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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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.

Original languageEnglish
Pages (from-to)5635-5647
Number of pages13
JournalIEEE Transactions on Power Systems
Volume38
Issue number6
DOIs
StatePublished - 1 Nov 2023

Keywords

  • Generation expansion planning
  • Wasserstein distance
  • concentrating solar power
  • demand-side resources
  • distributionally robust chance-constrained optimization
  • unit clustering

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