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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.19962 |
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| _version_ | 1866912050451578880 |
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| author | Tian, Pengchao Yan, Siqi Pan, Bikang Shi, Ye |
| author_facet | Tian, Pengchao Yan, Siqi Pan, Bikang Shi, Ye |
| contents | With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV scheduling control strategies have been developed to manage vehicle-to-grid (V2G) in coordination with the optimal power flow. In existing studies, such coordination optimization is formulated as a mixed-integer nonlinear programming (MINP), which is computationally challenging due to the binary EV charging and discharging variables. To address this challenge, we develop an efficient two-stage optimization method for this mixed-integer nonlinear coordination problem. This method first employs an efficient technique called the difference of convex (DC) to relax the integrality and reformulate MINP into a series of path-following continuous programming. Although the DC approach shows promising efficiency for solving MINP, it cannot guarantee the feasibility of the solutions. Consequently, we propose a trust region optimization method in stage two that constructs a trust region around DC's solution and then searches for the best feasible solution within this region. Our simulation results demonstrate that, compared to the open-source optimization solver SCIP, our proposed method significantly enhances computational efficiency while achieving near optimality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19962 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Two-Stage Optimization for Efficient V2G Coordination in Distribution Power System Tian, Pengchao Yan, Siqi Pan, Bikang Shi, Ye Computational Engineering, Finance, and Science With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV scheduling control strategies have been developed to manage vehicle-to-grid (V2G) in coordination with the optimal power flow. In existing studies, such coordination optimization is formulated as a mixed-integer nonlinear programming (MINP), which is computationally challenging due to the binary EV charging and discharging variables. To address this challenge, we develop an efficient two-stage optimization method for this mixed-integer nonlinear coordination problem. This method first employs an efficient technique called the difference of convex (DC) to relax the integrality and reformulate MINP into a series of path-following continuous programming. Although the DC approach shows promising efficiency for solving MINP, it cannot guarantee the feasibility of the solutions. Consequently, we propose a trust region optimization method in stage two that constructs a trust region around DC's solution and then searches for the best feasible solution within this region. Our simulation results demonstrate that, compared to the open-source optimization solver SCIP, our proposed method significantly enhances computational efficiency while achieving near optimality. |
| title | Two-Stage Optimization for Efficient V2G Coordination in Distribution Power System |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2409.19962 |