A Bilevel Optimisation Model for Distributed Energy Storage Considering Wind‐Solar Uncertainty and Carbon Trading

ABSTRACT To achieve the ‘Dual Carbon’ strategic goals, the integration of a high proportion of renewable energy poses significant challenges to the stability and economic efficiency of power systems. This paper proposes a bilevel optimisation method for distributed energy storage configuration that integrates wind‐solar power uncertainty and a carbon trading mechanism. First, a spectral normalisation generative adversarial network (SNGAN) is employed to generate a large number of wind‐solar power output scenarios. These scenarios are then reduced using the K‐means clustering algorithm to obtain typical representative scenarios, significantly improving the quality of scenario generation. Second, a bilevel optimisation model is constructed for distributed energy storage configuration considering a tiered carbon trading scheme. The upper‐level model aims to minimise the total cost, including energy storage investment, grid electricity purchase and carbon trading costs, whereas the lower‐level model focuses on minimising power grid vulnerability indices and active power losses. Finally, an improved Osprey Optimisation Algorithm is developed to solve the bilevel model. Simulation results demonstrate that the proposed method reduces annual carbon emissions by 22.6%, daily active power loss by 22.3% and daily electricity purchase cost by 9.2% compared to the case without ESS and carbon trading while significantly enhancing overall power system performance.