Towards a Sustainable Power System: A Three-Stage Demand Response Potential Evaluation Model

Haisheng Tan (), Peipei You, Sitao Li, Chengren Li, Chao Zhang, Hailang Zhou, Huicai Wang, Wenzhe Zhang and Huiru Zhao
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Haisheng Tan: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Peipei You: State Grid Energy Research Institute Co., Ltd., Beijing 102209, China
Sitao Li: State Grid Energy Research Institute Co., Ltd., Beijing 102209, China
Chengren Li: State Grid Energy Research Institute Co., Ltd., Beijing 102209, China
Chao Zhang: State Grid Energy Research Institute Co., Ltd., Beijing 102209, China
Hailang Zhou: Marketing Service Center of State Grid Chongqing Electric Power Company, Chongqing 400023, China
Huicai Wang: Marketing Service Center of State Grid Chongqing Electric Power Company, Chongqing 400023, China
Wenzhe Zhang: State Grid Chongqing Electric Power Company, Chongqing 400015, China
Huiru Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2024, vol. 16, issue 5, 1-21

Abstract: Developing flexible resources is a key strategy for advancing the development of new power systems and addressing the issue of climate change. Demand response is a crucial flexibility resource that is extensively employed due to its sustainability and economy. This work develops a three-stage demand response potential evaluation model based on “theoretical potential–realizable potential–multi-load aggregation potential” in response to the issues of inadequate consideration of numerous complicated agents and time in previous research. Firstly, the traditional method calculates the theoretical maximum demand response potential of a single industry in each period. Based on this, the industry characteristics are taken into account when establishing the demand response potential evaluation model. Lastly, the time variation of the demand response potential is taken into consideration when evaluating the demand response potential of multiple load aggregation. For the analysis, three industries are chosen as examples. The results show that the potential of peak shaving and valley filling obtained by using the model is smaller than that of the traditional method, the reduction range of peak cutting demand response potential calculated by multi-load aggregation is 19–100%, and the reduction range of valley filling demand response potential is 20–89%. The results are closer to reality, which is conducive to improving the accuracy of relevant departments in making relevant decisions and promoting the sustainable development of a new power system.

Keywords: demand response potential; multiple load; timing factors; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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