IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i7p3751-d777069.html
   My bibliography  Save this article

An Economic Dispatch Method of Microgrid Based on Fully Distributed ADMM Considering Demand Response

Author

Listed:
  • Dan Zhou

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Xiaodie Niu

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yuzhe Xie

    (State Grid Ningbo Power Supply Company, Ningbo 315000, China)

  • Peng Li

    (State Grid Ningbo Power Supply Company, Ningbo 315000, China)

  • Jiandi Fang

    (State Grid Ningbo Power Supply Company, Ningbo 315000, China)

  • Fanghong Guo

    (College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

Aiming at the problem that the existing alternating direction method of multipliers (ADMM) cannot realize totally distributed computation, a totally distributed improved ADMM algorithm that combines logarithmic barrier function and virtual agent is proposed. We also investigate economic dispatch for microgrids considering demand response based on day-ahead real-time pricing (RTP), which forms a source-load-storage collaborative optimization scheme. First, three general distributed energy sources (DERs), renewable energy resources (RESs), conventional DERs and energy storage systems (ESSs), are considered in the method. Second, the goal of economic dispatch is to minimize the sum of three energy generation costs and implement the optimal power allocation of dispatchable DERs. Specifically, the approach not only inherits the fast computational speed of ADMM but also uses barrier function and virtual agent to handle inequality and equality, respectively. Moreover, the approach requires no coordination center and only the communication between current agent and adjacent agent to achieve totally distributed solution for every iteration, which can preserve information privacy well. Finally, a 30-node microgrid system is used for case analysis, and the simulation results demonstrate the feasibility and effectiveness of the proposed approach. It can be found that, the proposed approach converges to the optima when p = 0.01, v = 100, t 0 = 0.01 and μ = 2.

Suggested Citation

  • Dan Zhou & Xiaodie Niu & Yuzhe Xie & Peng Li & Jiandi Fang & Fanghong Guo, 2022. "An Economic Dispatch Method of Microgrid Based on Fully Distributed ADMM Considering Demand Response," Sustainability, MDPI, vol. 14(7), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:7:p:3751-:d:777069
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/7/3751/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/7/3751/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Mohamed, Mohamed A. & Jin, Tao & Su, Wencong, 2020. "Multi-agent energy management of smart islands using primal-dual method of multipliers," Energy, Elsevier, vol. 208(C).
    3. Nwulu, Nnamdi I. & Xia, Xiaohua, 2017. "Optimal dispatch for a microgrid incorporating renewables and demand response," Renewable Energy, Elsevier, vol. 101(C), pages 16-28.
    4. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    5. Thamer Alquthami & Ahmad H. Milyani & Muhammad Awais & Muhammad B. Rasheed, 2021. "An Incentive Based Dynamic Pricing in Smart Grid: A Customer’s Perspective," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    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. Xiaoqing Bai & Chun Wei & Peijie Li & Dongliang Xiao, 2023. "Editorial for the Special Issue on Sustainable Power Systems and Optimization," Sustainability, MDPI, vol. 15(6), pages 1-3, March.

    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. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    2. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    3. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    4. Astriani, Yuli & Shafiullah, GM & Shahnia, Farhad, 2021. "Incentive determination of a demand response program for microgrids," Applied Energy, Elsevier, vol. 292(C).
    5. Andrew Blohm & Jaden Crawford & Steven A. Gabriel, 2021. "Demand Response as a Real-Time, Physical Hedge for Retail Electricity Providers: The Electric Reliability Council of Texas Market Case Study," Energies, MDPI, vol. 14(4), pages 1-16, February.
    6. Heydarian-Forushani, Ehsan & Golshan, Mohamad Esmail Hamedani & Shafie-khah, Miadreza & Catalão, João P.S., 2020. "A comprehensive linear model for demand response optimization problem," Energy, Elsevier, vol. 209(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. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    9. Xu, Jiuping & Liu, Tingting, 2020. "Technological paradigm-based approaches towards challenges and policy shifts for sustainable wind energy development," Energy Policy, Elsevier, vol. 142(C).
    10. Davide Deltetto & Davide Coraci & Giuseppe Pinto & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings," Energies, MDPI, vol. 14(10), pages 1-25, May.
    11. Ricardo Faia & Pedro Faria & Zita Vale & João Spinola, 2019. "Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House," Energies, MDPI, vol. 12(9), pages 1-18, April.
    12. Kong, Xiangyu & Kong, Deqian & Yao, Jingtao & Bai, Linquan & Xiao, Jie, 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning," Applied Energy, Elsevier, vol. 271(C).
    13. Mimica, Marko & Dominković, Dominik Franjo & Capuder, Tomislav & Krajačić, Goran, 2021. "On the value and potential of demand response in the smart island archipelago," Renewable Energy, Elsevier, vol. 176(C), pages 153-168.
    14. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Tian, Ning & Zhao, Wei, 2023. "Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility," Applied Energy, Elsevier, vol. 348(C).
    15. Aya Amer & Khaled Shaban & Ahmed Massoud, 2022. "Demand Response in HEMSs Using DRL and the Impact of Its Various Configurations and Environmental Changes," Energies, MDPI, vol. 15(21), pages 1-20, November.
    16. Hyung-Joon Kim & Mun-Kyeom Kim, 2019. "Multi-Objective Based Optimal Energy Management of Grid-Connected Microgrid Considering Advanced Demand Response," Energies, MDPI, vol. 12(21), pages 1-28, October.
    17. Sushmita Kujur & Hari Mohan Dubey & Surender Reddy Salkuti, 2023. "Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    18. 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.
    19. Haddadian, Hossein & Noroozian, Reza, 2017. "Optimal operation of active distribution systems based on microgrid structure," Renewable Energy, Elsevier, vol. 104(C), pages 197-210.
    20. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(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:gam:jsusta:v:14:y:2022:i:7:p:3751-:d:777069. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.