IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v314y2022ics0306261922002793.html
   My bibliography  Save this article

Development and implementation of multi-agent systems for demand response aggregators in an industrial context

Author

Listed:
  • Woltmann, Stefan
  • Kittel, Julia

Abstract

Demand Response (DR) mechanisms are an important pillar in the transition to a bigger share of renewable energy in the power grid. To utilize DR, industrial companies can offer load flexibility at DR markets by cooperating with DR aggregators. Numerous studies promote the use of multi-agent systems (MAS) in this domain, however there is a lack of actual implementations and real world adaption. The literature has identified several reasons for this. These include research that often focuses on a high level of abstraction, the assumption of homogeneous structures for the use of agents, and insufficient tools and solutions for industrial deployment. To help DR aggregators use MAS in their existing virtual power plants, this paper presents a MAS that enables a step-by-step switch to agent technology and enables them to implement the solutions developed by the research community to improve the management of their systems. The proposed technical implementation on standard industrial equipment and the required interfaces to flexibility provider and DR aggregator are derived from the German DR market requirements. A simulation of the MAS implemented on a laboratory setup via the Java Agent DEvelopment Framework will be presented. This should provide a foundation for the transition to real world adaption in the future, which will then be discussed to show the benefits and drawbacks of this solution.

Suggested Citation

  • Woltmann, Stefan & Kittel, Julia, 2022. "Development and implementation of multi-agent systems for demand response aggregators in an industrial context," Applied Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:appene:v:314:y:2022:i:c:s0306261922002793
    DOI: 10.1016/j.apenergy.2022.118841
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922002793
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.118841?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. Lu, Renzhi & Li, Yi-Chang & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2020. "Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management," Applied Energy, Elsevier, vol. 276(C).
    2. Kalliopi Kravari & Nick Bassiliades, 2015. "A Survey of Agent Platforms," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(1), pages 1-11.
    3. Davarzani, Sima & Granell, Ramon & Taylor, Gareth A. & Pisica, Ioana, 2019. "Implementation of a novel multi-agent system for demand response management in low-voltage distribution networks," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    4. Xi Wu & Ping Jiang & Jing Lu, 2014. "Multiagent-Based Distributed Load Shedding for Islanded Microgrids," Energies, MDPI, vol. 7(9), pages 1-13, September.
    5. Khan, Muhammad Waseem & Wang, Jie, 2017. "The research on multi-agent system for microgrid control and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1399-1411.
    6. Chendan Li & Mehdi Savaghebi & Josep M. Guerrero & Ernane A. A. Coelho & Juan C. Vasquez, 2016. "Operation Cost Minimization of Droop-Controlled AC Microgrids Using Multiagent-Based Distributed Control," Energies, MDPI, vol. 9(9), pages 1-19, September.
    7. Guelpa, Elisa & Verda, Vittorio, 2021. "Demand response and other demand side management techniques for district heating: A review," Energy, Elsevier, vol. 219(C).
    8. Hee-Jun Cha & Dong-Jun Won & Sang-Hyuk Kim & Il-Yop Chung & Byung-Moon Han, 2015. "Multi-Agent System-Based Microgrid Operation Strategy for Demand Response," Energies, MDPI, vol. 8(12), pages 1-15, December.
    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. Lantao Jing & Enyu Wei & Liang Wang & Jinkuo Li & Qiang Zhang, 2024. "A Multi-Type Dynamic Response Control Strategy for Energy Consumption," Energies, MDPI, vol. 17(13), pages 1-20, June.
    2. Helder Pereira & Bruno Ribeiro & Luis Gomes & Zita Vale, 2022. "Smart Grid Ecosystem Modeling Using a Novel Framework for Heterogenous Agent Communities," Sustainability, MDPI, vol. 14(23), pages 1-20, November.
    3. Elsir, Mohamed & Al-Sumaiti, Ameena Saad & El Moursi, Mohamed Shawky & Al-Awami, Ali Taleb, 2023. "Coordinating the day-ahead operation scheduling for demand response and water desalination plants in smart grid," Applied Energy, Elsevier, vol. 335(C).
    4. R. Kanesaraj Ramasamy & Fang-Fang Chua & Su-Cheng Haw & Chin-Kuan Ho, 2022. "WSFeIn: A Novel, Dynamic Web Service Composition Adapter for Cloud-Based Mobile Application," Sustainability, MDPI, vol. 14(21), pages 1-21, October.

    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. Silvia Marzal & Raul González-Medina & Robert Salas-Puente & Emilio Figueres & Gabriel Garcerá, 2017. "A Novel Locality Algorithm and Peer-to-Peer Communication Infrastructure for Optimizing Network Performance in Smart Microgrids," Energies, MDPI, vol. 10(9), pages 1-25, August.
    2. Stephanus Antonius Ananda & Jyh-Cherng Gu & Ming-Ta Yang & Jing-Min Wang & Jun-Da Chen & Yung-Ruei Chang & Yih-Der Lee & Chen-Min Chan & Chia-Hao Hsu, 2016. "Multi-Agent System Fault Protection with Topology Identification in Microgrids," Energies, MDPI, vol. 10(1), pages 1-21, December.
    3. Kaveh Dehghanpour & Christopher Colson & Hashem Nehrir, 2017. "A Survey on Smart Agent-Based Microgrids for Resilient/Self-Healing Grids," Energies, MDPI, vol. 10(5), pages 1-25, May.
    4. Emrani-Rahaghi, Pouria & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2023. "Efficient voltage control of low voltage distribution networks using integrated optimized energy management of networked residential multi-energy microgrids," Applied Energy, Elsevier, vol. 349(C).
    5. Zizzo, G. & Beccali, M. & Bonomolo, M. & Di Pietra, B. & Ippolito, M.G. & La Cascia, D. & Leone, G. & Lo Brano, V. & Monteleone, F., 2017. "A feasibility study of some DSM enabling solutions in small islands: The case of Lampedusa," Energy, Elsevier, vol. 140(P1), pages 1030-1046.
    6. Haifeng Liang & Yue Dong & Yuxi Huang & Can Zheng & Peng Li, 2018. "Modeling of Multiple Master–Slave Control under Island Microgrid and Stability Analysis Based on Control Parameter Configuration," Energies, MDPI, vol. 11(9), pages 1-18, August.
    7. Mohamed El-Hendawi & Hossam A. Gabbar & Gaber El-Saady & El-Nobi A. Ibrahim, 2018. "Control and EMS of a Grid-Connected Microgrid with Economical Analysis," Energies, MDPI, vol. 11(1), pages 1-20, January.
    8. Jicheng Liu & Fangqiu Xu & Shuaishuai Lin & Hua Cai & Suli Yan, 2018. "A Multi-Agent-Based Optimization Model for Microgrid Operation Using Dynamic Guiding Chaotic Search Particle Swarm Optimization," Energies, MDPI, vol. 11(12), pages 1-22, November.
    9. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2021. "Multi-objective optimization of district energy systems with demand response," Energy, Elsevier, vol. 227(C).
    10. Schumacher, Felix & Nussbaumer, Thomas, 2023. "Four approaches for the year-round operation of wood-fired heating plants with low pollutant emissions," Energy, Elsevier, vol. 278(C).
    11. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
    12. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
    13. Annala, Salla & Ruggiero, Salvatore & Kangas, Hanna-Liisa & Honkapuro, Samuli & Ohrling, Tiina, 2022. "Impact of home market on business development and internationalization of demand response firms," Energy, Elsevier, vol. 242(C).
    14. Fan, Dongming & Ren, Yi & Feng, Qiang & Liu, Yiliu & Wang, Zili & Lin, Jing, 2021. "Restoration of smart grids: Current status, challenges, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    15. Chao-Chung Hsu & Bi-Hai Jiang & Chun-Cheng Lin, 2023. "A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing," Energies, MDPI, vol. 16(22), pages 1-15, November.
    16. Guelpa, E. & Capone, M. & Sciacovelli, A. & Vasset, N. & Baviere, R. & Verda, V., 2023. "Reduction of supply temperature in existing district heating: A review of strategies and implementations," Energy, Elsevier, vol. 262(PB).
    17. Zhu, Dafeng & Yang, Bo & Liu, Yuxiang & Wang, Zhaojian & Ma, Kai & Guan, Xinping, 2022. "Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park," Applied Energy, Elsevier, vol. 311(C).
    18. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    19. Khanna, Tarun M., 2022. "Using agricultural demand for reducing costs of renewable energy integration in India," Energy, Elsevier, vol. 254(PC).
    20. Wang, Junya & Zhao, Qinfang & Ning, Ping & Wen, Shikun, 2024. "Greenhouse gas contribution and emission reduction potential prediction of China's aluminum industry," Energy, Elsevier, vol. 290(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:appene:v:314:y:2022:i:c:s0306261922002793. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.