Optimal BESS Sizing and Placement for Mitigating EV-Induced Voltage Violations: A Scalable Spatio-Temporal Adaptive Targeting Strategy
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914127888252928 |
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| author | Fang, Linhan Li, Xingpeng |
| author_facet | Fang, Linhan Li, Xingpeng |
| contents | The escalating adoption of electric vehicles (EVs) and the growing demand for charging solutions are driving a surge in EV charger installations in distribution networks. However, this rising EV load strains the distribution grid, causing severe voltage drops, particularly at feeder extremities. This study proposes a proactive voltage management (PVM) framework that can integrate Monte Carlo-based simulations of varying EV charging loads to (i) identify potential voltage violations through a voltage violation analysis (VVA) model, and (ii) then mitigate those violations with optimally-invested battery energy storage systems (BESS) through an optimal expansion planning (OEP) model. A novel spatio-temporal adaptive targeting (STAT) strategy is proposed to alleviate the computational complexity of the OEP model by defining a targeted OEP (T-OEP) model, solved by applying the OEP model to (i) a reduced set of representative critical time periods and (ii) candidate BESS installation nodes. The efficacy and scalability of the proposed approach are validated on 33-bus, 69-bus, and a large-scale 240-bus system. Results demonstrate that the strategic sizing and placement of BESS not only effectively mitigate voltage violations but also yield substantial cost savings on electricity purchases under time-of-use tariffs. This research offers a cost-effective and scalable solution for integrating high penetrations of EVs, providing crucial insights for future distribution network planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal BESS Sizing and Placement for Mitigating EV-Induced Voltage Violations: A Scalable Spatio-Temporal Adaptive Targeting Strategy Fang, Linhan Li, Xingpeng Systems and Control The escalating adoption of electric vehicles (EVs) and the growing demand for charging solutions are driving a surge in EV charger installations in distribution networks. However, this rising EV load strains the distribution grid, causing severe voltage drops, particularly at feeder extremities. This study proposes a proactive voltage management (PVM) framework that can integrate Monte Carlo-based simulations of varying EV charging loads to (i) identify potential voltage violations through a voltage violation analysis (VVA) model, and (ii) then mitigate those violations with optimally-invested battery energy storage systems (BESS) through an optimal expansion planning (OEP) model. A novel spatio-temporal adaptive targeting (STAT) strategy is proposed to alleviate the computational complexity of the OEP model by defining a targeted OEP (T-OEP) model, solved by applying the OEP model to (i) a reduced set of representative critical time periods and (ii) candidate BESS installation nodes. The efficacy and scalability of the proposed approach are validated on 33-bus, 69-bus, and a large-scale 240-bus system. Results demonstrate that the strategic sizing and placement of BESS not only effectively mitigate voltage violations but also yield substantial cost savings on electricity purchases under time-of-use tariffs. This research offers a cost-effective and scalable solution for integrating high penetrations of EVs, providing crucial insights for future distribution network planning. |
| title | Optimal BESS Sizing and Placement for Mitigating EV-Induced Voltage Violations: A Scalable Spatio-Temporal Adaptive Targeting Strategy |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.00297 |