IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v180y2019icp444-456.html
   My bibliography  Save this article

Agent and system dynamics-based hybrid modeling and simulation for multilateral bidding in electricity market

Author

Listed:
  • Wang, Jidong
  • Wu, Jiahui
  • Che, Yanbo

Abstract

The electricity industry consists of multiple parties on generation, transmission, distribution, trading and market supervision of electricity. Models for a single electricity firm are only suitable for studying the operational decisions of the particular firm. In order to study the bidding behavior of all the players in the electricity market, this paper proposes a hybrid simulation model (HSM) that combines agent-based simulation (ABS) and system dynamics simulation (SDS). With the proposed hybrid model, some input variables required for one simulation model can be obtained by the output of another simulation model. In order to improve the competitiveness of agents that participate in bidding, this paper incorporates the Reinforce Learning (RL) algorithm into the agents. Each agent can obtain information and adapt to the environment through the continuous interaction with the environment. With the hybrid simulation model, the dynamics of the entire market remain stable, the market clearing prices converge, and the market share is relatively uniform.

Suggested Citation

  • Wang, Jidong & Wu, Jiahui & Che, Yanbo, 2019. "Agent and system dynamics-based hybrid modeling and simulation for multilateral bidding in electricity market," Energy, Elsevier, vol. 180(C), pages 444-456.
    • Handle: RePEc:eee:energy:v:180:y:2019:i:c:p:444-456
    DOI: 10.1016/j.energy.2019.04.180
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219308199
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.04.180?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. Wallentin, Gudrun & Neuwirth, Christian, 2017. "Dynamic hybrid modelling: Switching between AB and SD designs of a predator-prey model," Ecological Modelling, Elsevier, vol. 345(C), pages 165-175.
    2. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    3. Lewe, J.-H. & Hivin, L.F. & Mavris, D.N., 2014. "A multi-paradigm approach to system dynamics modeling of intercity transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 188-202.
    4. Silva, Francisco & Teixeira, Brígida & Pinto, Tiago & Santos, Gabriel & Vale, Zita & Praça, Isabel, 2016. "Generation of realistic scenarios for multi-agent simulation of electricity markets," Energy, Elsevier, vol. 116(P1), pages 128-139.
    5. Vincenot, Christian Ernest & Giannino, Francesco & Rietkerk, Max & Moriya, Kazuyuki & Mazzoleni, Stefano, 2011. "Theoretical considerations on the combined use of System Dynamics and individual-based modeling in ecology," Ecological Modelling, Elsevier, vol. 222(1), pages 210-218.
    6. Reddy, S. Surender, 2017. "Optimal scheduling of thermal-wind-solar power system with storage," Renewable Energy, Elsevier, vol. 101(C), pages 1357-1368.
    7. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    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. Johannes Dahlke & Kristina Bogner & Matthias Mueller & Thomas Berger & Andreas Pyka & Bernd Ebersberger, 2020. "Is the Juice Worth the Squeeze? Machine Learning (ML) In and For Agent-Based Modelling (ABM)," Papers 2003.11985, arXiv.org.
    2. Wu, Zhaoyuan & Zhou, Ming & Zhang, Ting & Li, Gengyin & Zhang, Yan & Liu, Xiaojuan, 2020. "Imbalance settlement evaluation for China's balancing market design via an agent-based model with a multiple criteria decision analysis method," Energy Policy, Elsevier, vol. 139(C).
    3. José D. Morcillo & Fabiola Angulo & Carlos J. Franco, 2021. "Simulation and Analysis of Renewable and Nonrenewable Capacity Scenarios under Hybrid Modeling: A Case Study," Mathematics, MDPI, vol. 9(13), pages 1-26, July.
    4. Piao, Longjian & de Vries, Laurens & de Weerdt, Mathijs & Yorke-Smith, Neil, 2021. "Electricity markets for DC distribution systems: Locational pricing trumps wholesale pricing," Energy, Elsevier, vol. 214(C).
    5. Jidong Wang & Jiahui Wu & Yingchen Shi, 2022. "A Novel Energy Management Optimization Method for Commercial Users Based on Hybrid Simulation of Electricity Market Bidding," Energies, MDPI, vol. 15(12), pages 1-24, June.
    6. Dehghan, Hamed & Amin-Naseri, Mohammad Reza, 2022. "A simulation-based optimization model to determine optimal electricity prices under various scenarios considering stakeholders’ objectives," Energy, Elsevier, vol. 238(PC).
    7. Wu, Jiahui & Wang, Jidong & Kong, Xiangyu, 2022. "Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning," Energy, Elsevier, vol. 256(C).
    8. Yilun Luo & Esmaeil Ahmadi & Benjamin C. McLellan & Tetsuo Tezuka, 2022. "Will Capacity Mechanisms Conflict with Carbon Pricing?," Energies, MDPI, vol. 15(24), pages 1-25, December.
    9. Li, Wanying & Dong, Fugui & Ji, Zhengsen & Xia, Meijuan, 2023. "Analysis of the compound differential evolution game of new energy manufacturers’ two-stage market behavior under the weight of consumption responsibility," Energy, Elsevier, vol. 264(C).
    10. Ostadi, Bakhtiar & Motamedi Sedeh, Omid & Husseinzadeh Kashan, Ali, 2020. "Risk-based optimal bidding patterns in the deregulated power market using extended Markowitz model," Energy, Elsevier, vol. 191(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. Dianat, Fateme & Khodakarami, Vahid & Hosseini, Seyed-Hossein & Shakouri G, Hamed, 2022. "Combining game theory concepts and system dynamics for evaluating renewable electricity development in fossil-fuel-rich countries in the Middle East and North Africa," Renewable Energy, Elsevier, vol. 190(C), pages 805-821.
    2. Talaat, M. & Farahat, M.A. & Elkholy, M.H., 2019. "Renewable power integration: Experimental and simulation study to investigate the ability of integrating wave, solar and wind energies," Energy, Elsevier, vol. 170(C), pages 668-682.
    3. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    4. Ghiasi, Mohammad, 2019. "Detailed study, multi-objective optimization, and design of an AC-DC smart microgrid with hybrid renewable energy resources," Energy, Elsevier, vol. 169(C), pages 496-507.
    5. Zima, Wiesław, 2019. "Simulation of steam superheater operation under conditions of pressure decrease," Energy, Elsevier, vol. 172(C), pages 932-944.
    6. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
    7. van Zyl-Bulitta, Verena Helen & Ritzel, Christian & Stafford, William & Wong, James Gien, 2019. "A compass to guide through the myriad of sustainable energy transition options across the global North-South divide," Energy, Elsevier, vol. 181(C), pages 307-320.
    8. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    9. Khamdamov, T., 2022. "A brief overview of the evolution of computer simulations in economic research," Journal of the New Economic Association, New Economic Association, vol. 54(2), pages 189-207.
    10. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    11. Li, Wei-Qin & Chang, Li, 2018. "A combination model with variable weight optimization for short-term electrical load forecasting," Energy, Elsevier, vol. 164(C), pages 575-593.
    12. Hu, Maomao & Xiao, Fu & Jørgensen, John Bagterp & Wang, Shengwei, 2019. "Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 92-106.
    13. Silva, Felipe L.C. & Souza, Reinaldo C. & Cyrino Oliveira, Fernando L. & Lourenco, Plutarcho M. & Calili, Rodrigo F., 2018. "A bottom-up methodology for long term electricity consumption forecasting of an industrial sector - Application to pulp and paper sector in Brazil," Energy, Elsevier, vol. 144(C), pages 1107-1118.
    14. Wang, Jun & Cao, Junxing & Yuan, Shan & Cheng, Ming, 2021. "Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network," Energy, Elsevier, vol. 233(C).
    15. Wallentin, Gudrun, 2017. "Spatial simulation: A spatial perspective on individual-based ecology—a review," Ecological Modelling, Elsevier, vol. 350(C), pages 30-41.
    16. Barman, Mayur & Dev Choudhury, Nalin Behari, 2019. "Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept," Energy, Elsevier, vol. 174(C), pages 886-896.
    17. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    18. Huang, Lili & Wang, Jun, 2018. "Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network," Energy, Elsevier, vol. 151(C), pages 875-888.
    19. Li, Qian & Loy-Benitez, Jorge & Nam, KiJeon & Hwangbo, Soonho & Rashidi, Jouan & Yoo, ChangKyoo, 2019. "Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks," Energy, Elsevier, vol. 178(C), pages 277-292.
    20. Justin Pence & Zahra Mohaghegh, 2020. "A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1183-1211, June.

    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:180:y:2019:i:c:p:444-456. 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.