Modeling, optimization, and analysis of a virtual power plant demand response mechanism for the internal electricity market considering the uncertainty of renewable energy sources

Journal article

Ullah, Z., Arshad and Hassanin, H. 2022. Modeling, optimization, and analysis of a virtual power plant demand response mechanism for the internal electricity market considering the uncertainty of renewable energy sources. Energies. 15 (14), p. 5296. https://doi.org/doi.org/10.3390/en15145296
AuthorsUllah, Z., Arshad and Hassanin, H.
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
Year2022
JournalEnergies
Journal citation15 (14), p. 5296
PublisherMDPI
ISSN1996-1073
Digital Object Identifier (DOI)https://doi.org/doi.org/10.3390/en15145296
Official URLhttps://www.mdpi.com/1996-1073/15/14/5296/htm
Publication dates
Print21 Jul 2022
Publication process dates
Accepted20 Jul 2022
Deposited25 Jul 2022
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Open
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References

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