【摘要】 提出一种考虑峰谷电价机制下的激励性需求响应的微电网经济调度模型.为解决传统群智能优化算法寻优精度不高、收敛速度慢、易陷入局部最优的问题,提出一种多策略融合黏菌算法(MFSMA).引入自适应参数以确保MFSMA对搜索空间的彻底搜索;提出最优个体引导策略加快算法收敛并降低搜索的盲目性;引入精英反向学习策略避免算法陷入局部最优解;引入樽海鞘群算法的搜索模式提高算法收敛速度和精度.将MFSMA与其他算法在基准测试函数上进行比较,以验证其优越性,结果证明MFSMA在提高能源利用效率的同时可以最小化发电成本.更多还原

【Abstract】 We proposed an economic dispatch model for microgrids by considering incentive-based demand response and peak and valley tariff mechanism. Because the traditional swarm intelligence optimization algorithm has the problems of poor optimizing accuracy, slow convergence speed and easy to fall into the local optimum, a multi-strategy fusion slime mould algorithm(MFSMA) was put forward instead. The introduction of adaptive parameters ensured a thorough search of the search space; the optimal individual guidance strategy was proposed to accelerate the convergence of the algorithm and reduce the blindness of the search; the introduction of the elite reverse learning strategy was used to avoid the algorithm falling into the local optimum; the search pattern of the salp swarm algorithm was introduced to improve the convergence speed and convergence accuracy. Comparing MFSMA with other algorithms on the benchmark function verified the superiority of the algorithm, MFSMA could minimize the cost of power generation while improving the energy efficiency.更多还原