Efficient real-time cost management in renewable energy-powered microgrid with integrated electric vehicle charging/discharging control

The rapid proliferation of electric vehicles (EVs) has significantly escalated the strain on the public grid by exacerbating fluctuations and hindering widespread EV adoption. This paper presents a cutting-edge solution with a real-time cost optimization model tailored for AC/DC microgrid energy management. Leveraging a unique hybridization of particle-swarm optimization (PSO) and grey wolf optimization (GWO), our approach dynamically orchestrates energy flow and EV charging schedules. The model has been developed using MATLAB ۲۰۲۲a.Thus, a non-linear stochastic mathematical programming model optimizes EV charging and distributed energy resources (DERs) generation costs. We scrutinize our model across medium scale microgrid IEEE- ۳۷ Node systems—via real-time digital simulator (RTDS). Our multi-level control strategy ensures both immediate response to disturbances and long-term optimization, maintaining microgrid stability. Through meticulous real-time monitoring and control, our hybrid PSO-GWO algorithm delivers superior performance, slashing costs by ۱۵۲.۴۷ for medium scale microgrid while reducing execution time by ۰.۸۱ seconds ascompared to other metaheuristic algorithms. About ۳۶.۸۵% of the load is absorbed by EVs, with surplus power fed back to the main grid. This comprehensive approach not only enhances the cost-effectiveness but also fosters energy efficiency, affirming the efficacy of hybrid PSO-GWO in real-time microgrid management.

کلیدواژه ها:

Electric Vehicle ، Vehicle-to-grid ، Grid-to-vehicle ، particle swarm optimization ، Grey Wolf optimization ، Electric vehicle supply equipment