基于改进粒子群算法混合储能优化调度

Hybrid energy storage optimization scheduling based on improved particle swarm algorithm

大多数风电储能调度研究从经济运行成本或环境保护成本出发,调度方案缺少对储能投资企业收益和风电消纳的研究,考虑储能经济收益和风电功率消解对调度的影响,提出了一种基于改进粒子群算法的风电混合储能优化调度方法。首先建立一种蓄电池和超级电容混合储能的储能方案;其次基于分时售电、电价差异的影响因素,建立了以储能经济收益最大和风电功率消解最大的优化调度模型;最后提出了一种基于粒子群的改进优化算法,粒子群算法引入帕累托排序和拥挤度排序法,并进行了理论分析和仿真验证。结果表明,只考虑储能经济收益最大时收益明显增加,只考虑风电功率消解最大时消解明显增多,同时考虑储能经济收益最大和风电功率消解最大双目标时,对于两种目标,均有较好的优化结果。

Most of the wind energy storage scheduling research from the economic operation cost or environmental protection cost,scheduling scheme lacks of energy storage investment enterprise income and wind power dissipation research,this paper considers the energy storage economic income and wind power dissipation of power on the scheduling of the impact of this paper,puts forward a wind hybrid energy storage optimization scheduling method based on the improved particle swarm algorithm.Firstly,an energy storage scheme of hybrid storage of battery and supercapacitor is established,and secondly,based on the influence factors of time-sharing electricity sales and electricity price difference,an optimal scheduling model with the maximum economic gain of energy storage and the maximum power dissipation of wind power is established,and then an improved optimization algorithm based on particle swarm is proposed,which introduces the Pareto sorting and congestion sorting method,and the theoretical analysis and simulation verification are carried out.The results show that the revenue is obviously increased when only considering the maximum economic revenue of energy storage,the dissipation is obviously increased when only considering the maximum wind power dissipation,and when considering the dual objectives of the maximum economic revenue of energy storage and the maximum wind power power dissipation at the same time,there are better optimization results for both objectives.