基于改进遗传算法的配电网无功优化研究

有效降低配电网有功损失是配电网安全、经济运行的重要课题。为解决局部地区网损偏大的问题,文中将改进的遗传算法用于无功补偿优化。在考虑配电网拓扑结构的同时,设计了自适应遗传算子并构造了指数型适应度函数来提升遗传算法收敛速度和精度,充分发挥了遗传算法的全局随机快速搜索能力。优化某16节点算例的结果表明,配电网有功网损由6.76%下降到5.16%,电压达标率从70.61%提高到92.86%,表明该方法能够提高全局寻优精度,改善区域网络电压质量,同时也证明了该改进遗传算法用于无功优化的可行性和实用性。

Research on Reactive Power Optimization of Power Distribution Network Based on Improved Genetic Algorithm

Effectively reducing the loss of distribution network active power has been an important issue in the safe and economic operation of distribution networks. In order to solve the problem of large network loss in local areas,an improved genetic algorithm was proposed for reactive power compensation optimization. While considering the topological structure of distribution network, an adaptive genetic operator was designed and an exponential fitness function was constructed to promote the convergence speed and precision of genetic algorithm. In this way, the global random search capability of genetic algorithm could be fully utilized. The results of optimizing a 16-node study showed that the active network loss of the distribution network dropped from 6.76% to 5.16%,and the voltage qualification rate increased from 70.61% to 92.86%,indicating both the global optimization accuracy and the voltage quality of the regional network were improved. In addition, it also proved that this improved genetic algorithm is feasible for reactive power optimization.