IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v167y2019icp117-135.html
   My bibliography  Save this article

A price decision approach for multiple multi-energy-supply microgrids considering demand response

Author

Listed:
  • Li, Bei
  • Roche, Robin
  • Paire, Damien
  • Miraoui, Abdellatif

Abstract

Multi-energy-supply microgrids covering different types of demands are expected to play an important role in smart grids. Local generation, energy storage systems, and renewable energy sources can form load service entities, which can provide ancillary services to the utility grid and to consumers. On the other hand, microgrids can also sell energy to load service entities or the utility grid to obtain profits. But how the load service entities can decide the electricity selling price to multiple microgrids, and how the microgrids can decide the electricity selling price to load service entities are problems. In this paper, we present a price decision method for multiple microgrids considering demand response. Mixed integer linear programming is used to control the operation of each microgrid, and is also used to operate the load service entities. A genetic algorithm is used to search for the best price for each microgrid and the load service entities. The combined method is deployed in a decentralized way, namely, each microgrid runs its own operation problem. The simulation results show that the new searched price works better than a basic time of use price, which can reduce the operation cost of the whole system. The searching method is compared with a method based on the Cournot model. At last, a large system is tested, in which 4 load service entities, 16 microgrids and an IEEE30-node network are considered. In order to reduce the searching time, a neural network model is presented to estimate the operation of the whole system. Based on the neural network model, the prices are obtained, and the results show that the prices based on neural network model are better than with the time of use price.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:167:y:2019:i:c:p:117-135
    DOI: 10.1016/j.energy.2018.10.189
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218321868
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.10.189?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, Chunyu & Wang, Qi & Wang, Jianhui & Korpås, Magnus & Pinson, Pierre & Østergaard, Jacob & Khodayar, Mohammad E., 2016. "Trading strategies for distribution company with stochastic distributed energy resources," Applied Energy, Elsevier, vol. 177(C), pages 625-635.
    2. Li, Bei & Roche, Robin & Miraoui, Abdellatif, 2017. "Microgrid sizing with combined evolutionary algorithm and MILP unit commitment," Applied Energy, Elsevier, vol. 188(C), pages 547-562.
    3. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    4. Jalali, Mehdi & Zare, Kazem & Seyedi, Heresh, 2017. "Strategic decision-making of distribution network operator with multi-microgrids considering demand response program," Energy, Elsevier, vol. 141(C), pages 1059-1071.
    5. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2017. "Sizing of a stand-alone microgrid considering electric power, cooling/heating, hydrogen loads and hydrogen storage degradation," Applied Energy, Elsevier, vol. 205(C), pages 1244-1259.
    6. Yu, Mengmeng & Hong, Seung Ho, 2016. "Supply–demand balancing for power management in smart grid: A Stackelberg game approach," Applied Energy, Elsevier, vol. 164(C), pages 702-710.
    7. Celik, Berk & Roche, Robin & Suryanarayanan, Siddharth & Bouquain, David & Miraoui, Abdellatif, 2017. "Electric energy management in residential areas through coordination of multiple smart homes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 260-275.
    8. 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.
    9. Motevasel, Mehdi & Seifi, Ali Reza & Niknam, Taher, 2013. "Multi-objective energy management of CHP (combined heat and power)-based micro-grid," Energy, Elsevier, vol. 51(C), pages 123-136.
    10. Reihani, Ehsan & Motalleb, Mahdi & Thornton, Matsu & Ghorbani, Reza, 2016. "A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture," Applied Energy, Elsevier, vol. 183(C), pages 445-455.
    11. Xia, Lei & Ma, Zhenjun & Kokogiannakis, Georgios & Wang, Zhihua & Wang, Shugang, 2018. "A model-based design optimization strategy for ground source heat pump systems with integrated photovoltaic thermal collectors," Applied Energy, Elsevier, vol. 214(C), pages 178-190.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luo, Xi & Liu, Yanfeng & Feng, Pingan & Gao, Yuan & Guo, Zhenxiang, 2021. "Optimization of a solar-based integrated energy system considering interaction between generation, network, and demand side," Applied Energy, Elsevier, vol. 294(C).
    2. Morteza Vahid-Ghavidel & Mohammad Sadegh Javadi & Matthew Gough & Sérgio F. Santos & Miadreza Shafie-khah & João P.S. Catalão, 2020. "Demand Response Programs in Multi-Energy Systems: A Review," Energies, MDPI, vol. 13(17), pages 1-17, August.
    3. Li, Songrui & Zhang, Lihui & Nie, Lei & Wang, Jianing, 2022. "Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game," Energy, Elsevier, vol. 249(C).
    4. 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).
    5. Hossein Shayeghi & Elnaz Shahryari & Mohammad Moradzadeh & Pierluigi Siano, 2019. "A Survey on Microgrid Energy Management Considering Flexible Energy Sources," Energies, MDPI, vol. 12(11), pages 1-26, June.
    6. Longxi Li, 2020. "Optimal Coordination Strategies for Load Service Entity and Community Energy Systems Based on Centralized and Decentralized Approaches," Energies, MDPI, vol. 13(12), pages 1-22, June.
    7. Qiuyi Hong & Fanlin Meng & Jian Liu, 2023. "Customised Multi-Energy Pricing: Model and Solutions," Energies, MDPI, vol. 16(4), pages 1-31, February.
    8. Zhou, Kaile & Wei, Shuyu & Yang, Shanlin, 2019. "Time-of-use pricing model based on power supply chain for user-side microgrid," Applied Energy, Elsevier, vol. 248(C), pages 35-43.
    9. Li, Bei & Miao, Hongzhi & Li, Jiangchen, 2021. "Multiple hydrogen-based hybrid storage systems operation for microgrids: A combined TOPSIS and model predictive control methodology," Applied Energy, Elsevier, vol. 283(C).
    10. Wenting Zhao & Jun Lv & Xilong Yao & Juanjuan Zhao & Zhixin Jin & Yan Qiang & Zheng Che & Chunwu Wei, 2019. "Consortium Blockchain-Based Microgrid Market Transaction Research," Energies, MDPI, vol. 12(20), pages 1-22, October.
    11. Li, Longxi, 2021. "Coordination between smart distribution networks and multi-microgrids considering demand side management: A trilevel framework," Omega, Elsevier, vol. 102(C).

    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. de Souza Dutra, Michael David & Alguacil, Natalia, 2020. "Optimal residential users coordination via demand response: An exact distributed framework," Applied Energy, Elsevier, vol. 279(C).
    2. Tamás Kis & András Kovács & Csaba Mészáros, 2021. "On Optimistic and Pessimistic Bilevel Optimization Models for Demand Response Management," Energies, MDPI, vol. 14(8), pages 1-22, April.
    3. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    4. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    5. Wilke, Christoph & Bensmann, Astrid & Martin, Stefan & Utz, Annika & Hanke-Rauschenbach, Richard, 2018. "Optimal design of a district energy system including supply for fuel cell electric vehicles," Applied Energy, Elsevier, vol. 226(C), pages 129-144.
    6. Sheikhahmadi, P. & Bahramara, S. & Moshtagh, J. & Yazdani Damavandi, M., 2018. "A risk-based approach for modeling the strategic behavior of a distribution company in wholesale energy market," Applied Energy, Elsevier, vol. 214(C), pages 24-38.
    7. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    8. Dranka, Géremi Gilson & Ferreira, Paula & Vaz, A. Ismael F., 2021. "A review of co-optimization approaches for operational and planning problems in the energy sector," Applied Energy, Elsevier, vol. 304(C).
    9. Zhang, Xiaoyan & Zhu, Shanying & He, Jianping & Yang, Bo & Guan, Xinping, 2019. "Credit rating based real-time energy trading in microgrids," Applied Energy, Elsevier, vol. 236(C), pages 985-996.
    10. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    11. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    12. Pinto, Edwin S. & Gronier, Timothé & Franquet, Erwin & Serra, Luis M., 2023. "Opportunities and economic assessment for a third-party delivering electricity, heat and cold to residential buildings," Energy, Elsevier, vol. 272(C).
    13. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2018. "Optimal sizing of distributed generation in gas/electricity/heat supply networks," Energy, Elsevier, vol. 151(C), pages 675-688.
    14. Konstantakopoulos, Ioannis C. & Barkan, Andrew R. & He, Shiying & Veeravalli, Tanya & Liu, Huihan & Spanos, Costas, 2019. "A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure," Applied Energy, Elsevier, vol. 237(C), pages 810-821.
    15. András Kovács, 2021. "Inverse optimization approach to the identification of electricity consumer models," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(2), pages 521-537, June.
    16. Fan, Songli & Ai, Qian & Piao, Longjian, 2018. "Bargaining-based cooperative energy trading for distribution company and demand response," Applied Energy, Elsevier, vol. 226(C), pages 469-482.
    17. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
    18. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Pasquale, Stefano, 2019. "Fuel cell based hybrid renewable energy systems for off-grid telecom stations: Data analysis and system optimization," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    19. Wang, Yongli & Liu, Zhen & Wang, Jingyan & Du, Boxin & Qin, Yumeng & Liu, Xiaoli & Liu, Lin, 2023. "A Stackelberg game-based approach to transaction optimization for distributed integrated energy system," Energy, Elsevier, vol. 283(C).
    20. Li, Bei & Li, Jiangchen, 2021. "Probabilistic sizing of a low-carbon emission power system considering HVDC transmission and microgrid clusters," Applied Energy, Elsevier, vol. 304(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:eee:energy:v:167:y:2019:i:c:p:117-135. 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.