Integrated planning of source-grid-load-storage for regional power grids considering large-scale renewable energy integration

The large-scale integration of renewable energy has intensified transmission bottlenecks, hindered local absorption, and caused capacity allocation imbalance, posing challenges for grid planning due to the complex coupling between network topology and resource dynamics. To address these issues, this paper proposes an integrated source–grid–load–storage (SGLS) optimization method for multi-level power grids. First, an improved maximum-flow model incorporating power–reserve coupling and loss-weighted mechanisms is developed to establish a multi-timescale SGLS coordination framework, enabling dynamic balance among power generation, reserves, and network constraints across voltage levels. Second, a “hierarchical decoupling + global optimization” strategy combined with an improved multi-objective grey wolf optimizer (IMOGWO) is designed to enhance convergence and solution diversity. Simulation results show that, under the same annualized cost of RMB 2.559 billion, increasing key transmission capacity raises additional renewable capacity from 1083 MW to 1300 MW (+16.7 %) while reducing congestion; without grid expansion, energy storage coordination further alleviates congestion and lowers cost to RMB 2.45 billion, though capacity remains limited by grid constraints. Overall, grid expansion determines the upper limit of renewable integration, storage coordination enhances system efficiency, and the proposed integrated planning method achieves a balanced optimization among cost, capacity, reliability, and carbon reduction—offering a practical framework for high-renewable grid planning.