A novel multi-objective optimization approach for resilience enhancement considering integrated energy systems with renewable energy, energy storage, energy sharing, and demand-side management

Achieving resilience enhancement and carbon neutrality is a pressing global goal, and integrated energy systems (IESs) are emerging as a promising solution to meet the growing energy demands of diverse users sustainably and economically. However, the intricate structure and energy flow couplings within IESs present significant challenges for system optimization. This study introduces a novel multi-objective optimization model for designing and enhancing a Renewable Integrated Energy System (RIES) that incorporates renewable energy sources, energy storage technologies, and energy sharing mechanisms. The proposed method combines a gravity search algorithm (GSA) with a multi-objective optimization framework to enhance resilience, carbon reduction, and economic benefits. By integrating energy storage and renewable energy, the RIES minimizes energy losses and addresses the mismatch between renewable energy generation and user demand. The effectiveness of the proposed system was verified through a case study based on a standard IEEE test system. The experimental results demonstrate that adding wind turbines reduces system generation by 6.61 %, energy storage integration further reduces generation by 9.4 %, and including photovoltaics achieves a total reduction of 10.8 %. Furthermore, the proposed demand response program (DRP) effectively balances electricity supply and demand, achieving a 45 % reduction in the daily peak load. Optimization results also reveal that renewable energy utilization improves by 0.77 %, highlighting the enhanced operational efficiency and resilience of the system. This work presents a comprehensive framework for optimizing integrated energy systems, offering theoretical guidance and practical solutions for energy planners and policymakers.