基于期望后悔度的旋转备用多目标优化研究

MULTI-OBJECTIVE OPTIMIZATION OF SPINNING RESERVE BASED ON EXPECTED REGRET

考虑系统运行成本、旋转备用容量成本和能量成本,以及失负荷惩罚和弃风惩罚,并融入分时电价,提出一种期望后悔度度量指标,建立以系统总成本最小和惩罚费用最小为目标的双层优化模型,并对多目标量子粒子群算法进行改进。外层采用改进的多目标量子粒子群优化算法求解上、下旋转备用容量,内层采用优先顺序法求解机组组合和旋转备用的经济分配。从而得到成本期望后悔度-惩罚期望后悔度的Pareto前沿,并分析分时电价和权重系数对系统总成本和惩罚费用的影响。最后,通过算例验证所提优化模型和算法的有效性。

A metric of expected regret is proposed and a two-layer optimization model with the goal of minimum total system cost and minimum penalty cost is established,and the multi-objective quantum particle swarm algorithm is improved by considering the operation cost of the system,the capacity cost and energy cost of spinning reserve,as well as penalty cost for loss of load and wind curtailment and incorporating time-of-use electricity prices.In the outer layer of the model,the upward and downward spinning reserve capacities are obtained by using the improved multi-objective quantum particle swarm optimization algorithm.In the inner layer of the model,the unit commitment and economic distribution of the spinning reserve capacities are acquired by the priority method.Thus,the cost expectation regret-the Pareto fronts that punishes the expected regret are obtained,and the impact of the time-of-use electricity price and weight coefficient on the total cost of the system and the penalty fee are analyzed.Finally,the effectiveness of the proposed optimization model and algorithm is verified by the example.