Research on homestay spatial planning method based on optimal configuration of energy storage system in renewable energy distribution network

In the context of the current green transformation of the homestay industry and the large-scale application of renewable energy, optimizing the configuration of energy storage systems in the distribution network has become the key to balancing energy supply and demand, reducing operating costs, and improving grid efficiency. However, existing research has mostly focused on large-scale power grids, and methods for optimizing energy storage capacity and site selection in homestay space scenarios still need to be improved. To this end, this article constructs a two-layer optimization model: the upper layer aims to minimize costs and establishes an energy storage capacity optimization configuration model based on different renewable energy penetration rate constraints, and proposes a flexible selection method based on multiple penetration rates and charge discharge cycles; The lower level aims to minimize active power loss in the distribution network, and constructs an optimal energy storage location allocation model based on the centrality of network nodes. Taking the medium voltage benchmark distribution network system as the research object, a case study was conducted to calculate the optimal energy storage capacity and node configuration results through upper and lower-level models. Further, optimization algorithms were used to configure the distribution network system under different penetration rates, and compared with the proposed two-level model. Finally, the comprehensiveness and superiority of the model in balancing economy and grid efficiency in homestay spatial planning scenarios were verified. Taking a homestay scenario with a total fixed load of 5 MW as an example, when the penetration rate of renewable energy reaches 80%, the energy storage system reaches the optimal configuration—capacity peak adaptation, and the system operating cost is the lowest under a 12 h charging and discharging cycle; In terms of node location, the optimal nodes for the medium voltage reference distribution network are determined to be 3, 7, and 8 through the current betweenness centrality algorithm, which is more in line with the spatial layout and load demand of homestays than algorithms such as degree centrality and eigenvector centrality; Compared with traditional models, this dual layer optimization model achieves a renewable energy utilization rate of 95% and a space utilization rate of 85–92% at a penetration rate of 80%. It not only solves the mismatch between supply and demand, but also makes the energy storage capacity more reasonable as the penetration rate changes.