采用NSGA-Ⅱ混合智能算法的风电场多目标电网规划

Multi-objective Transmission Planning Associated With Wind Farms Applying NSGA-Ⅱ Hybrid Intelligent Algorithm

风电并网在实现节约化石能源和减少有害气体排放等效益的同时,也将对电力系统的可靠性造成一定的负面影响。为达到投资经济性、系统可靠性、环保效果的整体最优,构建了多目标风电场接入的输电线路与电网的联合优化规划模型;针对目标权重未知、人工神经网络(artificial neuralnetwork,ANN)收敛困难、无法合理决策等问题,采用方差最大化决策和分类逼近理想解的排序方法(technique fororder preference by similarity to an ideal solution,TOPSIS)缩小最优解的范围,并在此基础上提出了随机模拟、神经元网络和非劣排序遗传算法II(non-dominated sorting geneticalgorithm II,NSGA-Ⅱ)相结合的混合智能算法;对增加风电场的改进IEEE Garver-6系统进行计算分析,结果表明该方法具有较高的决策效率和计算精度,从而验证了所提出模型和方法的合理性和有效性。

Wind farms connected to grid would have effects on fossil energy saving, harmful gas emission reduction, and negatively on power systems reliability. In order to maximize integration result of investment efficiency, power system reliability, and environmental effects, this paper proposed the multi-objective transmission planning model with consideration of wind farms＇ connection lines. Aiming at solving unknown objective weights, artificial neural network （ANN） convergence and decision rationality difficulties, the hybrid intelligent algorithm combined with stochastic simulation, neural network, and non-dominated sorting genetic algorithm Ⅱ （NSGA-Ⅱ） was proposed, which could narrow the optimal solution scope by deviations maximization method and technique for order preference by similarity to an ideal solution （TOPSIS）. Wind farms were added to IEEE Garver-6 test system, and the modified test system study results show that the method has higher decision-making efficiency and calculation precision, and verify that the proposed model and method are reasonable and effective.