基于隐性备用约束的机组组合模型  

The unit commitment formulation with implicit reserve constraint

作　　者：张新松[1,2] 袁越[1,2] 傅质馨[1,2] 

机构地区：[1]河海大学能源电气学院,江苏南京210098 [2]河海大学可再生能源发电技术教育部工程研究中心,江苏南京210098

出　　处：《电力系统保护与控制》2013年第1期136-142,共7页Power System Protection and Control

基　　金：国家自然科学基金(51077041);南通市科技局应用研究项目(BK2012051)~~

摘　　要：提出了一种基于隐性备用约束的机组组合模型。与常规机组组合模型不同,该模型中的旋转备用约束并未明确给出,而是隐含在目标函数中的可靠性与经济性的平衡之中。采用解析法对机组组合方案的可靠性进行了评价,并在此基础上估算停电损失。可靠性评估过程中考虑了机组的随机故障与负荷的不确定性。采用遗传算法对模型进行了求解,除常规的复制、交叉、变异操作外,亦设计了智能变异操作算子以提升算法的寻优性能。基于某10机系统的仿真试验证了所提模型与算法的有效性。A novel unit commitment formulation with implicit spinning reserve （SR） constraints is proposed. Different with the traditional UC formulation, the constraints on the amount of SR is not given explicitly but implicitly in a tradeoff between the production costs and the interruption losses. An analytic algorithm is proposed to assess the reliability level of UC solutions, and the value of the interruption losses can be subsequently obtained. In the process of the reliability assessment, not only the stochastic failure of generator units but also the load demand uncertainties are all considered. Genetic algorithm （GA） is modified and utilized to solve the revised UC formulation proposed in this paper. Besides conventional operation of copy, crossover and mutation, a novel intelligent mutation operator （IMO） has been designed for the convergence enhancement. The simulation results on a certain 10-unit generation system demonstrate the feasibility of the proposed model and algorithm.

关 键 词：机组组合 旋转备用 可靠性 停电损失 遗传算法 智能变异 

分 类 号：TM732[电气工程—电力系统及自动化]