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<p>This study aims to develop an accurate and realistic model for the Dynamic Economic Emission Dispatch (DEED) problem in power systems integrating Wind–Solar renewable energy sources (RES) and Plug-in Electric Vehicles (PEVs). Four test systems were analyzed, consisting of 10-unit and 20-unit thermal power plants combined with RES and PEV fleets over a 24-hour scheduling horizon. The proposed DEED model incorporates the arrival–departure patterns and waiting-time constraints of PEVs, as well as the Under-Estimation (UE) and Over-Estimation (OE) cost uncertainties associated with wind and solar generation. To solve the model efficiently, the Equilibrium Optimizer (EO) algorithm was employed, and its performance was compared with several recently developed optimization techniques. The results indicate that EO provides superior cost–emission trade-offs, improved convergence behavior, and better handling of RES uncertainty. The study highlights that explicitly modelling PEV mobility constraints and RES prediction errors leads to more reliable dispatch strategies. The findings offer practical insights for system operators on enhancing cost precision, emission reduction, and operational resilience in future smart grids.</p>

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Dynamic economic emission dispatch in wind-solar plug-in electric vehicles: Equilibrium optimization with arrival-departure constraints and cost estimation precision

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Jatin Soni,
  • Kuntal Bhattacharjee

摘要

This study aims to develop an accurate and realistic model for the Dynamic Economic Emission Dispatch (DEED) problem in power systems integrating Wind–Solar renewable energy sources (RES) and Plug-in Electric Vehicles (PEVs). Four test systems were analyzed, consisting of 10-unit and 20-unit thermal power plants combined with RES and PEV fleets over a 24-hour scheduling horizon. The proposed DEED model incorporates the arrival–departure patterns and waiting-time constraints of PEVs, as well as the Under-Estimation (UE) and Over-Estimation (OE) cost uncertainties associated with wind and solar generation. To solve the model efficiently, the Equilibrium Optimizer (EO) algorithm was employed, and its performance was compared with several recently developed optimization techniques. The results indicate that EO provides superior cost–emission trade-offs, improved convergence behavior, and better handling of RES uncertainty. The study highlights that explicitly modelling PEV mobility constraints and RES prediction errors leads to more reliable dispatch strategies. The findings offer practical insights for system operators on enhancing cost precision, emission reduction, and operational resilience in future smart grids.