基于改进灰狼算法的综合能源系统优化调度

为解决综合能源系统中参与者利益与设备可靠运行冲突的问题,提出一种基于非合作博弈的综合能源系统优化策略.针对风、光和负荷的不确定性利用拉丁超立方抽样法与K-means聚类法生成预测出力典型模型.模型综合考虑源、网、荷、储的利益及可靠性问题,并在用户侧引入电动汽车柔性负荷,增强能源的利用率,分析各方在追求利益与运行可靠性时的均衡交互策略.对于传统灰狼算法狼群分布不均匀、搜寻猎物能力弱等问题,基于Hammersley序列产生更均匀的初始狼群,改进收敛因子的递减方式,并调整对越限个体的处理,产生高质量狼群的同时丰富了样本种类,减少寻优的时间和次数.通过算例分析,验证了本文模型及改进算法有效性.

Optimal Scheduling of Integrated Energy System Based on Improved Gray Wolf Algorithm

In order to solve the conflict between the interests of the participants and the reliable operation of the equipment in the integrated energy system, an integrated energy system optimization strategy based on non-cooperative game is proposed. For the uncertainty of wind, light and load, the Latin hypercube sampling method and K-means clustering method were used to generate typical models of predicted output. Considering the benefits and reliability of source, network, load and storage, the model leads into flexible load of electric vehicle on the user side to enhance the utilization rate of energy and analyzes the balanced interaction strategy of all parties in the pursuit of benefits and operational reliability. For the problems such as uneven distribution of wolves in the traditional Gray Wolf Algorithm and weak ability to search for prey, a more uniform initial Wolf pack was generated based on the Hammersley sequence, and the processing of off-limit individuals was adjusted to produce high-quality wolves, enrich the sample species, and reduce the time and times of optimization . The effectiveness of the model and the algorithm is verified.