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Real-time pricing based on convex hull method for smart grid with multiple generating units

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  • Li, Ningning
  • Gao, Yan

Abstract

With the growing demand and complex supply structure for electricity, higher requirements are proposed for generating units to regulate the peak load, resulting in significant increases in the startup cost. These issues bring urgent need to develop new pricing mechanisms. The real-time pricing (RTP) is an ideal pricing mechanism for clipping peak load and balancing supply and demand. This paper designs an RTP mechanism for the smart grid which integrates multiple generating units on the supply side and distributed renewable energy generation devices and energy storage systems on the demand side. Focusing on the interests of both supply and demand sides, a social welfare maximization model that incorporates startup costs of generating units is formulated. To tackle the discontinuity in the proposed model, the convex hull method is utilized to transform the primal model and derive the convex hull price which is widely regarded as an optimal price that can reflect the aggregate cost of generating units but has rarely extended to RTP. The related dual theory indicates that the convex hull price can be obtained by the Lagrange multiplier of the proposed social welfare maximization problem associated with the supply–demand balance constraint. Moreover, a distributed iterative algorithm based on the subgradient projection method is employed to solve the dual problem. Simulation results validate rationality and effectiveness of the proposed pricing mechanism.

Suggested Citation

  • Li, Ningning & Gao, Yan, 2023. "Real-time pricing based on convex hull method for smart grid with multiple generating units," Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:energy:v:285:y:2023:i:c:s0360544223029377
    DOI: 10.1016/j.energy.2023.129543
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    References listed on IDEAS

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    1. Liu, Jinqi & Wang, Jihong & Cardinal, Joel, 2022. "Evolution and reform of UK electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Yang, Zhichun & Yang, Fan & Min, Huaidong & Tian, Hao & Hu, Wei & Liu, Jian & Eghbalian, Nasrin, 2023. "Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources," Energy, Elsevier, vol. 263(PA).
    3. Ali, E.S. & Elazim, S.M. Abd & Balobaid, A.S., 2023. "Implementation of coyote optimization algorithm for solving unit commitment problem in power systems," Energy, Elsevier, vol. 263(PA).
    4. Abdi, Hamdi, 2021. "Profit-based unit commitment problem: A review of models, methods, challenges, and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    5. Li, Yuanyuan & Li, Junxiang & He, Jianjia & Zhang, Shuyuan, 2021. "The real-time pricing optimization model of smart grid based on the utility function of the logistic function," Energy, Elsevier, vol. 224(C).
    6. Fusco, Andrea & Gioffrè, Domenico & Francesco Castelli, Alessandro & Bovo, Cristian & Martelli, Emanuele, 2023. "A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services markets," Applied Energy, Elsevier, vol. 336(C).
    7. Stevens, Nicolas & Papavasiliou, Anthony, 2022. "Application of the Level Method for Computing Locational Convex Hull Prices," LIDAM Reprints CORE 3196, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Panagiotis Andrianesis & Dimitris Bertsimas & Michael C. Caramanis & William W. Hogan, 2020. "Computation of Convex Hull Prices in Electricity Markets with Non-Convexities using Dantzig-Wolfe Decomposition," Papers 2012.13331, arXiv.org, revised Oct 2021.
    9. Ding, Yixing & Xu, Qingshan & Hao, Lili & Xia, Yuanxing, 2023. "A Stackelberg Game-based robust optimization for user-side energy storage configuration and power pricing," Energy, Elsevier, vol. 283(C).
    10. 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).
    11. Yuan, Guanxiu & Gao, Yan & Ye, Bei, 2021. "Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response," Renewable Energy, Elsevier, vol. 179(C), pages 1424-1446.
    12. Stevens, Nicolas & Papavasiliou, Anthony, 2022. "Application of the Level Method for Computing Locational Convex Hull Prices," LIDAM Discussion Papers CORE 2022002, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    13. Wang, Ziyang & Sun, Mei & Gao, Cuixia & Wang, Xin & Ampimah, Benjamin Chris, 2021. "A new interactive real-time pricing mechanism of demand response based on an evaluation model," Applied Energy, Elsevier, vol. 295(C).
    14. Wen, Kai & Qiao, Dan & Nie, Chaofei & Lu, Yangfan & Wen, Feng & Zhang, Jing & Miao, Qing & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Multi-period supply and demand balance of large-scale and complex natural gas pipeline network: Economy and environment," Energy, Elsevier, vol. 264(C).
    15. Amiri-Pebdani, Sima & Alinaghian, Mahdi & Safarzadeh, Soroush, 2022. "Time-Of-Use pricing in an energy sustainable supply chain with government interventions: A game theory approach," Energy, Elsevier, vol. 255(C).
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