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<p>This paper presents a comprehensive multi-objective power management approach for integrating renewable energy systems inside vehicle-to-grid and V2X frameworks, enhanced by DR and P2P energy trading algorithms. The main aim of the study is to increase operational efficiency, decrease dependence on the grid and maximize economic benefits within connected multi-microgrid systems. The suggested approach incorporates thorough modeling of BESSs, solar and wind energy generation, and the dynamics of electric car charging and discharging while taking degradation factors and system uncertainties into account. The P2P algorithm enables local energy transactions between prosumers, while DR mechanisms promote flexible consumer participation to adjust to variations in demand. Simulation data was derived from a realistic case study involving three microgrids, including 20 households and an electric vehicle station with 25 parking spaces. Renewable generation and load profiles were obtained from historical datasets at hourly intervals over a 24-h period. Optimization was performed with mixed integer linear programming in MATLAB evaluating scenarios with and without P2P mechanism. Results indicate that integration of P2P trading decreases dependence on the main grid, with total electricity imports decreasing from 13,367 to 10,114&#xa0;kW, nearly 24.5%, by promoting P2P energy sharing among users. Battery charge–discharge cycles synchronize with optimal photovoltaic generation and load demands, improving system adaptability. The strategy decreases operational costs, improves renewable energy usage and improves energy supply resilience, thereby validating the feasibility of integrated vehicle-to-grid and V2X systems, DR and P2P frameworks for future smart grid applications.</p>

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Robust multi-objective optimization of power management in renewable energy-integrated V2G and V2X systems with demand response and peer-to-peer energy trading

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Yijun Lu,
  • Jui-Chan Huang

摘要

This paper presents a comprehensive multi-objective power management approach for integrating renewable energy systems inside vehicle-to-grid and V2X frameworks, enhanced by DR and P2P energy trading algorithms. The main aim of the study is to increase operational efficiency, decrease dependence on the grid and maximize economic benefits within connected multi-microgrid systems. The suggested approach incorporates thorough modeling of BESSs, solar and wind energy generation, and the dynamics of electric car charging and discharging while taking degradation factors and system uncertainties into account. The P2P algorithm enables local energy transactions between prosumers, while DR mechanisms promote flexible consumer participation to adjust to variations in demand. Simulation data was derived from a realistic case study involving three microgrids, including 20 households and an electric vehicle station with 25 parking spaces. Renewable generation and load profiles were obtained from historical datasets at hourly intervals over a 24-h period. Optimization was performed with mixed integer linear programming in MATLAB evaluating scenarios with and without P2P mechanism. Results indicate that integration of P2P trading decreases dependence on the main grid, with total electricity imports decreasing from 13,367 to 10,114 kW, nearly 24.5%, by promoting P2P energy sharing among users. Battery charge–discharge cycles synchronize with optimal photovoltaic generation and load demands, improving system adaptability. The strategy decreases operational costs, improves renewable energy usage and improves energy supply resilience, thereby validating the feasibility of integrated vehicle-to-grid and V2X systems, DR and P2P frameworks for future smart grid applications.