基于改进贪婪算法的多场景负荷组合优化

Multi-scenario Load Combinatorial Optimization Based on Improved Greedy Algorithm

随着售电侧市场化竞争格局的逐步形成,售电公司通过负荷组合优化,在用户侧可以提高负荷率,降低购电成本,在供电侧可以提高设备利用率,降低线路损耗,提高电网安全运行水平和供电质量。针对不同发展阶段的售电公司如何根据电力负荷的互补性、多样性整合优化用户资源的问题,首先基于用户负荷特性分析,提出了分别在购售电利益、交易电量及综合负荷率最大的场景下用户负荷组合优化模型。其次,对贪婪搜索算法进行改进,引入随机因子,解决算法在初始估计和优化效果判定上的缺陷,提高了算法求解的质量与精度。最后采用改进贪婪搜索算法求解不同场景下的组合优化模型,验证了该算法在求解该组合优化问题时具有更强的搜索能力和适应性。

With the gradual formation of sales side market competition pattern, the electricity companies can improve the safety level of power grid and the quality of power supply through load combinatorial optimization to improve the load rate and reduce the cost of electricity purchase on the users side, and to improve the equipment utilization rate and reduce the line loss on the power supply side. In order to integrate and optimize user resources according to the complementarity and diversity of power load, a user load combinatorial optimization model is proposed respectively for the scenarios of maximum purchase and sale benefits, maximum electricity transaction, and maximum comprehensive load rate based on the analysis of user load characteristics. And then, the greedy search algorithm is improved by introducing random factors to remedy the defects of the algorithm in determination of the initial estimation and optimization effects, thus improving the quality and accuracy of the algorithm. Finally, the improved greedy search algorithm is used to solve the load combinatorial optimization model under different scenarios. The improved algorithm has been verified to have stronger global search ability and adaptability in solving the combinatorial optimization problems.