Power generation–network–load–energy storage co-planning under uncertainty - Ma, Su; Liu, Lu; Cheng, Haozhong (Frontiers in Energy Research, 2024)
With the aggregation of renewable energy in the power system, the uncertainty brought by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models neglect to consider renewable energy uncertainty methods, specifically concern the renewable energy confidence, also the future possible scenarios, thus a confidencebased scenario cluster method is presented. The construction of the paper is, a novel GNLS (Generator, Network, Load, energy Storage)-Co planning model is proposed. First, a confidence-based scenarios cluster is built which can reflect uncertainties by clustering and analyzing wind, solar and load. Second, the proposed model focuses on load and energy storage co-planning, in addition, relevant flexible indices are used to assess the model. Last, the GNLS-Co planning model is built as a bi-level stochastic model on a continuous-time scales. The model is solved by benders decomposition algorithm. The method in this paper is validated by an IEEE RTS 24-bus and a real test system in China, to demonstrate the reduction of renewable energy curtailment and optimize economic in power system planning.