IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v310y2022ics0306261921017104.html
   My bibliography  Save this article

Minimizing pricing policies based on user load profiles and residential demand responses in smart grids

Author

Listed:
  • Rasheed, Muhammad Babar
  • R-Moreno, María D.

Abstract

This paper considers the time of use (TOU) pricing scheme to propose a consumer aware pricing policy (CAPP), where each customer receives a separate electricity pricing signal. These pricing signals are obtained based on individualized load demand patterns to optimally manage the flexible load demand. The main objective of CAPP is to reduce the peaks in overall system demand in such a way that the pricing signals remain non-discriminatory. To achieve this goal, firstly the mathematical model of CAPP comprising TOU electricity price, and its variation based on consumption patterns is formulated. Secondly, the proposed CAPP model is further extended by integrating renewable energy and storage sources to overcome the possible creation of rebound peaks due to scheduling. This objective is achieved by implementing a control variable modeling the upper bound of the low tariff area. Thirdly, the cost minimization optimization problem is solved by using a Genetic Algorithm (GA) with the objective of the fair price distribution. Numerical and simulation results are obtained to validate the proposed model in terms of convergence, optimality, and cost reduction objective function. Results reveal that each customer receives a separate electricity price signal based on his demand pattern without affecting the utility/retailer revenue. Furthermore, the total cost results are also compared with and without TOU & CAPP schemes to further validate the nondiscrimination in electricity price.

Suggested Citation

  • Rasheed, Muhammad Babar & R-Moreno, María D., 2022. "Minimizing pricing policies based on user load profiles and residential demand responses in smart grids," Applied Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261921017104
    DOI: 10.1016/j.apenergy.2021.118492
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921017104
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.118492?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. Urooj Asgher & Muhammad Babar Rasheed & Ameena Saad Al-Sumaiti & Atiq Ur-Rahman & Ihsan Ali & Amer Alzaidi & Abdullah Alamri, 2018. "Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources," Energies, MDPI, vol. 11(12), pages 1-26, December.
    2. Gao, Dian-ce & Sun, Yongjun & Lu, Yuehong, 2015. "A robust demand response control of commercial buildings for smart grid under load prediction uncertainty," Energy, Elsevier, vol. 93(P1), pages 275-283.
    3. 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.
    4. Neves, Diana & Brito, Miguel C. & Silva, Carlos A., 2016. "Impact of solar and wind forecast uncertainties on demand response of isolated microgrids," Renewable Energy, Elsevier, vol. 87(P2), pages 1003-1015.
    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. Dhiaa Halboot Muhsen & Haider Tarish Haider & Yaarob Al-Nidawi & Ghadeer Ghazi Shayea, 2023. "Operational Scheduling of Household Appliances by Using Triple-Objective Optimization Algorithm Integrated with Multi-Criteria Decision Making," Sustainability, MDPI, vol. 15(24), pages 1-24, December.
    2. Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Goh, Hui Hwang, 2023. "Incentive-based demand response under incomplete information based on the deep deterministic policy gradient," Applied Energy, Elsevier, vol. 351(C).
    3. Razzak, Abdur & Islam, Md. Tariqul & Roy, Palash & Razzaque, Md. Abdur & Hassan, Md. Rafiul & Hassan, Mohammad Mehedi, 2024. "Leveraging Deep Q-Learning to maximize consumer quality of experience in smart grid," Energy, Elsevier, vol. 290(C).
    4. Zafar Mahmood & Benmao Cheng & Naveed Anwer Butt & Ghani Ur Rehman & Muhammad Zubair & Afzal Badshah & Muhammad Aslam, 2023. "Efficient Scheduling of Home Energy Management Controller (HEMC) Using Heuristic Optimization Techniques," Sustainability, MDPI, vol. 15(2), pages 1-22, January.

    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. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. Huang, Pei & Sun, Yongjun, 2019. "A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty," Renewable Energy, Elsevier, vol. 134(C), pages 215-227.
    3. 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.
    4. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    5. Zhang, Yang & Yang, Qingyu & Li, Donghe & An, Dou, 2022. "A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility," Applied Energy, Elsevier, vol. 323(C).
    6. 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.
    7. 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).
    8. Huang, Pei & Wu, Hunjun & Huang, Gongsheng & Sun, Yongjun, 2018. "A top-down control method of nZEBs for performance optimization at nZEB-cluster-level," Energy, Elsevier, vol. 159(C), pages 891-904.
    9. 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).
    10. Xu, Ruoyu & Liu, Xiaochen & Liu, Xiaohua & Zhang, Tao, 2024. "Quantifying the energy flexibility potential of a centralized air-conditioning system: A field test study of hub airports," Energy, Elsevier, vol. 298(C).
    11. Vo-Van Thanh & Wencong Su & Bin Wang, 2022. "Optimal DC Microgrid Operation with Model Predictive Control-Based Voltage-Dependent Demand Response and Optimal Battery Dispatch," Energies, MDPI, vol. 15(6), pages 1-19, March.
    12. Gomez-Herrera, Juan A. & Anjos, Miguel F., 2018. "Optimal collaborative demand-response planner for smart residential buildings," Energy, Elsevier, vol. 161(C), pages 370-380.
    13. Xu, Fangyuan & Zhu, Weidong & Wang, Yi Fei & Lai, Chun Sing & Yuan, Haoliang & Zhao, Yujia & Guo, Siming & Fu, Zhengxin, 2022. "A new deregulated demand response scheme for load over-shifting city in regulated power market," Applied Energy, Elsevier, vol. 311(C).
    14. 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.
    15. Kalim Ullah & Sajjad Ali & Taimoor Ahmad Khan & Imran Khan & Sadaqat Jan & Ibrar Ali Shah & Ghulam Hafeez, 2020. "An Optimal Energy Optimization Strategy for Smart Grid Integrated with Renewable Energy Sources and Demand Response Programs," Energies, MDPI, vol. 13(21), pages 1-17, November.
    16. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    17. 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).
    18. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    19. Obara, Shin'ya & Sato, Katsuaki & Utsugi, Yuta, 2018. "Study on the operation optimization of an isolated island microgrid with renewable energy layout planning," Energy, Elsevier, vol. 161(C), pages 1211-1225.
    20. Seongwoo Lee & Joonho Seon & Byungsun Hwang & Soohyun Kim & Youngghyu Sun & Jinyoung Kim, 2024. "Recent Trends and Issues of Energy Management Systems Using Machine Learning," Energies, MDPI, vol. 17(3), pages 1-24, January.

    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:310:y:2022:i:c:s0306261921017104. 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.