IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i4p2080-d1074937.html
   My bibliography  Save this article

Customised Multi-Energy Pricing: Model and Solutions

Author

Listed:
  • Qiuyi Hong

    (Department of Mathematical Sciences, University of Essex, Colchester CO4 3SQ, UK)

  • Fanlin Meng

    (Alliance Manchester Business School, University of Manchester, Manchester M15 6PB, UK)

  • Jian Liu

    (Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA)

Abstract

With the increasing interdependence among energies (e.g., electricity, natural gas and heat) and the development of a decentralised energy system, a novel retail pricing scheme in the multi-energy market is demanded. Therefore, the problem of designing a customised multi-energy pricing scheme for energy retailers is investigated in this paper. In particular, the proposed pricing scheme is formulated as a bilevel optimisation problem. At the upper level, the energy retailer (leader) aims to maximise its profit. Microgrids (followers) equipped with energy converters, storage, renewable energy sources (RES) and demand response (DR) programs are located at the lower level and minimise their operational costs. Three hybrid algorithms combining metaheuristic algorithms (i.e., particle swarm optimisation (PSO), genetic algorithm (GA) and simulated annealing (SA)) with the mixed-integer linear program (MILP) are developed to solve the proposed bilevel problem. Numerical results verify the feasibility and effectiveness of the proposed model and solution algorithms. We find that GA outperforms other solution algorithms to obtain a higher retailer’s profit through comparison. In addition, the proposed customised pricing scheme could benefit the retailer’s profitability and net profit margin compared to the widely adopted uniform pricing scheme due to the reduction in the overall energy purchasing costs in the wholesale markets. Lastly, the negative correlations between the rated capacity and power of the energy storage and both retailer’s profit and the microgrid’s operational cost are illustrated.

Suggested Citation

  • Qiuyi Hong & Fanlin Meng & Jian Liu, 2023. "Customised Multi-Energy Pricing: Model and Solutions," Energies, MDPI, vol. 16(4), pages 1-31, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:2080-:d:1074937
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/4/2080/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/4/2080/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhu, Xu & Sun, Yuanzhang & Yang, Jun & Dou, Zhenlan & Li, Gaojunjie & Xu, Chengying & Wen, Yuxin, 2022. "Day-ahead energy pricing and management method for regional integrated energy systems considering multi-energy demand responses," Energy, Elsevier, vol. 251(C).
    2. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    3. 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).
    4. Hong, Qiuyi & Meng, Fanlin & Liu, Jian & Bo, Rui, 2023. "A bilevel game-theoretic decision-making framework for strategic retailers in both local and wholesale electricity markets," Applied Energy, Elsevier, vol. 330(PA).
    5. Shixiong Qi & Xiuli Wang & Xue Li & Tao Qian & Qiwen Zhang, 2019. "Enhancing Integrated Energy Distribution System Resilience through a Hierarchical Management Strategy in District Multi-Energy Systems," Sustainability, MDPI, vol. 11(15), pages 1-20, July.
    6. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2019. "A price decision approach for multiple multi-energy-supply microgrids considering demand response," Energy, Elsevier, vol. 167(C), pages 117-135.
    7. Zhang, Yan & Meng, Fanlin & Wang, Rui & Kazemtabrizi, Behzad & Shi, Jianmai, 2019. "Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid," Energy, Elsevier, vol. 179(C), pages 1265-1278.
    8. Haibing Wang & Chengmin Wang & Weiqing Sun & Muhammad Qasim Khan, 2022. "Energy Pricing and Management for the Integrated Energy Service Provider: A Stochastic Stackelberg Game Approach," Energies, MDPI, vol. 15(19), pages 1-15, October.
    9. Liu, Peiyun & Ding, Tao & Zou, Zhixiang & Yang, Yongheng, 2019. "Integrated demand response for a load serving entity in multi-energy market considering network constraints," Applied Energy, Elsevier, vol. 250(C), pages 512-529.
    10. Zugno, Marco & Morales, Juan Miguel & Pinson, Pierre & Madsen, Henrik, 2013. "A bilevel model for electricity retailers' participation in a demand response market environment," Energy Economics, Elsevier, vol. 36(C), pages 182-197.
    11. Dai, Yeming & Sun, Xilian & Qi, Yao & Leng, Mingming, 2021. "A real-time, personalized consumption-based pricing scheme for the consumptions of traditional and renewable energies," Renewable Energy, Elsevier, vol. 180(C), pages 452-466.
    12. Meng, Fanlin & Ma, Qian & Liu, Zixu & Zeng, Xiao-Jun, 2023. "Multiple dynamic pricing for demand response with adaptive clustering-based customer segmentation in smart grids," Applied Energy, Elsevier, vol. 333(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. Meng, Fanlin & Ma, Qian & Liu, Zixu & Zeng, Xiao-Jun, 2023. "Multiple dynamic pricing for demand response with adaptive clustering-based customer segmentation in smart grids," Applied Energy, Elsevier, vol. 333(C).
    2. Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
    3. Jiajia Li & Jinfu Liu & Peigang Yan & Xingshuo Li & Guowen Zhou & Daren Yu, 2021. "Operation Optimization of Integrated Energy System under a Renewable Energy Dominated Future Scene Considering Both Independence and Benefit: A Review," Energies, MDPI, vol. 14(4), pages 1-36, February.
    4. Zhou, Yuqi & Yu, Wenbin & Zhu, Shanying & Yang, Bo & He, Jianping, 2021. "Distributionally robust chance-constrained energy management of an integrated retailer in the multi-energy market," Applied Energy, Elsevier, vol. 286(C).
    5. van Beuzekom, Iris & Hodge, Bri-Mathias & Slootweg, Han, 2021. "Framework for optimization of long-term, multi-period investment planning of integrated urban energy systems," Applied Energy, Elsevier, vol. 292(C).
    6. Yi, Ji Hyun & Ko, Woong & Park, Jong-Keun & Park, Hyeongon, 2018. "Impact of carbon emission constraint on design of small scale multi-energy system," Energy, Elsevier, vol. 161(C), pages 792-808.
    7. Dominković, D.F. & Bačeković, I. & Sveinbjörnsson, D. & Pedersen, A.S. & Krajačić, G., 2017. "On the way towards smart energy supply in cities: The impact of interconnecting geographically distributed district heating grids on the energy system," Energy, Elsevier, vol. 137(C), pages 941-960.
    8. Lukas Kriechbaum & Philipp Gradl & Romeo Reichenhauser & Thomas Kienberger, 2020. "Modelling Grid Constraints in a Multi-Energy Municipal Energy System Using Cumulative Exergy Consumption Minimisation," Energies, MDPI, vol. 13(15), pages 1-23, July.
    9. Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    10. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    11. Sayegh, M.A. & Danielewicz, J. & Nannou, T. & Miniewicz, M. & Jadwiszczak, P. & Piekarska, K. & Jouhara, H., 2017. "Trends of European research and development in district heating technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1183-1192.
    12. Matija Kostelac & Lin Herenčić & Tomislav Capuder, 2022. "Planning and Operational Aspects of Individual and Clustered Multi-Energy Microgrid Options," Energies, MDPI, vol. 15(4), pages 1-17, February.
    13. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    14. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    15. David Drysdale & Brian Vad Mathiesen & Henrik Lund, 2019. "From Carbon Calculators to Energy System Analysis in Cities," Energies, MDPI, vol. 12(12), pages 1-21, June.
    16. 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).
    17. Beraldi, Patrizia, 2024. "Green retailer: A stochastic bi-level approach to support investment decisions in sustainable energy systems," Operations Research Perspectives, Elsevier, vol. 12(C).
    18. Bahl, Björn & Kümpel, Alexander & Seele, Hagen & Lampe, Matthias & Bardow, André, 2017. "Time-series aggregation for synthesis problems by bounding error in the objective function," Energy, Elsevier, vol. 135(C), pages 900-912.
    19. Cheng, Yaohua & Zhang, Ning & Kirschen, Daniel S. & Huang, Wujing & Kang, Chongqing, 2020. "Planning multiple energy systems for low-carbon districts with high penetration of renewable energy: An empirical study in China," Applied Energy, Elsevier, vol. 261(C).
    20. Morovat, Navid & Athienitis, Andreas K. & Candanedo, José Agustín & Nouanegue, Hervé Frank, 2024. "Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building," Energy, Elsevier, vol. 296(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:jeners:v:16:y:2023:i:4:p:2080-:d:1074937. 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.