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Study on Dynamic Pricing Strategy for Industrial Power Users Considering Demand Response Differences in Master–Slave Game

Author

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  • Shuxin Liu

    (The School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Jing Xu

    (The School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Chaojian Xing

    (The School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Yang Liu

    (State Grid Xuji Group Co., Ltd., Xuchang 461111, China)

  • Ersheng Tian

    (State Grid Xuji Group Co., Ltd., Xuchang 461111, China)

  • Jia Cui

    (The School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Junzhu Wei

    (The School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

With the deepening of power market reform, further study on power trading mechanisms has become the core issue of power market study. The development stage of the industrial electricity market requires efficient and flexible pricing mechanisms. Currently available pricing strategies are inadequate for demand response management. Therefore, this paper provides an in-depth study of the pricing mechanism in the industrial electricity market in the context of electricity market reform. It proposes a demand–response-based dynamic pricing strategy for industrial parks. The method proposes a dynamic pricing strategy for demand-side response in industrial parks based on master–slave game by establishing an exogenous model of demand-side response and incentives. Compared with the existing strategies, the strategy is more efficient and flexible, and effectively improves the economic efficiency of power trading and load regulation. Firstly, an exogenous model of demand-side response and incentive is built to characterize the demand-side response cost. The method focuses more on describing the exogenous characteristics of user incentives and response quantities. It only needs to analyze the exogenous indicators and random errors in various typical scenarios. The description of user demand-side response is more efficient. Secondly, a master–slave-game-based dynamic pricing strategy for industrial parks with demand-side response is proposed. The strategy is composed of a two-stage optimization. The primary regulation of customers is achieved by day-ahead time-of-use tariffs. The secondary regulation of customers is achieved by means of the same-day regulation of demand and purchase regarding clean electricity. The proposed two-stage price formation mechanism is more economical, more effective in load regulation, and improves the flexibility of industrial pricing. Finally, a case study is conducted on an industrial power user in a park in Liaoning Province. The results show that the proposed method is significantly better than existing methods in terms of improving the economic efficiency and load control effectiveness of the pricing strategy.

Suggested Citation

  • Shuxin Liu & Jing Xu & Chaojian Xing & Yang Liu & Ersheng Tian & Jia Cui & Junzhu Wei, 2023. "Study on Dynamic Pricing Strategy for Industrial Power Users Considering Demand Response Differences in Master–Slave Game," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12265-:d:1215097
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    References listed on IDEAS

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    1. Xiaobao Yu & Zhenyu Dong & Dandan Zheng, 2023. "Research on Critical Peak Price Decision Optimization Considering Industrial Consumer’s Risk Appetite under the Carbon Neutrality Goal," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
    2. Heydarian-Forushani, Ehsan & Golshan, Mohamad Esmail Hamedani & Shafie-khah, Miadreza & Catalão, João P.S., 2020. "A comprehensive linear model for demand response optimization problem," Energy, Elsevier, vol. 209(C).
    3. Smita Shandilya & Zdzislaw Szymanski & Shishir Kumar Shandilya & Ivan Izonin & Krishna Kant Singh, 2022. "Modeling and Comparative Analysis of Multi-Agent Cost Allocation Strategies Using Cooperative Game Theory for the Modern Electricity Market," Energies, MDPI, vol. 15(7), pages 1-14, March.
    4. Ahmed M. Nassef & Mohammad Ali Abdelkareem & Hussein M. Maghrabie & Ahmad Baroutaji, 2023. "Review of Metaheuristic Optimization Algorithms for Power Systems Problems," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    5. Marko Jelić & Marko Batić & Nikola Tomašević, 2021. "Demand-Side Flexibility Impact on Prosumer Energy System Planning," Energies, MDPI, vol. 14(21), pages 1-32, October.
    6. Ariana M. Pietrasanta & Mostafa F. Shaaban & Pio A. Aguirre & Sergio F. Mussati & Mohamed A. Hamouda, 2023. "Simulation and Optimization of Renewable Energy-Powered Desalination: A Bibliometric Analysis and Highlights of Recent Research," Sustainability, MDPI, vol. 15(12), pages 1-28, June.
    7. Senthil Sundaramoorthy & Dipti Kamath & Sachin Nimbalkar & Christopher Price & Thomas Wenning & Joseph Cresko, 2023. "Energy Efficiency as a Foundational Technology Pillar for Industrial Decarbonization," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    8. Sean Williams & Michael Short & Tracey Crosbie & Maryam Shadman-Pajouh, 2020. "A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services," Energies, MDPI, vol. 13(16), pages 1-30, August.
    9. Rami Al-Hajj & Ali Assi & Bilel Neji & Raymond Ghandour & Zaher Al Barakeh, 2023. "Transfer Learning for Renewable Energy Systems: A Survey," Sustainability, MDPI, vol. 15(11), pages 1-28, June.
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