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Hybrid uncertainty-based offering strategy for virtual power plants
Alahyari, A ; Sharif University of Technology | 2020
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- Type of Document: Article
- DOI: 10.1049/iet-rpg.2020.0249
- Publisher: Institution of Engineering and Technology , 2020
- 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. © The Institution of Engineering and Technology 2020
- Keywords:
- Electric energy storage ; Electric power generation ; Electric power plants ; Integer programming ; Profitability ; Wind power ; Distributed Energy Resources ; Mixed integer linear programming problems ; Optimal decisions ; Optimal operation ; Optimisations ; Robust optimisation ; Virtual power plants (VPP) ; Power markets
- Source: IET Renewable Power Generation ; Volume 14, Issue 13 , 2020 , Pages 2359-2366
- URL: https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-rpg.2020.0249
