Distributed Risk-Averse Optimization Scheduling of Hybrid Energy System with Complementary Renewable Energy Generation

Large-scale penetration of renewable energy generation brings various challenges to the power system in terms of safety, reliability, economy and flexibility. The development of large-scale, high-security energy-storage technology can effectively address these challenges and improve the capabilities of power systems in power-supply guarantee and flexible adjustment. This paper proposes a novel distributed risk-averse optimization scheduling model of a hybrid wind–solar–storage system based on the adjustability of the storage system and the complementarity of renewable energy generation. The correlation of wind power and photovoltaic generation is quantified based on a Copula function. A risk-averse operation optimization model is proposed using conditional value at risk to quantify the uncertainty of renewable energy generation. A linear formulation of conditional value at risk under typical scenarios is developed by Gibbs sampling the joint distribution and Fuzzy C-Means clustering algorithm. A distributed solution algorithm based on an alternating-direction method of multipliers is developed to derive the optimal scheduling of hybrid wind–solar–storage system in a distributed manner. Numerical case studies based on IEEE 34-bus distribution network verify the effectiveness of the proposed model in reducing the uncertainty impact of renewable energy generation on an upstream grid (the overall amount of renewable energy generation sent back to the upstream grid has decreased about 80.6%) and ensuring the operational security of hybrid wind–solar–storage system (overall voltage deviation within 5.6%).