Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Demand response scheduling algorithm for smart residential communities considering heterogeneous energy consumption
https://orcid.org/0000-0002-8001-8352
Abstract Smart residential communities tend to play an important role in demand response technologies due to their greater DR potential compared to single homes in smart grids. However, the heterogeneity in energy consumption between residents in a community leads to the low effectiveness of one-size-fits-all DR scheduling for a single home. To reflect the heterogeneity of energy consumption among residents and realize the demand response scheduling for a community, a centralized DR scheduling algorithm was presented. It introduces the willingness to pay to quantify the heterogeneity of residents' energy consumption by making full use of fuzzy clustering. Then, the quantized willingness to pay is applied to a Nash equilibrium game framework to maximize the individual surplus of residents, better adapting to all the demands of the residents in the community. Simulation results show that compared with the demand response scheduling algorithm without WTP, the presented demand response scheduling algorithm with WTP can reduce the amount of transferred data by about 30%. Compared with a demand response algorithm using game theory, the presented demand response scheduling algorithm can reduce the energy cost of the community by about 10% and satisfy the diversified demands of the residents while maintaining a small peaking-to-average ratio. © 2022 Elsevier Science. All rights reserved.
Demand response scheduling algorithm for smart residential communities considering heterogeneous energy consumption
https://orcid.org/0000-0002-8001-8352
Abstract Smart residential communities tend to play an important role in demand response technologies due to their greater DR potential compared to single homes in smart grids. However, the heterogeneity in energy consumption between residents in a community leads to the low effectiveness of one-size-fits-all DR scheduling for a single home. To reflect the heterogeneity of energy consumption among residents and realize the demand response scheduling for a community, a centralized DR scheduling algorithm was presented. It introduces the willingness to pay to quantify the heterogeneity of residents' energy consumption by making full use of fuzzy clustering. Then, the quantized willingness to pay is applied to a Nash equilibrium game framework to maximize the individual surplus of residents, better adapting to all the demands of the residents in the community. Simulation results show that compared with the demand response scheduling algorithm without WTP, the presented demand response scheduling algorithm with WTP can reduce the amount of transferred data by about 30%. Compared with a demand response algorithm using game theory, the presented demand response scheduling algorithm can reduce the energy cost of the community by about 10% and satisfy the diversified demands of the residents while maintaining a small peaking-to-average ratio. © 2022 Elsevier Science. All rights reserved.
Demand response scheduling algorithm for smart residential communities considering heterogeneous energy consumption
https://orcid.org/0000-0002-8001-8352
Abstract Smart residential communities tend to play an important role in demand response technologies due to their greater DR potential compared to single homes in smart grids. However, the heterogeneity in energy consumption between residents in a community leads to the low effectiveness of one-size-fits-all DR scheduling for a single home. To reflect the heterogeneity of energy consumption among residents and realize the demand response scheduling for a community, a centralized DR scheduling algorithm was presented. It introduces the willingness to pay to quantify the heterogeneity of residents' energy consumption by making full use of fuzzy clustering. Then, the quantized willingness to pay is applied to a Nash equilibrium game framework to maximize the individual surplus of residents, better adapting to all the demands of the residents in the community. Simulation results show that compared with the demand response scheduling algorithm without WTP, the presented demand response scheduling algorithm with WTP can reduce the amount of transferred data by about 30%. Compared with a demand response algorithm using game theory, the presented demand response scheduling algorithm can reduce the energy cost of the community by about 10% and satisfy the diversified demands of the residents while maintaining a small peaking-to-average ratio. © 2022 Elsevier Science. All rights reserved.
Demand response scheduling algorithm for smart residential communities considering heterogeneous energy consumption
Fan, X.M. (Autor:in) / Li, X.H. (Autor:in) / Ding, Y.M. (Autor:in) / He, J. (Autor:in) / Zhao, M. (Autor:in)
Energy and Buildings ; 279
22.11.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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