An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data Clustering
Background and Objectives: Different energy demand calls the need for utilizing Energy Hub Systems (EHS), but the economic dispatch issue has become complicated due to uncertainty in demand. So, scenario generation and reduction techniques are used to considering the uncertainty of the EH demand. De...
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| Format: | Article |
| Language: | English |
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Shahid Rajaee Teacher Training University
2020-07-01
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| Series: | Journal of Electrical and Computer Engineering Innovations |
| Subjects: | |
| Online Access: | https://jecei.sru.ac.ir/article_1468_aa44c69b3aa979184170309e6b775a71.pdf |
| _version_ | 1811306028626608128 |
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| author | H. Hosseinnejad S. Galvani P. Alemi |
| author_facet | H. Hosseinnejad S. Galvani P. Alemi |
| author_sort | H. Hosseinnejad |
| collection | DOAJ |
| description | Background and Objectives: Different energy demand calls the need for utilizing Energy Hub Systems (EHS), but the economic dispatch issue has become complicated due to uncertainty in demand. So, scenario generation and reduction techniques are used to considering the uncertainty of the EH demand. Dependent on the amount of fuel used, each system has various generation costs. Configuration selection stands as a challenging dilemma in the EHS designing besides economic problems. In this paper, the optimal EHS operation along with configuration issue is tackled.Methods: To do so, two EHS types are investigated to evaluate the configuration effect besides energy prices simultaneously change. Typically, the effect of the Demand Response (DR) feature is rarely considered in EHSs management which considered in this paper. Also, Metaheuristic Automatic Data Clustering (MADC) is used to reduce the decision-making problem dimension instead of using human decision makers in the subject of cluster center numbers and considering uncertainty. The "Shannon's Entropy" and the "TOPSIS" methods are also used in the decision-making. The study is carried out in MATLAB© and GAMS©.Results: In addition to minimizing the computational burden, the proposed EHS not only serves an enhancement in benefit by reducing the cost but also provides a semi-flat load curve in peak period by employing Emergency Demand Response Program (EDRP) and Time of Use (TOU).Conclusion: The results show that significant computational burden reduction is possible in the field of demand data by using automatic clustering method without human interference. In addition to the proposed configuration's results betterment, the approach demonstrated EH's configuration effect could consider as important as other features in the presence of DRPs for reaching desires of EHs customers which rarely considered. Also, "Shannon's Entropy" and the "TOPSIS" methods integration could select the best DRP scenario without human interference. The results of this study are encouraging and warrant further analysis and researches. |
| first_indexed | 2024-04-13T08:37:00Z |
| format | Article |
| id | doaj.art-bcd0739a75b541afac00194ae55ac458 |
| institution | Directory Open Access Journal |
| issn | 2322-3952 2345-3044 |
| language | English |
| last_indexed | 2024-04-13T08:37:00Z |
| publishDate | 2020-07-01 |
| publisher | Shahid Rajaee Teacher Training University |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering Innovations |
| spelling | doaj.art-bcd0739a75b541afac00194ae55ac4582022-12-22T02:54:03ZengShahid Rajaee Teacher Training UniversityJournal of Electrical and Computer Engineering Innovations2322-39522345-30442020-07-018223325410.22061/jecei.2020.7240.3751468An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data ClusteringH. Hosseinnejad0S. Galvani1P. Alemi2Department of Power Engineering, Urmia Branch, Islamic Azad University, Urmia, IranDepartment of Power Engineering, Urmia Branch, Islamic Azad University, Urmia, IranDepartment of Power Engineering, Urmia Branch, Islamic Azad University, Urmia, IranBackground and Objectives: Different energy demand calls the need for utilizing Energy Hub Systems (EHS), but the economic dispatch issue has become complicated due to uncertainty in demand. So, scenario generation and reduction techniques are used to considering the uncertainty of the EH demand. Dependent on the amount of fuel used, each system has various generation costs. Configuration selection stands as a challenging dilemma in the EHS designing besides economic problems. In this paper, the optimal EHS operation along with configuration issue is tackled.Methods: To do so, two EHS types are investigated to evaluate the configuration effect besides energy prices simultaneously change. Typically, the effect of the Demand Response (DR) feature is rarely considered in EHSs management which considered in this paper. Also, Metaheuristic Automatic Data Clustering (MADC) is used to reduce the decision-making problem dimension instead of using human decision makers in the subject of cluster center numbers and considering uncertainty. The "Shannon's Entropy" and the "TOPSIS" methods are also used in the decision-making. The study is carried out in MATLAB© and GAMS©.Results: In addition to minimizing the computational burden, the proposed EHS not only serves an enhancement in benefit by reducing the cost but also provides a semi-flat load curve in peak period by employing Emergency Demand Response Program (EDRP) and Time of Use (TOU).Conclusion: The results show that significant computational burden reduction is possible in the field of demand data by using automatic clustering method without human interference. In addition to the proposed configuration's results betterment, the approach demonstrated EH's configuration effect could consider as important as other features in the presence of DRPs for reaching desires of EHs customers which rarely considered. Also, "Shannon's Entropy" and the "TOPSIS" methods integration could select the best DRP scenario without human interference. The results of this study are encouraging and warrant further analysis and researches.https://jecei.sru.ac.ir/article_1468_aa44c69b3aa979184170309e6b775a71.pdfeconomic dispatchenergy hub (eh)configurationdemand responsemetaheuristic automatic data clustering (madc) |
| spellingShingle | H. Hosseinnejad S. Galvani P. Alemi An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data Clustering Journal of Electrical and Computer Engineering Innovations economic dispatch energy hub (eh) configuration demand response metaheuristic automatic data clustering (madc) |
| title | An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data Clustering |
| title_full | An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data Clustering |
| title_fullStr | An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data Clustering |
| title_full_unstemmed | An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data Clustering |
| title_short | An Efficient Configuration for Energy Hub to Peak Reduction Considering Demand Response Using Metaheuristic Automatic Data Clustering |
| title_sort | efficient configuration for energy hub to peak reduction considering demand response using metaheuristic automatic data clustering |
| topic | economic dispatch energy hub (eh) configuration demand response metaheuristic automatic data clustering (madc) |
| url | https://jecei.sru.ac.ir/article_1468_aa44c69b3aa979184170309e6b775a71.pdf |
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