基于改进白鲸算法的太阳能与生物质互补CCHP系统多目标优化研究

Research on Multi-objective Optimization of a CCHP System with Solar and Biomass Complementary Based on Improved Beluga Whale Algorithm

为解决可再生能源不稳定及消纳问题,构建了一种涵盖太阳能光伏/光热、电解水制氢及生物质气化甲烷化在内的冷热电联供(CCHP)系统;针对联供系统在高维多目标下的容量匹配问题,提出并验证了一种适用于高维多目标的改进白鲸(MOBWO)算法,其在改善分布性的同时提高了算法的收敛速度。基于此方法开展的案例分析表明:容量配置方案相比分产系统,虽然年总成本提高了19.8%,但是化石燃料消耗量降低了56.8%,CO_(2)排放量降低了53.1%,灵活性达到51.6%;在该案例模型下,改进白鲸算法较非支配排序遗传(NSGA-2)算法和多目标粒子群(MOPSO)算法有着更好的求解效果。

In order to solve the problems of instability and utilization of renewable energy,a combined cooling,heating,and power(CCHP)system was proposed that integrated solar photovoltaic/thermal,water electrolysis for hydrogen production,and biomass gasification and methanation.Aiming at the capacity matching problem of the CCHP system under high dimensional target,an improved beluga whale(MOBWO)algorithm was proposed and verified.The proposed algorithm improved the distribution of solutions and the convergence rate.A case study based on this method shows that compared with the individual systems,the fossil fuel consumption is reduced by 56.8%,the CO_(2) emission is reduced by 53.1%,and the flexibility reaches 51.6%,although the annual total cost of the optimal capacity allocation scheme is increased by 19.8%.In this case model,the MOBWO algorithm has better solving performance than non-dominated sorting genetic algorithm-II(NSGA-2)and multi-objective particle swarm optimization(MOPSO)algorithm.