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Dynamic bidding strategy for a demand response aggregator in the frequency regulation market

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  • Liu, Xin
  • Li, Yang
  • Lin, Xueshan
  • Guo, Jiqun
  • Shi, Yunpeng
  • Shen, Yunwei

Abstract

As a low-cost flexible resource, dynamic controllable load on the demand side offers potential for great application prospects in power system frequency regulation. To overcome the risks of various uncertain factors in electricity markets and realize the economic benefits of demand response, this study proposed a dynamic bidding strategy for demand-side resources to participate in the frequency regulation market by a demand response (DR) aggregator. A correlative uncertainty model of the market price and frequency regulation demand was constructed employing the copula function, while the corresponding copula conditional value-at-risk model was used as a market risk measurement index to quantify the DR aggregator’s decision risk. Consequently, an objective function that maximises the profit of the DR aggregator was established. Simultaneously, based on the analysis of the response potential of demand-side resources, a time-varying compensation method for the DR was proposed, and the bidding decision of the DR aggregator was dynamically optimised considering load deviation. Finally, case studies demonstrated that the accuracy and rationality of the uncertainty modelling are improved. The proposed dynamic optimisation method resulted in an increase of 16 % in operating profits. In addition, the revenue of users increased by 12 %. The impact of different risk preferences and the correlation between the stochastic electricity price and frequency regulation demand on the optimal decision result was analysed, based on which the manager of the DR aggregator can make decisions under different situations.

Suggested Citation

  • Liu, Xin & Li, Yang & Lin, Xueshan & Guo, Jiqun & Shi, Yunpeng & Shen, Yunwei, 2022. "Dynamic bidding strategy for a demand response aggregator in the frequency regulation market," Applied Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s030626192200407x
    DOI: 10.1016/j.apenergy.2022.118998
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    1. Elsir, Mohamed & Al-Sumaiti, Ameena Saad & El Moursi, Mohamed Shawky & Al-Awami, Ali Taleb, 2023. "Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid," Applied Energy, Elsevier, vol. 335(C).
    2. Liu, Xin & Li, Yang & Wang, Li & Tang, Junbo & Qiu, Haifeng & Berizzi, Alberto & Valentin, Ilea & Gao, Ciwei, 2024. "Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability," Energy, Elsevier, vol. 297(C).
    3. Wu, Shengyang & Ding, Zhaohao & Wang, Jingyu & Shi, Dongyuan, 2023. "Unveiling bidding uncertainties in electricity markets: A Bayesian deep learning framework based on accurate variational inference," Energy, Elsevier, vol. 276(C).
    4. Sun, Xiaotian & Xie, Haipeng & Qiu, Dawei & Xiao, Yunpeng & Bie, Zhaohong & Strbac, Goran, 2023. "Decentralized frequency regulation service provision for virtual power plants: A best response potential game approach," Applied Energy, Elsevier, vol. 352(C).
    5. Yang, Shaohua & Lao, Keng-Weng & Hui, Hongxun & Chen, Yulin, 2023. "A robustness-enhanced frequency regulation scheme for power system against multiple cyber and physical emergency events," Applied Energy, Elsevier, vol. 350(C).
    6. Ching-Jui Tien & Chia-Sheng Tu & Ming-Tang Tsai, 2022. "Risk Assessment of User Aggregators in Demand Bidding Markets," Energies, MDPI, vol. 16(1), pages 1-14, December.

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