A Distributionally Robust Approach for Transmission and Energy Storage Capacity Planning in a Remote Photovoltaic Power Plant

Photovoltaic (PV) power plants have developed rapidly in the past decade. In China, solar resources are abundant in the west, which is far away from the load center. The PV power plant there has to be connected to the power grid by a long transmission line. To further reduce the impact of its volatility on the power grid, energy storage can be built inside the PV power plant and the capacity requirement of the transmission line decreases. This paper proposes a method that jointly plans the capacity of the transmission and energy storage for a remote PV power plant. Since the historical data of the solar source may not be accurate enough, the distributionally robust optimization (DRO) technique based on Kullback-Leibler divergence (KL-divergence) is adopted, so that the difference between the empirical distribution and the real distribution is considered. Then a tractable form is derived, which comes down to linear programming. The effectiveness of the proposed method is shown through a test case of a 200MW PV power plant.