Modeling, Optimization, and Analysis of a Virtual Power Plant Demand Response Mechanism for the Internal Electricity Market Considering the Uncertainty of Renewable Energy Sources

Zahid Ullah, Arshad and Hany Hassanin
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Zahid Ullah: Institute for Globally Distributed Open Research and Education (IGDORE), Cleveland, Middlesbrough TS1 4JE, UK
Arshad: School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK
Hany Hassanin: School of Engineering, Technology, and Design, Canterbury Christ Church University, Canterbury CT1 1QU, UK

Energies, 2022, vol. 15, issue 14, 1-16

Abstract: The penetration of renewable energy sources (RESs) in the electrical power system has increased significantly over the past years due to increasing global concern about climate change. However, integrating RESs into the power market is highly problematic. The output of RESs such as wind turbines (WTs) and photovoltaics (PVs) is highly uncertain. Their correlation with load demand is not always guaranteed, which compromises system reliability. Distributed energy resources (DERs), especially demand response (DR) programs and energy storage systems (ESSs), are possible options to overcome these operational challenges under the virtual power plant (VPP) setting. This study investigates the impact of using a DR program and battery energy storage system (BESS) on the VPP’s internal electricity market, and also cost-minimization analysis from a utility viewpoint. Three different constrained optimal power flow (OPF) problems are solved such as base case, DR case, and BESS case to determine total incurred costs, locational marginal prices (LMPs), and generator commitments. A scenario tree approach is used to model the uncertainties associated with WTs, PVs, and load demand. The proposed model is investigated on a 14-bus distribution system. The simulation results obtained demonstrate a favorable impact of DR and a BESS on renewable operational challenges.

Keywords: renewable energy sources; demand response; energy storage; electricity market; uncertainty modeling; locational marginal prices; modeling; optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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