IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i4p525-d95718.html
   My bibliography  Save this article

A Bi-Level Coordinated Optimization Strategy for Smart Appliances Considering Online Demand Response Potential

Author

Listed:
  • Jia Ning

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Yi Tang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Qian Chen

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

  • Jianming Wang

    (Jiangsu Electric Power Company Research Institute, Nanjing 211103, Jiangsu, China)

  • Jianhua Zhou

    (Jiangsu Electric Power Company Research Institute, Nanjing 211103, Jiangsu, China)

  • Bingtuan Gao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, Jiangsu, China)

Abstract

Demand response (DR) is counted as an effective method when there is a large-capacity power shortage in the power system, which may lead to peak loads or a rapid ramp. This paper proposes a bi-level coordinated optimization strategy by quantitating the DR potential (DRP) of smart appliances to descend the steep ramp and balance the power energy. Based on dynamic characteristics of the smart appliances, the mathematic models of online DRP are presented. In the upper layer, a multi-agent coordinated distribution method is proposed to allocate the demand limit to each agent from the dispatching center considering the online DRP. In the lower layer, an optimal smart appliances-controlling strategy is presented to guarantee the total household power consumption of each agent below its demand limit considering the consumers’ comfort and response times. Simulation results indicate the feasibility of the proposed strategy.

Suggested Citation

  • Jia Ning & Yi Tang & Qian Chen & Jianming Wang & Jianhua Zhou & Bingtuan Gao, 2017. "A Bi-Level Coordinated Optimization Strategy for Smart Appliances Considering Online Demand Response Potential," Energies, MDPI, vol. 10(4), pages 1-16, April.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:525-:d:95718
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/4/525/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/4/525/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maytham S. Ahmed & Azah Mohamed & Raad Z. Homod & Hussain Shareef, 2016. "Hybrid LSA-ANN Based Home Energy Management Scheduling Controller for Residential Demand Response Strategy," Energies, MDPI, vol. 9(9), pages 1-20, September.
    2. Bishnu P. Bhattarai & Kurt S. Myers & Birgitte Bak-Jensen & Sumit Paudyal, 2017. "Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks," Energies, MDPI, vol. 10(1), pages 1-18, January.
    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. Jia Ning & Sipeng Hao & Aidong Zeng & Bin Chen & Yi Tang, 2021. "Research on Multi-Timescale Coordinated Method for Source-Grid-Load with Uncertain Renewable Energy Considering Demand Response," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    2. Stojiljković, Mirko M., 2017. "Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics," Energy, Elsevier, vol. 137(C), pages 1231-1251.
    3. Kanato Tamashiro & Talal Alharbi & Alexey Mikhaylov & Ashraf M. Hemeida & Narayanan Krishnan & Mohammed Elsayed Lotfy & Tomonobu Senjyu, 2021. "Investigation of Home Energy Management with Advanced Direct Load Control and Optimal Scheduling of Controllable Loads," Energies, MDPI, vol. 14(21), pages 1-14, November.
    4. Xiaofeng Liu & Qi Wang & Wenting Wang, 2019. "Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior," Energies, MDPI, vol. 12(19), pages 1-19, September.
    5. Munankarmi, Prateek & Maguire, Jeff & Balamurugan, Sivasathya Pradha & Blonsky, Michael & Roberts, David & Jin, Xin, 2021. "Community-scale interaction of energy efficiency and demand flexibility in residential buildings," Applied Energy, Elsevier, vol. 298(C).
    6. Qi Wang & Ping Chang & Runqing Bai & Wenfei Liu & Jianfeng Dai & Yi Tang, 2019. "Mitigation Strategy for Duck Curve in High Photovoltaic Penetration Power System Using Concentrating Solar Power Station," Energies, MDPI, vol. 12(18), pages 1-16, September.
    7. Chong Chen & Xuan Zhou & Xiaowei Yang & Zhiheng He & Zhuo Li & Zhengtian Li & Xiangning Lin & Ting Wen & Yixin Zhuo & Ning Tong, 2018. "Collaborative Optimal Pricing and Day-Ahead and Intra-Day Integrative Dispatch of the Active Distribution Network with Multi-Type Active Loads," Energies, MDPI, vol. 11(4), pages 1-22, April.

    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. Homod, Raad Z., 2018. "Analysis and optimization of HVAC control systems based on energy and performance considerations for smart buildings," Renewable Energy, Elsevier, vol. 126(C), pages 49-64.
    2. Giovanni Pau & Mario Collotta & Antonio Ruano & Jiahu Qin, 2017. "Smart Home Energy Management," Energies, MDPI, vol. 10(3), pages 1-5, March.
    3. 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.
    4. Waseem, Muhammad & Lin, Zhenzhi & Liu, Shengyuan & Zhang, Zhi & Aziz, Tarique & Khan, Danish, 2021. "Fuzzy compromised solution-based novel home appliances scheduling and demand response with optimal dispatch of distributed energy resources," Applied Energy, Elsevier, vol. 290(C).
    5. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.
    6. Adnan Ahmad & Asif Khan & Nadeem Javaid & Hafiz Majid Hussain & Wadood Abdul & Ahmad Almogren & Atif Alamri & Iftikhar Azim Niaz, 2017. "An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources," Energies, MDPI, vol. 10(4), pages 1-35, April.
    7. Lei Chen & Hongkun Chen & Jun Yang & Yanjuan Yu & Kaiwei Zhen & Yang Liu & Li Ren, 2017. "Coordinated Control of Superconducting Fault Current Limiter and Superconducting Magnetic Energy Storage for Transient Performance Enhancement of Grid-Connected Photovoltaic Generation System," Energies, MDPI, vol. 10(1), pages 1-23, January.
    8. Salah L. Zubaidi & Sadik K. Gharghan & Jayne Dooley & Rafid M. Alkhaddar & Mawada Abdellatif, 2018. "Short-Term Urban Water Demand Prediction Considering Weather Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4527-4542, November.
    9. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
    10. 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.
    11. Laihyuk Park & Yongwoon Jang & Hyoungchel Bae & Juho Lee & Chang Yun Park & Sungrae Cho, 2017. "Automated Energy Scheduling Algorithms for Residential Demand Response Systems," Energies, MDPI, vol. 10(9), pages 1-17, September.
    12. 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).
    13. Reza Aghayari & Heydar Maddah & Mohammad Hossein Ahmadi & Wei-Mon Yan & Nahid Ghasemi, 2018. "Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions," Energies, MDPI, vol. 11(5), pages 1-16, May.
    14. Salah L. Zubaidi & Sandra Ortega-Martorell & Patryk Kot & Rafid M. Alkhaddar & Mawada Abdellatif & Sadik K. Gharghan & Maytham S. Ahmed & Khalid Hashim, 2020. "A Method for Predicting Long-Term Municipal Water Demands Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1265-1279, February.
    15. Yeong Huei Lee & Mugahed Amran & Yee Yong Lee & Ahmad Beng Hong Kueh & Siaw Fui Kiew & Roman Fediuk & Nikolai Vatin & Yuriy Vasilev, 2021. "Thermal Behavior and Energy Efficiency of Modified Concretes in the Tropical Climate: A Systemic Review," Sustainability, MDPI, vol. 13(21), pages 1, October.
    16. S. Muhammad Bagher Sadati & Jamal Moshtagh & Miadreza Shafie-khah & João P. S. Catalão, 2017. "Risk-Based Bi-Level Model for Simultaneous Profit Maximization of a Smart Distribution Company and Electric Vehicle Parking Lot Owner," Energies, MDPI, vol. 10(11), pages 1-16, October.
    17. Mohammad Shakeri & Jagadeesh Pasupuleti & Nowshad Amin & Md. Rokonuzzaman & Foo Wah Low & Chong Tak Yaw & Nilofar Asim & Nurul Asma Samsudin & Sieh Kiong Tiong & Chong Kok Hen & Chin Wei Lai, 2020. "An Overview of the Building Energy Management System Considering the Demand Response Programs, Smart Strategies and Smart Grid," Energies, MDPI, vol. 13(13), pages 1-15, June.
    18. Christoforos Menos-Aikateriniadis & Ilias Lamprinos & Pavlos S. Georgilakis, 2022. "Particle Swarm Optimization in Residential Demand-Side Management: A Review on Scheduling and Control Algorithms for Demand Response Provision," Energies, MDPI, vol. 15(6), pages 1-26, March.
    19. Yuan Hong & Shengbin Wang & Ziyue Huang, 2017. "Efficient Energy Consumption Scheduling: Towards Effective Load Leveling," Energies, MDPI, vol. 10(1), pages 1-27, January.
    20. Brian O’Regan & Fabio Silva & Eoin O’Leidhin & Farah Tahir & Karen Mould & Barry Hayes & Vahid Hosseinnezhad & Ruzanna Chitchyan & Padraig Lyons, 2021. "P2P, CSC and TE: A Survey on Hardware, Software and Data," Energies, MDPI, vol. 14(13), pages 1-21, 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:gam:jeners:v:10:y:2017:i:4:p:525-:d:95718. 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.