Regulatory and financial hurdles for the installation of energy storage in UK distribution networks

Anuta, Oghenetejiri; Crossland, Andrew F.; Jones, Darren; Wade, Neal

doi:10.1049/cp.2012.0811

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…By reducing customer demand from the grid, storage can be used to reduce thermal losses in the transmission and distribution network [16], [17]. In the UK distribution network it is difficult to measure the effect on loss reduction throughout the entire network and the value in loss reduction for an small distributed unit is small [18]. However, the benefits from upgrade deferral are important and provide predictable revenues for network operators [17], [18].…”

Section: Figure 2-1: Locating Energy Storage In An LV Network: 1-at Tmentioning

confidence: 99%

“…3.5 Network model In order to compare these two topologies, the genetic algorithm with simulated annealing heuristic is applied to a real UK LV network. This network, which was used in [18], has 406 loads applied to 281 buses and 53 PV systems installed. There is a potential for a total of 247 domestic PV systems.…”

Section: Figure 3-3: Average Performance Of Different Heuristics Whermentioning

confidence: 99%

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Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing

Crossland

Jones

Wade

2014

International Journal of Electrical Power & Energy Systems

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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.

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Section: Figure 2-1: Locating Energy Storage In An LV Network: 1-at Tmentioning

confidence: 99%

Section: Figure 3-3: Average Performance Of Different Heuristics Whermentioning

confidence: 99%

Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing

Crossland

Jones

Wade

2014

International Journal of Electrical Power & Energy Systems

Self Cite

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“…This study presents heuristic methods which can be practically applied by DNOs to find a near optimal configuration of energy storage to solve a LV network voltage problem. This develops work in [8], where energy storage was shown to be financially feasible at the secondary transformer.…”

Section: Cired2013 Session 4 Paper No 0510mentioning

confidence: 99%

“…If suitably sized, these provide benefits such as prevention/reduction of reverse power flow or overvoltage problems by absorbing energy. Storage can also provide peak shaving and reduced losses by discharging stored energy [8].…”

Section: Introductionmentioning

confidence: 99%

Energy storage/demand side response in LV networks: design of cost based planning tools for network operators

Crossland

Jones

Wade

2013

22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013)

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With the high growth in distributed generation in the UK and resulting challenges in distribution, there is a need for network operators to develop cost based tools to plan for and control technologies such as energy storage. This paper presents three methods for locating storage in LV networks to solve voltage rise problems. The methods are compared and evaluated and it is shown that careful heuristic selection and storage located in homes can provide significant savings for network operators.

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“…In [3] appropriate size of storage systems has been investigated. In [4] financial analysis has been implemented to compute electricity storage systems cost. See [5], [6] for calculation of cost of storage and [7] for discussion about different categories of energy storage technologies.…”

Section: Introductionmentioning

confidence: 99%

Locational effects of variability of injected power on total cost

Cyrus

Lesieutre

2015

2015 IEEE Power and Energy Conference at Illinois (PECI)

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This paper introduces locational prices associated with power injection variability. The sensitivity of uncertainty in injected power on system cost is calculated using a probabilistic DC optimal power flow (DC-OPF). This sensitivity may be used as a price for regulation to equitably allocate costs associated with tracking power injections. In this context the price also provides a locational value to energy storage and hence can be used as a tool to investigate best locations for energy storage. As a case study, a 24-bus test system with uncertain variable wind power and uncertain variable loads is analyzed.

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Regulatory and financial hurdles for the installation of energy storage in UK distribution networks

Cited by 8 publications

References 6 publications

Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing

Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing

Energy storage/demand side response in LV networks: design of cost based planning tools for network operators

Locational effects of variability of injected power on total cost

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