Distributionally Robust Coordinated Expansion Planning for Generation, Transmission, and Demand Side Resources Considering the Benefits of Concentrating Solar Power Plants

Abstract

Renewable energy has gradually increased its investment and penetration due to technology innovation and cost reduction, turning power system expansion planning problem (SEP) into a complex task. Under this circumstance, this paper presents a long-term distributionally robust optimization (DRO) based planning model for power systems, which minimizes the total investment and operation costs along with expected penalty cost of load shedding and wind curtailment, while considering load and wind uncertainties from the data-driven ambiguity set. Besides generating units and transmission lines, demand side resources (DSR) and concentrating solar power (CSP) plants are also considered as investment candidates to meet the load growth and effectively alleviate load and wind uncertainties. Furthermore, the principal component analysis method is used to capture the generalized moment information of historical data to reduce conservativeness of planning decisions. The proposed model is recast as a mixed integer linear programming (MILP) model via duality theory and affine decision rules. Numerical case studies illustrate the effectiveness and scalability of the proposed DRO-based coordinated expansion planning model.