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Stochastic optimization of trigeneration systems for decision-making under long-term uncertainty in energy demands and prices

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  • Onishi, Viviani C.
  • Antunes, Carlos H.
  • Fraga, Eric S.
  • Cabezas, Heriberto

Abstract

Combined heating, cooling and power (CHCP) systems, so-called trigeneration, are widely accepted as more energy-efficient and environment-friendly alternatives to traditional separate energy generation. Nevertheless, the tasks of synthesis and optimization of trigeneration systems are strongly hampered by the long-term uncertainties in energy demands and prices. In this work, we introduce a new scenario-based model for the stochastic optimization of CHCP systems under uncertainty in several process design parameters. Energy generation operators are proposed to ensure the optimal sizing and operation of each equipment in each optimization scenario. Our main objective is to enhance energy efficiency by synthesizing the most cost-effective CHCP system able to operate in wide-ranging scenarios of energy demands and prices. For this purpose, uncertain design parameters are modelled as a set of loading and pricing scenarios with given probability of occurrence. The set of scenarios contains correlated energy prices described through a multivariate Normal distribution, which are generated via a Monte Carlo sampling technique with symmetric correlation matrix. The resulting stochastic multiscenario MINLP model is solved to global optimality by minimizing the expected total annualized cost. A thorough economic risk analysis underlines the effectiveness of the proposed methodology. This systematic approach represents a useful tool to support the decision-making process regarding system efficiency and robustness.

Suggested Citation

  • Onishi, Viviani C. & Antunes, Carlos H. & Fraga, Eric S. & Cabezas, Heriberto, 2019. "Stochastic optimization of trigeneration systems for decision-making under long-term uncertainty in energy demands and prices," Energy, Elsevier, vol. 175(C), pages 781-797.
    • Handle: RePEc:eee:energy:v:175:y:2019:i:c:p:781-797
    DOI: 10.1016/j.energy.2019.03.095
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