Adaptive and Collaborative Hierarchical Optimization Strategies for a Multi-Microgrid System Considering EV and Storage
The disordered nature of electric vehicle (EV) charging and user electricity consumption behaviors has intensified the strain on the grid. Meanwhile, energy storage technologies and microgrid interconnections still lack effective supply–consumption regulations and cost–benefit optimization mechanism...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/7/363 |
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| author | Yifeng He Tong Liu Zilong Wang Qiqi Ren Alian Chen |
| author_facet | Yifeng He Tong Liu Zilong Wang Qiqi Ren Alian Chen |
| author_sort | Yifeng He |
| collection | DOAJ |
| description | The disordered nature of electric vehicle (EV) charging and user electricity consumption behaviors has intensified the strain on the grid. Meanwhile, energy storage technologies and microgrid interconnections still lack effective supply–consumption regulations and cost–benefit optimization mechanisms. Therefore, the system’s operational efficiency holds significant potential for improvement. This paper proposes hierarchical optimization strategies for the multi-microgrid system to address these issues. In the lower layer, for the charging states of EVs in a single microgrid, an improved simulation method to enhance accuracy and a recursion mechanism of an energy storage margin band to facilitate intelligent EV-to-grid interaction are proposed. Additionally, in conjunction with demand management, an adaptive optimization method and a Pareto decision method are proposed to achieve optimal peak shaving and valley filling for both the EVs and load, yielding a 38.5% reduction in the total electricity procurement costs. The upper layer is built upon the EV–load management strategies of microgrids in the lower layers and evolves into a distributed interconnection structure. Furthermore, a dynamic optimization mechanism based on state mapping and a collaborative optimization method are proposed to improve storage benefits and energy synergies, achieving a 22.1% reduction in the total operating cost. The results provided demonstrate that the proposed strategy optimizes the operation of the multi-microgrid system, effectively enhancing the overall operational efficiency and economic performance. |
| format | Article |
| id | doaj-art-91c162ccd1774b99b888a7e40ead187c |
| institution | DOAJ |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-91c162ccd1774b99b888a7e40ead187c2025-08-20T03:13:57ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-06-0116736310.3390/wevj16070363Adaptive and Collaborative Hierarchical Optimization Strategies for a Multi-Microgrid System Considering EV and StorageYifeng He0Tong Liu1Zilong Wang2Qiqi Ren3Alian Chen4School of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaThe disordered nature of electric vehicle (EV) charging and user electricity consumption behaviors has intensified the strain on the grid. Meanwhile, energy storage technologies and microgrid interconnections still lack effective supply–consumption regulations and cost–benefit optimization mechanisms. Therefore, the system’s operational efficiency holds significant potential for improvement. This paper proposes hierarchical optimization strategies for the multi-microgrid system to address these issues. In the lower layer, for the charging states of EVs in a single microgrid, an improved simulation method to enhance accuracy and a recursion mechanism of an energy storage margin band to facilitate intelligent EV-to-grid interaction are proposed. Additionally, in conjunction with demand management, an adaptive optimization method and a Pareto decision method are proposed to achieve optimal peak shaving and valley filling for both the EVs and load, yielding a 38.5% reduction in the total electricity procurement costs. The upper layer is built upon the EV–load management strategies of microgrids in the lower layers and evolves into a distributed interconnection structure. Furthermore, a dynamic optimization mechanism based on state mapping and a collaborative optimization method are proposed to improve storage benefits and energy synergies, achieving a 22.1% reduction in the total operating cost. The results provided demonstrate that the proposed strategy optimizes the operation of the multi-microgrid system, effectively enhancing the overall operational efficiency and economic performance.https://www.mdpi.com/2032-6653/16/7/363electric vehicledemand responsemulti-microgrid systemadaptive optimizationpareto decision methodstorage management |
| spellingShingle | Yifeng He Tong Liu Zilong Wang Qiqi Ren Alian Chen Adaptive and Collaborative Hierarchical Optimization Strategies for a Multi-Microgrid System Considering EV and Storage World Electric Vehicle Journal electric vehicle demand response multi-microgrid system adaptive optimization pareto decision method storage management |
| title | Adaptive and Collaborative Hierarchical Optimization Strategies for a Multi-Microgrid System Considering EV and Storage |
| title_full | Adaptive and Collaborative Hierarchical Optimization Strategies for a Multi-Microgrid System Considering EV and Storage |
| title_fullStr | Adaptive and Collaborative Hierarchical Optimization Strategies for a Multi-Microgrid System Considering EV and Storage |
| title_full_unstemmed | Adaptive and Collaborative Hierarchical Optimization Strategies for a Multi-Microgrid System Considering EV and Storage |
| title_short | Adaptive and Collaborative Hierarchical Optimization Strategies for a Multi-Microgrid System Considering EV and Storage |
| title_sort | adaptive and collaborative hierarchical optimization strategies for a multi microgrid system considering ev and storage |
| topic | electric vehicle demand response multi-microgrid system adaptive optimization pareto decision method storage management |
| url | https://www.mdpi.com/2032-6653/16/7/363 |
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