基于HSMOPSO算法的微电网经济与环保协同优化方法

发布时间：2019-04-28 13:10

【摘要】：将微电网运行的经济性和环保性作为目标进行优化调度,可促进两者的协同优化。在算法上,传统多目标粒子群算法(MOPSO)采用拥挤距离法寻找集群最优解,局部性强而全局性较差。为此,首先构造了引入模糊相似矩阵的多目标粒子群算法(FMOPSO),以提高算法的全局性;然后综合两算法的优点,提出了混合策略下的多目标粒子群算法(HSMOPSO)。结果表明:将一欧洲典型微电网作为优化调度对象,采用HSMOPSO算法求得的非劣解集不仅更贴近真实的Pareto最优前端,且分布广而均匀,并且具备良好的多样性;在微电网中引入储能技术后,优化结果更靠近坐标原点,实现了Pareto改善。研究结果验证了所提优化算法兼具良好的局部搜索能力与全局搜索能力,同时也论证了引入储能技术可显著促进微电网经济与环保的协同优化。
[Abstract]:Taking the economy and environmental protection of microgrid operation as the objective to optimize the scheduling can promote the cooperative optimization of the two. In the algorithm, the traditional multi-objective particle swarm optimization algorithm (MOPSO) uses the crowded distance method to find the optimal solution of the cluster, and the locality is strong and the whole situation is poor. Therefore, the multi-objective particle swarm optimization (FMOPSO),) algorithm with fuzzy similarity matrix is first constructed to improve the global performance of the algorithm, and then the multi-objective particle swarm optimization (HSMOPSO).) algorithm based on hybrid strategy is proposed by combining the advantages of the two algorithms. The results show that, taking a typical European microgrid as the optimal dispatching object, the non-inferior solution set obtained by HSMOPSO algorithm is not only closer to the real Pareto optimal front end, but also widely distributed and uniformly distributed, and has good diversity. After the energy storage technology is introduced into the microgrid, the optimization result is closer to the coordinate origin, and the Pareto improvement is realized. The results show that the proposed optimization algorithm has both the local search ability and the global search ability. At the same time, the introduction of energy storage technology can significantly promote the cooperative optimization of microgrid economy and environmental protection.
【作者单位】：四川大学电气信息学院智能电网四川省重点实验室;
【基金】：四川省科技厅支撑项目(2014JY0191) 成都市科技项目(2015-HM01-00132-SF)~~
【分类号】：TM727

【相似文献】