Wasserstein Distributionally Robust Look-Ahead Economic Dispatch

• Published in: IEEE transactions on power systems Vol. 36; no. 3; pp. 2010 - 2022
• Main Authors: Poolla, Bala Kameshwar, Hota, Ashish R., Bolognani, Saverio, Callaway, Duncan S., Cherukuri, Ashish
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Chance-constrained optimization; conditional-value-at-risk; Constraints; data-driven approaches; distributionally robust optimization; Economics; Generators; Operators (mathematics); optimal power flow; Optimization; Power generation; Renewable energy sources; Robustness (mathematics); Transmission line matrix methods; Uncertainty
• DOI: 10.1109/TPWRS.2020.3034488
• ISSN: 0885-8950

We consider the problem of look-ahead economic dispatch (LAED) with uncertain renewable energy generation. The goal of this problem is to minimize the cost of conventional energy generation subject to uncertain operational constraints. The risk of violating these constraints must be below a given threshold for a family of probability distributions with characteristics similar to observed past data or predictions. We present two data-driven approaches based on two novel mathematical reformulations of this distributionally robust decision problem. The first one is a tractable convex program in which the uncertain constraints are defined via the distributionally robust conditional-value-at-risk. The second one is a scalable robust optimization program that yields an approximate distributionally robust chance-constrained LAED. Numerical experiments on the IEEE 39-bus system with real solar production data and forecasts illustrate the effectiveness of these approaches. We discuss how system operators should tune these techniques in order to seek the desired robustness-performance trade-off and we compare their computational scalability.