A Wasserstein Distance-Based Distributionally Robust Chance-Constrained Clustered Generation Expansion Planning Considering Flexible Resource Investments
Baorui Chen, Tianqi Liu, Xuan Liu, Chuan He, Lu Nan, Lei Wu, Xueneng Su, Jian Zhang
Abstract
This paper proposes a distributionally robust chance-constrained (DRCC) model for the clustered generation expansion planning (CGEP) of power systems. The proposed two-stage model minimizes the first-stage total cost along with the second-stage expected penalty cost with the worst-case probability distribution of renewable energy generation. A unit commitment model with flexibility constraints is embedded into the planning model, and the uncertainty is modeled via a Wasserstein distance (WD)-based ambiguity set. Demand-side resources (DSR) and concentrating solar power (CSP) plants are considered as candidates in the DRCC-CGEP model to enhance system flexibility, and the solution efficiency is improved through unit clustering. Furthermore, based on strong duality theory along with affine decision rule and conditional-value-at-risk approximation method, the proposed planning model is reformulated as a tractable mixed-integer linear programming problem. Numerical results show that the proposed WD-based DRCC-CGEP model is effective in improving the economics of the planning decisions while ensuring system reliability and maintaining computational efficiency.