IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v251y2019ic29.html
   My bibliography  Save this article

Welfare-aware strategic demand control in an intelligent market-based framework: Move towards sustainable smart grid

Author

Listed:
  • Taheri Tehrani, Mohammad
  • Afshin Hemmatyar, Ali Mohammad

Abstract

To address sustainability challenges appeared in today’s power grids, it is essential for emerging demand control paradigm to be adapted more to the lifestyle of the customers. In this paper, due to the ever-growing interconnectivity of the grids, a distributed Commodity Market (CM) framework is proposed in which intelligent agents embedded inside of customers want to maximize their preferred welfare through real-time demand of power from an energy market. Since there is not a comprehensive model for the grids, utilizing Reinforcement Learning (RL) proves that the global optimal performance is achieved in the Nash Equilibrium (NE) of the proposed framework. This solution not only maximizes the resource utilization of the market, but also allocates strategically optimal demands of the customers who have budget constraints. Considering buying power and diverse assumption-free interests of the customers are two of the novelties offered. To consider social objectives for the communities that may give priority to social values, a socially intelligent ability is added to the proposed framework as an option. In this case, social concepts such as social welfare and social fairness will be met among the customers. Finally, a framework is developed for the customers who have the aim to consider economic goals such as cost minimization in addition to welfare maximization. Considering simultaneous quantitative and qualitative goals in a joint optimization form for the budget-constrained customers without any restrictions on the customers’ preferences or the supply side is another novelty offered. The simulation results confirm that not only can the developed frameworks significantly improve the welfare in a stable manner, but also they are more successful in obtaining the demand during peak hours than just the economic frameworks proposed in the literature.

Suggested Citation

  • Taheri Tehrani, Mohammad & Afshin Hemmatyar, Ali Mohammad, 2019. "Welfare-aware strategic demand control in an intelligent market-based framework: Move towards sustainable smart grid," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:251:y:2019:i:c:29
    DOI: 10.1016/j.apenergy.2019.113325
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919309997
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.113325?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. William C. Brainard & Herbert E. Scarf, 2005. "How to Compute Equilibrium Prices in 1891," American Journal of Economics and Sociology, Wiley Blackwell, vol. 64(1), pages 57-83, January.
    2. Kelley, Morgan T. & Pattison, Richard C. & Baldick, Ross & Baldea, Michael, 2018. "An MILP framework for optimizing demand response operation of air separation units," Applied Energy, Elsevier, vol. 222(C), pages 951-966.
    3. E. Eisenberg, 1961. "Aggregation of Utility Functions," Management Science, INFORMS, vol. 7(4), pages 337-350, July.
    4. Herve Moulin, 2004. "Fair Division and Collective Welfare," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262633116, December.
    5. 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.
    6. Li, Pei-Hao & Pye, Steve, 2018. "Assessing the benefits of demand-side flexibility in residential and transport sectors from an integrated energy systems perspective," Applied Energy, Elsevier, vol. 228(C), pages 965-979.
    7. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    8. Malik, Anam & Ravishankar, Jayashri, 2018. "A hybrid control approach for regulating frequency through demand response," Applied Energy, Elsevier, vol. 210(C), pages 1347-1362.
    9. Anvari-Moghaddam, Amjad & Rahimi-Kian, Ashkan & Mirian, Maryam S. & Guerrero, Josep M., 2017. "A multi-agent based energy management solution for integrated buildings and microgrid system," Applied Energy, Elsevier, vol. 203(C), pages 41-56.
    10. Wang, Jianxiao & Zhong, Haiwang & Lai, Xiaowen & Xia, Qing & Shu, Chang & Kang, Chongqing, 2017. "Distributed real-time demand response based on Lagrangian multiplier optimal selection approach," Applied Energy, Elsevier, vol. 190(C), pages 949-959.
    11. Hu, Maomao & Xiao, Fu, 2018. "Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm," Applied Energy, Elsevier, vol. 219(C), pages 151-164.
    12. Kofinas, P. & Dounis, A.I. & Vouros, G.A., 2018. "Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids," Applied Energy, Elsevier, vol. 219(C), pages 53-67.
    13. Abdulaal, Ahmed & Moghaddass, Ramin & Asfour, Shihab, 2017. "Two-stage discrete-continuous multi-objective load optimization: An industrial consumer utility approach to demand response," Applied Energy, Elsevier, vol. 206(C), pages 206-221.
    14. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.
    15. Saffari, Mohammad & de Gracia, Alvaro & Fernández, Cèsar & Belusko, Martin & Boer, Dieter & Cabeza, Luisa F., 2018. "Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV," Applied Energy, Elsevier, vol. 211(C), pages 604-616.
    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. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    2. Zheng, Jun & Qi, Tiening & Hu, Xinyu & Pan, Qi & Zhang, Zhiyi & Guan, Aizhi & Ling, Wei & Peng, Tao & Wu, Jian & Wang, Wei, 2024. "Energy consumption assessment and economic analysis of a novel sustainable electro-machining auxiliary system," Applied Energy, Elsevier, vol. 357(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. Jordehi, A. Rezaee, 2019. "Optimisation of demand response in electric power systems, a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 308-319.
    2. Nikhil Garg & Ashish Goel & Benjamin Plaut, 2021. "Markets for public decision-making," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 56(4), pages 755-801, May.
    3. Ortega, Josué, 2020. "Multi-unit assignment under dichotomous preferences," Mathematical Social Sciences, Elsevier, vol. 103(C), pages 15-24.
    4. Fedor Sandomirskiy & Philip Ushchev, 2024. "The geometry of consumer preference aggregation," Papers 2405.06108, arXiv.org.
    5. Denizalp Goktas & Jiayi Zhao & Amy Greenwald, 2023. "T\^atonnement in Homothetic Fisher Markets," Papers 2306.04890, arXiv.org.
    6. 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).
    7. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.
    8. 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.
    9. Marcel García & Jose Aguilar & María D. R-Moreno, 2024. "An Autonomous Distributed Coordination Strategy for Sustainable Consumption in a Microgrid Based on a Bio-Inspired Approach," Energies, MDPI, vol. 17(3), pages 1-28, February.
    10. Qiu, Dawei & Ye, Yujian & Papadaskalopoulos, Dimitrios & Strbac, Goran, 2021. "Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach," Applied Energy, Elsevier, vol. 292(C).
    11. Navid Rezaei & Abdollah Ahmadi & Mohammadhossein Deihimi, 2022. "A Comprehensive Review of Demand-Side Management Based on Analysis of Productivity: Techniques and Applications," Energies, MDPI, vol. 15(20), pages 1-28, October.
    12. Yamashita, Daniela Yassuda & Vechiu, Ionel & Gaubert, Jean-Paul, 2020. "A review of hierarchical control for building microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    13. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    14. 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).
    15. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    16. Moshe Babaioff & Noam Nisan & Inbal Talgam-Cohen, 2021. "Competitive Equilibrium with Indivisible Goods and Generic Budgets," Mathematics of Operations Research, INFORMS, vol. 46(1), pages 382-403, February.
    17. Abdellatif Elmouatamid & Radouane Ouladsine & Mohamed Bakhouya & Najib El Kamoun & Mohammed Khaidar & Khalid Zine-Dine, 2020. "Review of Control and Energy Management Approaches in Micro-Grid Systems," Energies, MDPI, vol. 14(1), pages 1-30, December.
    18. Cheung, Yun Kuen & Cole, Richard & Devanur, Nikhil R., 2020. "Tatonnement beyond gross substitutes? Gradient descent to the rescue," Games and Economic Behavior, Elsevier, vol. 123(C), pages 295-326.
    19. Rocchetta, R. & Bellani, L. & Compare, M. & Zio, E. & Patelli, E., 2019. "A reinforcement learning framework for optimal operation and maintenance of power grids," Applied Energy, Elsevier, vol. 241(C), pages 291-301.
    20. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(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:251:y:2019:i:c:29. 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.