Wasserstein Distributionally Robust Look-Ahead Economic Dispatch
| Autor: | Duncan S. Callaway, Saverio Bolognani, Ashish R. Hota, Ashish Cherukuri, Bala Kameshwar Poolla |
|---|---|
| Přispěvatelé: | Optimization and Decision Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
distributionally robust optimization Computer science 020209 energy Energy Engineering and Power Technology Systems and Control (eess.SY) 02 engineering and technology data-driven approaches Electrical Engineering and Systems Science - Systems and Control Order (exchange) FOS: Mathematics FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Production (economics) Chance-constrained optimization Conditional-value-at-risk Data-driven approaches Distributionally robust optimization Optimal power flow optimal power flow Electrical and Electronic Engineering Mathematics - Optimization and Control Economic dispatch Robust optimization Decision problem Optimization and Control (math.OC) Scalability Probability distribution Look-ahead conditional-value-at-risk |
| Zdroj: | IEEE Transactions on Power Systems, 36 (3) IEEE Transactions on Power Systems, 36(3):9242289, 2010-2022. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| ISSN: | 0885-8950 1558-0679 |
| Popis: | 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. © 2021 IEEE IEEE Transactions on Power Systems, 36 (3) ISSN:0885-8950 ISSN:1558-0679 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|