Multi-objective stochastic model optimal operation of smart microgrids coalition with penetration renewable energy resources with demand responses.

The rapid transformation of energy systems necessitates innovative approaches to ensure cost-effective, reliable, and environmentally sustainable operation. This paper presents a novel multi-objective stochastic optimization model for the optimal operation of a coalition of interconnected smart microgrids, integrating renewable energy resources, demand response mechanisms, and electric vehicles (EVs) under uncertainty. The proposed framework simultaneously minimizes operational costs and environmental emissions while enhancing energy balance through demand-side management, dynamic energy trading, and EV-based vehicle-to-grid (V2G) services. A key innovation of this study is the development of an advanced hybrid solution methodology, combining the ε-constraint method for multi-objective optimization with Benders decomposition for computational efficiency. This approach enables the scalable solution of large-scale, scenario-based optimization problems while maintaining accuracy. The proposed model accounts for uncertain renewable generation and fluctuating energy demand, leveraging real-time flexibility through demand-side adjustments and bidirectional EV charging. Extensive case studies on a coalition of five interconnected microgrids highlight the advantages of coordinated energy management. Results demonstrate that cooperation among microgrids yields significant benefits compared to independent operation, including up to 22.7% reduction in total operational costs, 75% utilization of renewable energy resources, and a 31.1% decrease in carbon emissions. The integration of EV-based storage and demand response strategies further enhances system resilience and economic performance by dynamically adjusting energy consumption patterns and mitigating the intermittency of renewables. The findings underscore the potential of smart microgrid coalitions in reducing dependency on fossil fuels, improving grid stability, and creating economically viable, sustainable energy ecosystems. By incorporating a novel hybrid optimization technique and addressing the computational challenges of large-scale energy systems, this research provides a practical and scalable framework for future smart grid development, supporting a cleaner and more efficient energy transition.