IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v136y2014icp393-404.html
   My bibliography  Save this article

Stochastic risk-constrained short-term scheduling of industrial cogeneration systems in the presence of demand response programs

Author

Listed:
  • Alipour, Manijeh
  • Mohammadi-Ivatloo, Behnam
  • Zare, Kazem

Abstract

This paper presents a stochastic programming framework for solving the scheduling problem faced by an industrial customer with cogeneration facilities, conventional power production system, and heat only units. The power and heat demands of the customer are supplied considering demand response (DR) programs. In the proposed DR program, the responsive load can vary in different time intervals. In the paper, the heat-power dual dependency characteristic in different types of CHP units is taken into account. In addition, a heat buffer tank, with the ability of heat storage, has been incorporated in the proposed framework. The impact of the market and load uncertainties on the scheduling problem is characterized through a stochastic programming formulation. Autoregressive integrated moving average (ARIMA) technique is used to generate the electricity price and the customer demand scenarios. The daily and weekly seasonalities of demand and market prices are taken into account in the scenario generation procedure. The conditional value-at-risk (CVaR) methodology is implemented in order to limit the risk of expected profit due to market price and load forecast volatilities.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:136:y:2014:i:c:p:393-404
    DOI: 10.1016/j.apenergy.2014.09.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914009878
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2014.09.039?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. 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.
    2. Chang, Hsueh-Hsien & Yang, Hong-Tzer, 2009. "Applying a non-intrusive energy-management system to economic dispatch for a cogeneration system and power utility," Applied Energy, Elsevier, vol. 86(11), pages 2335-2343, November.
    3. Hu, Mengqi & Cho, Heejin, 2014. "A probability constrained multi-objective optimization model for CCHP system operation decision support," Applied Energy, Elsevier, vol. 116(C), pages 230-242.
    4. 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.
    5. Jitka Dupačová & Giorgio Consigli & Stein Wallace, 2000. "Scenarios for Multistage Stochastic Programs," Annals of Operations Research, Springer, vol. 100(1), pages 25-53, December.
    6. Chesi, Andrea & Ferrara, Giovanni & Ferrari, Lorenzo & Magnani, Sandro & Tarani, Fabio, 2013. "Influence of the heat storage size on the plant performance in a Smart User case study," Applied Energy, Elsevier, vol. 112(C), pages 1454-1465.
    7. Soroudi, Alireza & Amraee, Turaj, 2013. "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 376-384.
    8. Shnaiderman, Matan & Keren, Nir, 2014. "Cogeneration versus natural gas steam boiler: A techno-economic model," Applied Energy, Elsevier, vol. 131(C), pages 128-138.
    9. Buoro, Dario & Pinamonti, Piero & Reini, Mauro, 2014. "Optimization of a Distributed Cogeneration System with solar district heating," Applied Energy, Elsevier, vol. 124(C), pages 298-308.
    10. 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.
    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. Ferrari, Lorenzo & Esposito, Fabio & Becciani, Michele & Ferrara, Giovanni & Magnani, Sandro & Andreini, Mirko & Bellissima, Alessandro & Cantù, Matteo & Petretto, Giacomo & Pentolini, Massimo, 2017. "Development of an optimization algorithm for the energy management of an industrial Smart User," Applied Energy, Elsevier, vol. 208(C), pages 1468-1486.
    2. 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.
    3. Mongibello, Luigi & Bianco, Nicola & Caliano, Martina & Graditi, Giorgio, 2016. "Comparison between two different operation strategies for a heat-driven residential natural gas-fired CHP system: Heat dumping vs. load partialization," Applied Energy, Elsevier, vol. 184(C), pages 55-67.
    4. Fang, Tingting & Lahdelma, Risto, 2016. "Optimization of combined heat and power production with heat storage based on sliding time window method," Applied Energy, Elsevier, vol. 162(C), pages 723-732.
    5. 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.
    6. 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.
    7. 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).
    8. 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.
    9. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    10. 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.
    11. Roy, Nibir Baran & Das, Debapriya, 2024. "Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    12. 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.
    13. Howell, Shaun & Rezgui, Yacine & Hippolyte, Jean-Laurent & Jayan, Bejay & Li, Haijiang, 2017. "Towards the next generation of smart grids: Semantic and holonic multi-agent management of distributed energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 193-214.
    14. 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.
    15. SoltaniNejad Farsangi, Alireza & Hadayeghparast, Shahrzad & Mehdinejad, Mehdi & Shayanfar, Heidarali, 2018. "A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs," Energy, Elsevier, vol. 160(C), pages 257-274.
    16. Li, Longxi & Mu, Hailin & Li, Nan & Li, Miao, 2016. "Economic and environmental optimization for distributed energy resource systems coupled with district energy networks," Energy, Elsevier, vol. 109(C), pages 947-960.
    17. Bahl, Björn & Lampe, Matthias & Voll, Philip & Bardow, André, 2017. "Optimization-based identification and quantification of demand-side management potential for distributed energy supply systems," Energy, Elsevier, vol. 135(C), pages 889-899.
    18. 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.
    19. Wang, Haichao & Yin, Wusong & Abdollahi, Elnaz & Lahdelma, Risto & Jiao, Wenling, 2015. "Modelling and optimization of CHP based district heating system with renewable energy production and energy storage," Applied Energy, Elsevier, vol. 159(C), pages 401-421.
    20. Zhang, Lizhi & Kuang, Jiyuan & Sun, Bo & Li, Fan & Zhang, Chenghui, 2020. "A two-stage operation optimization method of integrated energy systems with demand response and energy storage," Energy, Elsevier, vol. 208(C).

    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:136:y:2014:i:c:p:393-404. 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.