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基于机会约束规划的输电系统规划方法
引用本文:杨宁,文福拴. 基于机会约束规划的输电系统规划方法[J]. 电力系统自动化, 2004, 28(14): 23-27
作者姓名:杨宁  文福拴
作者单位:浙江大学电机系,浙江省,杭州市,310027;香港大学电机电子工程学系,香港
基金项目:香港研究资助局资助项目,香港大学校科研和教改项目
摘    要:输电系统在电力市场中扮演着十分重要的角色,因为其提供了实现公平竞争的场所.输电系统的状况如容量充裕性对电力市场的竞争性有重要的影响.适当地扩展或加强输电系统对满足发电公司和用户的需要、消除或减缓输电系统阻塞具有重要作用.与传统的输电系统规划问题相比,电力市场环境下的输电系统规划更加复杂,需要处理的不确定性因素更多.机会约束规划是专门用于解决包含不确定性因素的优化问题的一类随机优化方法,适用于市场环境下的输电系统规划问题.文中将机会约束规划引入到市场环境下的输电系统规划研究中,给出了基于蒙特卡罗仿真和遗传算法的求解方法,为解决这一重要而困难的问题做了一些新的尝试,并通过一个算例系统说明了该方法的可行性.

关 键 词:输电系统规划  不确定性  机会约束规划  蒙特卡罗仿真  遗传算法
收稿时间:1900-01-01
修稿时间:1900-01-01

TRANSMISSION SYSTEM EXPANSION PLANNING BASED ON CHANCE CONSTRAINED PROGRAMMING
Yang Ning,Wen Fushuan. TRANSMISSION SYSTEM EXPANSION PLANNING BASED ON CHANCE CONSTRAINED PROGRAMMING[J]. Automation of Electric Power Systems, 2004, 28(14): 23-27
Authors:Yang Ning  Wen Fushuan
Abstract:The transmission system plays a critical role in providing access to all participants in a competitive electricity market for supply and delivery of electric power. Deregulation of the power industry brings many new challenges to the transmission system optimal planning problem, such as how to handle uncertain factors concerning the locations and capacities of new power plants as well as the demand growth for the transmission system planning period studied. Although the transmission system optimal planning problem has been extensively studied, available standard optimization models and methods cannot well solve this problem especially for the competitive electricity market environment with many uncertain factors. Given this background, a new method for the optimal transmission system expansion planning based on chance constrained programming is presented in this paper with several uncertainty factors such as the locations and capacities of new power plants as well as demand growth well taken into account. A stochastic optimization model is first formulated under the presumption that the locations and capacities of new power plants and future load demands could be modeled as specified probability distributions. A method is then presented for solving the optimization problem using the well-known Monte Carlo simulation method and the well-developed genetic algorithm. Finally, a numerical example is served for illustrating the essential features of the developed model and method.
Keywords:transmission system planning  uncertainty  chance constrained programming  Monte Carlo simulation  genetic algorithm
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