A Day-ahead Demand Response Potential Forecasting Approach Based on LSSA-BPNN Considering the Electricity-carbon Coupling Incentive Effects

With global warming, the need for a low-carbon transition of the power system is becoming more urgent. Previous DR potential forecasting models for load aggregators (LAs) only consider the peak-shaving potential of customers, ignore their potential to reduce carbon emissions, resulting in higher car...

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Vydáno v:Conference record (Industrial & Commercial Power Systems Technical Conference) s. 1 - 8
Hlavní autoři: Wang, Liyong, Sun, Qinfei, Li, Meiyi, Ge, Xinxin, Wang, Fei
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 21.05.2023
Témata:
Biological system modeling
Carbon dioxide
Couplings
Demand response
Electric potential
Electricity-carbon Coupling
Feature extraction
Load aggregator
Low-carbon
Potential forecasting
Predictive models
Scheduling
ISSN:2158-4907
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Shrnutí:With global warming, the need for a low-carbon transition of the power system is becoming more urgent. Previous DR potential forecasting models for load aggregators (LAs) only consider the peak-shaving potential of customers, ignore their potential to reduce carbon emissions, resulting in higher carbon emissions. To this end, A logistic sparrow search algorithm-back propagation neural network (LSSA-BPNN) based DR potential forecasting model for LAs in a low-carbon operation mode is proposed in this paper, which considers the dual incentive of electricity and carbon. First, customers are divided into different types according to their willingness of reducing economic cost and carbon emissions, and then the HEMS model considering the dual incentive of electricity and carbon is built. Second, the multiple influencing features are sorted according to the degree of importance by the RF model, which could reduce the redundancy of input features. Finally, based on the selected features, the LSSA-BPNN model is introduced to forecast the DR potential for LAs.
ISSN:2158-4907
DOI:10.1109/ICPS57144.2023.10142112