Data-Adaptive Robust Transmission Network Planning Incorporating Post-Contingency Demand Response

The significant development and increasing deployment of renewable generation in the modern power system introduces the challenge for dealing with uncertainty. In this paper, data-adaptive robust optimization is applied to the transmission network planning. By taking historical data correlation into account, the proposed model can achieve a lower expansion investment without sacrificing the robustness. Demand response is embedded in this model to relieve the overflow incurred by renewable generation fluctuation and N - 1 contingency. The model is decomposed into a master problem and several slave problems by column and constraint generation algorithm and then solved iteratively. The numerical simulation tested on Garver 6-bus system and the IEEE 118-bus system demonstrates the effect of demand response in reducing or postponing network construction. The proposed data-adaptive robust optimization is proved to be costeffective and computationally efficient.