Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.630064
Title: Application of stochastic and evolutionary methods to plan for the installation of energy storage in voltage constrained LV networks
Author: Crossland, Andrew Frederick
ISNI:       0000 0004 5351 7097
Awarding Body: Durham University
Current Institution: Durham University
Date of Award: 2014
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
Energy storage is widely considered to be an important component of a decarbonised power system if large amounts of renewable generation are to provide reliable electricity. However, storage is a highly capital intensive asset and clear business cases are needed before storage can be widely deployed. A proposed business case is using storage to prevent overvoltage in low voltage (LV) distribution networks to enable residential photovoltaic systems. Despite storage being widely considered for use in LV networks, there is little work comparing where storage might be installed in LV networks from the perspective of the owners of distribution networks (DNOs). This work addresses this in two ways. Firstly, a tool is developed to examine whether DNOs should support a free market for energy storage in which customers with PV purchase storage (e.g. battery systems) to improve their self-consumption. This reflects a recent policy in Germany. Secondly, a new (published) method is developed which considers how DNOs should purchase and locate storage to prevent overvoltage. Both tools use a snapshot approach by modelling the highest and lowest LV voltages. On their own, these tools enable a DNO to determine the cost of energy storage for a particular LV network with a particular set of loads and with PV installed by a given set of customers. However, in order to predict and understand the future viability of energy storage it is valuable to apply the tools to a large number of LV networks under realistic future scenarios for growth of photovoltaics in the UK power system. Therefore, the work extracts over 9,000 LV network models containing over 40,000 LV feeders from a GIS map of cables provided by one of the UK’s electricity distribution networks- Electricity North West. Applying the proposed tools to these 9,000 network models, the work is able to provide projections for how much LV energy storage would be installed under different scenarios. The cost of doing so is compared to the existing method of preventing reinforcement- LV network reconductoring. This is a novel way of assessing the viability of LV energy storage against traditional approaches and allows the work to draw the following conclusions about the market for energy storage in LV distribution networks in the UK: - Overvoltage as a result of PV could begin to occur in the next few years unless UK regulations for voltage levels are relaxed. There could be a large cost (hundreds of millions of pounds) to prevent this if the traditional approach of reconductoring is used. - If overvoltage begins to occur, a free market for energy storage (randomly purchased by electricity consumers) cannot offer large benefits to DNOs in reducing the reinforcement cost unless this is properly controlled, located and/or widely installed by customers. - Optimally located storage by the DNO can reduce overall reinforcement costs to mitigate overvoltage. This would enable more energy storage to balance renewable generation and present large savings to the power system. The exact topology of storage and the storage rating in each LV network could be determined using the tool proposed in this work.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.630064  DOI: Not available
Share: