Hybrid uncertainty-based offering strategy for virtual power plants

Arman Alahyari, Mehdi Ehsan, David Pozo, Meisam Farrokhifar

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

This study proposes an optimal day-ahead (DA) electricity market offering model for a virtual power plant (VPP) formed by a mix of renewable distributed energy resources along with energy storage, such as electric vehicles. Two sources of uncertainty are considered, namely, wind power generation, modelled by an uncertainty set, and DA market price, modelled by scenarios. Opposite to classical robust optimisation approaches, the authors model maps minimal (worst-case) profits to a conservativeness parameter, while the classical robust optimisation maps conservativeness parameter to worst-case profits. In this regard, by using their optimisation framework, a VPP operator only deals with setting a minimum-profit constraint, which is more sensible and easy for interpretation, while the required conservativeness is endogenously determined. The proposed mathematical model for constructing the offering curve is a hierarchical four-level robust optimisation problem. The first level represents the optimal decision on the price-quantity offer bids; the second- and third-level relate to the optimal identification of conservativeness parameter; and the fourth-level represents the optimal operation of the VPP managed assets. The four-level model is reformulated as a single-level mixed-integer linear programming problem. The proposed approach and its applicability are verified using numerical simulations.

Original languageEnglish
Pages (from-to)2359-2366
Number of pages8
JournalIET Renewable Power Generation
Volume14
Issue number13
DOIs
Publication statusPublished - 5 Oct 2020

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