基于多目标多任务进化算法的含可再生能源混合发电系统优化调度

可再生能源发电的快速发展为电力系统的安全和经济运行带来了新的挑战。在此背景下,构建了能够计及火电阀点效应非线性,风电、光伏发电系统出力不确定性和水电一次能源浪费的多目标优化调度模型。假设风速服从Weibull分布、光照服从Beta分布的前提下,含可再生能源混合发电系统优化模型综合考虑了能源利用、环境保护、成本以及损耗等限制因素。在此基础上,创新的引入了多目标多任务进化算法,同时优化多个任务的多个目标,并行处理多个发电系统的优化调度问题,从而大幅提高了搜索速度。仿真算例采用标准IEEE30节点和IEEE118节点系统,验证了该算法在解决多目标多任务多电源发电系统优化问题时的优越性。

Optimal Scheduling of Hybrid Power Generation System with Renewable Energy Based on Multi-objective and Multi-task Evolutionary Algorithm

The rapid development of renewable energy generation has brought new challenges to the safe and economic operation of power systems. In this context, this paper constructed a multi-objective optimal dispatching model considering the non-linearity of thermal power valve point effect, the output uncertainty of wind power and photovoltaic power generation systems and the waste of primary energy of hydropower. Assuming that the wind speed obeys Weibull distribution and the illumination obeys Beta distribution, the optimization model of hybrid power generation system with renewable energy takes into account the constraints of energy utilization, environmental protection, cost and loss. On this basis, this paper introduces an innovative multi-objective and multi-task evolutionary algorithm to optimize multi-tasks and multi-objectives simultaneously, and to optimize the scheduling of multi-generation system in parallel, thus greatly improving the search speed. This paper adopts standard IEEE 30-bus and IEEE 118-bus systems in simulations. The simulation results demonstrate the superiority of the proposed algorithm in optimizing multi-objective, multi-task and multi-power generation system.