文章摘要
丁强,李锴,朱泽磊,等.基于变量降维的大电网经济调度优化方法[J].电力系统自动化,2017,41(18):130-136. DOI: 10.7500/AEPS20170222005.
DING Qiang,LI Kai,ZHU Zelei, et al.Economic Dispatch Optimization Method for Large-scale Power Grid Based on Variable Dimension Reduction[J].Automation of Electric Power Systems,2017,41(18):130-136. DOI: 10.7500/AEPS20170222005.
基于变量降维的大电网经济调度优化方法
Economic Dispatch Optimization Method for Large-scale Power Grid Based on Variable Dimension Reduction
DOI:10.7500/AEPS20170222005
关键词: 经济调度  发电计划  大电网  降维  Benders分解
KeyWords: economic dispatch  generation scheduling  large power grid  dimension reduction  Benders decomposition
上网日期:2017-05-09
基金项目:国家电网公司科技项目“大电网调度计划并行协同策略及关键技术研究”
作者单位E-mail
丁强 中国电力科学研究院, 北京市 100192  
李锴 国家电网华中电力调控分中心, 湖北省武汉市 430077  
朱泽磊 中国电力科学研究院, 北京市 100192 zhuzelei@epri.sgcc.com.cn 
郑晓雨 国家电力调度控制中心, 北京市 100031  
李旻 国网四川电力调度控制中心, 四川省成都市 610041  
刘聪 中国电力科学研究院, 北京市 100192  
摘要:
      随着电网规模扩大,经济调度问题因变量增多,维度巨大,造成求解困难。文中利用Benders分解法,将大电网安全约束经济调度问题分解为无安全约束的优化主问题和网络潮流安全约束子问题,将子问题安全约束返回主问题进行模型重构,通过主子问题协调最终实现问题求解。优化问题求解过程中,根据当前运行信息,对机组调整价值进行甄别,将其中对安全和经济贡献小的机组进行集中处理,同时根据重要断面潮流负荷变化的物理特征对研究时段进行检验,将非重点关注的相邻时段进行简化合并,实现压缩机组变量和时段数量、降低求解维度的目的。最后,算例验证表明基于变量降维的优化方法可以减少大电网优化时间,同时保持较高的求解精度,满足工程化计算要求。
Abstract:
      With the development of power grid's scale, because of the increase of variables and huge dimensions, it will be increasingly difficult to solve the economic dispatch problem. In order to overcome the difficulty, the Benders decomposition method is used to decompose the security constrained economic dispatch problem into the main optimization problem without security constraints and the subordinate power flow problem with security constraints. The security constraints on the sub-problem are returned to the main problem by model reconstruction. And the original problem can be solved through coordination between main problem and sub-problem. In the process of solving the optimization problem, the adjusted values of units are distinguished according to current operation information. The units with small safety and economic contributions are centrally addressed. At the same time, the optimization time intervals are tested according to the physical characteristics of power flow and load change on the important section, and the unimportant neighbouring time intervals are simplified and combined. Then the number of unit variables and time intervals can be reduced, so as to lower the goal of solving dimensions. Finally, the results show that the proposed optimization method can greatly reduce the optimization time while retaining a high precision required by engineering calculation.
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