Configuration Method of Energy Storage for Wind Farms Considering Wind Power Uncertainty and Wind Curtailment Constraint

The large-scale integration of wind power has caused serious curtailment problems and the configuration of energy storage in wind farms can significantly reduce the curtailment. Considering the uncertainty and curtailment rate constraint of wind power, this paper focuses on the energy storage configuration in wind farms based on distributionally robust optimization method. Firstly, an empirical distribution function for wind power is estimated based on historical data. Considering the wind power uncertainties, a set of probability distribution functions for wind power output is established, which takes the Kullback-Leibler (KL) divergence as distance measurement of distribution function. Secondly, the requirement of wind power curtailment rate is modelled as a robust chance constraint with respect to the worst distribution in the probability distribution function set. And a distributionally robust optimization model is proposed with the objective of minimizing the investment cost of energy storage and curtailment rate constraints. Finally, the robust chance constraint is transformed to a traditional one by correcting risk thresholds in chance constraint. Through convex approximation and sampling average, a linear program is constructed for highly efficient solution. A case study of the proposed model is conducted on IEEE 30-bus system, and the results are compared with the traditional stochastic programming and robust optimization models, which demonstrate the advantage of the proposed model in dealing with wind power uncertainties by balancing the conservatism and robustness effectively. © 2020 Automation of Electric Power Systems Press.