Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets
The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator (LA). Therefore, this paper regards the flexible user-side resources as a virtual energy storage (VES), and uses the traditional narrow sense energy storage (NSES) to alleviate the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9497857/ |
| _version_ | 1828786704641687552 |
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| author | Weiqing Sun Wei Liu Wei Xiang Jie Zhang |
| author_facet | Weiqing Sun Wei Liu Wei Xiang Jie Zhang |
| author_sort | Weiqing Sun |
| collection | DOAJ |
| description | The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator (LA). Therefore, this paper regards the flexible user-side resources as a virtual energy storage (VES), and uses the traditional narrow sense energy storage (NSES) to alleviate the uncertainty of VES. In order to further enhance the competitive advantage of LA in electricity market transactions, the opertion mechanism of LA in day-ahead and real-time market is analyzed, respectively. Besides, truncated normal distribution is used to simulate the response accuracy of VES, and the response model of NSES is constructed at the same time. Then, the hierarchical market access index (HMAI) is introduced to quantify the risk of LA being eliminated in the market competition. Finally, combined with the priority response strategy of VES and HMAI, the capacity allocation model of NSES is established. As the capacity model is nonlinear, Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it. In order to verify the effectiveness of the model, the data from PJM market in the United States is used for testing. Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response, and the economic benefit of LA can be increased by 52.2% at its maximum. Through the reasonable NSES capacity allocation, LA is encouraged to improve its own resource level, thus forming a virtuous circle of market competition. |
| first_indexed | 2024-12-12T00:17:34Z |
| format | Article |
| id | doaj.art-84c24c8c1d1d492e93438a694f4c49b5 |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-12-12T00:17:34Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-84c24c8c1d1d492e93438a694f4c49b52022-12-22T00:44:50ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202022-01-011041021103110.35833/MPCE.2020.0007379497857Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity MarketsWeiqing Sun0Wei Liu1Wei Xiang2Jie Zhang3School of Mechanical Engineering, University of Shanghai for Science and Technology,Shanghai,China,200093School of Mechanical Engineering, University of Shanghai for Science and Technology,Shanghai,China,200093Shanghai Shenergy New Power Storage R&D Co., Ltd.,Shanghai,China,201419School of Mechanical Engineering, University of Shanghai for Science and Technology,Shanghai,China,200093The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator (LA). Therefore, this paper regards the flexible user-side resources as a virtual energy storage (VES), and uses the traditional narrow sense energy storage (NSES) to alleviate the uncertainty of VES. In order to further enhance the competitive advantage of LA in electricity market transactions, the opertion mechanism of LA in day-ahead and real-time market is analyzed, respectively. Besides, truncated normal distribution is used to simulate the response accuracy of VES, and the response model of NSES is constructed at the same time. Then, the hierarchical market access index (HMAI) is introduced to quantify the risk of LA being eliminated in the market competition. Finally, combined with the priority response strategy of VES and HMAI, the capacity allocation model of NSES is established. As the capacity model is nonlinear, Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it. In order to verify the effectiveness of the model, the data from PJM market in the United States is used for testing. Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response, and the economic benefit of LA can be increased by 52.2% at its maximum. Through the reasonable NSES capacity allocation, LA is encouraged to improve its own resource level, thus forming a virtuous circle of market competition.https://ieeexplore.ieee.org/document/9497857/Load aggregatorgeneralized energy storagenarrow sense energy storagecapacity allocation strategyancillary service market |
| spellingShingle | Weiqing Sun Wei Liu Wei Xiang Jie Zhang Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets Journal of Modern Power Systems and Clean Energy Load aggregator generalized energy storage narrow sense energy storage capacity allocation strategy ancillary service market |
| title | Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets |
| title_full | Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets |
| title_fullStr | Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets |
| title_full_unstemmed | Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets |
| title_short | Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets |
| title_sort | generalized energy storage allocation strategies for load aggregator in hierarchical electricity markets |
| topic | Load aggregator generalized energy storage narrow sense energy storage capacity allocation strategy ancillary service market |
| url | https://ieeexplore.ieee.org/document/9497857/ |
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