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Optimal operation of a virtual power plant in frequency constrained electricity market
Mousavi, M ; Sharif University of Technology | 2019
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- Type of Document: Article
- DOI: 10.1049/iet-gtd.2018.5204
- Publisher: Institution of Engineering and Technology , 2019
- Abstract:
- In this study, optimal offering strategy problem of a virtual power plant (VPP) as a price-maker player in day-ahead frequency constrained electricity market is presented. The optimal offering strategy problem is modelled as a bi-level optimisation problem. In the upper-level problem, the total profit of VPP is maximised. In the lower-level problem, the clearing conditions of frequency constrained electricity market are modelled. The proposed bi-level optimisation problem is reformulated as a mathematical programming with equilibrium constraints (MPEC) problem by using Karush-Kuhn-Tucker conditions. Then, the proposed MPEC problem, which is non-linear and hard to solve by commercial solvers, is transformed into a mixed-integer linear programming problem by using strong duality theorem and big-number mathematical technique. Here, stochastic optimisation is included in the modelling to enable the VPP for optimisation in the presence of uncertainties, e.g. renewable energy source generations, demands, and offering strategies of rivals. Finally, the effectiveness of proposed model is investigated by implementing it on various case studies. © The Institution of Engineering and Technology
- Keywords:
- Linear programming ; Optimisation ; Pricing ; Renewable energy sources ; Commerce ; Costs ; Electric industry ; Integer programming ; Linear programming ; Mathematical programming ; Natural resources ; Power plants ; Renewable energy resources ; Stochastic systems ; Uncertainty analysis ; Duality (mathematics) ; Karush Kuhn tucker condition ; Mathematical programming with equilibrium constraints ; Mixed integer linear programming problems ; Optimisations ; Renewable energy source ; Strong duality theorems ; Virtual power plants (VPP) ; Power markets
- Source: IET Generation, Transmission and Distribution ; Volume 13, Issue 11 , 2019 , Pages 2015-2023 ; 17518687 (ISSN)
- URL: https://ieeexplore.ieee.org/document/8746908