IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v168y2019icp1119-1127.html
   My bibliography  Save this article

Real-time price-based demand response model for combined heat and power systems

Author

Listed:
  • Alipour, Manijeh
  • Zare, Kazem
  • Seyedi, Heresh
  • Jalali, Mehdi

Abstract

Real-time electricity pricing could promote the adaptation of demand response programs in the presence of price volatility in smart grids. This paper proposes a real-time price-based demand response management model for heat and power consumers. In the proposed demand response program (DRP) the responsive electrical load can vary in different time intervals. In addition, total power and heat demands of the consumer are met, without any curtailment. The price uncertainty is envisaged through robust optimization for minimizing the worst-case electricity and heat demand procurement cost while flexibly adjusting the solution robustness. The proposed model can be easily embedded in the energy management system of the customer equipped with combined heat and power systems (CHPs), a power-only unit, a boiler unit and a heat buffer tank (HBT) and makes it possible to achieve a minimum cost. In this paper, the dual dependency characteristic of the heat and power in different types of CHPs has been taken into account. Furthermore, technical constraints, i.e. minimizing number of start-ups and shut-downs, ramp rate limits and minimum up/down-time limits of generation facilities are satisfied. Numerical simulations confirming the applicability and effectiveness of the proposed model are provided. According to the simulation results, there is a significant increase in the daily cost of the consumer without smart grid technology in comparison with the consumer employing the proposed real-time model.

Suggested Citation

  • Alipour, Manijeh & Zare, Kazem & Seyedi, Heresh & Jalali, Mehdi, 2019. "Real-time price-based demand response model for combined heat and power systems," Energy, Elsevier, vol. 168(C), pages 1119-1127.
    • Handle: RePEc:eee:energy:v:168:y:2019:i:c:p:1119-1127
    DOI: 10.1016/j.energy.2018.11.150
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218323612
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.11.150?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. Ferrari, M.L. & Cuneo, A. & Pascenti, M. & Traverso, A., 2017. "Real-time state of charge estimation in thermal storage vessels applied to a smart polygeneration grid," Applied Energy, Elsevier, vol. 206(C), pages 90-100.
    2. Kia, Mohsen & Nazar, Mehrdad Setayesh & Sepasian, Mohammad Sadegh & Heidari, Alireza & Siano, Pierluigi, 2017. "Optimal day ahead scheduling of combined heat and power units with electrical and thermal storage considering security constraint of power system," Energy, Elsevier, vol. 120(C), pages 241-252.
    3. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    4. Alipour, Manijeh & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2014. "Short-term scheduling of combined heat and power generation units in the presence of demand response programs," Energy, Elsevier, vol. 71(C), pages 289-301.
    5. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
    6. Sun, Zeyi & Li, Lin & Bego, Andres & Dababneh, Fadwa, 2015. "Customer-side electricity load management for sustainable manufacturing systems utilizing combined heat and power generation system," International Journal of Production Economics, Elsevier, vol. 165(C), pages 112-119.
    7. Stephen P. Holland & Erin T. Mansur, 2006. "The Short-Run Effects of Time-Varying Prices in Competitive Electricity Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 127-156.
    8. Christidis, Andreas & Koch, Christoph & Pottel, Lothar & Tsatsaronis, George, 2012. "The contribution of heat storage to the profitable operation of combined heat and power plants in liberalized electricity markets," Energy, Elsevier, vol. 41(1), pages 75-82.
    9. Kopanos, Georgios M. & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "Energy production planning of a network of micro combined heat and power generators," Applied Energy, Elsevier, vol. 102(C), pages 1522-1534.
    10. Florian Kuhnlenz & Pedro H. J. Nardelli & Santtu Karhinen & Rauli Svento, 2017. "Implementing Flexible Demand: Real-time Price vs. Market Integration," Papers 1709.02667, arXiv.org, revised Feb 2018.
    11. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.
    12. Alipour, Manijeh & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2016. "Optimal risk-constrained participation of industrial cogeneration systems in the day-ahead energy markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 421-432.
    13. Alipour, Manijeh & Zare, Kazem & Seyedi, Heresh, 2018. "A multi-follower bilevel stochastic programming approach for energy management of combined heat and power micro-grids," Energy, Elsevier, vol. 149(C), pages 135-146.
    14. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Zare, Kazem, 2014. "Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs," Applied Energy, Elsevier, vol. 136(C), pages 393-404.
    15. Kühnlenz, Florian & Nardelli, Pedro H.J. & Karhinen, Santtu & Svento, Rauli, 2018. "Implementing flexible demand: Real-time price vs. market integration," Energy, Elsevier, vol. 149(C), pages 550-565.
    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. Ghasemi-Marzbali, Ali & Shafiei, Mohammad & Ahmadiahangar, Roya, 2023. "Day-ahead economical planning of multi-vector energy district considering demand response program," Applied Energy, Elsevier, vol. 332(C).
    2. Li, Yuanyuan & Li, Junxiang & He, Jianjia & Zhang, Shuyuan, 2021. "The real-time pricing optimization model of smart grid based on the utility function of the logistic function," Energy, Elsevier, vol. 224(C).
    3. Adlband, Nahid & Biguesh, Mehrzad & Mohammadi, Mohammad, 2020. "A privacy-preserving and aggregate load controlling decentralized energy consumption scheduling scheme," Energy, Elsevier, vol. 198(C).
    4. Jun Dong & Shilin Nie & Hui Huang & Peiwen Yang & Anyuan Fu & Jin Lin, 2019. "Research on Economic Operation Strategy of CHP Microgrid Considering Renewable Energy Sources and Integrated Energy Demand Response," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    5. Shen, Ziqi & Wei, Wei & Wu, Lei & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Economic dispatch of power systems with LMP-dependent demands: A non-iterative MILP model," Energy, Elsevier, vol. 233(C).
    6. Markus Fleschutz & Markus Bohlayer & Marco Braun & Michael D. Murphy, 2022. "Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems," Sustainability, MDPI, vol. 14(13), pages 1-38, June.
    7. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    8. Wang, Fei & Ge, Xinxin & Yang, Peng & Li, Kangping & Mi, Zengqiang & Siano, Pierluigi & Duić, Neven, 2020. "Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing," Energy, Elsevier, vol. 213(C).
    9. Mohammad Hossein Nejati Amiri & Mehdi Mehdinejad & Amin Mohammadpour Shotorbani & Heidarali Shayanfar, 2023. "Heuristic Retailer’s Day-Ahead Pricing Based on Online-Learning of Prosumer’s Optimal Energy Management Model," Energies, MDPI, vol. 16(3), pages 1-21, January.
    10. Ioannis P. Panapakidis & Nikolaos Koltsaklis & Georgios C. Christoforidis, 2020. "A Novel Integrated Profit Maximization Model for Retailers under Varied Penetration Levels of Photovoltaic Systems," Energies, MDPI, vol. 14(1), pages 1-27, December.
    11. 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).
    12. Arega Getaneh Abate & Rosana Riccardi & Carlos Ruiz, 2021. "Dynamic tariffs-based demand response in retail electricity market under uncertainty," Papers 2105.03405, arXiv.org, revised Feb 2024.

    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. Misaghian, M.S. & Saffari, M. & Kia, M. & Heidari, A. & Shafie-khah, M. & Catalão, J.P.S., 2018. "Tri-level optimization of industrial microgrids considering renewable energy sources, combined heat and power units, thermal and electrical storage systems," Energy, Elsevier, vol. 161(C), pages 396-411.
    2. Elnaz Davoodi & Salar Balaei-Sani & Behnam Mohammadi-Ivatloo & Mehdi Abapour, 2021. "Flexible Continuous-Time Modeling for Multi-Objective Day-Ahead Scheduling of CHP Units," Sustainability, MDPI, vol. 13(9), pages 1-18, April.
    3. Kia, Mohsen & Setayesh Nazar, Mehrdad & Sepasian, Mohammad Sadegh & Heidari, Alireza & Sharaf, Adel M., 2017. "Coordination of heat and power scheduling in micro-grid considering inter-zonal power exchanges," Energy, Elsevier, vol. 141(C), pages 519-536.
    4. Alipour, Manijeh & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2016. "Optimal risk-constrained participation of industrial cogeneration systems in the day-ahead energy markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 421-432.
    5. Mohammadi, Mohammad & Noorollahi, Younes & Mohammadi-ivatloo, Behnam & Yousefi, Hossein, 2017. "Energy hub: From a model to a concept – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1512-1527.
    6. Alipour, Manijeh & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2014. "Short-term scheduling of combined heat and power generation units in the presence of demand response programs," Energy, Elsevier, vol. 71(C), pages 289-301.
    7. Kia, Mohsen & Setayesh Nazar, Mehrdad & Sepasian, Mohammad Sadegh & Heidari, Alireza & Siano, Pierluigi, 2017. "An efficient linear model for optimal day ahead scheduling of CHP units in active distribution networks considering load commitment programs," Energy, Elsevier, vol. 139(C), pages 798-817.
    8. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Zare, Kazem, 2014. "Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs," Applied Energy, Elsevier, vol. 136(C), pages 393-404.
    9. Kia, Mohsen & Nazar, Mehrdad Setayesh & Sepasian, Mohammad Sadegh & Heidari, Alireza & Siano, Pierluigi, 2017. "Optimal day ahead scheduling of combined heat and power units with electrical and thermal storage considering security constraint of power system," Energy, Elsevier, vol. 120(C), pages 241-252.
    10. Milis, Kevin & Peremans, Herbert & Van Passel, Steven, 2018. "Steering the adoption of battery storage through electricity tariff design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 125-139.
    11. Olamaei, Javad & Nazari, Mohammad Esmaeil & Bahravar, Sepideh, 2018. "Economic environmental unit commitment for integrated CCHP-thermal-heat only system with considerations for valve-point effect based on a heuristic optimization algorithm," Energy, Elsevier, vol. 159(C), pages 737-750.
    12. Kumbartzky, Nadine & Schacht, Matthias & Schulz, Katrin & Werners, Brigitte, 2017. "Optimal operation of a CHP plant participating in the German electricity balancing and day-ahead spot market," European Journal of Operational Research, Elsevier, vol. 261(1), pages 390-404.
    13. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    14. 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.
    15. Romero-Quete, David & Garcia, Javier Rosero, 2019. "An affine arithmetic-model predictive control approach for optimal economic dispatch of combined heat and power microgrids," Applied Energy, Elsevier, vol. 242(C), pages 1436-1447.
    16. Valdes, Javier & Poque González, Axel Bastián & Masip Macia, Yunesky & Dorner, Wolfgang & Ramirez Camargo, Luis, 2020. "Unveiling the potential for combined heat and power in Chilean industry - A policy perspective," Energy Policy, Elsevier, vol. 140(C).
    17. Mughees, Neelam & Jaffery, Mujtaba Hussain & Mughees, Anam & Ansari, Ejaz Ahmad & Mughees, Abdullah, 2023. "Reinforcement learning-based composite differential evolution for integrated demand response scheme in industrial microgrids," Applied Energy, Elsevier, vol. 342(C).
    18. Najafi, Arsalan & Falaghi, Hamid & Contreras, Javier & Ramezani, Maryam, 2016. "Medium-term energy hub management subject to electricity price and wind uncertainty," Applied Energy, Elsevier, vol. 168(C), pages 418-433.
    19. Li, Simon & Berrio, Denering & Fang, Yanda, 2022. "Heat balance modelling and simulation of non-mixing buffer tank design for hydronic heating applications," Energy, Elsevier, vol. 244(PB).
    20. 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.

    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:168:y:2019:i:c:p:1119-1127. 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.