Systematic Categorization of Optimization Strategies for Virtual Power Plants

Amit Kumer Podder, Sayemul Islam, Nallapaneni Manoj Kumar, Aneesh A. Chand, Pulivarthi Nageswara Rao, Kushal A. Prasad, T. Logeswaran and Kabir A. Mamun
Additional contact information
Amit Kumer Podder: Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
Sayemul Islam: Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
Nallapaneni Manoj Kumar: School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong
Aneesh A. Chand: School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
Pulivarthi Nageswara Rao: Department of Electrical Electronics and Communication Engineering, Gandhi Institute of Technology and Management (Deemed to be University), Visakhapatnam 530045, Andhra Pradesh, India
Kushal A. Prasad: School of Engineering and Physics, The University of the South Pacific, Suva, Fiji
T. Logeswaran: Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Erode 638060, Tamil Nadu, India
Kabir A. Mamun: School of Engineering and Physics, The University of the South Pacific, Suva, Fiji

Energies, 2020, vol. 13, issue 23, 1-46

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.

Keywords: virtual power plants; digital electricity; optimization strategies; distributed energy resources; renewable energy resources; energy management; energy scheduling; distributed generation; real-time energy markets; electricity market; demand response; optimization in virtual power plants; price-based unit commitment model; intelligent technique in power management; day-ahead scheduling (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/23/6251/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/23/6251/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:23:p:6251-:d:452165

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().