基于改进贪婪算法的多场景负荷组合优化

doi:10.11930/j.issn.1004-9649.201812087

中国电力 ›› 2020, Vol. 53 ›› Issue (5): 1-9.DOI: 10.11930/j.issn.1004-9649.201812087

基于改进贪婪算法的多场景负荷组合优化

王岩¹, 蒋静², 何恒靖¹, 高赐威², 肖勇¹

1. 南方电网科学研究院，广东广州 510080;
2. 东南大学电气工程学院，江苏南京 210096

收稿日期:2018-12-25 修回日期:2019-08-19 发布日期:2020-05-05
作者简介:王岩(1979-),男,硕士,高级工程师,从事电动汽车、需求响应、智能用电技术研究,E-mail:wangyan2@csg.cn;蒋静(1993-),女,硕士研究生,从事电力市场、需求侧管理研究,E-mail:220172694@seu.edu.cn;何恒靖(1986-),男,博士,工程师,从事计量系统技术、非侵入式负荷辨识技术、大数据技术研究,E-mail:hehj@csg.cn;高赐威(1977-),男,通信作者,教授,从事电力市场、电力需求侧管理及需求响应、电力规划、电动汽车接入电网研究,E-mail:ciwei.gao@seu.edu.cn
基金资助:
国家自然科学基金资助项目（基于空调负荷储能建模的负荷聚合与运行调度关键技术，51577029，计及不确定性的多时空尺度需求响应模型及应用研究，51277028）；南方电网公司科技项目（基于用电数据价值挖掘的客户行为分析与增值服务关键技术研究，ZBKJXM20170079）

Multi-scenario Load Combinatorial Optimization Based on Improved Greedy Algorithm

WANG Yan¹, JIANG Jing², HE Hengjing¹, GAO Ciwei², XIAO Yong¹

1. China Southern Power Grid Research Institute, Guangzhou 510080, China;
2. School of Electrical Engineering, Southeast University, Nanjing 210096, China

Received:2018-12-25 Revised:2019-08-19 Published:2020-05-05
Supported by:
This work is supported by National Natural Science Foundation of China (Key Technology of Load Aggregation and Operation Scheduling Based on Energy Storage Modeling of Air Conditioning Load, No.51577029，Demand Response Model and Application Research under Multi-temporal Scale Considering the Uncertainty, No.51277028) and Science and Technology Project of Southern Power Grid Corporation of China (Research on Customer Behavior Analysis and Value-Added Services Key Technologies Based on the Value Mining of Power Data, No.ZBKJXM20170079)

摘要/Abstract

摘要： 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。

关键词: 电力市场, 负荷组合优化, 负荷率, 购电成本, 改进贪婪算法

Abstract: 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.

Key words: electricity market, load combinatorial optimization, load rate, electricity purchase cost, improved greedy algorithm

王岩, 蒋静, 何恒靖, 高赐威, 肖勇. 基于改进贪婪算法的多场景负荷组合优化[J]. 中国电力, 2020, 53(5): 1-9.

WANG Yan, JIANG Jing, HE Hengjing, GAO Ciwei, XIAO Yong. Multi-scenario Load Combinatorial Optimization Based on Improved Greedy Algorithm[J]. Electric Power, 2020, 53(5): 1-9.

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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

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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

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