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A detailed MILP optimization model for combined cooling, heat and power system operation planning

Author

Listed:
  • Bischi, Aldo
  • Taccari, Leonardo
  • Martelli, Emanuele
  • Amaldi, Edoardo
  • Manzolini, Giampaolo
  • Silva, Paolo
  • Campanari, Stefano
  • Macchi, Ennio

Abstract

A detailed optimization model is presented for planning the short-term operation of combined cooling, heat and power (CCHP) energy systems. The purpose is, given the design of a cogeneration system, to determine an operating schedule that minimizes the total operating and maintenance costs minus the revenue due to the electricity sold to the grid, while taking into account time-varying loads, tariffs and ambient conditions. The model considers the simultaneous use of different prime movers (generating electricity and heat), boilers, compression heat pumps and chillers, and absorption chillers to satisfy given electricity, heat and cooling demands. Heat and cooling load can be stored in storage tanks. Units can have one or two operative variables, highly nonlinear performance curves describing their off-design behavior, and limitations or penalizations affecting their start-up/shut-down operations. To exploit the effectiveness of state-of-the-art Mixed Integer Linear Program (MILP) solvers, the resulting Mixed Integer Nonlinear Programming (MINLP) model is converted into a MILP by appropriate piecewise linear approximation of the nonlinear performance curves. The model, written in the AMPL modeling language, has been tested on several plant test cases. The computational results are discussed in terms of the quality of the solutions, the linearization accuracy and the computational time.

Suggested Citation

  • Bischi, Aldo & Taccari, Leonardo & Martelli, Emanuele & Amaldi, Edoardo & Manzolini, Giampaolo & Silva, Paolo & Campanari, Stefano & Macchi, Ennio, 2014. "A detailed MILP optimization model for combined cooling, heat and power system operation planning," Energy, Elsevier, vol. 74(C), pages 12-26.
  • Handle: RePEc:eee:energy:v:74:y:2014:i:c:p:12-26
    DOI: 10.1016/j.energy.2014.02.042
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    References listed on IDEAS

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    1. 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.
    2. Makkonen, Simo & Lahdelma, Risto, 2006. "Non-convex power plant modelling in energy optimisation," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1113-1126, June.
    3. Mitra, Sumit & Sun, Lige & Grossmann, Ignacio E., 2013. "Optimal scheduling of industrial combined heat and power plants under time-sensitive electricity prices," Energy, Elsevier, vol. 54(C), pages 194-211.
    4. Fazlollahi, Samira & Mandel, Pierre & Becker, Gwenaelle & Maréchal, Francois, 2012. "Methods for multi-objective investment and operating optimization of complex energy systems," Energy, Elsevier, vol. 45(1), pages 12-22.
    5. Buoro, D. & Casisi, M. & De Nardi, A. & Pinamonti, P. & Reini, M., 2013. "Multicriteria optimization of a distributed energy supply system for an industrial area," Energy, Elsevier, vol. 58(C), pages 128-137.
    6. Lahdelma, Risto & Hakonen, Henri, 2003. "An efficient linear programming algorithm for combined heat and power production," European Journal of Operational Research, Elsevier, vol. 148(1), pages 141-151, July.
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