Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach

Jannat Ul Ain Binte Wasif Ali; Syed Ali Abbas Kazmi; Abdullah Altamimi; Zafar A. Khan; Omar Alrumayh; M. Mahad Malik

doi:10.1109/ACCESS.2022.3169707

IEEE Access (Jan 2022)

Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach

Jannat Ul Ain Binte Wasif Ali,
Syed Ali Abbas Kazmi,
Abdullah Altamimi,
Zafar A. Khan,
Omar Alrumayh,
M. Mahad Malik

Affiliations

Jannat Ul Ain Binte Wasif Ali: U.S. Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, Pakistan
Syed Ali Abbas Kazmi: U.S. Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, Pakistan
Abdullah Altamimi: ORCiD; Department of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah, Saudi Arabia
Zafar A. Khan: ORCiD; Department of Electrical Engineering, Mirpur University of Science and Technology, Mirpur, Pakistan
Omar Alrumayh: ORCiD; Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah, Saudi Arabia
M. Mahad Malik: U.S. Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, Pakistan

DOI: https://doi.org/10.1109/ACCESS.2022.3169707
Journal volume & issue: Vol. 10
pp. 50062 – 50077

Abstract

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A virtual power plant (VPP) is a cloud based distributed power plant that aggregates the capacities of diverse distributed energy resources (DERs) for the purpose of enhancing power generation as well as trading or selling power on the electricity market. The main issue faced while working on VPPs is energy management. Smart energy management of a VPP is a complex problem due to the coordinated operation of diverse energy resources and their associated uncertainties. This research paper proposes a real-time (RT) smart energy management model for a VPP using a multi-objective, multi-level optimization-based approach. The VPP consists of a solar, wind and thermal power unit, along with an energy storage unit and some flexible demands. The term multi-level refers to three different energy levels depicted as three homes comprising of different amounts of loads. RT operation of a VPP is enabled by exploiting the bidirectional communication infrastructure. Multi-objective RT smart energy management is implemented on a community-based dwelling system using three alternative algorithms i.e., hybrid optimal stopping rule (H-OSR), hybrid particle swarm optimization (H-PSO) and advanced multi-objective grey wolf optimization (AMO-GWO). The proposed technique focuses on achieving the objectives of optimal load scheduling, real-time pricing, efficient energy consumption, emission reduction, cost minimization and maximization of customer comfort altogether. A comparative analysis is performed among the three algorithms in which the calculated real-time prices are compared with each other. It is observed that on average H-PSO performs 7.86 % better than H-OSR whereas AMO-GWO performs 10.49% better than H-OSR and 5.7% better than H-P-SO. This paper concludes that AMO-GWO is the briskest, most economical, and efficient optimization algorithm for RT smart energy management of a VPP.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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