可再生能源, 多目标鲸鱼优化算法, 液态二氧化碳储能系统, 多目标优化调度

Existing multi-objective whale optimization algorithms frequently exhibit premature convergence when addressing microgrid scheduling models. The inherent volatility and uncertainty of renewable energy integration further complicate operational stability. To mitigate these challenges, a novel multi-objective optimization framework is introduced for microgrids incorporating LECS(Liquid Carbon Dioxide Energy Storage), through an enhanced algorithmic approach termed IMOWOA(Improved Multi-Objective Whale Optimization). The proposed methodology employs an infinite folding iterative chaotic map for population initialization, creating diverse candidate solutions through nonlinear dynamic processes. An adaptive grid mechanism enhances elite solution selection while maintaining Pareto optimality with reduced computational complexity. To prevent premature convergence, a hybrid exploration strategy combining sine and cosine operations is used sustaining population diversity during evolutionary iterations. This framework coordinates dispatch operations of heterogeneous energy resources in microgrids, leveraging the operational flexibility of LECS(Liquid Carbon Dioxide Energy Storage) to improve system efficiency. Experimental results demonstrate remarkable improvements in scheduling effectiveness and power regulation accuracy, offering a practical solution for maintaining microgrid sustainability amidst renewable energy variability.

renewable energy, multi-objective whale optimization algorithm, liquid carbon dioxide energy storage system, multi-objective optimal scheduling