摘要
电力公司报价策略是一个两层优化问题,其中第一层ISO模型是为保证社会公共效益最大化而制定的市场清除价模型,确定参与发电的电力公司;第二层是发电公司期望利润最大的模型。采用启发式算法求解简单易行,具有全局最优解,且与初始点选择无关。本文运用改进后的混沌粒子群优化算法(PSO)求解电力公司利润最大的优化问题,并与确定性方法和基本粒子群的计算结果进行了比较。此方法在IEEE30节点6机系统验证了有效性。
Power companies＇ bidding strategy is a double-optimization problem, the top of the ISO model is to ensure the maximization of social and public benefits, which is to determine the participation of the electricity generating companies. The second layer is the largest profit model of power generating companies. Using a simple heuristic algorithm, the optimal solution is overall, and has nothing to do with the initial selection. In this paper, an improved use of the Particle Swarm Optimization （PSO） for the largest profit optimization problem is made, and the deterministic method and the results are compared. On the IEEE30 six-node system,the effectiveness of the method is verified.
出处
《计算机工程与科学》
CSCD
北大核心
2010年第2期150-153,共4页
Computer Engineering & Science
基金
湖南省自然科学基金资助项目(06JJ50109)
湖南省科技计划资助项目(06FJ3161)
关键词
两层优化模型
混沌
价格策略
粒子群优化算法
启发式算法
市场清除价模型
optimization of a two-tier model
chaos
price strategy
particle swarm optimization
heuristic algorithm
market elimination price model
