A Distributionally Robust optimization Model for the Decomposition of Contract Electricity Considering Uncertainty of Wind Power

In recent years, the rapid development of the Distributed Generation (DG) and the uncertainty of load has brought new challenges to the decomposition of contract electricity. The traditional stochastic optimization and robust optimization methods may result in over-conservative or over-risky decisions when dealing with the uncertainty of wind power and load. Therefore, this paper proposes a distributionally robust optimization model for the decomposition of contract electricity, which regards the difference between the unit's monthly power generation and the contract electricity as one of its optimization target. This model includes not only the units, wind turbine, but also the electrical storage system. Then, based on the typical scenario data of wind power and load, and the regulation characteristics for different decision variables, a data-driven based two-stage distributionally robust reactive power optimization model is set up, in which the uncertainty probability distribution confidence set is simultaneously constrained by 1-norm and ∞-norm. Finally, by the Columns and Constraints Generation algorithm, the IEEE 33-bus system is applied to verify the effectiveness of proposed.