IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipbs0306261924017070.html
   My bibliography  Save this article

Demand response with pricing schemes and consumers mode constraints for energy balancing in smart grids

Author

Listed:
  • Hua, Lyu-Guang
  • Hafeez, Ghulam
  • Alghamdi, Baheej
  • Alghamdi, Hisham
  • Khan, Farrukh Aslam
  • Ullah, Safeer

Abstract

Demand response (DR) is currently gaining widespread attention because it persuades consumers to participate actively in the electricity market, intending to balance power demand with available generation. The DR shaves peak energy use by shifting or curtailing demand or driving energy conservation concerning price variation or incentive payments to meet growing energy demand with the available generation, and provides new opportunities for power usage scheduling. To avail this opportunity, this work proposes DR energy management scheme (DREMS) based on multi-objective wind-driven optimization (MOWDO) algorithm for the power scheduling problem of the smart home, which has smart appliances: controllable (power and time) and critical appliances. The first class of appliances are controllable with adjustable power and time. On the other hand, critical appliances do not tolerate adjustment in time or power. The first class contributes to utility bill payment and peak-to-average demand ratio (PADR) minimization, and the second class contributes to comfort enhancement. The aim is to reduce utility bills, PADR, and discomfort, and achieve the desired trade-off between payment and PADR, and payment and discomfort. The power usage scheduling problem is formulated as an optimization problem with integer programming for four modes of operation. The DREMS based on the MOWDO algorithm resolves the optimization problem, producing an optimal operation schedule for four modes of operation. To assess its effectiveness, the MOWDO algorithm is compared with the existing MOPSO algorithm using various performance metrics. Simulation results demonstrate that the MOWDO returned an optimized operation schedule that successfully achieves the desired trade-off between payment and PADR, as well as payment and discomfort.

Suggested Citation

  • Hua, Lyu-Guang & Hafeez, Ghulam & Alghamdi, Baheej & Alghamdi, Hisham & Khan, Farrukh Aslam & Ullah, Safeer, 2025. "Demand response with pricing schemes and consumers mode constraints for energy balancing in smart grids," Applied Energy, Elsevier, vol. 377(PB).
  • Handle: RePEc:eee:appene:v:377:y:2025:i:pb:s0306261924017070
    DOI: 10.1016/j.apenergy.2024.124324
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924017070
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.124324?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Chuyao & Ji, Jie & Yang, Hongxing, 2024. "Day-ahead schedule optimization of household appliances for demand flexibility: Case study on PV/T powered buildings," Energy, Elsevier, vol. 289(C).
    2. Wu, Jiaman & Lu, Chenbei & Wu, Chenye & Shi, Jian & Gonzalez, Marta C. & Wang, Dan & Han, Zhu, 2024. "A cluster-based appliance-level-of-use demand response program design," Applied Energy, Elsevier, vol. 362(C).
    3. Bartusch, Cajsa & Juslin, Peter & Stikvoort, Britt & Yang-Wallentin, Fan & Öhrlund, Isak, 2024. "Opening the black box of demand response: Exploring the cognitive processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. Fahad R. Albogamy & Sajjad Ali Khan & Ghulam Hafeez & Sadia Murawwat & Sheraz Khan & Syed Irtaza Haider & Abdul Basit & Klaus-Dieter Thoben, 2022. "Real-Time Energy Management and Load Scheduling with Renewable Energy Integration in Smart Grid," Sustainability, MDPI, vol. 14(3), pages 1-28, February.
    5. Alzahrani, Ahmad & Sajjad, Khizar & Hafeez, Ghulam & Murawwat, Sadia & Khan, Sheraz & Khan, Farrukh Aslam, 2023. "Real-time energy optimization and scheduling of buildings integrated with renewable microgrid," Applied Energy, Elsevier, vol. 335(C).
    6. 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).
    7. Bilal Naji Alhasnawi & Basil H. Jasim & M. Dolores Esteban, 2020. "A New Robust Energy Management and Control Strategy for a Hybrid Microgrid System Based on Green Energy," Sustainability, MDPI, vol. 12(14), pages 1-28, July.
    8. Alirezazadeh, Atefeh & Rashidinejad, Masoud & Abdollahi, Amir & Afzali, Peyman & Bakhshai, Alireza, 2020. "A new flexible model for generation scheduling in a smart grid," Energy, Elsevier, vol. 191(C).
    9. Liu, Youquan & Li, Huazhen & Zhu, Jiawei & Lin, Yishuai & Lei, Weidong, 2023. "Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm," Energy, Elsevier, vol. 262(PA).
    10. Tang, Hong & Wang, Shengwei, 2023. "Game-theoretic optimization of demand-side flexibility engagement considering the perspectives of different stakeholders and multiple flexibility services," Applied Energy, Elsevier, vol. 332(C).
    11. Blumsack, Seth & Fernandez, Alisha, 2012. "Ready or not, here comes the smart grid!," Energy, Elsevier, vol. 37(1), pages 61-68.
    12. Zhu, Jiawei & Lin, Yishuai & Lei, Weidong & Liu, Youquan & Tao, Mengling, 2019. "Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm," Energy, Elsevier, vol. 171(C), pages 944-955.
    13. Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael, 2021. "Mixed-integer linear programming based optimization strategies for renewable energy communities," Energy, Elsevier, vol. 237(C).
    14. Ajoulabadi, Ata & Ravadanegh, Sajad Najafi & Behnam Mohammadi-Ivatloo,, 2020. "Flexible scheduling of reconfigurable microgrid-based distribution networks considering demand response program," Energy, Elsevier, vol. 196(C).
    15. Li, Xiao Hui & Hong, Seung Ho, 2014. "User-expected price-based demand response algorithm for a home-to-grid system," Energy, Elsevier, vol. 64(C), pages 437-449.
    16. Ibrahim, Charles & Mougharbel, Imad & Kanaan, Hadi Y. & Daher, Nivine Abou & Georges, Semaan & Saad, Maarouf, 2022. "A review on the deployment of demand response programs with multiple aspects coexistence over smart grid platform," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    17. Ullah, Kalim & Hafeez, Ghulam & Khan, Imran & Jan, Sadaqat & Javaid, Nadeem, 2021. "A multi-objective energy optimization in smart grid with high penetration of renewable energy sources," Applied Energy, Elsevier, vol. 299(C).
    Full references (including those not matched with items on IDEAS)

    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. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    2. Soares, Ana & Antunes, Carlos Henggeler & Oliveira, Carlos & Gomes, Álvaro, 2014. "A multi-objective genetic approach to domestic load scheduling in an energy management system," Energy, Elsevier, vol. 77(C), pages 144-152.
    3. Alzahrani, Ahmad & Sajjad, Khizar & Hafeez, Ghulam & Murawwat, Sadia & Khan, Sheraz & Khan, Farrukh Aslam, 2023. "Real-time energy optimization and scheduling of buildings integrated with renewable microgrid," Applied Energy, Elsevier, vol. 335(C).
    4. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2014. "Scheduling policies for two-state smart-home appliances in dynamic electricity pricing environments," Energy, Elsevier, vol. 69(C), pages 455-469.
    5. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).
    6. Durillon, Benoit & Bossu, Adrien, 2024. "Environmental assessment of smart energy management systems at distribution level — A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    7. Liu, Youquan & Li, Huazhen & Zhu, Jiawei & Lin, Yishuai & Lei, Weidong, 2023. "Multi-objective optimal scheduling of household appliances for demand side management using a hybrid heuristic algorithm," Energy, Elsevier, vol. 262(PA).
    8. Hong, Seung Ho & Yu, Mengmeng & Huang, Xuefei, 2015. "A real-time demand response algorithm for heterogeneous devices in buildings and homes," Energy, Elsevier, vol. 80(C), pages 123-132.
    9. Erdinc, Ozan, 2014. "Economic impacts of small-scale own generating and storage units, and electric vehicles under different demand response strategies for smart households," Applied Energy, Elsevier, vol. 126(C), pages 142-150.
    10. Yi Zhang & Tian Lan & Wei Hu, 2023. "A Two-Stage Robust Optimization Microgrid Model Considering Carbon Trading and Demand Response," Sustainability, MDPI, vol. 15(19), pages 1-22, October.
    11. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    12. Wang, Tonghe & Hua, Haochen & Shi, Tianying & Wang, Rui & Sun, Yizhong & Naidoo, Pathmanathan, 2024. "A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy," Applied Energy, Elsevier, vol. 356(C).
    13. Kovacic, Zora & Giampietro, Mario, 2015. "Empty promises or promising futures? The case of smart grids," Energy, Elsevier, vol. 93(P1), pages 67-74.
    14. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    15. Zhu, Junpeng & Meng, Dexin & Dong, Xiaofeng & Fu, Zhixin & Yuan, Yue, 2023. "An integrated electricity - hydrogen market design for renewable-rich energy system considering mobile hydrogen storage," Renewable Energy, Elsevier, vol. 202(C), pages 961-972.
    16. Ayman Al-Quraan & Muhannad Al-Qaisi, 2021. "Modelling, Design and Control of a Standalone Hybrid PV-Wind Micro-Grid System," Energies, MDPI, vol. 14(16), pages 1-23, August.
    17. Li, Xiao Hui & Hong, Seung Ho, 2014. "User-expected price-based demand response algorithm for a home-to-grid system," Energy, Elsevier, vol. 64(C), pages 437-449.
    18. Li, Ling-Ling & Ji, Bing-Xiang & Liu, Guan-Chen & Yuan, Jian-Ping & Tseng, Shuan-Wei & Lim, Ming K. & Tseng, Ming-Lang, 2024. "Grid-connected multi-microgrid system operational scheduling optimization: A hierarchical improved marine predators algorithm," Energy, Elsevier, vol. 294(C).
    19. Mariuzzo, Ivan & Fina, Bernadette & Stroemer, Stefan & Corinaldesi, Carlo & Raugi, Marco, 2025. "Grid-friendly optimization of energy communities through enhanced multiple participation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    20. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:377:y:2025:i:pb:s0306261924017070. 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.