新能源基地直流外送系统多时间尺度鲁棒协同优化调度OA

To address the scheduling issues caused by the instability of wind and photovoltaic outputs in DC transmission systems,a coordinated optimal scheduling method for wind-photovoltaic-thermal-storage DC systems integrating multi-time-scale optimization and adaptive robust optimization was proposed.A two-stage coordinated optimal scheduling model for wind-photovoltaic-thermal-storage DC systems was established with the objective of maximizing comprehensive benefits,including economic,environmental,system and flexibility benefits,subject to constraints such as power balance and energy storage state of charge(SOC).In the day-ahead planning stage,the objective function and constraints were established,and the multi-objective problem was solved using the non-dominated sorting genetic algorithm Ⅱ.In the intraday rolling optimization stage,a deviation set describing the uncertainty of wind and photovoltaic outputs was constructed,and a min-max robust optimization method was employed.The model was solved using the column-and-constraint generation algorithm to ensure that transmission power met medium-and long-term DC transmission plans under extreme conditions.The simulation results demonstrated that the proposed strategy achieved the lowest comprehensive cost under scenario 4,which was approximately 7％lower than that under scenario 1.The fluctuation amplitude of medium-and long-term transmission power reduced by more than 30％.After intraday rolling optimization,the tracking error of the transmission power was controlled within 2％,and the robust optimization model still obtained a globally optimal solution satisfying hard constraints under the worst-case scenario.The proposed scheduling strategy can maximize comprehensive benefits in the DC transmission systems of new energy bases,ensuring that real-time transmission power closely tracks medium and long-term transmission plans.Furthermore,the robust optimization model can still obtain a globally optimal solution satisfying hard constraints even when considering uncertainty scenarios under the worst-case scenario.