Improving Integrated Energy Systems With Improved Particle Swarm Optimization: Co‐Optimizing Renewables, Electric Vehicles, Gas Systems, and Demand Management

The increasing adoption of renewable energy sources and energy storage systems has led to growing interest in energy management. Power‐to‐gas (P2G) technology is becoming more significant for grid operators, as it allows excess electricity from renewable sources or battery storage to be converted into gas, which is then distributed through gas networks. The combination of gas‐distributed generation units and P2G technology creates an interaction between the electricity and gas grids. This research focuses on optimizing integrated energy systems to enhance both energy efficiency and profitability. Due to the uncertainty in renewable energy output and fluctuating electricity prices, a scenario‐based model is utilized. Additionally, demand‐side management (DSM) plays a pivotal role in reducing electricity demand peaks and increasing profits. The study presents a hybrid model based on mixed‐integer nonlinear programming (MINLP) to optimize electric and gas systems simultaneously. The model includes key components such as distributed generation (DG), P2G, energy storage technologies (EST), electric vehicles (EVs), and DSM. An enhanced particle swarm optimization (I‐PSO) algorithm is employed to tackle this nonlinear optimization challenge, offering better performance compared to other methods. The proposed model is tested using a 33‐bus distribution system, and the results from various scenarios highlight its effectiveness in optimizing integrated energy systems. The I‐PSO algorithm improves optimization performance by about 3.4% compared to competing methods, demonstrating its effectiveness in solving complex energy management problems.