Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks
The authors propose a two-stage sequential configuration method for energy storage systems to solve the problems of the heavy load, low voltage, and increased network loss caused by the large number of electric vehicle (EV) charging piles and distributed photovoltaic (PV) access in urban, old and un...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/24/6325 |
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| author | Hu Cao Lingling Ma Guoying Liu Zhijian Liu Hang Dong |
| author_facet | Hu Cao Lingling Ma Guoying Liu Zhijian Liu Hang Dong |
| author_sort | Hu Cao |
| collection | DOAJ |
| description | The authors propose a two-stage sequential configuration method for energy storage systems to solve the problems of the heavy load, low voltage, and increased network loss caused by the large number of electric vehicle (EV) charging piles and distributed photovoltaic (PV) access in urban, old and unbalanced distribution networks. At the stage of selecting the location of energy storage, a comprehensive power flow sensitivity variance (CPFSV) is defined to determine the location of the energy storage. At the energy storage capacity configuration stage, the energy storage capacity is optimized by considering the benefits of peak shaving and valley filling, energy storage costs, and distribution network voltage deviations. Finally, simulations are conducted using a modified IEEE-33-node system, and the results obtained using the improved beluga whale optimization algorithm show that the peak-to-valley difference of the system after the addition of energy storage decreased by 43.7% and 51.1% compared to the original system and the system with EV and PV resources added, respectively. The maximum CPFSV of the system decreased by 52% and 75.1%, respectively. In addition, the engineering value of this method is verified through a real-machine system with 199 nodes in a district of Kunming. Therefore, the energy storage configuration method proposed in this article can provide a reference for solving the outstanding problems caused by the large-scale access of EVs and PVs to the distribution network. |
| format | Article |
| id | doaj-art-d0ffb5b760934a3ba16fd72fe2f73805 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-d0ffb5b760934a3ba16fd72fe2f738052025-08-20T02:55:36ZengMDPI AGEnergies1996-10732024-12-011724632510.3390/en17246325Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution NetworksHu Cao0Lingling Ma1Guoying Liu2Zhijian Liu3Hang Dong4Kunming Power Supply Design Institute Co., Ltd., Kunming 650118, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, ChinaThe authors propose a two-stage sequential configuration method for energy storage systems to solve the problems of the heavy load, low voltage, and increased network loss caused by the large number of electric vehicle (EV) charging piles and distributed photovoltaic (PV) access in urban, old and unbalanced distribution networks. At the stage of selecting the location of energy storage, a comprehensive power flow sensitivity variance (CPFSV) is defined to determine the location of the energy storage. At the energy storage capacity configuration stage, the energy storage capacity is optimized by considering the benefits of peak shaving and valley filling, energy storage costs, and distribution network voltage deviations. Finally, simulations are conducted using a modified IEEE-33-node system, and the results obtained using the improved beluga whale optimization algorithm show that the peak-to-valley difference of the system after the addition of energy storage decreased by 43.7% and 51.1% compared to the original system and the system with EV and PV resources added, respectively. The maximum CPFSV of the system decreased by 52% and 75.1%, respectively. In addition, the engineering value of this method is verified through a real-machine system with 199 nodes in a district of Kunming. Therefore, the energy storage configuration method proposed in this article can provide a reference for solving the outstanding problems caused by the large-scale access of EVs and PVs to the distribution network.https://www.mdpi.com/1996-1073/17/24/6325energy storage site selection and capacity determinationdistribution networkcomprehensive power flow sensitivity variancebeluga whale optimization algorithmelectric vehiclesphotovoltaic consumption |
| spellingShingle | Hu Cao Lingling Ma Guoying Liu Zhijian Liu Hang Dong Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks Energies energy storage site selection and capacity determination distribution network comprehensive power flow sensitivity variance beluga whale optimization algorithm electric vehicles photovoltaic consumption |
| title | Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks |
| title_full | Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks |
| title_fullStr | Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks |
| title_full_unstemmed | Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks |
| title_short | Two-Stage Energy Storage Allocation Considering Voltage Management and Loss Reduction Requirements in Unbalanced Distribution Networks |
| title_sort | two stage energy storage allocation considering voltage management and loss reduction requirements in unbalanced distribution networks |
| topic | energy storage site selection and capacity determination distribution network comprehensive power flow sensitivity variance beluga whale optimization algorithm electric vehicles photovoltaic consumption |
| url | https://www.mdpi.com/1996-1073/17/24/6325 |
| work_keys_str_mv | AT hucao twostageenergystorageallocationconsideringvoltagemanagementandlossreductionrequirementsinunbalanceddistributionnetworks AT linglingma twostageenergystorageallocationconsideringvoltagemanagementandlossreductionrequirementsinunbalanceddistributionnetworks AT guoyingliu twostageenergystorageallocationconsideringvoltagemanagementandlossreductionrequirementsinunbalanceddistributionnetworks AT zhijianliu twostageenergystorageallocationconsideringvoltagemanagementandlossreductionrequirementsinunbalanceddistributionnetworks AT hangdong twostageenergystorageallocationconsideringvoltagemanagementandlossreductionrequirementsinunbalanceddistributionnetworks |