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Optimisation and Management of Virtual Power Plants Energy Mix Trading Model

1Institute for Globally Distributed Open Research and Education (IGDORE), Cleveland, Middlesbrough, TS1 4JE, United Kingdom

2Mehran University of Science and Technology, Indus Hwy, Jamshoro Sindh, 76062, Pakistan

3University of Bradford, Richmond Rd, Bradford, BD7 1DP, United Kingdom

Received: 19 Jun 2021; Revised: 31 Aug 2021; Accepted: 30 Sep 2021; Available online: 5 Oct 2021; Published: 1 Feb 2022.
Editor(s): Grigorios Kyriakopoulos
Open Access Copyright (c) 2022 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
. In this study, a robust optimisation method (ROM) is proposed with aim to achieve optimal scheduling of virtual power plants (VPPs) in the day-ahead electricity markets where electricity prices are highly uncertain. Our VPP is a collection of various distributed energy resources (DERs), flexible loads, and energy storage systems that are coordinated and operated as a single entity. In this study, an offer and bid-based energy trading mechanism is proposed where participating members in the VPP setting can sell or buy to/from the day-ahead electricity market to maximise social welfare (SW). SW is defined as the maximisation of end-users benefits and minimisation of energy costs. The optimisation problem is solved as a mixed-integer linear programming model taking the informed decisions at various levels of uncertainty of the market prices. The benefits of the proposed approach are consistency in solution accuracy and traceability due to less computational burden and this would be beneficial for the VPP operators. The robustness of the proposed mathematical model and method is confirmed in a case study approach using a distribution system with 18-buses. Simulation results illustrate that in the highest robustness scenario, profit is reduced marginally, however, the VPP showed robustness towards the day-ahead market (DAM) price uncertainty
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Keywords: Renewable energies, Distributed energy resources; Power systems integration; Electricity market

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