prosumer as part of a commercial virtual power plant and provides incentives by bidding prices for the commodity part. • Local distribution system operator (DSO) aims at an efficient and stable operation of its network. The DSO, in turn, reimburses Households connected to its network to achieve an optimal network load. This can be done by either giving incentives based for increasing/decreasing the production (in certain parts of) the network as well as

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Commercial Aggregation

Commercial Balancing

SH

SH

SH

SH CAA

SH



SH

CAA

SH

SH

SH

SH

SH

SH CAA

SH SH



DISTRIBUTION LEVEL

SH CAA

SH

TRADE & SUPPLY

Forward & Futures Market & Power Exchange; Balancing markets; etc. WHOLESALE MARKET

•

incentives for increasing/decreasing the consumption. The DSO operates the prosumer as a part of a technical virtual power plant and provides incentives by setting appropriate price levels for energy transport In order to operate a technical VPP in a proper manner the DSO should on the one hand be able to determine the local load on the transformers and different network segments behind the transformer to detect congestions in the network. On the other hand the DSO should have insight in the actual price levels on the CAA as well as on the imbalance market. Based on this information they should form a proper price bid to achieve the desired network situation.

SH SH

SH

SH SH

CAA

Network Service Aggregation



Distribution Services



TRANSMISSION LEVEL

TSO

Transmission System Services

Individual operating parameters, contracts, or Bids & Offers Intermediated level operating parameters, contracts, or Bids & Offers Aggregated operating parameters, contracts, or Bids & Offers CAA

Commercial Aggregating Agent

SH

Smart House

Figure IV-5. Aggregation of Households for dual control goals: Technical Aggregation for ancillary services delivery to DSOs / TSOs and Commercial Aggregation for integration into energy markets.

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Figure IV-5. visualizes the aggregation (= pooling) levels for the dual control strategies of technical and commercial aggregation. Both the DSO and the commercial party aggregate and optimize over a high number of prosumer sites. In each grid area all Households (technical systems) are communicating with one single aggregating system of the local DSO. However, in the same area different prosumers can have contracts with different commercial aggregators. The local Commercial Aggregating Agent (CAA) acts as an intermediate between the three stakeholders.

IV.1.5 Agent based coordination in dual markets For the coordination between households the dual market view leads to aggregation levels where household agents coincide with incentive agents from two different parties, which may have conflicting interests at any moment in time. The negotiations and the delivery of the active services are automatically performed by the Household interface. Those houses receiving non-conflicting incentives from both parties will be more willing to respond. In this way a global merit order list emerges which balances between the stakes of all three involved parties.

Figure IV-6. Cross-section Aggregation Market

Figure IV-6 focuses on a cross section from Figure IV-5. in which all households are clustered that belong to one distribution segment of the grid (one common substation) and that are connected to one commercial operator (a virtual power plant or a utility). The CAA node functions as a PowerMatcher aggregation node that receives the bids from the households. A commercial operator may enter this same CAA node with an incentive in order to provoke a demand or supply response from the households. This incentive can also be in the form of a bid function, offering a high price for electricity if the commercial operator is in need of electricity (e.g. if the commercial operator is short in its high level market) and offering low prices for electricity if it is long. Similarly the technical operator (the DSO) may place bids on the CAA node based on the state of the network and set high prices in case of e.g. network congestion, thereby invoking less demand at the local grid and more supply. In demonstration A several scenarios will study different aspects of the dual market model.

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Candidate scenarios are commercial portfolio imbalance reduction, substation peak reduction, local energy use optimization and economic performance. Structuring of markets In Figure IV-5. each household belongs to one cross-section. Looking at the cell1 / cell 2 concepts developed in the CRISP project and described elsewhere in the WP2 deliverables a reconfiguration of cells may force a household to become part of another grid segment by opening or closing switches. This will require a reconfiguration of the markets and the household (e.g. its agent) has to be notified to get subscribed to another CAA node. In this way the PowerMatcher concept can contribute to quickly rebalancing a distribution cell. Input from the PowerMatcher markets may even give the technical operator insight in cells becoming imbalanced due to a threatening shortage of generation capacity, which may be a trigger for reconfiguring the network.

IV.1.6 Supporting Functionality Apart from the primary functionality required in Demo A to build-up and control the VPP additional supporting functionality is required. The supporting functionality can be categorized as follows: • Online Trouble Shooting Essential lessons learned from earlier field tests (CRISP, Gasunie) is the ability for remote maintenance and support of the (agent) software. As with every software developments the code might contain bugs but new versions of the software with improved functionality is most likely to be developed during the period of the field test. With a 30 – 40 households to maintain it is obvious that one cannot maintain the software in a time and cost effective way without remote maintenance possibilities [Gasunie-2008]. It is not unlikely that the home installations may fail at any point of time. This can be induced by faulty hardware, failing software or improper user intervention. Remote monitoring and (predictive) maintenance support functionality is essential to maintain the complete VPP. Such a type of functionality will become more and more important when the local intelligence of devices increases over time and will be developed anyway apart from smart grid concepts. Our opinion is that standardization of a protocol to support (predictive) remote maintenance support is beyond the scope of Integral. • User feedback Participants in the field test need to be informed about the project and the performance of their installation. Otherwise they most likely get distracted and switch of the smart agents since they most likely already have some kind of “Big brother is watching you” feeling. Therefore some kind of dashboard is required which provides functionality to: + Give insight into their energy consumption and production. + Gives insight into the achieved CO2 reduction. + Gives insight into the actual costs benefits. + Anonymous peer group comparison, which gives insight into their “energy performance” with respect to the average / best of this peer group. + Adjust their lifestyle based parameters for the flexible thermostat. + To give user feedback.

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All personal data should be kept confidential and should be secured from unauthorized third party access. •

PR & Management information

Both for PR and Project Management purposes it is essential that the performance of the total VPP can be monitored, controlled, displayed and demonstrated. Most likely this will be some kind of web page that participating parties can access to show how well the VPP is performing. One should be able to zoom in into the different CAA but also be able to get an overall view of the VPP. This should include the Peak and Load Shift effects, Price developments and number of transactions on the CAA’s or CO2 reduction between others. • Data Collection and Analysis Underlying of the whole experiment will be a data collection database to support both analysis of the results, but also provide the data for the user feedback, PR and management information. Although it seems trivial that such functionality is required, collection of 15’’ time interval data of all devices and households will require a substantial effort.

IV.2 Demonstration B This section describes: • High Level description of Demonstration B. • Network configuration for Demo B • Priority setting for Demo B site • External Power Network commutation • Control and Communication element – ZigBee • Central control system – Intelligent PC control system

IV.2.1 High Level description of Demonstration B The purpose of this section is to describe the approach to be implemented at the demonstrator B site in which critical situations are provoked and simulated, including the response options. At first the existing functional situation is described, followed by a description of the occurrence of what critical situations are taken along. Technologies available for the demonstrator B are presented, including procedures for Critical Situation tests, as described in the INTEGRAL project. In order to reach the objective, the system (hardware and software) and facilities where demonstrator B is installed and that will be used for critical situations test, are described. Existing technology is described as concerning the priority of consumables and possibilities to exercise load shedding or islanding in critical conditions. The external network simulation and related different functional options are described. Furthers measures and the procedures are to be included in the test site description.

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IV.2.2 Network configuration for Demonstration B The ‘parador’ MAS ROIG is an old Catalan farmhouse from the XVI century situated at the fringe of village Llagostera, in the province of Girona. Principal activities developed at present are: • • • • • • •

•

Living Farming practices: pigs, lambs, goose, ducks… … Exploitation of horticulture Office with basic infrastructure (computers, printers, ADSL Wi-Fi, scanners, photocopy machine, etcetera) Forest Exploitation: Re-conversion of an eucalyptus forest towards a Mediterranean forest. For 20 years sustainable forest exploitation is implemented. Astronomic observation centre (In construction together with the Astronomic Centre from Girona) Photovoltaic production central with a capacity of 100 kWp (injection in the distribution network at the nearby the industrial polygon). Already it’s accepted by the government, the 100 kWp solar central is waiting for the new spain feed-in-tariff law and also for the Industrial Area construction. However it will be "on-grid connection" without any Microgrid link. Educative activities and wide dissemination. During the last years over 3000 persons have visited Mas Roig. From basic school educative centres, high schools up to university and research centres and government institutions. This accompanied by a continuous press attention through all means of communication, newspapers, articles in the renewable energy sector and TV attention (both the Catalan TV3 as Spanish TVE).

Mas Roig is a singular space which is working with FSA3 technology as unique source of energy for more than ten years (besides a heat recovering chimney). Therefore it is a technical and educational reference offering an exceptional setting where this project will be executed. El Mas Roig is guided by Dr. Francesc Sureda, inventor of the FSA system and active supported in the execution of the INTEGRAL project. Demonstrator B will be implemented at Mas Roig, which will have the configuration as Figure IV-7. The different energy elements in the system can be classified as RE Generators, Accumulators, Consumers, Converters, Control Elements, Communication elements, Commuter, Central Control: All the loads have the same supply path. When we are under critical conditions, the DSM system disconnects the critical loads by a contactor located close the charge.

3

FSA system is patented by wattpic, it describes the system, which provides autonomous movement to

photovoltaic and calorific elements to follow automatically sun radiation.

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Multiple Zig Bee nodes at each load inside home

C1, C2, Cn

G2

A3

Zig Bee

Zig Bee Zig Bee Zig Bee

G3

Zig Bee

G1 Intelligent Control PC with Zig Bee master node

A1 G4

A2

Figure IV-7. Micro-network scheme. Components of generation and consumption

Renewable Energy Sources (RESs): • • • •

Photovoltaic power station (G1) (with a battery) Photovoltaic power station (G2) (with a battery) Wind mill (G3) (with a battery) Diesel Engine (G4)

RE Generator PV_FSA_I

PV Brand HELIOS

PV Power 1

PV_FSA_2

ATERSA

1,2

PV_FSA_3 Wind Mill

Siliken Fatronik

1,2 2

Diesel Engine

Electra Molins

14

Battery

Converter

Others

24Vcc, 1000 Ah 24Vcc, 1400 Ah No battery 24Vcc, 1500 Ah No Battery

HEART EMS 2.800 24 Vcc Xantrex SW3024

10 years old

windstreampower 2,5 Kw

Out of order

4 years old

Is managed by Xantrex SW

Table IV-1. Renewable Energy Sources' parameters

In order to measure the charge on accumulators, the open circuit voltage measurement is used by means of voltage meters embedded into zigbee nodes (see system interconnection description)

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Accumulators: Apart from the batteries associated to the RE Generators, we will have: • Auxiliary Battery (A1) • Hot Water Tank (A2) • Freezer (A3) Concerning those 3 energy storage elements or accumulators only A1 may provide power in some cases. A2 and A3 are indeed Consumers, which may store energy in the form of hot water for sanitary use (A2) and cold for food or other storage purposes (A3). Accumulator Auxiliary Battery Hot Water Tank Freezer

Parameters Do not exist 300 liters 240 L

Brand Lapesa

As in previous case, for accumulator’s charge measurement, the open circuit voltage measurement is used by means of voltage meters coupled to Zig Bee nodes (see system interconnection description)

IV.2.3 Priority settings Demonstration B site Consumers - Loads: The consumers in Mas Roig have associated a priority property, for example (h)igh, (m)edium, and (l)ow priority. There are 3 subsystems of consumers: • Home (C1) • Office (C2) • Warehouse (C3) The following table show the available consumer elements (within Home and Office) with consumptions and priority levels – taken from “Centre d’interpretació de la Sostenibilitat”). Within each of the subsystems different consuming devices are defined with each of them a different level of priority. One of the first activities in critical situations is closing the lower priority or main consuming devices. The devices are grouped depending on the nature of functioning. The decision making process and related algorithms is to be defined, jointly with NTUA and IDEA Functional electricity groups at MaS Roig: Lights Electrical Applicances Cleaning electrical application Climatization Water / irrigation IT systems Multimedia Example of electricity devices and level of priority is given below, a complete overview is described in the next section:

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Lights (Low Power lights)

Priority level

Hall 1 x 24 W

L

Kitchen 1 x 24 W

M

Living room 1x24 W + 1 x 11 W = 35 W

H

Studio 1x24. W + 1 x 11 W = 35 W

L

Toilette 1 x 11 W.

H

Dorm-room 1x 24 W

M

Outside 2 x11 w = 22W

L

Total Illumination consumption: 175 W x 3 hours per day approx. = 525 W/24 hours Table IV-2. Priority settings of consumption for Demo B

Converters / Inverters Most RE (Renewable Energy) Generators need converters / inverters to convert from DC to AC. The switching of these devices is controlled by intelligent system thorough zig bee control devices. Reading of power load and generation could be read from those elements thorough measurement with associated zig bee hardware. Converter

RES within HEART EMS 2.800 24 PV_FSA_I Vcc

Xantrex SW3024 windstreampower 2,5 Kw

PV_FSA_2 PV_FSA_3 Wind Mill Diesel Engine

Table IV-3. Converters/Inverters associated with RE Generators of Demo B

IV.2.4 Control and Communication element – ZigBee Some elements are controlled thorough ZigBee nodes. These wireless elements provide the communication capabilities that, attached to intelligent control, provide the infrastructure for intelligent control. This technology allows Mas Roig to be equipped with a variety of critical operation procedures possibilities. What is ZigBee? : ZigBee is a low-cost, low-power, wireless mesh networking standard. The low cost allows the technology to be widely deployed in wireless control and monitoring applications, the low power-usage allows longer life with smaller batteries, and the mesh networking provides high reliability and larger range. Why ZigBee? : The ZigBee elements are to be developed and installed in Mas Roig facilities. Different brands and configurations have been considered. We analysed the Zigbee development kits present on the market. Two candidates were selected: Chipcon (Texas Page 51 of 75


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Instrument: CC2430), and Ember (EM250). CRIC has experience on EMBER kit development, from the point of view of software and hardware. In fact, we developed Zigbee hardware using the Transceiver EM2420. Recently, there has appeared the EM250, a System-on-Chip that combines a 2.4 GhZ IEEE 802.15.4 compliant radio transceiver with a 16-bit microcontroller similar to the CC2430 from Chipcon, but with a very interesting features. The EM250 provide better performance than the CC2430 mainly in three points: •

The sensitivity is better: -98 dBm in front of -94 dBm. This means that for the same Radio-Frequency power the EMBER chip permits a 50% more than coverage in sighton-line.

•

The power transmission allowed without power amplifiers is higher: +3 (normal mode) ÷ +5 (boost mode) dbm in front of 0 dbm. This characteristic will even increment the coverage again in a 41% (+3 dBm) or 78% (+5 dBm).

•

Better consumption. The data sheet of CC2430 shows a bit lower consumption than the EM250. That is true. But we have also to take into account that the distance allowed for the Chipcon chip is worst (due to its worst sensitivity and low RF power transmission) an important drawback especially in indoor applications. So if we compare the ratio coverage/consumption, the EMBER chip is much better than the chipcon chip.

EMBER and TI/Chipcon are both promoters of the Zigbee Alliance, but the software from EMBER, its Zigbee Stack, are used in USA to test if the Zigbee products developed are Zigbee compliant. This means that the Zigbee Stack from EMBER is normally the latest one, including the last Zigbee updates. Summarising, the EMBER kit has been selected because is better from the point of view of software and hardware, and we have experience on it.

Figure IV-8. EM250

System-on-Chip overview

On the next table is exposed the features of this chip: the transceiver plus a microprocessor:

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Hardware Platform Frequency Number of Channels OperatingTemperature Transmit Power Output RF Data Rate Receiver Sensitivity Adjacent channel rejection (Supply / 5 MH ) Voltage

General

Performance

Power Requirements

Microprocessor Capabilities

Total Current N l/B Current Total N l/B Sleep current

RX TX

Architecture Frequency Memory EEPROM General Purpose Registers Peripheral Features Interfaces Others Table IV-4. EM250

Figure IV-9. Zig

Features

Ember ISM 2.4 GHz 16 Channels -40 to 85 ºC (industrial) 2 mW (3dBm) / 3.2mW(5dBm) 250 kbps - 97.5 / - 98.5 46 / 39 (EM) 2.1 – 3.6 V 35.5 / 37.5 mA 35.5 / 41.5 mA 1 µA 16-bit XAP2b µC 12 MHz 128 kB Flash / 5 KB SRAM EEPROM emulation 17 x 16 bit 2 timers / RTC / PWM / 4h UART, l 12 SPI bi ADC I2C, Watchdog timer / Power-on R /i l RC ill /

Bee nodes based on EM250 developed by CRIC

Radio nodes based on EM250 has been already developed by CRIC in two different designs. The second version is based on Ember reference design and improves the radio performance, as well as the board dimensions. The concept is that each wireless element will have an associated specific zig bee node, with specific capabilities (on loads, consumption is measured and can be switched on and of; on generators, it can be switched on and off, and accumulator voltage and frequency can be

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measured). Once a new node is connected, automatically it will detect the wireless network in which is operating and will join to the network. A particular node has high relevance: the main node allocated to the Central control PC, which acts as master, and manages and control all the wireless nodes.

IV.2.5 External power network commutation Mas Roig is an isolated power network. The connection to the general power network is going to be simulated using simulation software provided by the French partner (IDEA) There is a commuter control, which will allow commuting the power source between the (simulated) external general power and the internal RE generators power. This commuter is zig bee based also and allows controlling the right phase and voltage for a satisfactory connection. The development of electrical power supply network of the Demo B site at Mas Roig can be graphically visualised with the following graph:

MICRO-GRID 220 Vca, 50 Hz C: Demand

A:

Accumulators

G: Generation

Intelligent Management System

C1: Priority Demands C2: Prorrogables A C3: Prorrogables B C4: Additional

A1: Battery group nr.3 & Converter cc/ca nr. 3 A2: Sanitation, hot Water System A3: Water Deposit A4: Freezer

G1: FSA_Autonomous (FV + converter nr. 1 cc/ca + Battery group nr. 1) G2: FSA_GRID (FV + CC/CA nr. 2 ) G3: Micro-aero generator (CC/CA nr. 2 + Battery group 1) G4: Micro-CHP. Electricity Generator (bio-diesel) con alternator 15 kVA and heat exchange

Simulation External power network (simulated)

Figure IV-10. Electrical Power Supply network of the Demo B site at Mas Roig

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IV.2.6 External power network Available options to simulate the external power network: • Option 1 Use an inverter or and medium size generator (with the speed and excitation control ability) to control an equivalent dynamic system close collapsing (from a record). Record the failure procedure, after a blackout, at the buses of our distribution network. This recorded file must include some parameters of point (V,f) which will be used as the set-point for the inverter with the control loop (V,f). • Option 2 Use an inverter or and medium size generator (with the speed and excitation control ability) to control an equivalent dynamic system close collapsing (file of point anyway) thank to the control command loop which have the more or less first order answers (depend on the dynamic identified with Eurostag or other simulation software). • Option 3 This is the remote control from Grenoble with the real time simulator RT-Lab. We can control an inverter which have V/f regulation with RT-Lab in Grenoble, and we could hold of the controlled collapses of network and see whether a contribution of a cell LV0 can help and save the network. This option needs a high efficiency communication protocol. RT-LAB is a distributed real-time platform that facilitates the design process for engineering systems by taking engineers from Simulink dynamic models to real-time with hardware-inthe-loop, in a very short time, it provides also tools for running simulations of highly complex models on a network of distributed run-time targets, communicating via ultra low-latency technologies, in order to achieve the required performance. You have to design and validate a model by analyzing the system to be modeled and implementing the model in the dynamic simulation software. RT-LAB is designed to automate the execution of simulations for models made with offline dynamic simulation software, like Simulink, in a real-time multiprocessing environment. RT-LAB is fully scalable, enabling you to separate mathematical models into blocks to be run in parallel on a cluster of machines, without changing the model’s behaviour, introducing real-time glitches, or causing deadlocks.

IV.2.7 Test Software control system For test proposals, an initial version of software has been developed. This one provides the interfaces for power measurement and control (unfortunately at the moment the software in only in Catalan language version). Through the intelligent software, the system can access, by means of the Zigbee network, to particular load and generators status, configuration and control, and it provides access to the intelligent system on intelligent criteria control rules and different parameters.

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Configuration access to photovoltaic pannel

Access to diesel generator configuration

After individual configuration, intelligent parameters can be configured

Loads configuration

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Figure IV-11. Test of Software control systems for Demo B

IV.2.8 Central control system – Intelligent PC control system One PC (with Internet connection and telematic connectivity to the control elements) hosts an intelligent demand management system (DMS), and controls the network thorough a USB connection to a ZigBee node which acts as control net master. This DMS has a user interface which allows to: - Configure the system: add, remove, edit all kind of elements (consumers, generators, etc.) - Configure working modes taking into account different factors and data (consumer priorities, main grid status, battery levels, etc.) - Choose a working mode (night/day, season, level of criticity state, etc.) - Monitor the status of the system (stability, performance, quality) The intelligent control software system has intelligent behaviour based on: -

Sensing on the system: data about battery levels and consumer loads in a certain time. Control decisions of consumer (and generators?) switching on and off to avoid system failures and to optimize the stability of the system. Criteria might come from: o o o o o

o

System configuration and operation modes (conservative, reliable, etc..). Current consumers load and batteries level Working mode chosen by user or automatically by calendar and clock History of the system. Reconnection processes: re-connection of consumers, especially after a blackout, will depend on consumer priorities. Apart from priority other properties might be considered, such as consumers type (reactive or capacitive, etc.), and the interrelationship among consumer elements. Reaction against pre-detected adverse conditions – failure signals: the system, if a critical situation is detected in advance due to some announcement signals (to be detailed but, for example, before a power failure Page 57 of 75


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o

o o

there might be a voltage drop and drift detection, etc), will proceed to the isolation of the micro-network, and will compute and decide which are the vital consumers to keep on, their consumption, the battery loads, and plan next future in terms of use of batteries and future disconnection of consumers, provided the computation of time in which the system can survive active. Economic-related decisions about connection to the general power network: taking into consideration current power prices, current configuration and status of the micro-network, history, and battery loads, a decision can be worked out to optimize economical aspects between using external power or internal power, and sell the available generated power to the external network Weather, day/night and season factors: some preventive decisions about generation and consumer connections and disconnections can be worked out tied to those factors and the current consumer loads and battery levels. Quality, performance and stability of the electric micro-network.

AI based techniques to be used to provide this intelligent behaviour and control might be rules-based expert system and case based reasoning.

IV.3 Demonstration C This section describes: • High Level description of Demonstration C. • Fault distance computation function • Fault passage indicators (FIs). • Coordination between Fault Passage Indicator and Fault distance computation to locate the faulty section in the distribution network • Service restoration

IV.3.1 High Level description of Demonstration C One of the novel ideas emerges recently to protest the EPS against catastrophic failures is the use of self-healing approaches (SHA). The objective of the SHA is to evaluate power system behavior in real-time, prepare the power system to withstanding eventually combination of contingencies, and accommodate fast recovery from emergency state to normal state. When a disturbance occurs in the network, the power system model together with existing operation conditions, the information and communication networks are used to determine the degree of the disturbance. Once it is determined that the disturbance affects a wide area of the system, SHA break up the system into smaller parts and to alter the effect of the disturbance. The power system operators always try to limit to the smallest part operated at a slightly degraded level as possible. The entire system could be restored after the disturbance was eliminated. In the case of contemporary distribution systems, with the help of ICT and ADA systems, the changes of topology are flexibly carried out following the presence of a permanent disturbance. The objective of these changes consists of re-supply the maximum of consumers who were affected by the fault in order to reduce outage time and operating cost. Fault will be consecutively detected, localized and isolated with the advanced approaches. And the service restoration will be the final task of the self-healing procedure. The distribution operator is responsible to achieve the highest level of self-healing for the distribution system with existed available control facilities.

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However, fault location in distribution networks is always complex due to their nonhomogeneity of line, fault resistance, load uncertainty and unbalance. The fact that a feeder has many branches adds a big difficulty in locating the fault although the fault distance from a substation could be evaluated. In order to determine exactly the faulty section when a fault occurs in distribution network with or without DER, an interesting approach which combines FI with fault distance computation should be developed. In general, SHA is expected to including the three high level functions: • fault distance computation • fault location and isolation by combination of FI with fault distance computation • fault isolation and service restoration. In the following part of this paper, some definitions and explications will be presented in order to prepare for the experimental demonstration C (Demo C) within the framework of INTEGRAL project.

Figure IV-12. Test bench µ-Network configuration for Demo C in France

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Figure IV-13. Network Modeling for the Demo C in France

IV.3.2 Fault distance computation Basically, there are two different families of approach for fault distance computation in distribution network. One, so-called determinist, is based on electrical computation and signal processing. The other approach, so-called heuristic is based on "artificial intelligent system" to evaluate the fault distance [PENKOV-2006] . However, these approaches have not taken into account the presence of DG in the computation. In [CRISP-D1.4], an impedance based approach with the appropriated changes for calculation with the presence of DG was proposed. This method is based on the symmetrical components calculations to compute the fault loop impedance with the parameters during the fault measured at fundamental frequency (50Hz). This method is called "loop impedance at 50Hz". The data needed are the impedances of the machines, transformers, conductors and the fault current of sources, as well as the network topology. The fault current from DGs can be determined by on site measurements or by estimation from DG models.

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Single phase to earth fault Equivalent schema with symmetric components

Analytical equations

Z boucle =

VPC − I1d _ bf .Z ld . x

 ( Z1ti + Z1li )  1 +  .I1i Z Z + ( ) 2 ti 2 li   imag ( Z boucle ) x= ω .Lmoyen

Lmoyen = where:

imag ( Z ld + Z li + Z lo ) 3 x:

fault

distance

estimation (m)

Phase to phase fault Equivalent schema with symmetric components


imag( x=

imag(Zld )

Kd =1+ Kc =

 1  1 V1d   − Kc.(Z1ld + Z1td  2.Kd  2.Kd  I1d 

(Z1td + Z1ld ) (Z + Z ) ; Ki =1+ 1ti 1li ; (Z2td + Z2ld ) (Z2ti + Z2li )

Kd + Ki Ki

where: x: fault distance estimation (m) Three phases fault Equivalent schema with symmetric components


 1  Vd  − Z1td − Z1ld   imag     K d  I1d x= imag ( Z ld ) where: x: fault distance estimation (m) Figure IV-14 . Equivalent schema and analytical equations for fault distance computation with the presence of Distributed Generation (1-feeder substation; 2-DG; t- transformer; d-direct; i-inverse; o-zero sequence)

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Figure IV-14 shows the equivalent diagrams and analytical equations for fault distance computation for different fault types with the presence of Distributed Generation. For the purpose to have a best fault location with the highest impact of DGs, synchronous machine is chosen as the additional source in the network. This is because of comparing with the others DGs interface with distribution grid like induction machine and inverter; the synchronous machine may supply a fault with a much more important transient current, proportioned to its nominal power. The load is assumed to be concentrated at the end of line: one-end concentrated load. It is noticed that only the DGs which have the common path of fault current with the substation are taken into account on the fault distance calculation.

IV.3.3 Fault Passage Indicators (FIs) Fault Passage Indicators are small devices installed along the distribution feeders. They are clamped around a cable that measure current and/or voltage signals to detect the passage of fault through their connection point. The inputs of FIs are generally the current and the voltage at the connection point. When an abnormal operation condition appears in the system such as the extreme high level of current or extreme small level of voltage rather than the nominal values, FIs will detect and signal the fault existence in the network. The residual voltage and current can be taken into account for the detection of dissymmetric fault. These inputs are able to be obtained from potential or current transformers from the measurement of electromagnetic field.

Sensor

Processor

Signaling

Supply Figure IV-15. Constitution diagram of FIs

The information resulted from FIs may be used for the location of fault in the case of permanent fault or for the location of area affected in the case of non-permanent fault. Regarding to their information provided, these fault indicators can be divided into two types: • Non-directional FIs based on a simple current criterion. They can recognize the passage of fault current but can not determine the direction of fault. This FPI can be used for the case of important fault current like single-phase faults with impedance grounded neutral and for multi-phase faults. • Directional FIs based on a directional criterion which can be used for the case of small fault current like single-phase to earth with compensated or isolated neutral). They can recognize the passage of fault as well as the direction of fault seen from their connection point. They have the similar principle of detection for multi-phase fault as the non-directional FI, but they analyze also the variation of aptitude of the residual currents and voltages appeared following the single-phase to earth fault to indicate the fault direction. The signals emitted by the fault detectors can be supplied in a local way in the detector itself with help of a local ICT system that records the events or can be sent to a centralized point by a communication system. The indicator placed at strategic location along feeders or derivations, associated with the switches can provide fast location enabling reduction in outage times. This contributes to enhance service to the customers thereby improving the quality of electricity supply. In the next section, the use of FI in combination with the fault distance computation for targeted fault location will be presented.

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Influence of DER on signaling resulted from FIs Since the presence of DER can influence on the amplitude as well as the direction of fault current. Depending on the type of fault, the power of DER and the type of FI using in a distribution network, two types of phenomenon should be distinguished: + Single phase to earth and phase to phase to earth:

Figure IV-16. Circulation of Single phase to earth fault current with the presence of DER

The path flow of residual current when a single phase to earth occurs in a distribution network with the presence of DG is presented in the Figure IV-16. Because of a distributed generator should be connected via wye-delta coupled LV/MV power transformer to limit the fault impact on the DG by isolating it from network, the ground faults remain "invisible" on the primary side. In the others words, DGs do not contribute to the capacitive current. In consequence, single phase to earth fault current is not changed with the presence of DGs. In other words, DG does not influence on signaling of FIs in the case of single phase or earthed phase to phase fault. + Three phases and phase to phase faults Non-directional FIs is usually used for fault of multiplied phase. In this situation, depending on the importance of fault current provided by the DG, the signaling of FIs can be made an error. If the fault current is sufficiently important (higher than the threshold of FIs) and DG connected downstream fault, the FIs placed between DER and the fault point can signal the existence of fault. Thus, fault location with the information resulted from FIs is not correct. In this case, directional FPI should be used in order to assure the good fault location.

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F1 FI_1

FI_2

FI_3

F1 FI_4 DG 2

FI_1

FI_2

FI_3

FI_4 DG 2 DG 1

DG 1

b) F1 – DG1 power important – DG2 power non-important

a) F1 – DG1 power non-important – DG2 power non-important

F1 FI_1

FI_2

FI_3

FI_4 DG 2

FI_1

FI_2

FI_3

DG 2

DG 1

DG 1 c) F1 – DG1 power non-important – DR2 power important

FI_4 F2

d) F2 –DG2 power important

Figure IV-17. Influence of DER on the signaling of FIs

Figure IV-17 shows the incorrect signals resulted from non-directional FIs. These errors are caused by misunderstanding of the fault current path with the presence of DG in the network.

IV.3.4 FI - based fault location The fault distance computation with the impedance based approaches can indicate the estimated distance of fault from the substation. But this estimation does not enable the precise location of fault due to many points in the network being satisfied this distance. In order to locate exactly the faulty section, it is necessary to enquire further information about the emergency state of network. A method using in combination FIs results with fault distance computation for fault location in distribution networks was developed in GIE-IDEA in cooperation with G2Elab (Grenoble InP). The advantages of this method consist in its high autonomous level for fault location with an associated ICT infrastructure. This method is very efficient in case that there are many branches and ramifications in distribution network. The principles of fault location system are described in the following illustrated figures.

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Substation

HV/MV

Remote Control Switch: close/open Manual Control Switch: close/open FPI: detected/non-detected Circuit breaker: close/open LV Load MV Load

DR

switch: close/open Fault distance estimation

FPI: detected/non-detected Circuit breaker: close/open

Figure IV-18. Fault distance computation

This distribution network works normally in opened loop operation. Firstly, when a fault appears in the network, the reclosing sequences are carried out to determine the permanent fault existence. If the reclosing procedure is not successful, the main circuit breaker is opened and an alarm is sent to the operator to inform him about the existing feeder no more supplied. After the acquisition of information about the emergency state, the fault distance evaluation approach will be applied, and the operator can know the distance of fault from the substation (Figure IV-19). Then, the signaling provided by FIs (both non-directional and directional) allows operator to determine the correct fault path among many paths corresponding the estimated distance. Finally, the system operator can fix the exact faulty section in analyzing the information resulted from FIs, using eventually some intelligent algorithm of selection (Figure IV-20).

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Substation HV/MV

Fault trajectory

DR

Figure IV-19. Fault path determination in combination with FIs

Substation HV/MV

Fault location

DR

Detected FPI

Figure IV-20. Exact fault location

IV.3.5 Service restoration After the fault is located, the operators have to execute several operations to reenergize the sane portions of network and reduce as maximal as possible the customers affected by the fault. That is called Service Restoration. From the fault position, the network may be divided into two parts: downstream fault part and upstream fault part. In order to reenergize the downstream fault part, the system operators must close the feeder circuit breaker to have the power flow from substation. Whereas, the upstream fault sane portion of network can be supplied by closing the tie switches to connect to another substation. The illustrations of service restoration procedure are presented from Figure IV-21 to Figure IV-23. Operation for service restoration is organized into several steps corresponding "remote control" (for switches or FI type remote control) or "in location" (for switches or FI type manual control).

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INTEGRAL: Integrated ICT-platform for Distributed Control in Electricity Grids Substation

Remote Control Switch: close/open Manual Control Switch: close/open

HV/MV

FPI: detected/non-detected Circuit breaker: close/open LV Load MV Load TS-2

DR RCS-1

MCS-1

TS-1

TS-4 MCS-2 MCS-3

TS-3

Figure IV-21. Isolation of fault affected zone by opening the feeder circuit breaker

In the first time of fault, all of feeder is out of service due to the opening of feeder circuit breaker and tie switches. A large non-energized zone including many sane portions of network can be observed in the Figure IV-21. Substation


HV/MV


DR RCS-1

MCS-1

TS-1

TS-4 MCS-2 MCS-3

TS-3

Figure IV-22. Reduction of fault affected zone by opening the remote switches and closing circuit breaker

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Then, the operator makes a decision to close the circuit breaker of the feeder in order to reenergize the upstream fault portion. For instance, the remote control switch RCS-1 is opened to isolate the affected zone. The faulted zone can be reduced (Figure IV-22). Substation


HV/MV


DR RCS-1

MCS-1 TS-4

TS-1 MCS-2 MCS-3

TS-3

Figure IV-23. Reduction maximum of fault affected zone by opening the manual switches and closing the tie remote control switches

Finally, the fault affected zone can be reduced even more by opening the manual switches (MS-1,2,3) and closing the tie switches (TS-3,4) as in Figure IV-23. The sane portions of network are supposed to be supplied by the others nearby feeders. For example, in Figure IV-23 the sane portion upstream fault may be reenergized by the feeders of S2 and S3. However, there may be a problem if the sane portions which can be reenergized by the nearby feeders are too significant. This will provoke an overload for the new feeders or new lines related to their thermal limit. For instance, in Figure IV-24, after the closing of remote tie switch TS-4, feeder S3 is overload. In such case, there may be two situations: + the electric lines in the network were planned with a large margin. That means they are anticipated to take the more important load rather than the nominal value. Thus, the operators do not have any worry about line overload during service restoration operation. + the capacity of line in the network is slightly enough for the normal operation with a small margin. That means the operators have to take into account the possibility of overload for the new lines during service restoration operation. The operators thus should look for the optimal topology of network so that the maximum portion of isolated network is rescued and thermal constraints of all lines after the reconfiguration are satisfied. In [ENACHEANU-2007], a constrained optimization program was developed with the objective function is as following:

f objectif

 I = ∑ k  k  I k MAX

   

m

where: Ik : current in the line k Ikmax : maximal current admissible for line k

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INTEGRAL: Integrated ICT-platform for Distributed Control in Electricity Grids Substation


HV/MV

FPI: detected/non-detected Circuit breaker: close/open LV Load MV Load

DR

Figure IV-24. Service restoration for sane portion by connection to the nearby feeders

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V Conclusions This document has demonstrated the integration of ICT systems and decentralized intelligence into the distribution network operation in order to improve the global efficiency in different operation conditions. Each system contains their specific characteristic and functionality to achieve the different operation tasks. Three operation modes of network: normal, critical and emergency could be firstly assured and then improved in the effective manners thanks to the specific functions defined in the document. An integrated ICT concept could contribute to the following objectives: • Reduce peak loads. Balance Supply-Demand could be assured by the Power Matcher Coordination. • Prevent dangerous situations. • Minimize effects of network failures during critical operations. • Black Start of isolated networks. • Fault location and service restoration. Realize the “self-healing” concept. In the context of deregulation market, different operators and market parties have different incentives and priorities in their business goals. Therefore, these systems standardized systems with clear predefined functions are necessary to ensure that after implementation, the stability and control off the network is actually improved. Each of these market parties has different interest and objectives in each grid level. It is the challenge for INTEGRAL project to develop a non-ambiguous view on an automated coordination system that meets the requirements for each market role at each grid level. Therefore, the high level functions, which will be able to implemented into the three demonstrations, have been presented in order to create the theory based for the following demonstrations: • Demo A for normal operation will be carried out in Holland by ECN/GET • Demo B for critical operation will be carried out in Spain by WATTPIC/ CRIC • Demo C for emergency operation will be carried out in France by GIE-IDEA/INPG Within the INTEGRAL framework, the geographical and electrical criterion will be proposed to make the ICTs dimension feasible. The results of CRISP and MICROGRID projects will be pursued. The practical algorithm for the high level functionality which can be implemented to the demonstrations will be described in the deliverable 2.2. of the Working Pakage WP2. Finally, the demonstrations will be carried out within the WP 5, 6 and 7 of the INTERGRAL Project.

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References [ASHOK-2001]: S Ashok,R Banerjee. "An Optimization Mode for Industrial Load Management." Transactions on Power Systems, Vol. 16, No. 4, 2001 [BENOIT-2007]: Benoit Bletterie, "Interconnection Requirements for DER in Europe Impact and opportunities of DER for network operation". http://www.eh.gov.hu/gcpdocs/200704/solidderhungarianseminar_bletterie.pdf [BOEDA-2007]: Didier BOEDA, Guillaume VERNEAU, et al. "Load Control to Balance Limited or Intermittent Production." CIRED - 19th International Conference on Electricity Distribution, Vienna, 21-24 May 2007, 2007 [CAIRE-2002] : Raphaël Caire, Nicolas Retière, Sylvain Martino, Christophe Andrieu, Nouredine Hadjsaid « Impact Assessment of LV Distributed Generation on MV Distribution Network », IEEE Power Engineering Society 2002 Summer Meeting, 21-25 July 2002, Chicago, USA [CEIDS-2004]: The CEIDS and Intelligrid vision is explained following the link: http://www.epri.com/IntelliGrid/ [CORTINAS-1999] : D. Cortinas, P. Juston « Assessing the impact of dispersed generation on medium voltage networks: analysis methods », Electric Power Engineering, 1999. PowerTech Budapest 99. International Conference on , 29 Aug.-2 Sept. 1999 [CRISP-D1.4]: Ch. Andrieu et. Al. "Fault detection, analysis and diagnostics in high DG distribution systems" Deliverable D1.4, 2006. http://www.ecn.nl/crisp/deliverables/D1.4.pdf [CRISP-D1.7]: Ch. Andrieu et. Al. Distributed network architectures, Deliverable D1.7, 2006. http://www.ecn.nl/crisp/deliverables/D1.7.pdf [ENACHEANU-2008]: B. Enacheanu, B Raison, et al. "Radial Network Reconfiguration Using Genetic Algorithm Based on the Matroid Theory." IEEE Transaction on Power System Vol. 23, No. 1, pp 186-195, 2008 [ENACHEANU-2007]: B. Enacheanu "Operation software tools for distribution network operators." Doctoral Thesis - Grenoble Institute of Technology 2007 [EUDEEP-2006]: The website of this project can be found at: http://www.eudeep.com/. In the project a thorough classification of load types has been given. [FENIX-2007] The project-website is located at http://www.fenix-project.org/ [FlexibelWP1-2006]: Mohamed Choukri BenHabib, Jorge Duarte, TU/Eindhoven Maarten Hommelberg, René Kamphuis, Cor Warmer, ECN, Flexible electricity grids Power Electronic System and ICT-requirements for novel electricity distribution grids, 2006. http://www.flexible-electricitynetworks.nl/rapporten/flexible_electricity_grids_power_electronic_system_and_ICT_requirem ents_final.pdf

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[FRAISSE-2001] : Jean Luc Fraisse, F. Boulanger, Philippe Juston, Pierre Lemerle, O. Jeannin, « Praogen, a tool for studying connection of generating plant onto the mediumvoltage network », Electricity Distribution, 2001. Part 1: Contributions. CIRED. 16th International Conference and Exhibition on (IEE Conf. Publ No. 482) ,Volume: 4 , 18-21 June 2001 [Gasunie-2008] Aggregatie van micro-wkk's in een virtuele centrale: First trial smart power System, Hommelberg et al., ECN 2007 http://www.ecn.nl/docs/library/report/2007/e07055.pdf and A field test using agents for coordination of residential micro-chp, Warmer et al. 14th International Conference on Intelligent System Applications to Power Systems (ISAP), Kaohsiung, Taiwan, 4-8 november 2007. [GOODMAN-2005]: F Goodman, M McGranaghan. "EPRI research plan for advanced distribution automation." Power Engineering Society General Meeting Vol. 3, pp 2620, 2005 [GridWise-2006]; A complete http://www.gridwise.org/

description

of

the

initiative

can

be

found

at:

[HADJSAID-1999] Hadjsaid, N.; Canard, J.-F.; Dumas, F. "Dispersed generation impact on distribution networks", Computer Applications in Power, IEEE, Volume 12, Issue 2, April 1999 Page(s):22 - 28, Digital Object Identifier 10.1109/67.755642 [HAUSHEER-2007]: D.K. Hausheer. PeerMart. "Secure decentralised pricing and accounting for peer-to-peer systems." Dissertation at the Swiss Federal Institute of Technology, Zurich, 2007. [IRED-2006] On the conference website the conference program can be found: http://www.2ndintegrationconference.com/agenda.asp [KAMPHUIS-2003]: E-Box, A residential gateway for cost management and. sustainability. IG Kamphuis, ECN-C-03-017, Petten, NL. www.energie.nl/nel/nl03e0706.html [KAMPHUIS-2004]: René Kamphuis, Per Carlsson, et al. "Market oriented online supplydemand matching." CRISP - Livrable D1.2, 2004 [KOK-2006] J.K. Kok, C.J. Warmer and I.G. Kamphuis, PowerMatcher: multiagent control in the electricity infrastructure, IEEE Systems Journal, 2006. [KUPZOG-2007]: Friederich Kupzog,Charlotte Roesener. management." IEEE Transaction on Power System, 2007

"A

closer

look

on

load

[MIAO-2001] : Z. Miao, R.L. Klein, M.A. Choudhry « Case Study of Distributed Generation on the Power Distribution System », Proc. of NIAPS, Texas A&M University, USA, October 1516 2001 [MicroGrids-2006]; See http://microgrids.power.ece.ntua.gr/calendar.htm; a recent survey on MicroGrids has been given in IEEE Power & Applications, April, 2008. [PENKOV-2006]: Delcho Penkov. "Fault location in electrical MV networks with Distirbuted Generators." Doctoral Thesis - Grenoble Institut of Technology, 2006

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[PHAM CONG-2006]: Duc Pham Cong. "Fault detecton and location in the distribution networks with dispersed generation insertion." Doctoral Thesis - Grenoble Institut of Technology, 2006 [PHAM-2005]: T.T.H. Pham, Y Bésanger, et al. "A new restoration process in power systems with large scale of dispersed generation." IEEE PES Transmission and Distribution Congerence & Exposition, New Orleans - USA, 2005 [PLATT-2007]: Glenn Platt. "The Decentralised Control of Electricity Networks- Intelligent and Self-Healing Systems. CSIRO Energy Technology." Grid Interop Forum, 2007. [RICHARDOT-2006]: Olivier RICHARDOT. "Coordinated Voltage Control in Distribution Networks using Distributed Generation." Doctor Thesis, Grenoble Insitut of Technology, FRANCE, 2006 [SMARTGRIDS-2006]: European Smartgrids Technology Platform, Vision and Strategy for Europe’s Electricity Networks of the future. EUR22040, 2006. [SUSTELNET-2006]: an extensive overview of the project has been given at: ftp://ftp.cordis.europa.eu/pub/eesd/docs/ev260901_poster_sustelnet.pdf [SUTHERLAND-2006]: P.E Sutherland, F.R Goodman, et al. "Feeder and Network Evolution for the Distribution System of the Future." IEEE, PES, Transmission and Distribution Conference and Exhibition, pp 348-353, 2006 [RTE-2006]: Electrical Energy Statistics for France france.com/htm/an/mediatheque/vie_publi_annu_stats_2006.jsp"

2006,

"http://www.rte-

[YASUHIRO-2004]: Hayashi Yasuhiro,Matsuki Junya. "Loss Minimum Configuration of Distribution System Considering N-1 Security of Dispersed Generators." IEEE Transaction on Power System, Vol. 19, No. 1, 2004 [WARMER-2007]: Cor Warmer, Maarten Hommelberg, René Kamphuis and Koen Kok. "Market integration of flexible demand and DG-RES supply - a new approach for demand response." CIRED, May 2007. [WEDDE-2006]: Wedde, H.F.; Lehnhoff, S.; Handschin, E.; Krause, O. "DEZENT, Real-time multi-agent support for decentralized management of electric power." 18th Euromicro Conference on Real-time Systems, 2006.

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Acronyms and Abbreviations ADA AMR APX BUSMOD CHP CRISP DG-RES DNO DER DG DRR DSM DMS DSP DEMS EPS GSM ICT ISO IEA LAN LV MV PES PRP PV SDM TSO RA IIDC

Advanced Distribution Automation Automated Meter Reading Amsterdam Power eXchange BUSiness MODels in a world characterized by distributed generation Combined Heat and Power generation distributed intelligence in CRitical Infrastructures for Sustainable Power Distributed Generation with Renewable Energy Sources Distribution Network Operator Distributed Energy Resources (DER) Distributed Generation Demand Response Resources Demand Side Management Distribution Management System (EMS for distribution network) Digital Signal Processor Distributed Energy Management System Electric Power System Global System for Mobile communications Information and Communication Technology Independent System Operator ( ~ TSO, USA context); International Standards Organization International Energy Agency Local Area Network Low Voltage Medium Voltage Power Electronics System Programme Responsible Party Photo-Voltaic Supply and Demand Matching Transmission System Operator Ressource Agent Integrated ICT - platform based Distributed Control

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Executive Summary This deliverable aims to determine some specific functions for information and communication systems integrated into the distribution networks in order to take the best advantage of the local resources such as controllable loads and decentralized generators based (or not, on renewable). On the one hand, ICTs systems should play an essential coordination to promote the participation of DER/RES to the energy market by the intelligent tools of management and aggregation of large number of intermittent source and/or controllable load. On the other hand, the high penetration level of DER/RES into distribution network may provoke also many technical impacts to the network and then many difficulties for the systems operators. So that, before having some expected economic goals, the security of the network in presence of DER/RES needs to be assured with the help of ICTs systems. In the other work, the lack of specific functions for ICTs in the control and protection of distribution network jeopardizes the achievement of the required goals. Therefore, in this deliverable, the specific functionalities which permit assure the efficiency (commercially aspect) and secure (physical aspect) operation of the future distribution network based on common ICT systems available. These functionalities are also the preparation for the three substantial demonstrations of INTEGRAL project which cover the full range of different operating condition: o normal operating conditions of DER/RES aggregations, showing their potential to reduce grid power imbalances, optimize local power and energy management, minimize cost etc. o critical operating conditions of DER/RES aggregations, showing stability when gridintegrated. o emergency operating conditions, showing self-healing capabilities of DER/RES aggregations.

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