@phdthesis{nokey,
title = {A market analysis of customer-connected mass energy storage},
author = {Neil McIlwaine},
url = {https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.854974
https://pure.qub.ac.uk/files/320007520/Thesis_Neil_McIlwaine_rev_33rev1_NMC.pdf},
year = {2022},
date = {2022-06-01},
institution = {Queen's University Belfast},
abstract = {The electricity operators on the island of Ireland have policy objectives to generate at least 70% of electricity from renewable sources by 2030. The source of this renewable power will mainly be wind and storage is needed to facilitate this transition. However, to date the roll out and market uptake of storage has been slow in the Irish grid. Therefore, this research undertook a market analysis of the technical and economic value of distributed mass energy storage to examine storage considering these targets. The research uses the Irish market as a case study with specific modelling on the Northern Ireland system which is a subset of the overall market. The modelling and the results of the research are applicable and relevant to all regions which operate with a high share of renewables. The research had four parts. In part 1, a global techno-economic review of the status of energy storage and power quality services focusing on ten countries with differing political, social, and economic trends was undertaken. This led to a combined strengths, weaknesses, opportunities, and threats (SWOT) appraisal informed by the data and information from the ten countries response to embedded and distributed renewable generation and storage. The SWOT analysis is then coupled to a Pugh chart to indicate optimal concept choice in the later analyses. Then in part 2, a gap analysis of the ten countries to determine the frameworks and approaches used to regulate, plan, and operate retail electricity markets was carried out in order to inform the modelling. Next in part 3, a suite of financial models was developed to quantify the market revenue available for battery storage investment that could provide ancillary services, network congestion relief and response to local system events. Then a dynamic economic dispatch model in MATLAB was developed to test the economic production schedule with and without battery storage and a unit commitment model was developed to determine the costs of providing system reserve using fossil fuel generation so a comparison could be made in the scenario where the reserve is provided by battery storage. The key finding is that the revenue available from the current schemes are insufficient to attract investment in energy storage. It is recommended that system operators reform the existing schemes, design new schemes and look to the wider benefits that energy storage brings to fossil fuels generation. Finally, in part 4, a unit commitment wholesale electricity market model of the SEM focusing on the Northern Ireland system was developed in Energy Exemplar's PLEXOS for Power Systems. It makes for an interesting case study for other jurisdictions as it is an electrically isolated grid with limited interconnection and storage but operating with a high share of renewables. Here four combinations of wind generation and load were assessed to measure the effect of varying levels of battery storage. The benefits of storage were clearly demonstrated with reductions in emission levels and generation costs, load smoothing, ramping reduction, reduced maintenance and reduced curtailment of renewables. For example, the monthly model run with 300 MW of battery storage at 70% SNSP resulted in a generation cost decrease of £500k, an emission decrease of 28k tonnes CO2, and total ramping decrease of 478 hours compared to the no storage scenario. Currently revenue streams for provision of these benefits associated with generation and demonstrated by the modelling do not exist. Therefore, it is recommended that these services are properly valued in order to attract future investment. Overall, this research clearly demonstrates the gap that exists between the positive benefits of battery storage and the less than adequate revenue being pitched to attract investment into technology to achieve climate change targets with recommendations made to address this based on the findings. In fact, an optimum level of storage exists which is dependent on demand and wind generation. The research in this thesis indicates this level to be between 200 MW and 300 MW. A report published in the year 2021 by the system operator stated an expected storage in Northern Ireland of 200 MW by 2030. Therefore, this expected storage rating needs revised based on the results of the research. The key recommendation is that the regulators and the grid operators urgently revisit the current schemes and restructure them otherwise we may have power quality and supply issues into the future as current fossil fuel, mainly gas generators are mothballed. },
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
