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<p>The increasing prevalence of electric vehicles, electric vehicle charging stations (EVCSs), and renewable energy sources presents significant issues for the management of electricity markets. This paper proposes an optimal placement framework for EVCSs using a novel locational marginal pricing (LMP)-based incremental load power index, which identifies the most critical buses and lines in terms of both price sensitivity and MVA flows. Furthermore, a bi-level transactive energy trading model has been developed to maximize the profitability of the EVCS in the energy trading market while considering different distribution network constraints based on the optimal placement of EVCSs on a modified system. The overall payoff distribution for each EVCS has been determined using cooperative game theory, specifically through the asymmetric Nash bargaining approach. The implementation of a bi-level transactive energy trading mechanism optimizes the profit of EVCS and reduces charging costs for EV owners by determining the optimal location. The proposed method’s effectiveness has been evaluated using a modified IEEE 33-bus system. The results show a notable improvement in the placement of EVCS and the efficiency of energy transactions throughout the grid.</p>

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An efficient allocation of electric vehicle charging stations with optimized bi-level energy trading for enhanced system economic sustainability

  • 期刊论文

  • 发表日期：

    2025年11月22日

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  • Naresh Boda,
  • Prashant Kumar Tiwari,
  • Asheesh Kumar Singh

摘要

The increasing prevalence of electric vehicles, electric vehicle charging stations (EVCSs), and renewable energy sources presents significant issues for the management of electricity markets. This paper proposes an optimal placement framework for EVCSs using a novel locational marginal pricing (LMP)-based incremental load power index, which identifies the most critical buses and lines in terms of both price sensitivity and MVA flows. Furthermore, a bi-level transactive energy trading model has been developed to maximize the profitability of the EVCS in the energy trading market while considering different distribution network constraints based on the optimal placement of EVCSs on a modified system. The overall payoff distribution for each EVCS has been determined using cooperative game theory, specifically through the asymmetric Nash bargaining approach. The implementation of a bi-level transactive energy trading mechanism optimizes the profit of EVCS and reduces charging costs for EV owners by determining the optimal location. The proposed method’s effectiveness has been evaluated using a modified IEEE 33-bus system. The results show a notable improvement in the placement of EVCS and the efficiency of energy transactions throughout the grid.