PGA在电力系统电源规划中的应用

Generation expansion planning of power system by partheno-genetic alogorithm

目前,遗传算法作为一种基于人工智能技术的优化算法在电力系统电源规划中已经得到广泛应用。然而其遗传操作繁杂、计算量庞大、早熟收敛等问题使其应用受到局限。本文提出一种自然分段式编码成功地将单亲遗传算法PGA引入电源规划中,通过使用该方法可以大大简化电源规划的计算量、避免早熟收敛、提高计算效率。算例结果表明:该算法可以成功解决包含各种类型电源的规划问题,并且还有广阔的发展空间。

Genetic algorithm has been widely used in the Generation Expansion planning as an artificial intelligence algorithm. However, the utilization of Genetic Algorithm in practice was limited due to complicated operation, huge calculating and immature convergence. In this thesis, divided natural coding method is proposed to bring PGA to Generation Expansion planning,by this means, the calculating will be declined, immature convergence be avoided while the afficiency of calculating will be increased largely. The result of competition examples shows clearly that this algorithm can resolve the planning of power system including different kinds of generations successfully. Besides, PGA will be improved wide by wide.