IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v207y2020ics0360544220313748.html
   My bibliography  Save this article

Many-criteria optimality of coordinated demand response with heterogeneous households

Author

Listed:
  • Zhou, Bin
  • Cao, Yingping
  • Li, Canbing
  • Wu, Qiuwei
  • Liu, Nian
  • Huang, Sheng
  • Wang, Huaizhi

Abstract

This paper proposes a bilevel multi-house energy management (MHEM) framework to coordinate the residential demand response (DR) of heterogeneous households based on many-criteria optimality. In the upper level, the loss of life (LOL) cost of transformers is formulated into the DR cost model, and a stochastic scheduling is implemented to determine the optimum amount of transformer load deferment and curtailment. The lower level aims to optimally allocate the transformer load from the aggregator to individual households, and a many-criteria DR optimality model is proposed to maximize the multi-house benefits from DR while achieving coordination of the DR participation. Furthermore, a hypercube spatial transformation based classification and sorting scheme is developed to form an evolutionary many-objective (EMO) algorithm in order to solve the many-criteria decision making (MCDM) problem of coordinated DR with numerous participants. The performance of the proposed method was benchmarked and validated on different scaled neighborhood systems over a 24-h scheduling horizon, and comparative results demonstrated its superiority and optimality in solving many-house DR problems.

Suggested Citation

  • Zhou, Bin & Cao, Yingping & Li, Canbing & Wu, Qiuwei & Liu, Nian & Huang, Sheng & Wang, Huaizhi, 2020. "Many-criteria optimality of coordinated demand response with heterogeneous households," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313748
    DOI: 10.1016/j.energy.2020.118267
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220313748
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118267?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. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    2. Ramos Muñoz, Edgar & Razeghi, Ghazal & Zhang, Li & Jabbari, Faryar, 2016. "Electric vehicle charging algorithms for coordination of the grid and distribution transformer levels," Energy, Elsevier, vol. 113(C), pages 930-942.
    3. Finn, P. & Fitzpatrick, C. & Connolly, D., 2012. "Demand side management of electric car charging: Benefits for consumer and grid," Energy, Elsevier, vol. 42(1), pages 358-363.
    4. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "Residential demand response scheme based on adaptive consumption level pricing," Energy, Elsevier, vol. 113(C), pages 301-308.
    5. Thakur, Jagruti & Chakraborty, Basab, 2016. "Demand side management in developing nations: A mitigating tool for energy imbalance and peak load management," Energy, Elsevier, vol. 114(C), pages 895-912.
    6. Hou, Qingchun & Zhang, Ning & Du, Ershun & Miao, Miao & Peng, Fei & Kang, Chongqing, 2019. "Probabilistic duck curve in high PV penetration power system: Concept, modeling, and empirical analysis in China," Applied Energy, Elsevier, vol. 242(C), pages 205-215.
    7. Adhikari, Rajendra & Pipattanasomporn, M. & Rahman, S., 2018. "An algorithm for optimal management of aggregated HVAC power demand using smart thermostats," Applied Energy, Elsevier, vol. 217(C), pages 166-177.
    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. Ildar Daminov & Rémy Rigo-Mariani & Raphael Caire & Anton Prokhorov & Marie-Cécile Alvarez-Hérault, 2021. "Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime," Energies, MDPI, vol. 14(5), pages 1-27, March.
    2. Haider, Haider Tarish & Muhsen, Dhiaa Halboot & Al-Nidawi, Yaarob Mahjoob & Khatib, Tamer & See, Ong Hang, 2022. "A novel approach for multi-objective cost-peak optimization for demand response of a residential area in smart grids," Energy, Elsevier, vol. 254(PB).
    3. Rajaa Naji EL idrissi & Mohammed Ouassaid & Mohamed Maaroufi & Zineb Cabrane & Jonghoon Kim, 2023. "Optimal Cooperative Power Management Framework for Smart Buildings Using Bidirectional Electric Vehicle Modes," Energies, MDPI, vol. 16(5), pages 1-22, February.
    4. 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).

    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. Nizami, Sohrab & Tushar, Wayes & Hossain, M.J. & Yuen, Chau & Saha, Tapan & Poor, H. Vincent, 2022. "Transactive energy for low voltage residential networks: A review," Applied Energy, Elsevier, vol. 323(C).
    2. Matthew Gough & Sérgio F. Santos & Mohammed Javadi & Rui Castro & João P. S. Catalão, 2020. "Prosumer Flexibility: A Comprehensive State-of-the-Art Review and Scientometric Analysis," Energies, MDPI, vol. 13(11), pages 1-32, May.
    3. Gao, Zhikun & Yu, Junqi & Zhao, Anjun & Hu, Qun & Yang, Siyuan, 2022. "A hybrid method of cooling load forecasting for large commercial building based on extreme learning machine," Energy, Elsevier, vol. 238(PC).
    4. Nikos Kampelis & Elisavet Tsekeri & Dionysia Kolokotsa & Kostas Kalaitzakis & Daniela Isidori & Cristina Cristalli, 2018. "Development of Demand Response Energy Management Optimization at Building and District Levels Using Genetic Algorithm and Artificial Neural Network Modelling Power Predictions," Energies, MDPI, vol. 11(11), pages 1-22, November.
    5. Lim, Keumju & Lee, Jongsu & Lee, Hyunjoo, 2021. "Implementing automated residential demand response in South Korea: Consumer preferences and market potential," Utilities Policy, Elsevier, vol. 70(C).
    6. Nizami, M.S.H. & Haque, A.N.M.M. & Nguyen, P.H. & Hossain, M.J., 2019. "On the application of Home Energy Management Systems for power grid support," Energy, Elsevier, vol. 188(C).
    7. Danish Mahmood & Nadeem Javaid & Sheraz Ahmed & Imran Ahmed & Iftikhar Azim Niaz & Wadood Abdul & Sanaa Ghouzali, 2017. "Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation," Energies, MDPI, vol. 10(3), pages 1-25, March.
    8. Flavio Martins & Maria Fatima Almeida & Rodrigo Calili & Agatha Oliveira, 2020. "Design Thinking Applied to Smart Home Projects: A User-Centric and Sustainable Perspective," Sustainability, MDPI, vol. 12(23), pages 1-27, December.
    9. Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
    10. Xiong, Rui & Sun, Fengchun & He, Hongwen & Nguyen, Trong Duy, 2013. "A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles," Energy, Elsevier, vol. 63(C), pages 295-308.
    11. Bertolini, Marina & D'Alpaos, Chiara & Moretto, Michele, 2018. "Do Smart Grids boost investments in domestic PV plants? Evidence from the Italian electricity market," Energy, Elsevier, vol. 149(C), pages 890-902.
    12. Jia, Kunqi & Guo, Ge & Xiao, Jucheng & Zhou, Huan & Wang, Zhihua & He, Guangyu, 2019. "Data compression approach for the home energy management system," Applied Energy, Elsevier, vol. 247(C), pages 643-656.
    13. Alagoz, B. Baykant & Kaygusuz, Asim & Akcin, Murat & Alagoz, Serkan, 2013. "A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market," Energy, Elsevier, vol. 59(C), pages 95-104.
    14. Chitchai Srithapon & Prasanta Ghosh & Apirat Siritaratiwat & Rongrit Chatthaworn, 2020. "Optimization of Electric Vehicle Charging Scheduling in Urban Village Networks Considering Energy Arbitrage and Distribution Cost," Energies, MDPI, vol. 13(2), pages 1-20, January.
    15. Talaat, M. & Hatata, A.Y. & Alsayyari, Abdulaziz S. & Alblawi, Adel, 2020. "A smart load management system based on the grasshopper optimization algorithm using the under-frequency load shedding approach," Energy, Elsevier, vol. 190(C).
    16. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    17. Ioannis Karakitsios & Dimitrios Lagos & Aris Dimeas & Nikos Hatziargyriou, 2023. "How Can EVs Support High RES Penetration in Islands," Energies, MDPI, vol. 16(1), pages 1-17, January.
    18. Chen, Chien-fei & Nelson, Hannah & Xu, Xiaojing & Bonilla, Gregory & Jones, Nicholas, 2021. "Beyond technology adoption: Examining home energy management systems, energy burdens and climate change perceptions during COVID-19 pandemic," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    19. Ioanna-M. Chatzigeorgiou & Christos Diou & Kyriakos C. Chatzidimitriou & Georgios T. Andreou, 2021. "Demand Response Alert Service Based on Appliance Modeling," Energies, MDPI, vol. 14(10), pages 1-15, May.
    20. Mahmoud H. Elkholy & Tomonobu Senjyu & Mohammed Elsayed Lotfy & Abdelrahman Elgarhy & Nehad S. Ali & Tamer S. Gaafar, 2022. "Design and Implementation of a Real-Time Smart Home Management System Considering Energy Saving," Sustainability, MDPI, vol. 14(21), pages 1-22, October.

    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:207:y:2020:i:c:s0360544220313748. 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.