考虑价格和激励/补偿耦合机制的微网容量多层内嵌优化

Multi-layer Embedded Optimization of Microgrid Capacity Considering Price and Incentive/Compensation Coupling Mechanism

为充分挖掘供需侧双向交互平抑微网源荷差异的能力,根据价格型和激励补偿型响应的优劣势互补关系,提出了一种结合不同响应机制的源荷协同优化思路。首先,在内层荷端构建了需求侧响应上、下双层优化模型,上层根据净负荷曲线划分峰、平、谷时段,以微网各时段净负荷绝对值之和最小为目标优化分时电价,下层计及柔性负荷参与调度的积极性,以电舒适度和经济性构成满意度指标优化激励/补偿系数。然后,在外层搭建了综合考量经济性和可靠性的源端容量优化模型。最后,基于多层内嵌机理耦合源荷两端优化模型,构建了结合多目标粒子群算法(MOPSO)和粒子群-帝国竞争算法(PSO-ICA)的求解模型。仿真结果表明,得益于需求侧综合响应机制和求解模型结构的优越性,所提方法能够在满足用户需求的基础上提升微网的经济性和可靠性。

This study proposes a collaborative optimization idea of source and load sides by combining multiple response mechanisms to fully tap the ability of two-way interaction between supply and demand sides to stabilize the difference between source and load in the microgrid.This idea is based on the complementary relationship between the advantages and disadvantages of price response and incentive/compensation response.Firstly,an upper and lower bi-level optimization model of demand-side response is constructed at the load end of the inner layer.The upper layer divides the peak and valley periods according to the net load curve and optimizes the time-of-use(TOU)tariff with the objective of minimizing the sum of the absolute value of the net load in each period of the microgrid.The lower layer optimizes the incentive/compensation coefficient with the satisfaction indexes consisting of electricity comfort and economy,which takes into account the enthusiasm of the flexible load to participate in dispatching.Secondly,a source-end capacity optimization model considering economy and reliability is built in the outer layer.Finally,the optimization models of both the source and load are coupled through the multi-layer embedded mechanism,and a solution model is constructed,which combines the multi-objective particle swarm optimization(MOPSO)algorithm and the particle swarm optimization–imperial competition algorithm(PSO-ICA).Simulations show that the proposed method can improve the economy and reliability of the microgrid on the basis of meeting the needs of users due to the advantages of the demand-side comprehensive response mechanism and solution model structure.