Article ID Journal Published Year Pages File Type
479756 European Journal of Operational Research 2015 19 Pages PDF
Abstract

•Optimization of the self-scheduling, and market strategy of an electricity producer.•An adaptive two-stage robust optimization model is proposed, two variants are considered.•Two case studies are analyzed, and several instances are investigated.•Risk analysis are made by solving each instance for multiple values of budget parameters.•The two variants of the algorithms have a similar performance, with some exceptions.

This paper addresses the optimization under uncertainty of the self-scheduling, forward contracting, and pool involvement of an electricity producer operating a mixed power generation station, which combines thermal, hydro and wind sources, and uses a two stage adaptive robust optimization approach. In this problem the wind power production and the electricity pool price are considered to be uncertain, and are described by uncertainty convex sets. To solve this problem, two variants of a constraint generation algorithm are proposed, and their application and characteristics discussed. Both algorithms are used to solve two case studies based on two producers, each operating equivalent generation units, differing only in the thermal units’ characteristics. Their market strategies are investigated for three different scenarios, corresponding to as many instances of electricity price forecasts. The effect of the producers’ approach, whether conservative or more risk prone, is also investigated by solving each instance for multiple values of the so-called budget parameter. It was possible to conclude that this parameter influences markedly the producers’ strategy, in terms of scheduling, profit, forward contracting, and pool involvement. These findings are presented and analyzed in detail, and an attempted rationale is proposed to explain the less intuitive outcomes. Regarding the computational results, these show that for some instances, the two variants of the algorithms have a similar performance, while for a particular subset of them one variant has a clear superiority.

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Physical Sciences and Engineering Computer Science Computer Science (General)
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