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

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  • 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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Stephen P. Holland & Erin T. Mansur, 2006. "The Short-Run Effects of Time-Varying Prices in Competitive Electricity Markets," The Energy Journal, , vol. 27(4), pages 127-156, October.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
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    11. 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.
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