LAPSE:2023.27872
Published Article

LAPSE:2023.27872
Systematic Categorization of Optimization Strategies for Virtual Power Plants
April 11, 2023
Abstract
Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.
Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development.
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Keywords
day-ahead scheduling, demand response, digital electricity, distributed energy resources, distributed generation, electricity market, energy management, energy scheduling, intelligent technique in power management, optimization in virtual power plants, optimization strategies, price-based unit commitment model, real-time energy markets, renewable energy resources, virtual power plants
Subject
Suggested Citation
Podder AK, Islam S, Kumar NM, Chand AA, Rao PN, Prasad KA, Logeswaran T, Mamun KA. Systematic Categorization of Optimization Strategies for Virtual Power Plants. (2023). LAPSE:2023.27872
Author Affiliations
Podder AK: Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh [ORCID]
Islam S: Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh [ORCID]
Kumar NM: School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong [ORCID]
Chand AA: School of Engineering and Physics, The University of the South Pacific, Suva, Fiji [ORCID]
Rao PN: Department of Electrical Electronics and Communication Engineering, Gandhi Institute of Technology and Management (Deemed to be University), Visakhapatnam 530045, Andhra Pradesh, India
Prasad KA: School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
Logeswaran T: Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Erode 638060, Tamil Nadu, India
Mamun KA: School of Engineering and Physics, The University of the South Pacific, Suva, Fiji [ORCID]
Islam S: Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh [ORCID]
Kumar NM: School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong [ORCID]
Chand AA: School of Engineering and Physics, The University of the South Pacific, Suva, Fiji [ORCID]
Rao PN: Department of Electrical Electronics and Communication Engineering, Gandhi Institute of Technology and Management (Deemed to be University), Visakhapatnam 530045, Andhra Pradesh, India
Prasad KA: School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
Logeswaran T: Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Erode 638060, Tamil Nadu, India
Mamun KA: School of Engineering and Physics, The University of the South Pacific, Suva, Fiji [ORCID]
Journal Name
Energies
Volume
13
Issue
23
Article Number
E6251
Year
2020
Publication Date
2020-11-27
ISSN
1996-1073
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Original Submission
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PII: en13236251, Publication Type: Review
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