Adjustable robust optimal power flow with the price of robustness for large-scale power systems

DC optimal power flow (DCOPF) has been widely used in modern power system operation and planning. With the consideration of the stochastic renewable resources integration, an adjustable robust DCOPF is studied in this work in combination with generator participation factors to obtain an optimal solution that can immunise against all realisations of the renewable resource output variability. However, the robust optimisation may lead to an extreme conservative optimal solution compared to the traditional deterministic optimisation. Therefore, the price of robustness is taken into account which provides a tradeoff between the robust optimal solution and the traditional optimal solution for decision makers. According to the duality theory, the robust DCOPF is transformed into a convex quadratic program, which has a large number of linear constraints. In order to efficiently solve the robust DCOPF for applications in large-scale power systems, an inactive constraints reduction technique is introduced to identify the inactive security constraints before solving the proposed model, which greatly improves the computational performance. Numerical results on the IEEE 14-bus, 118-bus and other six large Polish test systems validate the effectiveness of the proposed method.