Demand Response Potential Day-Ahead Forecasting Approach Based on LSSA-BPNN Considering the Electricity-Carbon Coupling Incentive Effects

In order to address the challenges of global climate change and to achieve the goals of sustainable development, the need for a low-carbon transition of the power system is becoming more urgent. However, previous demand response (DR) potential forecasting models for load aggregators (LAs) only consi...

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Vydané v:	IEEE transactions on industry applications Ročník 60; číslo 3; s. 4505 - 4516
Hlavní autori:	Zhang, Yiran, Ge, Xinxin, Li, Meiyi, Li, Nan, Wang, Fei, Wang, Liyong, Sun, Qinfei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Back propagation networks Biological system modeling Carbon Carbon dioxide Carbon emissions Climate change Customers Data models Day-ahead forecasting Demand response demand response potential Economic impact Electric potential Electric power demand Electric power systems Electricity electricity-carbon coupling Electrode potentials Emissions Energy management Forecasting Home automation load aggregator Load modeling low-carbon Low-carbon economy Neural networks Power consumption Power system planning Redundancy Residential energy Search algorithms Sustainable development
ISSN:	0093-9994, 1939-9367
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Abstract	In order to address the challenges of global climate change and to achieve the goals of sustainable development, the need for a low-carbon transition of the power system is becoming more urgent. However, previous demand response (DR) potential forecasting models for load aggregators (LAs) only consider the potential for customers to adjust their loads, without fully exploiting the potential to reduce their carbon emissions. To this end, a logistic sparrow search algorithm- back propagation neural network (LSSA-BPNN) based DR potential and carbon reduction potential forecasting model for LAs is proposed in this paper, which considers the dual incentive of electricity and carbon. First, a novel home energy management system (HEMS) model is developed, which considers the dual incentive of electricity and carbon. The model's objective function consists of two components: minimizing economic costs and reducing carbon emissions. Weight coefficients are introduced to characterize the power consumption preferences of different customer types. Subsequently, simulations are conducted to generate data related to DR potential and carbon reduction potential of LAs. Second, the multiple influencing features of DR potential and carbon reduction potential are sorted according to the degree of importance by the RF model respectively, which could reduce the redundancy of input features. Finally, based on the selected features, the LSSA-BPNN model is established to obtain the forecasting results of the DR potential and carbon reduction potential for LAs. The effectiveness and superiority of the proposed model have been verified using a real dataset in Austin.
AbstractList	In order to address the challenges of global climate change and to achieve the goals of sustainable development, the need for a low-carbon transition of the power system is becoming more urgent. However, previous demand response (DR) potential forecasting models for load aggregators (LAs) only consider the potential for customers to adjust their loads, without fully exploiting the potential to reduce their carbon emissions. To this end, a logistic sparrow search algorithm- back propagation neural network (LSSA-BPNN) based DR potential and carbon reduction potential forecasting model for LAs is proposed in this paper, which considers the dual incentive of electricity and carbon. First, a novel home energy management system (HEMS) model is developed, which considers the dual incentive of electricity and carbon. The model's objective function consists of two components: minimizing economic costs and reducing carbon emissions. Weight coefficients are introduced to characterize the power consumption preferences of different customer types. Subsequently, simulations are conducted to generate data related to DR potential and carbon reduction potential of LAs. Second, the multiple influencing features of DR potential and carbon reduction potential are sorted according to the degree of importance by the RF model respectively, which could reduce the redundancy of input features. Finally, based on the selected features, the LSSA-BPNN model is established to obtain the forecasting results of the DR potential and carbon reduction potential for LAs. The effectiveness and superiority of the proposed model have been verified using a real dataset in Austin.
Author	Wang, Fei Li, Meiyi Li, Nan Zhang, Yiran Sun, Qinfei Wang, Liyong Ge, Xinxin
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Cites_doi	10.1109/TEVC.2019.2901012 10.1016/j.energy.2020.117885 10.1109/TIA.2020.3014575 10.1109/TNNLS.2020.3042434 10.1109/ICPS57144.2023.10142112 10.1109/PESGM.2016.7742002 10.1109/TSG.2023.3308055 10.1049/gtd2.12963 10.1109/TIA.2023.3273186 10.1109/TSG.2023.3318418 10.1016/j.apenergy.2022.118947 10.1109/TSG.2017.2710358 10.1109/TSG.2021.3105747 10.1016/j.energy.2017.11.142 10.1016/j.apenergy.2019.114139 10.1049/rpg2.12600 10.1016/j.rser.2022.113125 10.1109/TPWRS.2020.2966732 10.1109/TIA.2023.3296065 10.1016/j.apenergy.2017.05.098 10.1109/TSG.2017.2693121 10.1038/s41467-022-30747-0 10.1109/TSG.2023.3286697 10.1109/TIA.2022.3202756 10.1109/TIA.2022.3148351 10.1109/JSTARS.2023.3284019 10.1109/TII.2016.2585122 10.1016/j.energy.2022.125837 10.35833/MPCE.2022.000632 10.1016/j.apenergy.2022.119579 10.1109/TIM.2019.2903615 10.35833/MPCE.2021.000058 10.1016/j.energy.2022.126505 10.1109/TPWRS.2021.3050837 10.1109/TII.2016.2578184 10.1016/j.apenergy.2020.114525 10.35833/MPCE.2021.000535 10.35833/MPCE.2022.000434 10.1109/TSG.2012.2183649 10.1109/TIA.2023.3334715 10.35833/MPCE.2020.000152 10.1109/TSG.2023.3299668 10.1016/j.segan.2021.100452 10.1016/j.apenergy.2019.03.121 10.1109/TIA.2020.3035553 10.1016/j.energy.2023.129716 10.1109/TSG.2016.2636873 10.1016/j.apenergy.2022.120628 10.3390/en11040770 10.1109/TITS.2018.2854827 10.1016/j.apenergy.2020.116418 10.1016/j.enbuild.2016.03.061 10.1016/j.energy.2020.118765 10.1109/TIA.2022.3144244 10.1109/TII.2016.2635082 10.1109/TII.2019.2916566 10.1016/j.apenergy.2018.02.058
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SubjectTerms	Artificial neural networks Back propagation networks Biological system modeling Carbon Carbon dioxide Carbon emissions Climate change Customers Data models Day-ahead forecasting Demand response demand response potential Economic impact Electric potential Electric power demand Electric power systems Electricity electricity-carbon coupling Electrode potentials Emissions Energy management Forecasting Home automation load aggregator Load modeling low-carbon Low-carbon economy Neural networks Power consumption Power system planning Redundancy Residential energy Search algorithms Sustainable development
Title	Demand Response Potential Day-Ahead Forecasting Approach Based on LSSA-BPNN Considering the Electricity-Carbon Coupling Incentive Effects
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