Optimal Scheduling and Compensation Pricing Method for Load Aggregators Based on Limited Peak Shaving Budget and Time Segment Value

Hanyu Yang, Zhihao Sun, Xun Dou (), Linxi Li, Jiancheng Yu, Xianxu Huo and Chao Pang
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Hanyu Yang: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Zhihao Sun: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Xun Dou: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Linxi Li: College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211816, China
Jiancheng Yu: State Grid Tianjin Electric Power Company, Electric Power Research Institute, Tianjin 300384, China
Xianxu Huo: State Grid Tianjin Electric Power Company, Tianjin 300010, China
Chao Pang: State Grid Tianjin Electric Power Company, Electric Power Research Institute, Tianjin 300384, China

Energies, 2024, vol. 17, issue 22, 1-21

Abstract: Load-side peak shaving is an effective measure to alleviate power supply–demand imbalance. As a key link between a vast array of small- and medium-sized adjustable resources and the bulk power system, load aggregators (LAs) typically allocate peak shaving budgets using fixed pricing methods based on peak shaving demand forecasts. However, due to the randomness of supply and demand, fluctuations in peak shaving demand occur, making it a significant technical challenge to meet peak shaving needs under limited budget allocations. To address this issue, this paper first conducts a clustering analysis of various adjustable load characteristics to derive typical electricity consumption curves, and then proposes a differentiated calculation method for the value of multi-time-segment peak shaving. Subsequently, an optimization model for LA scheduling and compensation pricing is established based on the limited peak shaving budget and time-segment peak shaving value. While ensuring the economic benefits of LAs, the model also analyzes the impact of different peak shaving budget allocations on the scale of peak shaving that can be achieved. Finally, case studies demonstrate that, compared to traditional fixed compensation pricing, the proposed pricing method reduces scheduling costs by an average of 16.5%, while significantly improving the overall satisfaction of adjustable users.

Keywords: adjustable load; load aggregators; optimal scheduling; peaking value; budget; compensation pricing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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