reconfiguration and automation, and post-contingency Demand. Side Response (DSR) as ... heuristic searches to model optimal reinforcements and C2C interventions ..... premises after a construction period. Scenario 3 represents a.
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Economic Assessment of Distribution Network Reinforcement Deferral Through PostContingency Demand Response E. A. Martinez Cesena, Member, IEEE, and P. Mancarella, Senior Member, IEEE
Abstract--This paper presents a methodology for the economic assessment of smarter solutions used for distribution network reinforcement planning; specifically, the methodology was developed to assess the economic value from deploying network reconfiguration and automation, and post-contingency Demand Side Response (DSR) as recommended by the Capacity to Customers (C2C) method, which is currently being tested in the UK at medium voltage levels (i.e., 11kV and 6.6kV). The C2C method combines the aforementioned smart solutions to increase distribution network capacity (limited by security considerations) and bring about several social benefits at the distribution level (e.g., reduced power losses and increased reliability); thus deferring or even averting costly network and substation reinforcements. The proposed methodology relies on scenarios, optimisations and a Cost Benefit Analysis (CBA) framework to assess the C2C method compared with traditional reinforcement practices. The approach is illustrated using a real UK distribution network where the C2C method is currently being tested, and general conclusions from other such networks are also discussed. The results highlight the conditions that encourage the deployment of C2C interventions as an alternative to or in combination with traditional reinforcements. Index Terms-- Cost benefit analysis, demand response, distribution network planning, network reinforcement deferral, optimisation.
I. INTRODUCTION
T
HE capacity of most distribution networks in the UK has been historically determined based on the rationale that (i) energy supply must be guaranteed for all (or most) customers after credible contingencies occur and (ii) control is mainly preventive and provided from the supply side. Accordingly, large investments in spare network capacity are typically made to meet security criteria (i.e., N-1 considerations and P2/6 engineering recommendations used in the UK [1]). This spare network capacity is seldom used, as it is strictly reserved for emergency conditions that, in some cases, may occur once every 5 years or less frequently. This traditional planning philosophy may not be the most cost effective alternative, especially in the light of emerging smarter grids where control may be corrective too, and provided in part from the consumption side as Demand Side Response (DSR). E. A. Martínez Ceseña and P. Mancarella are with the University of Manchester (UoM), School of Electrical and Electronic Engineering (EEE), Electrical Energy and Power Systems Group, Manchester, M13 9PL, U.K. (email: {eduardo.martinezcesena, p.mancarella}@manchester.ac.uk).
Based on this, Electricity North West Limited (ENWL), which is one of the distribution network operators (DNOs) in the UK, has recently proposed the Capacity to Customers (C2C) method as a means of updating traditional distribution network planning practices based on the smarter grid paradigm [2]. The C2C method comprises a combination of network automation and reconfiguration, and postcontingency DSR response solutions, which are expected to (i) defer or avoid costly network reinforcements via releasing part of the untapped emergency capacity for everyday use and (ii) bring about several network benefits such as decreased power losses and increased reliability among others. However, these benefits have yet to be quantified and compared with the costs inherent to the C2C method (e.g., investments in network automation and DSR payments, among others). In the light of the above, this work presents a methodology for the economic assessment of solutions used for distribution network planning; particularly the smarter grid solutions recommended by the C2C method. The proposed approach is based on (i) a Cost Benefit Analysis (CBA) framework consistent with UK regulations to quantify the economic value of the network solutions under study, (ii) scenarios to represent the effects of uncertainty on the value of each solution and (iii) bespoke optimisation engines based on heuristic searches to model optimal reinforcements and C2C interventions independently or in combination. The methodology is used to assess the potential value of the C2C method in one of the 36 Trial UK networks where the method is currently being tested. General results from studies carried out for other Trial networks are also discussed with the objective of highlighting the conditions that motivate the deployment of the C2C method. The rest of the paper is structured as follows. Section II provides a thorough description of the C2C method. Section III introduces the proposed methodology. Sections IV and V present the case study and associated key results and conclusions, respectively. II. THE C2C METHOD In the UK, distribution networks at the 6.6kV and 11kV levels typically comprise two or more radial feeders interconnected through a Normally Open Point (NOP) as shown in Fig. 1. The operation of the NOP is typically restricted to emergency conditions after a fault occurs in one
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of the feeders and supply has to be provided through an adjacent feeder. In such a case, all customers in the affected feeder are initially disconnected while the protections system isolates the fault along with several customers (normally within three minutes). Afterwards, depending on the location of the fault, a section of the feeder connected to the NOP may become isolated from the point of connection. Customers in this section of the feeder are then supplied via the neighbouring feeder after the NOP is closed manually (typically within an hour hour). This practice is analogous to N-1 security considerations and is put in place to comply with engineering recommendations P2/6 [1]. Due to the abovementioned practices, both distribution feeders have to be significantly oversized to meet own demand during normal operations, and demand from adjacent feeders during emergency conditions. The resulting emergency capacity is seldom used as faults occur rarely; in some cases once every five years or less frequently. Based on this, under current practices, demand growth in one feeder may lead to costly reinforcements in one or both feeders (and the substation) to provide emergency capacity that is rarely used. These practices are based on supply-side control and preventive security and may not be economically efficient in the light of emerging smarter grid solutions that facilitate postfault corrective control, which can be provided in part by the consumption-side as DSR. Based on this, ENWL recently introduced the C2C method at the medium voltage level (currently under trial at the 6.6kV and 11kV levels). The C2C method is a corrective control philosophy based on network reconfiguration and automation and post-fault DSR solutions specially devised as an alternative to costly network reinforcements, as well as a means for reducing social costs at the distribution level [2]. The C2C method is expected to defer (or even avoid) costly network reinforcements by releasing emergency capacity for everyday use. This is achieved by allowing demand to grow beyond the security (post-contingency) limits imposed by security criteria (e.g., the P2/6 engineering recommendations in the UK) while still meeting security requirements by calling DSR to mitigate thermal and voltage issues that might arise during emergency conditions. By allowing demand to grow beyond traditional security limits, the C2C interventions would defer costly network reinforcements. Furthermore, considering
that (i) reinforcements must be committed several years before demand is predicted to reach the secure capacity of the network due to the lead time needed to carry out the work (e.g., 3 years) and (ii) demand growth forecasts can be substantially uncertain under real life conditions; C2C interventions may avoid reinforcements whenever demand does not grow as forecasted. Thus, C2C interventions (with a lead time of roughly a year) may be particularly attractive under practical conditions characterised by significant uncertainty. It is important to note that post-fault DSR would seldom be needed by the C2C scheme as emergencies occur rarely (at the trial sate, the C2C method is only considered for highly reliable systems) and, even during emergency conditions, thermal or voltage issues are unlikely to occur unless the emergency conditions extend to peak time. Besides network reinforcement deferral, the C2C method is expected to decrease power losses via network reconfiguration and increase network reliability via network automation (view Fig. 2). The NOP would now be operated normally closed creating a ring and bringing about immediate power losses reductions. The NOP and at least one recloser in each feeder would now be automated. Thus, if a fault were to occur in the ring, all customers in both feeders would be disconnected for less than three minutes; however, apart from the customers that are isolated along with the fault, all other customers would be restored within three minutes (the typical time for the automatic switching scheme to operate). As a result, the C2C method is expected to improve system reliability by reducing Customer Interruptions (CI) of longer than three minutes, which are the only interruptions regulated in the UK. Hence, it is clear how the C2C solution can be an attractive alternative (or complement) to traditional reinforcement practices. However, the associated economic benefits have yet to be quantified and compared against the costs inherent to the C2C method (e.g., costs of automation, DSR capacity payments and so forth) using a suitable methodology as the one presented in the next section. III. METHODOLOGY The methodology proposed in this work relies on a CBA framework, scenarios and optimisation engines to assess the economic attractiveness of deploying the C2C method as an Point of connection
Point of connection Loads: Typical Feeder 1
Feeder 1
Feeder 2
Normally open
Constraints: Preventive security Thermal Voltage Configuration: Radial Expectations: –
NOP Fig. 1. Traditional network comprising two radial feeders
Feeder 2
Normally closed DSR NOP
Loads: Typical Interruptible Constraints: Corrective security Thermal Voltage Configuration: Ring Expectations: Increased capacity Increased reliability
Fig. 2. C2C networks
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alternative or in combination with traditional reinforcements. It is worth noticing that, even though the approach has been formulated specifically to address the solutions considered by the C2C method and traditional reinforcement practices, other network solutions may also be considered. A. CBA framework The CBA framework used in this methodology is consistent with the new UK regulations, namely the first Revenues = Incentives + Innovations + Outputs Electricity Distribution (RIIO-ED1) price control [3]. The RIIO-ED1 price control has been proposed in the UK as a means to incentivise DNOs to propose new and smarter distribution-level solutions that drive economic or social benefits, such as the C2C method. In line with the RIIO-ED1 price control, the UK regulator (Ofgem) introduced a deterministic CBA framework for DNOs to assess network reinforcement alternatives. This CBA framework (with several modifications explained below) is included in the proposed approach as a base for the economic assessment of distribution network solutions. According to the CBA framework, distribution network expansion costs should be assessed based on the Net Present Costs (NPC) criterion as denoted by (1) – (7) [4]. _ _
%&' = +%_
= 0.85 × = 0.15 ×
=!
=
( )
_" #
_ − = %&' × *& _ +
NPC =
01
+%_ (1 + )
_
( ),
(1)
( ),
(2)
$
(3)
− %&' +
)
(4) (5) (6) (7)
_ and _ are shares of where the investment costs ( ) that can be recovered over time , (i.e., lead to profits for the DNO in the form of network charges) and immediately, respectively; is the depreciation of all capitalized investments over the depreciation lifetime ( _" # $ ), %&' is the regulated asset value (UK regulation limits DNO expenditure by constraining this parameter), is the cost of capital, *& is the pre-tax weighted average cost of capital, +%_ are the investment costs associated with network reinforcements (incurred mainly by DNOs and new customers connected to the network) in a given year, the subscript (3) denotes the nth network solution intervention in year 3, and both subscripts 3 and 31 denote time periods (years). It is important to highlight that the original CBA framework proposed by Ofgem relies on the Net Present Value (NPV) criterion to compare all network solutions under assessment with a baseline based on business as usual traditional reinforcement practices. Accordingly if the
associated NPV is positive, the proposed solution is more attractive than the baseline. This approach has the disadvantage of concealing the characteristics of the baseline and the actual benefits and costs associated with the network solutions under assessment. Based on this, it was decided to base the proposed methodology on the NPC criterion, which leads to the same results as the NPV while separating the baseline (which is always presented for comparison purposes) from the network solution under assessment. B. Scenario assessment The CBA framework proposed by Ofgem is deterministic as it only allows the consideration of a single scenario that is meant to represent the most likely unfolding of underlying uncertainty (demand growth in this work). This approach is not ideal for the assessment of the C2C method or other smarter solutions that increase network capacity and can have a significantly different economic performance in different scenarios. In specific, the use of a single scenario would disregard the value of flexibility from post-fault DSR solution to defer or avoid network reinforcements under different conditions. In the light of this, the proposed methodology considers several potential scenarios for demand growth. C. Optimisation engines Another area of improvement of Ofgem’s CBA framework is the manner in which investment schemes are formulated. The framework does not provide a systematic approach to define the baseline or the network solution under assessment; leaving this task entirely to the DNOs. As a result, different DNOs may formulate different baselines and the amount of network solutions that are assessed via the CBA may be limited. Based on this, in this work, the CBA framework has been automated and combined with two bespoke optimisation engines that allow the systematic formulation of the baseline and other solutions under assessment. The optimisation engines, namely greedy and exhaustive engines, are based on heuristic searches that can be implemented in most programming or simulation software (e.g., MATLAB in this work), and can be used by DNOs that may not use commercial optimisation software. The greedy optimisation engine (see Fig. 3) is based on greedy searches to identify a series of cheapest immediate investment decisions (including doing nothing) needed to ensure that the network meets feasibility constraints (e.g., voltage and capacity limits, security considerations and so forth). The algorithm begins by proposing an investment scheme in which no investments are made throughout the planning horizon ( (3) = 1 ∀3) for a given scenario (the approach is applied to every scenario under consideration). Afterwards, the feasibility of the investment scheme (series of investment decisions associated with each year throughout the planning horizon) is assessed based on the expected demand growth for different years (3 = 3 + 1). In case the investment scheme is unfeasible in a given year, the different available network solutions ( (3) = (3) + 1) are assessed, and the best solution (feasible solution with the lowest NPC) is implemented that year. Eventually, an investment scheme comprising feasible investment decisions throughout the
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Initialise investment scheme - (3) = 1 (do-nothing) ∀3 - 3 = 1 (first investment period) Yes Feasible? No
Yes
(3) ≤ +?
No
(3) = (3) + 1
Initialise investment scheme - (3) = 1 (do-nothing) ∀3 - 3 = 45 (end of planning horizon) 3 =3+1 3 > 45?
No Feasible? No
Yes CBA
CBA
Yes
3 =3+1
(3) ≤ +?
3 > 45?
No
Yes
End No
3 =3−1
No
Yes (3) = (3) + 1 3 =3−2
(3 ) = 0 ∀3 > 3
Best? CBA Yes
3=3−1
Save investment decision
Fig. 3. Flow diagram of the greedy search engine
planning horizon (45 years) is formulated and assessed with the CBA framework. The rationale of the greedy search is consistent with Ofgem’s definition of the baseline (i.e., cheapest traditional investment scheme available) as long as the only network solutions considered are substation upgrades and network reinforcements to one or both feeders. In addition, the greedy search is adequate to model a C2C based investment Scheme (C2CS) where the underlying smarter solutions are used as alternatives to traditional reinforcements recommended by the baseline. However, this optimisation engine is not adequate to identify optimal investment schemes that may combine traditional reinforcement and C2C solutions, as greedy searches cannot guarantee optimality [5]. For that reason, a second optimisation engine based on exhaustive searches is proposed (see Fig. 4). It can be argued that even though an exhaustive search can guarantee optimality, it may also be computationally expensive or even unfeasible in the face of a large search space [5]. However, as with the problem addressed in this work, the use of exhaustive searches becomes an attractive and efficient option if the search space can be limited. In the light of this, the proposed exhaustive optimisation engine limits the search space by systematically disregarding unfeasible investment decisions that would lead to thermal, voltage or security constraint violations, and discards conflicting investments (e.g., investments in reinforcements that would not increase the capacity of the system). Similarly to the greedy engine, the proposed exhaustive algorithm begins by formulating an investment scheme that recommends doing nothing every year throughout the planning horizon ( (3) = 1 ∀3 ). However, this time, the algorithm begins by assessing the feasibility of the solution from the last year of the planning horizon (3 = 45). If the investment scheme is feasible until year 45, it will be assessed with the CBA framework and stored as the best investment scheme identified so far. Otherwise, the algorithm will search (3 = 3 − 1) for the last year when the investment scheme was
No Best? Yes Save investment scheme
Yes Continue search? No End
Fig. 4. Flow diagram of the optimisation engine
feasible. Afterwards, when a year when the investment scheme is feasible has been found, new combinations of investment decisions are proposed for the later years ( (3) = (3) + 1) (e.g., reinforce the network, C2C interventions and so forth). In cases where no investment solutions are available ( (3) > +) or the investment scheme becomes unfeasible, the algorithm searches once again for the last year when the investment scheme was feasible and proposes a different combination of investment solutions. This process effectively disregards the unfeasible search space, and assesses all investment schemes that are feasible throughout the planning horizon (3 > 45) with the CBA. The optimal investment scheme is taken as the best of the feasible investment schemes. As a summary, the greedy optimisation engine is used to formulate the baseline (business-as-usual investment scheme) and C2CS (investment scheme that recommends C2C interventions as alternatives to reinforcements), and the exhaustive optimisation engine is used to formulate an Optimal investment Scheme based on Investment costs minimisation (OSI) that may combine traditional reinforcement and C2C based solutions. D. Additional considerations The proposed approach is built upon the recommendations from experts and technical studies of the prospective system before and after implementing a given network solution. This information comprises: • The maximum capacity of the existing system and prospective systems after a solution is implemented, and the associated power losses. This is calculated based on AC power flows simulations that take into account P2/6 engineering recommendations for the feeders and
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• •
classical N-1 security criteria for the substation, and security ratings (i.e., up to 20% overload for two hours). CI estimates and demand growth scenarios forecasted by experts. Potential network reinforcements based on credible upgrades of one or both feeders and the substation. IV. CASE STUDY
As a case study example, let us assume that ENWL’s “Heywood” 6.6kV urban network (See Fig. 5) is close to maximum capacity. That is, feeder headroom is 3% and substation spare capacity is 9.7%. Accordingly, the DNO needs to assess the options to reinforce the network based on business as usual practices, C2C interventions or a combination of both alternatives. Note that this is a hypothetical situation as this is one of the Trial networks where the C2C method is currently being tested. In addition, based on current outputs from the C2C project [2], consider that (i) the price of DSR in the area is 22k£/MWp for flexible capacity provided during peak time, (ii) the cost for automating two reclosers and the NOP is 19k£, (iii) automation brings about a CI reduction of 50%, (iv) the lead times (including planning and execution) for a substation upgrade, a network reinforcement and a C2C intervention are 3 years, 2 years and 1 year, respectively; and (v) the costs associated with reinforcing the network (both feeders) and the substation are 276k£ and 338k£, respectively. The DNO envisions three probable demand growth scenarios for this area (view Fig. 6). Scenario 1 represents the possibility Point of connection
that demand grows steadily during the next few years due to, for instance, new customer connections in the area. Scenario 2 depicts a case where demand growth is negligible for a few years before increasing steadily as in Scenario 1. This could be attributed, for example, to the connection of new housing premises after a construction period. Scenario 3 represents a case where demand reverts to current levels after increasing for a few years. This can represent, for instance, new customer connections for a few years followed by a large implementation of energy efficiency measures (e.g., housing insulation). This scenario can also be used to roughly model the risk of demand not growing as expected, which would typically lead to costly investments in spare network capacity that would only be needed to meet security criteria for a short period if ever. Based on the greedy and exhaustive engines presented in this work, the baseline, C2CS and OSI for the abovementioned scenarios are as shown in Table I, Table II and Table III, respectively. It can be seen that, as expected, the baseline recommends reinforcements (subject to planning and implementation lead time) whenever maximum capacity is reached in a particular scenario, and the C2CS follows a similar logic although recommending C2C interventions instead of reinforcements. The OSI has the flexibility to select the optimal investment decisions in every scenario which may include combinations of network reinforcements and C2C interventions as in the first few scenarios. The NPC, and the power losses and CI corresponding to the different investment schemes are presented in Table IV and Table V, respectively. The results show that the C2C method can be a cost effective altenative to traditional reinforcements, particularly in the face of demand growth risks that may lead to costly investments in spare capacity that is only needed for a short period as in scenario 3. In such a case, the C2C method offers the flexibility to meet network constraints via DSR deployment at a relatively low cost (compared with network TABLE I.
NOP
Scenario 1
2
Feeder 1 Feeder 2
3
BASELINE INVESTMENT SCHEME
Actions: Reinforce the network in year 4 and the substation in year 9 Reinforce the network in year 9 and the substation in year 17 Reinforce the network in year 5
Fig. 5. Heywood 6.6kV distribution network. TABLE II.
Demand growth (%)
20
Substation's maximum capacity
Scenario 1
15
Network's maximum capacity 10
C2CS RECOMMENDATIONS
Scenario 1
Actions: Perform a C2C intervention in year 5
2
Perform a C2C intervention in year 10
3
Perform a C2C intervention in year 6
Scenario 2 TABLE III.
5
Scenario 3 0 1
6
11
16
21
Time period (years) Fig. 6. Demand growth scenarios considered for the study
Scenario 1
2 3
OSI RECOMMENDATIONS
Actions: Reinforce the network in year 4 and perform a C2C intervention in year 11 Reinforce the network in year 9 and perform a C2C intervention in year 19 Perform a C2C intervention in year 6
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reinforcements). In other scenarios where demand may grow substantially (i.e., scenario 1 and 2), the associated DSR costs would rise accordingly and encourage the DNO to reinforce part of the network to limit DSR payments, which is the recommentation of the OSI. The same conclusions can be drawn from studying other networks where the C2C method is being tested (not included in this work due to space constraints). It is worth notticing that uncertaintymodelling (i.e., the use of scenarios) is critical for highlighting the fundamentally different investment decisions and costs that may arise under diverse conditions (e.g., the OSI in the last two scenarios), which could not be observed or quantified with deterministic assessments based on a single scenario. As can be deduced from Table V, power losses (annual average for 45 years without discounting) can be minimised by combining network reinforcements and the C2C method (closing the ring). In this case study (and most Trial networks), power losses can be reduced by connecting the feeders as a ring; although greater losses reductions can be achieved by reinforcing the network. This wass assessed via power flow calculations for both network configurations and considering reinforcements (view Fig. 7). Network automation brings about significant CI (annual average per 100 customers for 45 years without discounting) reductions; thus the C2CS and OSI tend to outperform the baseline in this regard. Even though, the OSI recommends C2C interventions, its associated CI are higher than those of the C2CS in the first two scenarios because the C2C interventions are recommended at a later time. This trend is observed in other networks whenever a single network TABLE IV.
Scenario 1 2 3
Baseline 295 215 26
TABLE V.
Scenario 1 2 3
AGGREGATED NPC NPC (k£) C2CS 144 63 18
OSI 101 38 18
AVERAGE LOSSES AND CI
Losses (MWh/year) Baseline C2CS OSI 146 215 122 145 200 128 122 176 176
CI (events/year) Baseline C2CS OSI 10 6 6 10 6 7 10 6 6
Losses (MWh/year)
300
Radial
reinforcement provides sufficient headroom to meet demand growth throughout the scenario (as in the example). In other cases where multiple network reinforcements would be needed to meet demand growth in different years, the OSI tends to recommend C2C interventions before (or instead) network reinforcements. The CI associated with the OSI and C2CS tend to be the same in these cases. It is important to emphasize that the network under study is highly reliable (the average CI of ENWL was roughly 47 events per year in 2011 [6]), which is currently one of the pre-requisites for the implementation of the C2C method. As a result, reliability improvements brought about by network automation from C2C interventions may not seem consequential. However, reliability may become critical if the C2C method becomes business as usual and is implemented in less reliable networks. V. CONCLUSION This work evaluated the techno-economic benefits from using a network automation and reconfiguration and DSR based corrective control philosophy, namely the C2C method, in the context of distribution network reinforcement planning using a bespoke methodology based on a CBA framework consistent with UK regulations, bespoke optimisation engines, and on AC load flow studies. The results highlight that the C2C method can be an economically attractive alternative to costly network and substation reinforcements, particularly in the face of uncertainty (e.g., if demand may not grow as forecasted). In addition, it can be attractive to combine the C2C method with network reinforcements to limit DSR costs. It was also observed that the C2C method can bring about increased expected reliability and reduced power losses (when combined with network reinforcements). In the light of this, proper CBA frameworks supported by optimisation models (even if relatively simple as shown here for wider applications from network operators) as proposed in this work are critical to facilitate the proper implementation of the DSR schemes under conditions that facilitate both economic and social benefits. Future studies aim at consolidating this framework taking into account optimal planning strategies in the face of evolving uncertainty. VI. ACKNOWLEDGMENT The authors are grateful to Victoria Turnham, Paul Turner and Dr. Rita Shaw from ENWL for the continuous support and feedback provided during this work.
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VII. REFERENCES
Ring [1]
200 150
Reinforced radial
100
Reinforced ring
50 0
5
10
15
20
Time period (years) Fig. 7. Power losses associated with different solutions
[2] [3] [4] [5] [6]
R. Allan, G. Strbac, P. Djapic and K. Jarret, “Developing the P2/6 Methodology,” UMIST, Tech. Rep. DG/CG/00023/REP, 2013. ENWL, Capacity to Customers – Key Documents, [Online]. Available: http://www.enwl.co.uk/c2c/about-c2c/key-documents Ofgem, Network Regulation – The RIIO model, [Online]. Available: https://www.ofgem.gov.uk/network-regulation-%E2%80%93-riio-model Ofgem, Template CBA RIIO-ED1 v4, 2013. J. Pearl, Heuristics: Intelligent Search Strategies for Computer Problem Solving. Boston, MA, USA: Addison-Wesley, 1984. Ofgem, ElectricityDistribution Annual Report 2010-11, [Online]. Available: https://www.ofgem.gov.uk/ofgempublications/46553/electricitydistributionannualreportfor201011.pdf.
