Economic, Environment, and Technical Energy Management in Smart Microgrid With Optimal Participation of the Plug‐In Electric Vehicles and Adaptive Demand Response

This article presents a multiobjective framework for energy management and scheduling specifically designed for a microgrid that includes electric vehicles (EVs) and demand response (DR). The primary innovations consist of a unique Dependency Reduction Index (DRI) objective function aimed at enhancing the microgrid’s self‐sufficiency. The optimization problem is tackled using an Improved Newton–Raphson Method (INRM). The adaptive sigma point approach is employed to represent uncertainties associated with load, EV, and renewable energy sources. The results demonstrate that incorporating the DRI objective significantly enhances the energy independence of the microgrid, resulting in lower operational and emission costs. In summary, the paper claims that the proposed optimization framework successfully balances economic, environmental, and resilience goals, offering a practical and superior solution for modern microgrid operations. The implementation of the DRI objective led to remarkable enhancements in microgrid operational autonomy, achieving a 5.69% reduction in operational costs and a 4.48% decrease in emissions. This illustrates that dependency‐aware optimization fundamentally transforms resource allocation strategies, reshaping the microgrid operational model into one that relies less on the grid through the strategic coordination of charging stations and storage systems, fostering a scenario where improved self‐sufficiency and economic efficiency support each other rather than conflict. The coordination of charging stations with the microgrid, guided by the DRI, resulted in a 12.78% reduction in DR costs, thanks to the sophisticated management of battery storage, EV, and EB charging stations within a hierarchical storage framework that operates synergistically across various case studies.

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