Newcastle University ePrintsCrossland AF, Jones D, Wade NS. Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing.This work is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License ePrints -Newcastle University ePrints http://eprint.ncl.ac.ukPlanning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing International Journal of Electrical Power & Energy Systems http://dx.AbstractIn light of the expansion of domestic photovoltaic (PV) systems in the UK, there are concerns of voltage rise within LV networks. Consequently, network operators are interested in the costs and benefits of different technologies to manage their assets. This paper examines the particular case for distributed energy storage. A heuristic planning tool is developed using a genetic algorithm with simulated annealing to investigate the problem of locating and sizing energy storage within LV networks. This is applied to investigate the configuration and topologies of storage to solve voltage rise problems as a result of increased penetration of PV. Under a threshold PV penetration, it is shown that distributed storage offers a financially viable alternative to reconductoring the LV network. Further, it is shown that a configuration of single phase storage located within the customer home can solve the voltage problem using less energy than a three phase system located on the street. NomenclatureRandom number in the interval zero to one, applied in simulated annealing Capital cost of storage unit [£] Installation cost of each storage unit [£] Power cost of particular energy storage technology [£/kW] Energy cost of particular energy storage technology [£/kWh] Cost of reconductoring the network [V] Permissible depth of discharge [%] Capacity of energy storage unit [kWh] Roulette wheel fitness of solution during algorithm round Probability that solution is selected during algorithm round Rating of energy storage unit [kW] Length of time that storage operates at full power [h] Temperature, applied in simulated annealing Highest voltage in LV network when storage solution is implemented [V] Round trip efficiency of particular energy storage technology [%]Please cite this article as: Crossland, A.F., et al., Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing.

Wade NS. An international review of the implications of regulatory and electricity market structures on the emergence of grid scale electricity storage.

A method for the coordination of multiple battery energy storage systems (BESSs) is proposed for voltage control in low-voltage distribution networks (LVDNs). The main objective of this method is to solve over-voltage problems with multiple suitably sized energy storage systems. The performance of coordinated control is compared with noncoordinated control using both a real-time digital simulator and a MATLAB model of a real U.K. LVDN with a high installed capacity of solar photovoltaics. This is used to show that coordinated control is robust and effective at preventing voltage rise problems in LVDNs. The proposed coordinated control scheme is able to use the BESSs more evenly, and therefore reduces the costs of battery replacement to the storage operator in terms of both number of batteries and maintenance visits.Index Terms-Battery energy storage systems (BESSs), coordinated voltage control, distributed generation, low-voltage distribution network (LVDN), real-time digital simulator.

Expansion of photovoltaic (PV) generation is increasing the challenge for network operators to keep voltages within operational limits. Voltage rise occurs in low voltage (LV) networks when distributed generators export, particularly at times of low demand. However, there is little work quantifying the scale of voltage issues and subsequently potential solutions across large numbers of real networks. In this paper, a method is presented to analyse a large quantity of geographically and topographically varying distribution networks. The impact of PV on voltages in 9163 real LV distribution networks is then quantified. One potential mitigation measure is increased network demand to reduce voltages. In this work, location algorithms are used to identify where increased demand, through energy storage, has the greatest effect on overvoltage. The study explores the impact on overvoltage of two modes of storage installation reflecting differing routes to adoption: purchase of storage by homeowners and purchase by network operators. These scenarios are compared with traditional re-conductoring in the 9163 networks. It is shown that to avoid violation of absolute voltage limits, storage should be installed at strategically important locations. Storage in homes reduces overvoltage, offering clear benefits to the network operator, but very wide deployment is required to completely remove the need for reinforcement.

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