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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Veröffentlicht in:Results in engineering Jg. 22; S. 102265
Hauptverfasser: El-Afifi, Magda I., Sedhom, Bishoy E., Eladl, Abdelfattah A., Elgamal, Mohamed, Siano, Pierluigi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2024
Elsevier
Schlagworte:
Archimedes optimization algorithm
Day-ahead and real-time scheduling
Demand side management
Genetic algorithm
Smart homes
Demand side management
Archimedes optimization algorithm
Genetic algorithm
Smart homes
Day-ahead and real-time scheduling
ISSN:2590-1230, 2590-1230
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Zusammenfassung: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.
ISSN:2590-1230
2590-1230
DOI:10.1016/j.rineng.2024.102265