IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i8p3248-d1374949.html
   My bibliography  Save this article

Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives

Author

Listed:
  • Wenhui Zhao

    (College of Economics and Management, Shanghai Electric Power University, Shanghai 201306, China)

  • Zilin Wu

    (College of Economics and Management, Shanghai Electric Power University, Shanghai 201306, China)

  • Bo Zhou

    (College of Economics and Management, Shanghai Electric Power University, Shanghai 201306, China)

  • Jiaoqian Gao

    (Qingpu Power Supply Company, State Grid Shanghai Electric Power Company, Shanghai 201700, China)

Abstract

In China, the inversion between peak periods of wind and photovoltaic (PV) power (WPVP) generation and peak periods of electricity demand leads to a mismatch between electricity demand and supply, resulting in a significant loss of WPVP. In this context, this article proposes an improved demand response (DR) strategy to enhance the consumption of WPVP. Firstly, we use feature selection methods to screen variables related to response quantity and, based on the results, establish a response potential prediction model using random forest algorithm. Then, we design a subsidy price update formula and the subsidy price constraint conditions that consider user response characteristics and predict the response potential of users under differentiated subsidy price. Subsequently, after multiple iterations of the price update formula, the final subsidy and response potential of the user can be determined. Finally, we establish a user ranking sequence based on response potential. The case analysis shows that differentiated price strategy and response potential prediction model can address the shortcomings of existing DR strategies, enabling users to declare response quantity more reasonably and the grid to formulate subsidy price more fairly. Through an improved DR strategy, the consumption rate of WPVP has increased by 12%.

Suggested Citation

  • Wenhui Zhao & Zilin Wu & Bo Zhou & Jiaoqian Gao, 2024. "Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives," Sustainability, MDPI, vol. 16(8), pages 1-22, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3248-:d:1374949
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/8/3248/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/8/3248/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Yongli & Li, Fang & Yang, Jiale & Zhou, Minhan & Song, Fuhao & Zhang, Danyang & Xue, Lu & Zhu, Jinrong, 2020. "Demand response evaluation of RIES based on improved matter-element extension model," Energy, Elsevier, vol. 212(C).
    2. Kong, Xiangyu & Wang, Zhengtao & Liu, Chao & Zhang, Delong & Gao, Hongchao, 2023. "Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants," Applied Energy, Elsevier, vol. 334(C).
    3. Shi, Renwei & Jiao, Zaibin, 2023. "Individual household demand response potential evaluation and identification based on machine learning algorithms," Energy, Elsevier, vol. 266(C).
    4. Dai, Xuemei & Li, Yaping & Zhang, Kaifeng & Feng, Wei, 2020. "A robust offering strategy for wind producers considering uncertainties of demand response and wind power," Applied Energy, Elsevier, vol. 279(C).
    5. Qi Wang & Ping Chang & Runqing Bai & Wenfei Liu & Jianfeng Dai & Yi Tang, 2019. "Mitigation Strategy for Duck Curve in High Photovoltaic Penetration Power System Using Concentrating Solar Power Station," Energies, MDPI, vol. 12(18), pages 1-16, September.
    6. Kong, Xiangyu & Kong, Deqian & Yao, Jingtao & Bai, Linquan & Xiao, Jie, 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning," Applied Energy, Elsevier, vol. 271(C).
    7. Liu, Di & Qin, Zhaoming & Hua, Haochen & Ding, Yi & Cao, Junwei, 2023. "Incremental incentive mechanism design for diversified consumers in demand response," Applied Energy, Elsevier, vol. 329(C).
    8. Wohlfarth, Katharina & Klobasa, Marian & Gutknecht, Ralph, 2020. "Demand response in the service sector – Theoretical, technical and practical potentials," Applied Energy, Elsevier, vol. 258(C).
    9. Leinauer, Christina & Schott, Paul & Fridgen, Gilbert & Keller, Robert & Ollig, Philipp & Weibelzahl, Martin, 2022. "Obstacles to demand response: Why industrial companies do not adapt their power consumption to volatile power generation," Energy Policy, Elsevier, vol. 165(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hui Wang & Yao Xu, 2024. "Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study," Energies, MDPI, vol. 17(11), pages 1-15, May.
    2. Yan Li & Zhiwen Liu & Chong Shao & Bingjun Lin & Jiayu Rong & Nan Dong & Buyun Su & Yuejia Hong, 2024. "A Method for Evaluating Demand Response Potential of Industrial Loads Based on Fuzzy Control," Energies, MDPI, vol. 17(20), pages 1-14, October.
    3. Gao, Hongchao & Jin, Tai & Feng, Cheng & Li, Chuyi & Chen, Qixin & Kang, Chongqing, 2024. "Review of virtual power plant operations: Resource coordination and multidimensional interaction," Applied Energy, Elsevier, vol. 357(C).
    4. Debnath, Kumar Biswajit & Jenkins, David P. & Patidar, Sandhya & Peacock, Andrew D., 2024. "Remote work might unlock solar PV's potential of cracking the ‘Duck Curve’," Applied Energy, Elsevier, vol. 367(C).
    5. Wang, Tonghe & Hua, Haochen & Shi, Tianying & Wang, Rui & Sun, Yizhong & Naidoo, Pathmanathan, 2024. "A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy," Applied Energy, Elsevier, vol. 356(C).
    6. Hao Wu & Lin Zhou & Yihao Wan & Qiang Liu & Siyu Zhou, 2019. "A Mixed Uncertainty Power Flow Algorithm-Based Centralized Photovoltaic (PV) Cluster," Energies, MDPI, vol. 12(20), pages 1-16, October.
    7. Jiao, P.H. & Chen, J.J. & Cai, X. & Wang, L.L. & Zhao, Y.L. & Zhang, X.H. & Chen, W.G., 2021. "Joint active and reactive for allocation of renewable energy and energy storage under uncertain coupling," Applied Energy, Elsevier, vol. 302(C).
    8. Chen, Ting & Vandendriessche, Frederik, 2023. "Enabling independent flexibility service providers to participate in electricity markets: A legal analysis of the Belgium case," Utilities Policy, Elsevier, vol. 81(C).
    9. Capper, Timothy & Kuriakose, Jaise & Sharmina, Maria, 2024. "Facilitating domestic demand response in Britain’s electricity system," Utilities Policy, Elsevier, vol. 89(C).
    10. Sæle, Hanne & Sperstad, Iver Bakken & Wang Hoiem, Kristian & Mathiesen, Vivi, 2023. "Understanding barriers to utilising flexibility in operation and planning of the electricity distribution system – Classification frameworks with applications to Norway," Energy Policy, Elsevier, vol. 180(C).
    11. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    12. Li, He & Wang, Pengyu & Fang, Debin, 2024. "Differentiated pricing for the retail electricity provider optimizing demand response to renewable energy fluctuations," Energy Economics, Elsevier, vol. 136(C).
    13. Kiguchi, Y. & Weeks, M. & Arakawa, R., 2021. "Predicting winners and losers under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 236(C).
    14. Zhang, Li & Gao, Yan & Zhu, Hongbo & Tao, Li, 2022. "Bi-level stochastic real-time pricing model in multi-energy generation system: A reinforcement learning approach," Energy, Elsevier, vol. 239(PA).
    15. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2021. "Energy equipment sizing and operation optimisation for prosumer industrial SMEs – A lifetime approach," Applied Energy, Elsevier, vol. 299(C).
    16. Luwen Pan & Jiajia Chen, 2024. "Optimal Energy Storage Configuration of Prosumers with Uncertain Photovoltaic in the Presence of Customized Pricing-Based Demand Response," Sustainability, MDPI, vol. 16(6), pages 1-18, March.
    17. Wufeng Qiao & Zepeng Yang & Bo Peng & Xiaoyu Cai & Yuanyuan Zhang, 2024. "Integrated Evaluation Method of Bus Lane Traffic Benefit Based on Multi-Source Data," Mathematics, MDPI, vol. 12(17), pages 1-23, August.
    18. Kanato Tamashiro & Talal Alharbi & Alexey Mikhaylov & Ashraf M. Hemeida & Narayanan Krishnan & Mohammed Elsayed Lotfy & Tomonobu Senjyu, 2021. "Investigation of Home Energy Management with Advanced Direct Load Control and Optimal Scheduling of Controllable Loads," Energies, MDPI, vol. 14(21), pages 1-14, November.
    19. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
    20. Rusche, Simon & Weissflog., Jan & Wenninger, Simon & Häckel, Björn, 2023. "How flexible are energy flexibilities? Developing a flexibility score for revenue and risk analysis in industrial demand-side management," Applied Energy, Elsevier, vol. 345(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3248-:d:1374949. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.