基于改进粒子群的风电-火电-蓄热电锅炉联合优化调度

蓄热式电锅炉是增加风电-火电系统弃风消纳的重要手段,但是当前风电成本仍高于火电成本,风电消纳和降低蓄热式电锅炉用能成本存在矛盾,考虑多方效益,合理优化风电、火电机组及蓄热式电锅炉的出力,对于降低用能成本、提高风电消纳具有重要意义。文章建立了考虑多目标的含风电、火电机组和蓄热式电锅炉的优化调度数学模型,通过采用偏小型隶属函数将以风电、火电机组运行成本最低和弃风量最小的多目标函数转化为单目标函数,在满足各部分约束条件下求取隶属度最佳的风电、火电机组出力。进一步,提出一种改进的粒子群算法,对上述模型求解。该算法可以跳出局部最优解,具有较快的收敛速度和较高的计算精度。最后,基于某电网的实际数据进行仿真,仿真结果验证了所提方法的有效性。

Combined optimal dispatch of wind power-thermal power-restoring power boiler based on modified particle swarm

Regenerative electric boilers are an important means to increase the abandonment of wind power-thermal power systems, but the current cost of wind power is still higher than the cost of thermal power. There is a contradiction between wind power consumption and the reduction of energy cost of regenerative electric boilers. Optimizing the output of wind power, thermal power units and regenerative electric boilers is of great significance for reducing energy costs and improving wind power consumption. The article establishes a mathematical model for optimal scheduling of wind power, thermal power units, and regenerative electric boilers that take into account multiple objectives. By adopting a small-scale membership function, the multi-objective function with the lowest operating cost of wind power and thermal power units and the smallest abandonment of wind power is converted into a single objective function, and the output of the wind power and thermal power units with the best membership degree is obtained under the constraints of each part. Further, a modified particle swarm optimization algorithm is proposed to solve the above model. The algorithm can jump out of the local optimal solution, and has a faster convergence speed and a higher calculation accuracy. Finally, based on the actual data of a power grid, the simulation results verify the effectiveness of the proposed method.