摘要
电源规划是电力系统电源布局的战略决策,在电力系统规划中处于十分重要的地位。其核心问题是要确定在规划期内随着负荷的增长,系统应在何时、何地、建什么类型、多大容量的电厂。由于其本身的的复杂性,用传统的优化方法求解需采取简化措施,寻求一个满足各种约束条件和可靠性指标及环保要求的最优电源建设方案,以满足系统负荷发展的需要。为此,提出一种粒子群算法与模拟退火算法结合的模拟退火粒子群算法,并将其用于求解复杂的、非线性的水火电混合电力系统(包含核电)电源规划问题。该组合算法在粒子群算法中引入了模拟退火算法成功的提高了基本粒子群算法的全局搜索能力。算例结果表明:该算法能可靠、快速的收敛到全局最优解,特别适合于大型电力系统的中长期电源规划。
Generation expansion planning of power system is directed by systems engineering, using new optimization and computer teehnology, search a best generation construction scheme to satisfy the load developemtn. The scheme should also satisfy all constraint conditions, reliability target and environment protected demanding. Generation expansion planning is a strategic decision-making of generation disposition in the power system. In other words, it＇s very important. The core of generation expansion planning is confirming when, where, which type and how much it＇s generate capacity to construct generations with the development of load in the planning period. For the better plan, generation expansion planning relates to lots of problems such as load forecasting and choosing the site of generation; it also relates to many departments. To sum up, it is an extraordinary complex task. Owning the complexity of generation expansion planning, we have to predigest it when solve the problem with a variety of classical optimization methods, howbeit, which decrease the precision of planning. Recently, artificial intelligence algorithms hae made a rapid progress. This new algorithms can solve some disperse or non-proturding nonlinear system problems, which are different from the classical ones. Through making some researches on them, we can find that excellent applicability in the generation expansion planning, In these algorithms, we imitate biologic characteristics, and then abstract these characteristics. Based on them, smoe effective search algorithms have been discovered. Besides, kinds of artificial intelligence algorithms have been used in fact wide by wide, such as GA, ES, EP, ANN etc. During this time, particle swarm optimization（PSO） is put forward step by step. If we systematize and standardize these algorithms, studying on their mathematical principle, it will be very important in the theory and practice field that we solve the planning with them. A simulated annealing particle swarm optimization algorithm（SA-
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2006年第4期104-107,共4页
High Voltage Engineering
关键词
电源规划
模拟退火粒子群算法
环保约束
加速变步长搜索法
可靠性计算
generation expansion planning
SA-PSO
environment constraint
accelerated search method with variable step
reliability evaluation
