基于改进鲸鱼优化算法的微网系统能量优化管理

针对包含多种可再生能源的冷热电联供型微网系统的能量优化问题,为了优化其运行过程的经济效益和环境效益,本文提出一种基于改进鲸鱼优化算法的多时间尺度下能量优化方法,首先根据长短期记忆网络(Long Short Term Memory,LSTM)预测得到的可再生能源出力和负荷需求预先制定调度规划,然后以此预测数据为基础,采用改进鲸鱼优化算法调整可控设备出力,优化微网系统的运行成本和固定成本。将该方法应用于某楼宇冷热电联供型微网,结果表明,在满足负荷需求的基础上使得经济成本平均降低4.03%且经济效益更优。

Energy optimization management of microgrid based on improved whale optimization algorithm

Aiming at the energy optimization problem of the combined cooling, heating and power microgrid system containing multiple renewable energy sources, in order to optimize the economic and environmental benefits of its operation process, this paper proposes a multi-time scale energy optimization based on an improved whale optimization algorithm The method firstly formulates a scheduling plan based on the renewable energy output and load demand predicted by the Long Short Term Memory (LSTM) network, and then uses the improved whale optimization algorithm to adjust the output of controllable equipment based on the predicted data. Optimize the economic and environmental benefits of the microgrid system. This method is applied to a building"s combined cooling, heating and power microgrid, and the results showthat on the basis of meeting the load demand, the economic cost is reduced by 4.03% on average and the environmental benefits are better.