Bilevel programming for price-based electricity auctions: a revenue-constrained case
This paper describes the application of bilevel programming to a class of real-life problems in the field of electric power systems. Within the context of electricity markets, market-clearing procedures, i.e., auction models, are used by an independent entity to schedule generation offers and consum...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2015
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/11fda7da436246b885176e9eb24555a5 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | This paper describes the application of bilevel programming to a class of real-life problems in the field of electric power systems. Within the context of electricity markets, market-clearing procedures, i.e., auction models, are used by an independent entity to schedule generation offers and consumption bids as well as to determine market-clearing prices. This paper addresses a mathematically challenging type of auction, denoted as price-based market clearing, wherein, as a distinctive feature, market-clearing prices are explicitly incorporated in the formulation of the optimization process. This paper shows that bilevel programming provides a suitable modeling framework for price-based market clearing. Furthermore, based on practical modeling aspects, an equivalent single-level primal-dual transformation into a mixed-integer program can be implemented. Such transformation relies on the application of duality theory of linear programming. The bilevel programming framework for price-based market clearing is applied to a revenue-constrained auction model similar to those used in several European electricity markets. As a major contribution, bilinear terms associated with both generation revenue constraints and the duality-based transformation are equivalently converted into linear forms with no additional binary variables. Simulation results show the effective performance of the proposed approach and its superiority over current industry practice. |
|---|