Two-Stage Robust Planning for Transmission Network Considering Adaptive Decision of Carbon Trading Volume

Abstract

Low carbon is the future development trend of new power system, and the simulation of horizontal annual carbon trading volume at the planning level plays an important role in realizing the low-carbon economic operation of future power grid. Based on this understanding, a robust planning method for transmission network considering adaptive decision-making of carbon trading volume is proposed. First, a measurement method of carbon quota trading cost based on the baseline method is constructed, and a two-stage robust programming model considering the uncertainty of wind power and load is established considering the constraint of carbon trading volume. Then, the relax-and-enforce decoupling method is used to decouple the sub-problem into security feasibility detection sub-problem and low carbon feasibility detection sub-problem according to time, and the two-stage robust programming model is solved by using the column and constraint generation algorithm. Based on the cut information feedback from the security constraint and the carbon quota trading volume constraint in each uncertain scenario, the mapping relationship between the feasible solution space of the model and the setting of the threshold parameter of carbon quota trading volume is analyzed, and the upper and lower limits of carbon trading volume are adaptively decided. Finally, the improved IEEE-RTS 24-node system is simulated to analyze the impacts of the low-carbon planning model, carbon quota trading volume, and carbon price on the planning results. The results show that the proposed model realizes the coordinated optimization of the planning scheme in terms of economy and low carbon.

Keywords

• robust optimization
• transmission network
• carbon trading
• carbon quota
• time decoupling