. (2010) 'Evaluating the bene ts of an electrical energy storage system in a future smart grid.', Energy policy., 38 (11). pp. 7180-7188. Further information on publisher's website:http://dx.doi.org/10.1016/j.enpol.2010.07.045Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in Energy policy. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractInterest in electrical energy storage systems is increasing as the opportunities for their application become more compelling in an industry with a back-drop of ageing assets, increasing distributed generation and a desire to transform networks into Smart Grids. A field trial of an energy storage system designed and built by ABB is taking place on a section of 11kV distribution network operated by EDF Energy Networks in Great Britain. This paper reports on the findings from simulation software developed at Durham University that evaluates the benefits brought by operating an energy storage system in response to multiple events on multiple networks. The tool manages the allocation of a finite energy resource to achieve the most beneficial shared operation across two adjacent areas of distribution network. Simulations account for the key energy storage system parameters of capacity and power rating. Results for events requiring voltage control and power flow management show how the choice of operating strategy influences the benefits achieved. The wider implications of these results are discussed to provide an assessment of the role of electrical energy storage systems in future Smart Grids.
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.
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.
Wade NS. An international review of the implications of regulatory and electricity market structures on the emergence of grid scale electricity storage.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.