ADN source-load-storage cooperative two-layer optimal allocation based on ICSQPSO algorithm

With the continuous advancement of smart grid technology and the extensive application of renewable energy, the traditional one-way passive distribution network is gradually transforming into a two-way interactive, multi-dimensional, and coordinated active distribution network (ADN). However, the stochastic and temporal nature of distributed generation (DG) output, coupled with the volatility of loads, poses significant challenges to the resource allocation and operation regulation of ADNs. To address these challenges, this paper proposes a two-layer optimization method for ADN source-load-storage coordination, taking into account demand response. The upper layer, referred to as the planning layer, aims to determine the optimal siting and capacity setting scheme for each device within the active distribution network. Conversely, the lower layer, known as the operation layer, focuses on deriving the optimal scheduling scheme for each flexible resource, including the demand response load. To manage the complexity inherent in the two-layer planning problem, an improved hybrid cuckoo search-based quantum-behaved particle swarm optimization (ICSQPSO) algorithm is introduced. This algorithm enhances computational efficiency and mitigates the risk of falling into local optima. The effectiveness of the proposed model and algorithm is subsequently verified through simulation using the IEEE33 algorithm.