基于CQPSO算法的含风电场电力系统经济调度研究

为了克服风电场出力波动的不利影响,提高调度经济性,构建含常规火力发电燃料费用、风电预测误差备用费用以及风电弃风成本的多目标动态调度模型,并提出一种混沌量子粒子群算法对模型进行求解。标准测试函数的仿真结果表明本算法比对照算法具有较高的收敛精度和稳定性。对含风电场的IEEE30节点系统算例进行仿真实验,结果表明采用混沌量子粒子群算法对调度模型求解的调度费用最低。

RESEARCH OF ECONOMIC DISPATCH OF POWER SYSTEM WITH WIND FARMS BASED ON CHAOS QUANTUM PARTICLE SWARM OPTIMIZATION ALGORITHM

In order to overcome the adverse effects of wind farm output fluctuation and improve the scheduling economy, a multi-objective dynamic scheduling model including conventional thermal power fuel cost, wind power prediction error reserve cost and wind power abandonment cost were constructed, and a chaos quantum particle swarm optimization algorithm was proposed to solve the model. The simulation results of the standard test function showed that the algorithm had higher convergence accuracy and stability than the control algorithm. The simulation results of IEEE30-node power system with wind farm showed that the scheduling cost of using chaotic quantum particle swarm optimization algorithm to solve the scheduling model was the lowest.