Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach
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 i...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9761880/ |
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| author | Jannat Ul Ain Binte Wasif Ali Syed Ali Abbas Kazmi Abdullah Altamimi Zafar A. Khan Omar Alrumayh M. Mahad Malik |
| author_facet | Jannat Ul Ain Binte Wasif Ali Syed Ali Abbas Kazmi Abdullah Altamimi Zafar A. Khan Omar Alrumayh M. Mahad Malik |
| author_sort | Jannat Ul Ain Binte Wasif Ali |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T02:47:26Z |
| format | Article |
| id | doaj.art-471c2af9ede64db19b79003528b683f3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T02:47:26Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-471c2af9ede64db19b79003528b683f32022-12-22T00:40:58ZengIEEEIEEE Access2169-35362022-01-0110500625007710.1109/ACCESS.2022.31697079761880Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based ApproachJannat Ul Ain Binte Wasif Ali0Syed Ali Abbas Kazmi1Abdullah Altamimi2https://orcid.org/0000-0002-0236-7872Zafar A. Khan3https://orcid.org/0000-0003-3149-6865Omar Alrumayh4https://orcid.org/0000-0002-3412-2853M. Mahad Malik5U.S. Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, PakistanU.S. Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, PakistanDepartment of Electrical Engineering, College of Engineering, Majmaah University, Al-Majmaah, Saudi ArabiaDepartment of Electrical Engineering, Mirpur University of Science and Technology, Mirpur, PakistanDepartment of Electrical Engineering, College of Engineering, Qassim University, Unaizah, Saudi ArabiaU.S. Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, PakistanA 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.https://ieeexplore.ieee.org/document/9761880/Hybrid optimal stopping rulehybrid particle swarm optimizationJADEmulti-agent systemmulti-objective grey wolf optimizationsmart energy management |
| spellingShingle | Jannat Ul Ain Binte Wasif Ali Syed Ali Abbas Kazmi Abdullah Altamimi Zafar A. Khan Omar Alrumayh M. Mahad Malik Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach IEEE Access Hybrid optimal stopping rule hybrid particle swarm optimization JADE multi-agent system multi-objective grey wolf optimization smart energy management |
| title | Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach |
| title_full | Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach |
| title_fullStr | Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach |
| title_full_unstemmed | Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach |
| title_short | Smart Energy Management in Virtual Power Plant Paradigm With a New Improved Multilevel Optimization Based Approach |
| title_sort | smart energy management in virtual power plant paradigm with a new improved multilevel optimization based approach |
| topic | Hybrid optimal stopping rule hybrid particle swarm optimization JADE multi-agent system multi-objective grey wolf optimization smart energy management |
| url | https://ieeexplore.ieee.org/document/9761880/ |
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