IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v285y2023ics0360544223029377.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223029377
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129543?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Liu, Jinqi & Wang, Jihong & Cardinal, Joel, 2022. "Evolution and reform of UK electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    3. 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.
    4. 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).
    5. 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).
    6. 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).
    7. 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).
    8. 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).
    9. 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).
    10. 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).
    11. 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.
    12. 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).
    13. 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).
    14. 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).
    15. 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).
    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. Stevens, Nicolas & Papavasiliou, Anthony & Smeers, Yves, 2024. "On some advantages of convex hull pricing for the European electricity auction," Energy Economics, Elsevier, vol. 134(C).
    2. Dong, Jizhe & Li, Yuanhan & Zuo, Shi & Wu, Xiaomei & Zhang, Zuyao & Du, Jiang, 2023. "An intraperiod arbitrary ramping-rate changing model in unit commitment," Energy, Elsevier, vol. 284(C).
    3. Wang, Yudong & Hu, Junjie, 2023. "Two-stage energy management method of integrated energy system considering pre-transaction behavior of energy service provider and users," Energy, Elsevier, vol. 271(C).
    4. Qu, Deqiang & Li, Junxiang & Ma, Xiaojia, 2024. "Distributed real-time pricing of smart grid considering individual differences," Omega, Elsevier, vol. 127(C).
    5. Feng, Fenling & Zhang, Jiaqi & Liu, Chengguang, 2023. "Integrated pricing mechanism of China Railway Express whole-process logistics based on the Stackelberg game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    6. Hossein Lotfi & Mohammad Hasan Nikkhah, 2024. "Multi-Objective Profit-Based Unit Commitment with Renewable Energy and Energy Storage Units Using a Modified Optimization Method," Sustainability, MDPI, vol. 16(4), pages 1-28, February.
    7. 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).
    8. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    9. Amiri-Pebdani, Sima & Alinaghian, Mahdi & Khosroshahi, Hossein, 2023. "Pricing in competitive energy supply chains considering government interventions to support CCS under cap-and-trade regulations: A game-theoretic approach," Energy Policy, Elsevier, vol. 179(C).
    10. Hanwen Wang & Xiang Li & Haojun Hu & Yizhou Zhou, 2024. "Distributed Dispatch and Profit Allocation for Parks Using Co-Operative Game Theory and the Generalized Nash Bargaining Approach," Energies, MDPI, vol. 17(23), pages 1-19, December.
    11. Qiuyi Hong & Fanlin Meng & Jian Liu, 2023. "Customised Multi-Energy Pricing: Model and Solutions," Energies, MDPI, vol. 16(4), pages 1-31, February.
    12. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    13. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    14. Diaa Salman & Mehmet Kusaf, 2021. "Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting," Sustainability, MDPI, vol. 13(24), pages 1-22, December.
    15. Feng, Jie & Ran, Lun & Wang, Zhiyuan & Zhang, Mengling, 2024. "Optimal energy scheduling of virtual power plant integrating electric vehicles and energy storage systems under uncertainty," Energy, Elsevier, vol. 309(C).
    16. Han, Ouzhu & Ding, Tao & Zhang, Xiaosheng & Mu, Chenggang & He, Xinran & Zhang, Hongji & Jia, Wenhao & Ma, Zhoujun, 2023. "A shared energy storage business model for data center clusters considering renewable energy uncertainties," Renewable Energy, Elsevier, vol. 202(C), pages 1273-1290.
    17. Ji, Jingna & Li, Tao & Yang, Lei, 2023. "Pricing and carbon reduction strategies for vertically differentiated firms under Cap-and-Trade regulation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    18. Li, J.Y. & Chen, J.J. & Wang, Y.X. & Chen, W.G., 2024. "Combining multi-step reconfiguration with many-objective reduction as iterative bi-level scheduling for stochastic distribution network," Energy, Elsevier, vol. 290(C).
    19. Lingling Hu & Junming Zhou & Feng Jiang & Guangming Xie & Jie Hu & Qinglie Mo, 2023. "Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization," Sustainability, MDPI, vol. 16(1), pages 1-25, December.
    20. Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.

    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:eee:energy:v:285:y:2023:i:c:s0360544223029377. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.