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On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation

Author

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  • Antonio Piacentino

    (Department of Energy, Information Engineering and Mathematical Models, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy)

  • Roberto Gallea

    (Sistema Informativo di Ateneo, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy)

  • Pietro Catrini

    (Department of Energy, Information Engineering and Mathematical Models, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy)

  • Fabio Cardona

    (Department of Energy, Information Engineering and Mathematical Models, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy)

  • Domenico Panno

    (Department of Energy, Information Engineering and Mathematical Models, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy)

Abstract

Cogeneration and trigeneration plants are widely recognized as promising technologies for increasing energy efficiency in buildings. However, their overall potential is scarcely exploited, due to the difficulties in achieving economic viability and the risk of investment related to uncertainties in future energy loads and prices. Several stochastic optimization models have been proposed in the literature to account for uncertainties, but these instruments share in a common reliance on user-defined probability functions for each stochastic parameter. Being such functions hard to predict, in this paper an analysis of the influence of erroneous estimation of the uncertain energy loads and prices on the optimal plant design and operation is proposed. With reference to a hotel building, a number of realistic scenarios is developed, exploring all the most frequent errors occurring in the estimation of energy loads and prices. Then, profit-oriented optimizations are performed for the examined scenarios, by means of a deterministic mixed integer linear programming algorithm. From a comparison between the achieved results, it emerges that: (i) the plant profitability is prevalently influenced by the average “spark-spread” (i.e., ratio between electricity and fuel price) and, secondarily, from the shape of the daily price profiles; (ii) the “optimal sizes” of the main components are scarcely influenced by the daily load profiles, while they are more strictly related with the average “power to heat” and “power to cooling” ratios of the building.

Suggested Citation

  • Antonio Piacentino & Roberto Gallea & Pietro Catrini & Fabio Cardona & Domenico Panno, 2016. "On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation," Energies, MDPI, vol. 9(12), pages 1-31, December.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1049-:d:85085
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    References listed on IDEAS

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    1. Dumitrascu Gheorghe & Feidt Michel & Popescu Aristotel & Grigorean Stefan, 2019. "Endoreversible Trigeneration Cycle Design Based on Finite Physical Dimensions Thermodynamics," Energies, MDPI, vol. 12(16), pages 1-21, August.
    2. Volpe, R. & Catrini, P. & Piacentino, A. & Fichera, A., 2022. "An agent-based model to support the preliminary design and operation of heating and power grids with cogeneration units and photovoltaic panels in densely populated areas," Energy, Elsevier, vol. 261(PB).
    3. 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.

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