基于免疫记忆粒子群优化算法的风火联合系统的多目标优化调度

Wind Power Integrated with Thermal System Multi-objective Optimal Dispatch Based on Immune Memory Particle Swarm Optimization Algorithm

随着清洁可再生的随机性和间歇性风电大规模接入,其对电力系统发电调度的影响不容忽视。考虑风电机组的环境效益和并网对系统安全造成的影响,建立基于多目标机会约束规划模型,将燃煤机组发电成本最小、污染气体排放量最小作为目标,并计及系统的正、负旋转备用容量约束。使用风电出力的分布函数将随机模型转化为确定模型,采用模糊化处理技术将多目标优化转化为单目标优化,利用免疫记忆粒子群优化算法求解模型。最后,以修改后IEEE-30节点系统模型测试算例MATLAB仿真为例,通过算例仿真结果比较分析,验证了所建立模型的正确性以及算法的快速性、有效性和收敛性。

With clean, renewable randomness and intermittent large-scale wind power connected into the power systems, the influences of it should be considered in power system dispatch. This paper establishes multi-objective chance constrained programming model considering environmental benefits of wind turbines and grid security caused by wind farm into power grid. Minimizing both the fuel cost and emission of atmospheric pollutants of thermal generators are considered as objective functions, the up spinning reserve and the down spinning reserve constraints are included in the model. In order to solve the model, this paper uses the cumulative distribution function of wind power to transform the stochastic optimization problem to a deterministic one, adopts fuzzy technology to transform the multi-objective optimization to a single objective optimization and applies IM-PSO. Finally, by comparing and analyzing the results of simulation, the revised model IEEE 30-bus system MATLAB simulation test studies have demonstrated the correctness of the proposed model and the rapidness, effectiveness, convergence of the proposed algorithm.