Two-stage chance-constrained unit commitment based on optimal wind power consumption point considering battery energy storage

Wind power generation has developed rapidly in recent decades due to low carbon emissions. However, the significant uncertainty makes it uneconomic or even unreliable to be consumed, which hinders the development of wind energy. To guarantee the minimum wind utilisation level without jeopardising system reliability and cost-effectiveness, this study proposes a concept of optimal wind power consumption point. Based on that, a two-stage chance-constrained unit commitment model is presented to co-optimise the day-ahead energy and reserve schedules, which achieves a reasonable trade-off between robustness and costs. The battery energy storage is also investigated to enhance system flexibility and promote wind consumption. The joint chance constraint is dealt with through a sample average approximation method in bilinear forms. The resulting large-scale mixed-integer programming is decomposed into the master and subproblem formulations and then solved iteratively by the developed bilinear Benders decomposition (BBD) method. To achieve computational tractability, several techniques are used to enhance the convergence property of BBD and accelerate the solution process, with a novel optimality-check-only bilinear Benders decomposition method proposed. Case studies on six-bus, IEEE 118-bus and 236-bus systems demonstrate the effectiveness of the proposed model and algorithm.