Demand side management strategy for smart building using multi-objective hybrid optimization technique
This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load d...
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| Vydané v: | Results in engineering Ročník 22; s. 102265 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.06.2024
Elsevier |
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| ISSN: | 2590-1230, 2590-1230 |
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| Abstract | This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and real-time basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %.
•HAG efficiently manages appliances for cost savings and user safety.•Dynamic programming optimizes appliance scheduling in real-time.•MKP framework explores optimal solutions for energy management.•Minimizes energy costs, CO2 emissions, and power demands. |
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| AbstractList | This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and real-time basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %. This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and real-time basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %. •HAG efficiently manages appliances for cost savings and user safety.•Dynamic programming optimizes appliance scheduling in real-time.•MKP framework explores optimal solutions for energy management.•Minimizes energy costs, CO2 emissions, and power demands. |
| ArticleNumber | 102265 |
| Author | El-Afifi, Magda I. Siano, Pierluigi Sedhom, Bishoy E. Elgamal, Mohamed Eladl, Abdelfattah A. |
| Author_xml | – sequence: 1 givenname: Magda I. surname: El-Afifi fullname: El-Afifi, Magda I. organization: Dept. of Electrical Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt – sequence: 2 givenname: Bishoy E. orcidid: 0000-0001-9223-694X surname: Sedhom fullname: Sedhom, Bishoy E. organization: Dept. of Electrical Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt – sequence: 3 givenname: Abdelfattah A. surname: Eladl fullname: Eladl, Abdelfattah A. organization: Dept. of Electrical Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt – sequence: 4 givenname: Mohamed surname: Elgamal fullname: Elgamal, Mohamed organization: Dept. of Electrical Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt – sequence: 5 givenname: Pierluigi surname: Siano fullname: Siano, Pierluigi email: psiano@unisa.it organization: Department of Management & Innovation Systems, University of Salerno, Fisciano, (SA), 84084, Italy |
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| Keywords | Demand side management Archimedes optimization algorithm Genetic algorithm Smart homes Day-ahead and real-time scheduling |
| Language | English |
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