Deliverable (D) No: 5.2 Short report on infrastructure and ... - PlanGridEV

Distribution grid planning and operational principles for EV mass roll-out while enabling DER integration

Deliverable (D) No: 5.2

Short report on infrastructure and system updates Author: Date:

Version:

Enel Distribuzione 20.01.2015 0.5

www.PlanGridEV.eu

Confidential (Y / N): N The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 608957.

D5.2 Short report on infrastructure and system updates

Title of the Deliverable

Short report on infrastructure and system updates

WP number 5 Task title

WP title

Main Author

Cristina Silvestri/ENEL DISTRIBUZIONE Giovanni Coppola/ENEL DISTRIBUZIONE Luis Silvestre/EDP Eoghan O’Callaghan/ESB Armin Gaul/RWE Dr. Armin Gaul, Stefan Greve

Project partners involved Editors

WP leader

Validation / Real Testing EDP Update of infrastructure and systems to enable validation activity (t16-t19)

Type (Distribution level)  PU, Public  PP, Restricted to other program participants (including the Commission Services)  RE, Restricted to other a group specified by the consortium (including the Commission Services)  CO, Confidential, only for members of the consortium (including the Commission Services) Status  In Process  In Revision  Approved Further information

www.PlanGridEV.eu

150821_AG_PlanGridEV Deliverable 5 2 v0 5_submitted_editor.docx Page 3 of 30


Executive Summary The PlanGridEV Deliverable 5.2 presents the outcomes of Task 5.2 where, based on the use cases identified in T5.1, the necessary updates made to the system infrastructure are reported in order to prepare for the validation activity (T5.3). The document details, in particular, technical conditions and foreseen upgrades of the selected infrastructures chosen in T5.1 for the different test beds. For the Italy test bed, based on the use cases selected in PlanGridEV Deliverable 5.1, the execution of the demonstration will require a real time LV Distribution Management System in order to deliver a proof of concept of the service “Planned Demand Response: Enhanced RENs (Renewable Resources) integration” as described in D2.2. Alongside the LV DMS to deliver the service described above, there are some technological enablers of such service which are already in operation as part of Enel’s infrastructure for electric mobility: -

A real-time monitoring, operation and maintenance back-end system for EV charging stations.

-

A real-time communication uplink between EV charging stations and operation back-end.

-

A communication protocol capable of hosting Power Modulation Level object as defined in Deliverable D5.1.

-

A charging station capable of receiving a Power Modulation Level object and converting it through a PWM signal, in accordance at least with ISO/IEC 61851-1 and possibly with ISO/IEC 61851-23 and/or ISO 15118.

For the Germany Test Bed a lot of new equipment has been installed in the LV Grids of the Smart Operator Project: -

Intelligent Meters.

-

Adjustable On-Load Transformer.

-

Network Storage.

-

Low Voltage Switch.

-

Home Energy Controller (HEC).

These technologies will be selected according to the grid area and will be used either in combination or separately. The central controlling function will be carried out by the Smart Operator.



The Irish test bed is used to evaluate the scenarios “conventional” and “safe”. As outlined in Deliverable 5.1, the test bed consists of one site where three electric vehicles are deployed on a 33kVA MV/LV transformer fed from a single phase spur on a three phase MV network which feeds 4 residential customers. The demonstration will consist of four stages: •

No electric vehicles connected

•

Electric vehicles deployed but charging restricted

•

Electric vehicles deployed with unrestricted charging

•

Intelligent charging.

From a DSO point of view, a monitoring device which will record power, current and voltage will be installed on the LV side of the 33kVA transformer, and an end of line monitor will also be installed in the property of the customer, furthest from the transformer, to monitor the end of line voltage. The monitoring device at the transformer uses ZigBee wireless communications and a cellular modem to transfer the data.

Finally, Portugal Test Bed development aims at lowering EV penetration impact by postponing or avoiding “copper” investments in order to sustain EVs adoption. The proper allocation in the LV grid of the charging demand and PV generation within the day would allow the DSO to keep the LV and MV grid with today’s configuration, without replacing transformers or lines that would otherwise be overloaded due to EV charging. As all the tests will be performed with the EDP software planning tool DPlan, no particular technical updates (electric infrastructure or ICT technologies) will be needed to develop the tests.

According to the work plan, all the necessary updates for test beds have been deployed and validation testing can begin (MS3 reached).



Table of contents Executive Summary ................................................................................................................................ 5 Table of contents ..................................................................................................................................... 7 List of figures .......................................................................................................................................... 8 List of tables ............................................................................................................................................ 9 Abbreviations and Acronyms ................................................................................................................ 10 1. Introduction ................................................................................................................................... 12 1.1. Scope of the document .......................................................................................................... 12 1.2. Structure of the document ..................................................................................................... 12 2. Update of infrastructure for Italy’s Test bed (Enel) ...................................................................... 13 2.1. Overview of Test bed ............................................................................................................ 13 2.2. Technical updates for test bed execution............................................................................... 14 2.3. ENEL Test bed updates mapping to use cases ...................................................................... 16 3. Update of infrastructure for Germany Test bed (RWE) ................................................................ 17 3.1. Overview of test bed.............................................................................................................. 17 3.2. Technical updates for test bed execution............................................................................... 19 3.3. RWE Test bed updates mapping to use cases ....................................................................... 22 4. Update of infrastructure for Ireland Test bed (ESB) ..................................................................... 23 4.1. Overview of test bed.............................................................................................................. 23 4.2. Technical updates for test bed execution............................................................................... 23 4.3. ESB Test bed updates mapping to use cases ......................................................................... 24 5. Update of infrastructure for Portugal Test bed (EDP)................................................................... 25 5.1. Overview of test bed.............................................................................................................. 25 5.2. Technical updates for test bed execution............................................................................... 26 5.3. EDP Test bed updates mapping to use cases ......................................................................... 28 6. References ..................................................................................................................................... 29 6.1. Project documents ................................................................................................................. 29 7. Revisions ....................................................................................................................................... 30 7.1. Track changes ........................................................................................................................ 30



List of figures FIGURE 2.1 FRAMEWORK ARCHITECTURE OF ELECTRIC MOBILITY AND EMBEDDED IT INTERFACES (IF 1 TO 8) ...................................................................................................................... 13 FIGURE 3.1- BASIC CONCEPT OF THE SMART OPERATOR .......................................................... 17 FIGURE 3.2 - TEST BED KISSELBACH ........................................................................................ 18 FIGURE 3.3 - EQUIPMENT THAT HAS BEEN INSTALLED IN THE RWE TEST BEDS ............................ 19 FIGURE 3.4 - EQUIPMENT THAT HAS BEEN INSTALLED IN THE RWE TEST BED .............................. 20 FIGURE 5.1 - EDP DTC AND EDP ENERGY BOX ....................................................................... 26 FIGURE 5.2 – EQUIPMENT AND COMMUNICATIONS SCHEME ....................................................... 27



List of tables TABLE 1 ACRONYMS ................................................................................................................ 10 TABLE 2 ENEL TEST BED: SYSTEM UPDATES MAPPING TO EV SMART CHARGING UC .................... 16 TABLE 3 RWE TEST BED: SYSTEM UPDATES MAPPING TO EV SMART CHARGING UC.................... 22 TABLE 4 ESB TEST BED: SYSTEM UPDATES MAPPING TO EV SMART CHARGING UC ..................... 24 TABLE 5 EDP TEST BED: SYSTEM UPDATES MAPPING TO EV SMART CHARGING UC..................... 28



Abbreviations and Acronyms Table 1 Acronyms BAU

Business as Usual

DER

Distributed Energy Resources

DMS

Distribution Management System

DR

Demand Response

DSO

Distribution System Operator

E2P

Energy to the Power

EMM

Electric Mobility Management system

EV EVSE EVSEO

Electric Vehicle Electric Vehicle Supply Equipment EVSE operator

EVSP

Electric Vehicle Service Provider

HEC

Home Energy Controller

HMI

Human-Machine Interface

ICT

Information and communications technology

IF

Interface

IT

Information technology

L&G

Load and generation

LV

Low Voltage

MV

Medium Voltage

OLTC

On-Load Tap Changer

POD

Point of delivery

PWM

Pulse Width Modulation

PV

Photovoltaic

REN

Renewable resources

RES

Renewable Energy Sources

SOC

State Of Charge

TOU

Time Of Use

TSO

Transmission System Operator

VCDT

Voltage Controlled Distribution Transformers



WP

Work Package



1. Introduction 1.1. Scope of the document Deliverable D5.2 is the outcome of PlanGridEV Task 5.2, where the necessary technical updates of the four PlanGridEV test beds were identified in accordance to the previous activities of the project. More precisely, the work carried out by this task leads to the definition, for each test bed, of the technical conditions and/or technical updates that are necessary on the selected infrastructure that will host the test execution. The document serves therefore as an agenda for the technical development in progress, as it has been derived from the high-level scenarios of WP2 and in accordance with the selected use cases of Task 5.1 for each test bed.

1.2. Structure of the document The document comprises the following main sections, each of them reporting an overview of the ongoing implementations / updates for each PlanGridEV test bed. Section 2: Update of infrastructure for Italy’s Test bed (Enel) Section 3: Update of infrastructure for Germany’s Test bed (RWE) Section 4: Update of infrastructure for Ireland’s Test bed (ESB) Section 5: Update of infrastructure for Portugal’s Test bed (EDP)



2. Update of infrastructure for Italy’s Test bed (Enel) 2.1. Overview of Test bed Based on the use cases selected for the Italy test bed in PlanGridEV Deliverable 5.1, the execution of the Italy’s demonstration will require a real time Distribution Management System (DMS) in order to deliver a proof of concept of the service “Planned Demand Response: Enhanced RENs (Renewable Resources) integration” as described in D2.2. A DMS is an IT system usually leveraged by Distribution System Operators (DSO) in order to execute power flow calculation and assessments with regards to a physical grid description. A premiere LV installation of this system is implemented in PlanGridEV project, in order to perform the validation of a smart charging program in a LV area where Enel’s Electric Vehicle Supply Equipment systems (EVSEs) are installed. The LV DMS will guarantee that the Load Management target issued by a DER Operator and/or a DSO will be executed by an EVSE Operator in accordance with the power flow calculation and assessment over the physical LV grid where the EVSEs are installed. The Load Management target (as defined in PlanGridEV Deliverable 5.1) will be issued at simulation level considering a large penetration of EVSEs in the target demo LV area. A portion of the whole Load Management target request will be eventually executed on the existing EVSEs (reasonably a cluster of 5 to 10 EVSEs as installed in today’s market condition in the target demo LV area). The Load Management target validated by the DMS will feed the optimization algorithm already existing within Enel’s EVSE Operation back-end, where the EV Customer preferences will be taken into account. As a consequence of optimization tradeoff between EV Customer preferences (Initial SOC, Final SOC, Time Of Departure) and the DSO/DER Operator constraints, each EVSEs belonging to the Load Area (as defined in PlanGridEV Deliverable D5.1) will be controlled accordingly.

Figure 2.1 Framework architecture of electric mobility and embedded IT interfaces (IF 1 to 8)



2.2. Technical updates for test bed execution The execution of Italy’s test bed relies on existing devices and systems which partially fulfills the global requirements of a “Planned Demand Response: Enhanced RENs integration” service as described in Deliverable D2.2. Hereby, there is a short list of the technological enablers of such service, which is already in operation as part of Enel’s infrastructure for electric mobility. -

A real-time monitoring, operation and maintenance back-end system for EV charging stations. A real-time communication uplink between EV charging stations and operation back-end. A communication protocol capable of hosting Power Modulation Level object as defined in Deliverable D5.1. A charging station product which is capable of receiving a Power Modulation Level object and converting it through PWM signal in accordance at least with ISO/IEC 61851-1 and possibly with ISO/IEC 61851-23 and/or ISO 15118.

The following additional steps on top of existing Enel’s infrastructure are being implemented in PlanGridEV project in order to deliver a proof a concept of the above mentioned D2.2 service: -

(A) Premiere installation of a LV DMS to validate Load Management targets issued by DSO / DER Operators and EVSE back-end, validating interface (IF) 3, 7 and 8. (B) EV Customer preferences HMI for real-time communication of Initial SOC, Final SOC, Time Of Departure, implementing IF 5.

(A) Installation and updates of LV DMS in order to comply with the above mentioned purpose o

Data exchange with DMS MV

For the selected demonstration area, the results of state estimation calculations in MV network will be imported on DMS LV. The DMS LV will use to evaluate the state of the network for the LV network.

o

Data exchange with EMM Platform

EMM Platform is the Enel’s EVSE Back-end operation system which sets the recharging parameters; moreover, the EMM dynamically receives the measured data from all the EV recharges. The following data must be sent to DMS LV in order to validate a Load Management target: -

Geographical position. Network position. Maximum recharge power. Number of vehicles rechargeable at the same time.



o

Traditional functions of MV DMS

According to the experience with the MV DMS, the following necessities have to be addressed: a. b. c.

o

Complete performance indices of considered network including calculation of losses for different network states (peak load, light load). Identification of overloaded substations and sections in network (not respected current and/or voltage limits). Substations and feeders where operation reliability is violated, according to some operation security criteria.

Load forecasting and generation forecasting

The Load Forecasting tool will be used for short, medium and long-term load forecasting of the considered network. It uses adequate mathematical model for estimation of future consumption and demand development. The model is based on actual and previous period consumption data and includes empirical conditions and projections (urban plans, heating conditions, granted consumer's demand requests and future development). The forecast is divided on small consumer's areas to enable generation of different scenarios. The Generation Forecasting will be used to take into account the effect of actual and expected DER in LV grid.

o

Verification of connection

According to the analysis of the actual and forecasted network states, it is possible to foresee the problems to be solved in the future, with the connection of a large number of EVs. Using mathematical optimization methods and users' empirical experience, different (alternative) scenarios are generated. A long term action could be reconstruction of supply substations or feeders, building new sections or consumer's substations in the MV network, putting out of operation and dismantling of some installations. A short term action could be the modulation of the recharge power in some EVSE. Thanks to the connection with EMM, the DMS can suggest the correct settings.

(B) EV Customer HMI for smart charging A customized version of “Enel Drive” end-user application for e-mobility services (http://www.eneldrive.it) will be developed and demonstrated, in order to input the necessary boundary conditions from customers perspective into the optimization tradeoff algorithm hosted by Enel’s EMM Platform. Such a customized smart phone application will be used within the project in order to produce a significant amount of EV charging processes performed through a smart scheduling.



2.3. ENEL Test bed updates mapping to use cases The following table shows the use case applicable to the Enel test bed, as defined in D5.1, and the systems that will be used during the validation of the operational methods that will take place in the frame of task 5.3. Updates will be highlighted in the last column. Table 2 Enel test bed: system updates mapping to EV smart charging UC

No.

1

2

3

No. 1

2

Use Case no. 3: EV smart charging (through optimum schedule definition) Step Scenario Step System and functions required no. LV DMS: Data exchange with MV DSM Load and generation forecasting DSO provides load schedule to Obtaining load 1 Performance indices calculation EVSEO schedule Identification of alarms in network Network scenario generation Data exchange with EVSEO backend EV user HMI (smart phone app): EVSP communicates end-user Obtaining EV user 1 EV user preferences comm. to EVSEO backend preferences to EVSEO preferences application (EVSP=EVSEO) EVSEO backend (EMM): EV charge schedule definition by 1 Communication with LV DSM EVSEO EV user preferences use EVSEO backend (EMM): Controlling EV Main functions already in operation charge 2 EV charge control by EVSEO IEC 61851-23 and ISO/IEC 15118 protocols in the future EVSEO backend (EMM): EVSEO communicates EV charge 3 schedule to the DSO Communication with LV DSM Use Case no. 4: EV smart charging (real-time control) Step Scenario Step System and functions required no. LV DMS: DSO provides new load schedule Obtaining new 1 for an area when grid conditions Real time DER and load parameter monitoring load schedule change Short term load and generation forecasting EVSEO backend (EMM): EV charge schedule definition by 1 Communication with LV DSM the EVSEO EV user preferences use EVSEO backend (EMM): Controlling EV Main functions already in operation charge 2 EV charge control by the EVSEO IEC 61851-23 and ISO/IEC 15118 protocols in the future EVSEO backend (EMM): EVSEO communicates EV charge 3 schedule to the DSO Communication with LV DSM



3. Update of infrastructure for Germany Test bed (RWE) 3.1. Overview of test bed As already described in D5.1, RWE makes use of a test bed, which was established for the field test of the RWE Project Smart Operator. In the three test beds Kisselbach, Wertachau and Wincheringen many smart grid components including stationary battery storage, renewable energy generation from photovoltaic technology, measurement in the sub stations and electric vehicles have been deployed. This makes the test bed Kisselbach an ideal base for the proactive and, partly even, the smart grid use case of PlanGridEV. The centralized and ‘smart’ steering device, the Smart Operator, makes it possible to monitor the entire low voltage grid and – if required – to trigger steering signals to prevent potential grid instability and impermissible voltage values. An additional goal of the grid operation is to prevent overload of grid assets. To achieve this there are several possibilities, such as operating a storage asset, exploiting the flexibility of local consumption and/or generation of electricity in households and on-load-switching of the tap changer at the substation (Figure 3.1).

Figure 3.1- Basic Concept of the Smart Operator

The following Figure 3.2 shows a diagram of the field test area Kisselbach.



Figure 3.2 - Test Bed Kisselbach

The “Smart Operator” is the heart of the intelligent grid in Kisselbach: -

It communicates with feeders, consumers and storage facilities via state-of-the-art communications technologies, e. g. fiber optics and power line communications. It collects data on power supply, loads and storage. Capacities are calculated, analysed and then used for supply and load prognoses. The “Smart Operator” aligns all these parameters. It optimizes grid efficiencies by balancing supply and loads.



3.2. Technical updates for test bed execution In order to be ready for the tests a lot of new equipment has been installed in the LV Grids of the Smart Operator Project (Figure 3.3).

Figure 3.3 - Equipment that has been installed in the RWE test beds In the project various innovative grid components are tested. These are to be used intelligently by the Smart Operator to guarantee stable and safe grid operation. The local grid transformer will be replaced by a Voltage Controlled Distribution Transformers (VCDT) using an integrated on-load tap changer (OLTC), which can regulate the voltage in 9 steps of 2.5% each to provide a broad range of the possible voltage bandwidth supply. Furthermore, four different kinds of battery storage systems are introduced. 1) Large grid storage systems with a capacity of 150 kWh are used for peak shaving. The size of the reduction in power and further storage behavior will be examined to analyze the optimal relation of capacity to power (E2P-ratio). Based on these elaborations, a reduction of a PV peak, e.g. from 60 kW nominal power to 30 kW leveled feed-in (and hence, an E2P-ratio of 150kWh/30kW=5 hours) should be possible. This storage is intended to reduce the burden on the transformer in the event of large PV-feed-in peaks. 2) A further possible application for the grid storage of 150 kWh is to maintain the voltage at the end of the line. Here, depending on the grid conditions active and/ or reactive power can be consumed or supplied. 3) In addition to the large battery systems, smaller units with capacities of 30 kWh are used as decentralized assets in order to contribute to (balanced) voltage levels. For this, these assets are positioned next to selected PV generators. 4) Furthermore, battery systems are installed in households to increase the flexibility of the consumption/generation, which is enabled by the local battery in the operation of the Home Energy Controller HEC (see details below). 150821_AG_PlanGridEV Deliverable 5 2 v0 5_submitted_editor.docx Page 19 of 30


Grid separation switches are to be used to control the grid topology, so that, if necessary, two branches can be joined together to form a closed ring structure. Charging stations are used for electric vehicles to charge the batteries by cable connection. The communication with controllable loads in households takes place via installed intelligence (Home energy controller - HEC). The HEC controls the loads and energy generation in the households including, among others, PV generators, heat pumps, white goods and battery storages. For the household appliances the HEC works out the optimal timetable. For this, different optimization objectives are applicable, e.g. maximize the self-consumption of locally generated electricity. Furthermore, the HEC may adapt the local energy profile to react on incentives and steering signals provided by the ‘Smart Operator’ (e.g. in cases of grid problems). The HEC uses information from weather forecasts and combines this with consumer behaviour patterns to produce a forecast of the consumption/generation profile. Thus, it seeks to optimize usage, so that e.g. a heat pump is preferably switched on at midday and the temperature in the refrigerator is reduced if sufficient PV feed-in is given. If the Smart Operator chooses a load profile for the next few hours the HEC tries to realize it and the schedule is passed on to the components. The customer can intervene at any time and still has complete freedom to do as he or she pleases, so that the HEC and the Smart Operator have to react on unforeseen customer behavior. In case the temperature in the refrigerator rises above the permitted level, the refrigerator will start anyway even if this is not foreseen in the HEC’s prognosis (Figure 3.4).

Figure 3.4 - Equipment that has been installed in the RWE test bed



These technologies will be selected according to the grid area and will be used either in combination or separately. The central controlling function will be carried out by the Smart Operator. The test bed in Kisselbach is characterized by the following properties: -

Availability of a fiber optic network for transmitting data from the smart meters to the attachment sites in the sub stations. A real-time monitoring, operation and maintenance back-end system for EV charging stations A real-time communication uplink between EV charging stations and operation back-end. A communication protocol capable of hosting power modulation between the Smart Operator and the installed Equipment incl. the charging units. Communication via fiber optic cable. 135 out of 200 households equipped with smart meters. 12 PV-Installations (170 kWp). 21 off-peak storage heating systems. 21 electric heated hot water storages. 6 heat pumps. 1 e-bike charging unit. 1 controllable grid transformer 630 MVA. 2 EV charging units (with two 22kW type 2 connections each), which are capable of receiving a power modulation level information and converting it through PWM signal in accordance at least with ISO/IEC 61851-1. They will be upgraded to ISO 15118 within the project.

The following additional steps on top of existing RWE’s infrastructure have been implemented in PlanGridEV project in order to deliver a proof a concept of the above mentioned D2.2 service: -

(A) Premiere installation of a Smart Operator computer which controls the local grid.

-

(B) EV Customer preferences HMI for real-time communication of Initial SOC, Final SOC, Time of Departure.



3.3. RWE Test bed updates mapping to use cases The following table shows the use case applicable to the RWE test bed, as defined in D5.1, and the systems that will be used during the validation of the operational methods that will take place in the frame of task 5.3. Updates will be highlighted in the last column. Table 3 RWE test bed: system updates mapping to EV smart charging UC

No.

1

2

3

4

5

6

Use Case no. 5: Real time optimization of the distribution network operation Step Scenario Step System and functions required no. Smart meters (in network and end-user facilities) Real time monitoring of the main Monitoring the Energy supply, consumption and storage 1 EVSE: grid parameters distribution grid EV charge information Grid state Grid state estimation based on 1 Currently available backend application estimation metered values Weather monitoring equipment: Weather data information collection HEC (Home energy controller): Obtaining weather forecast and 1 load & generation (L&G) End user load and generation forecast calculation schedules by the DMS (it also uses weather information) Load and EV user HMI: generation EV user preferences information forecasting Smart Operator (DMS): Local grid efficiency optimization Load and generation forecasting HEC (Home energy controller): 2 for the next 24 hours Load and generation optimisation at home level Reacts to Smart operation signals if available Power flow optimisation to Network analysis 1 choose the optimum operation Currently available backend application strategy Smart Operator (DMS): Central control for local grid Voltage Controlled Distribution Transformers: On-load voltage variation Network storage systems: Grid asset control (switching Peak shaving for large PV feed-in peaks 1 signals) Voltage control at line end Balanced voltage levels next to PV generators LV switches: Grid topology control (connect two branches to form a ring structure) Controlling the grid Smart Operator (DMS): Grid efficiency optimization Central control HEC (Home energy controller): Load & generation control Storage systems: End-user control through HEC 2 devices Increase energy demand/generation flexibility at residential level Energy generation systems: PV, micro CHP, Controllable loads: EVSE/EV (now IEC 61851-1, in the future ISO/IEC 15118), hot water storage, heat pump, white goods Smart meters (in network and end-user facilities) Monitoring the distribution grid Energy supply, consumption and storage 1 EVSE: after reconfiguration Performance EV charge information monitoring Smart Operator (DMS): Performance rating (weighting of 2 Algorithm assigning a high weight to successful control actions) switching options



4. Update of infrastructure for Ireland Test bed (ESB) 4.1. Overview of test bed The Irish test bed is used to evaluate the scenarios “conventional” and “safe”. As outlined in Deliverable 5.1, the test bed consists of one site where three electric vehicles are deployed on a 33kVA MV/LV transformer fed from a single phase spur on a 3 phase MV network which feeds four residential customers. The demonstration will consist of four stages: -

No electric vehicles connected. Electric vehicles deployed but charging restricted. Electric vehicles deployed with unrestricted charging. Intelligent charging.

Compared to other jurisdictions, Ireland has extremely low levels of DER, therefore the test bed does not include DER. The test bed that is implemented will allow examination of the impact of high penetration levels of electric vehicles on a typical rural network. Intelligent charging will be utilised to examine its capabilities in resolving network congestion. ESB electric vehicle public charging infrastructure is located in the area surrounding the test bed, which ensures it is more feasible for participants to use the vehicles and also provides a more realistic mix of vehicle charging.

Figure 4.1: ESB eCars Public Charge Point & Electric Vehicle

4.2. Technical updates for test bed execution The scope of the demonstration does not require network reinforcement. However a number of devices were installed to ensure the trial provides the required results. From a DSO point of view, a monitoring device which will record power, current and voltage will be installed on the LV side of the 33kVA transformer, and an end of line monitor will also be installed in the property of the customer, furthest from the transformer, to monitor the end of line voltage. The monitoring device at the transformer uses ZigBee wireless communications and a cellular modem to transfer the data.



The EVSE have installed a charge point timer and profile meter at each of the three participant properties. The programming of the timer will determine which scenario is being tested. Minor alterations had to be made to the EVs and the charge points to facilitate the timers. The profile meters will record the load of each electric vehicle. The profile meters uses cellular technology to transfer the recorded data.

4.3. ESB Test bed updates mapping to use cases The following table shows the use case applicable to the ESB test bed, as defined in D5.1, and the systems that will be used during the validation of the operational methods that will take place in the frame of task 5.3. Updates will be highlighted in the last column. Table 4 ESB test bed: system updates mapping to EV smart charging UC

No.

1

2

Use Case no. 2: Off-line monitoring and EV demand related risk identification Step Scenario Step System and functions required no. Metering device at the MV/LV transformer: Monitoring of power, current and voltage in the LV Remote monitoring of the main side Monitoring the 1 EVSE meter and timer: network parameters distribution grid Meters record charge profiles Timer to create different EV charge scenarios Network analysis Network impact analysis based 1 Based on currently available backend applications (historical data) on monitoring data



5. Update of infrastructure for Portugal Test bed (EDP) 5.1. Overview of test bed EDP test bed is linked to the Conventional scenario where switching is used as only means for network operation and it is closer to the Safe scenario when on/off type control over EV and DER is performed. No grid reinforcements are expected but where simply reconfiguration process is not enough, the investment will be minimized by applying the above concept. No EVSE operator (EVSEO) is involved and no ToU tariff is foreseen. Test Bed development aims at lowering EV penetration impact by postponing or avoiding “copper” investments in order to sustain EVs adoption. The proper allocation in the LV grid of the charging demand and PV generation within the day would allow the DSO to keep the LV and MV grid with today’s configuration, without replacing transformers or lines that would otherwise be overloaded due to EV charging. This Conventional-Safe scenario (EV and DER on/off control possible but no reinforcements expected, no EVSEO, no ToU tariffs) is directly linked with the Service nº 3 (D5.1 – Paragraph 2.1): Planned DR: Load management according to long term minimization of electricity grid investments. The correct allocation of EV load and DER generation within LV feeders will be achieved by simply operate switches remotely. To determine the correct/optimal location of the mentioned LV switches a BaU simulations will be deployed, where DER generation, EV charge and customer data will be used as input for network reconfiguration (meshed networks) matching the loading/generation scenarios defined in Deliverable D1.2 (Future Scenarios). Furthermore, an optimal EV and DER control strategies, meaning a set of decision criteria to operate the remote controlled LV switches in order to perform load transfer between LV feeders, will be developed and will be the basis for establish new Operational Methods. This Dynamic Strategy Management tests will be carried out considering that EVs and DERs are controlled optimally. The benefits of optimal reconfigurable operation will be measured by comparing simulation results through either a static daily configuration or a dynamic configuration within the day.



5.2. Technical updates for test bed execution As all the tests will be performed with the EDP software planning tool DPlan no particular technical updates (electric infrastructure or ICT technologies) will be needed to develop the tests. All the data needed to feed the software planning tool DPlan (customers and EV load diagrams and DER output diagrams) is already available and it is obtained through technology already installed in our EDP Project Inovgrid. The image below shows the two equipment used to acquire grid data implemented in the MV/LV substations (EDP DTC) and in the customer facilities (EDP Energy Box).

Figure 5.1 - EDP DTC and EDP Energy Box

The technology to acquire the above information/data is based in the traditional cellular mobile systems like GPRS for the EDP DTC (data acquisition installed in MV/LV substations) and in NPLC and/or GPRS for the EDP Energy Box (customer and DER data). The information acquired by the two devices above described will help us to better understand and characterize the MV and LV network. As EDP Test Bed aim to test LV networks the simulations will be based on the information provided by the EDP Boxes. The following image represents the principle scheme linked to the interconnection of the above equipment and the communication technologies used between them.



Figure 5.2 – Equipment and Communications scheme

Regarding EV customers, in the EDP Test bed the simulations will take into account the possibility that EVs have to charge as soon as they arrive at the charging point and without any limitation on the power to be demanded. Therefore, no Charge Management or Type of Charge management is previewed and consequently there are no necessity to implement any new communication system between the EV and the charging station. As stated in D5.1, the validation test that will be deployed in EDP Test Bed will seek to evaluate the flexibility of the network aiming to maximizing the presence of electric vehicles (EVs) and distributed energy resources (DER). This flexibility will be provided by future inexpensive remote-controlled switchgear and will be simulated in the planning software DPlan. The technology to operate these new devices is based in a GPRS system/communication as it is already implemented in MV remote controlled switchgear systems.



5.3. EDP Test bed updates mapping to use cases The following table shows the use case applicable to the EDP test bed, as defined in D5.1, and the systems that will be used during the validation of the operational methods that will take place in the frame of task 5.3. This case is different from the others since tests will be based on simulations and, therefore, it requires no additional systems. Real historical data from network monitoring will be used as input for the calculations instead of the forecasted data that should be used in a real network operation case. Table 5 EDP test bed: system updates mapping to EV smart charging UC

No.

1

2

3

4

5

Use Case no. 1: Load management through network configuration and EV/DER on/off control Step Scenario Step System and functions required no. Meters at MV/LV substation (EDP DTC): Weather forecast and L&G 1 "schedule" acquisition Energy distribution data will be used as input for Load and the simulations generation (L&G) L&G forecasting for the next day Meters at end user facilities (EDP Energy box): forecasting 2 based on external and historical Consumed and generated energy data will be data used as input for the simulations Power flow optimisation to select Planning software (DPlan): 1 the optimum solution EDP Planning tool LV network switches: Network analysis Simulated operation on LV switches for load Network configuration selection 2 transfer between feeders will be tested for network for the following day operation optimization (Dynamic Strategy Mngmt) Network status monitoring to Due to the fact that tests will be simulated this part identify required actions aiming will not be accomplished in the project framework 1 LV network switches: at the optimum solution Controlling the grid Based in GPRS communications The technology is already implemented in MV 2 Grid asset control remote controlled switchgears Monitoring distribution grid 1 Meters: as in scenario no. 1 Monitoring the parameters to check deviations distribution grid Based on currently available backend applications 2 Alert identification Load and generation control 1 Controlling load signals (on/off) sent if required No control is foreseen on loads, including EVs, or and generation DER Performance monitoring for (L&G) 2 indirect L&G control actions



6. References 6.1. Project documents List of reference document produced in the project or part of the grant agreement [DOW] – Description of Work [GA] – Grant Agreement [CA] – Consortium Agreement [1] Silvestri C., Coppola G. Technical Requirements for tools/methods for smart grid integration of EVs, PlanGridEV project deliverable 2.2. [2] Gaul. A., Selection of use cases and testing infrastructure at DSOs, PlanGridEV project deliverable 5.1, v1, 11/11/2014



7. Revisions 7.1. Track changes Name

Date (dd.mm.jjjj)

Version

Cristina Silvestri (ENEL)

01.12.2014

0.1

Adriano Santiangeli (CIRPS)

16.01.2015

0.3

Raul Rodriguez (Tecnalia)

Cristina Silvestri (ENEL)

Armin Gaul (RWE) Armin Gaul

15.01.2015 20.01.2015 20.01.2015 21.01.2015

0.2 0.4 0.5 0.5

Changes Subject of change page First draft with Enel contribution as example Comments and amendment proposal General Review

Adopted comments by CIRPS and Tecnalia Adoped comments in the RWE section General Review
