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A Comprehensive Methodology for the Integrated Optimal Sizing and Operation of Cogeneration Systems with Thermal Energy Storage

Author

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  • Luca Urbanucci

    (Department of Energy, Systems, Territory, and Constructions Engineering (DESTEC), University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy)

  • Francesco D’Ettorre

    (Department of Energy, Systems, Territory, and Constructions Engineering (DESTEC), University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy)

  • Daniele Testi

    (Department of Energy, Systems, Territory, and Constructions Engineering (DESTEC), University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy)

Abstract

Cogeneration systems are widely acknowledged as a viable solution to reduce energy consumption and costs, and CO 2 emissions. Nonetheless, their performance is highly dependent on their capacity and operational strategy, and optimization methods are required to fully exploit their potential. Among the available technical possibilities to maximize their performance, the integration of thermal energy storage is recognized as one of the most effective solutions. The introduction of a storage device further complicates the identification of the optimal equipment capacity and operation. This work presents a cutting-edge methodology for the optimal design and operation of cogeneration systems with thermal energy storage. A two-level algorithm is proposed to reap the benefits of the mixed integer linear programming formulation for the optimal operation problem, while overcoming its main drawbacks by means of a genetic algorithm at the design level. Part-load effects on nominal efficiency, variation of the unitary cost of the components in relation to their size, and the effect of the storage volume on its thermal losses are considered. Moreover, a novel formulation of the optimization problem is proposed to better characterize the heat losses and operation of the thermal energy storage. A rolling-horizon technique is implemented to reduce the computational time required for the optimization, without affecting the quality of the results. Furthermore, the proposed methodology is adopted to design a cogeneration system for a secondary school in San Francisco, California, which is optimized in terms of the equivalent annual cost. The results show that the optimally sized cogeneration unit directly meets around 70% of both the electric and thermal demands, while the thermal energy storage additionally covers 16% of the heat demands.

Suggested Citation

  • Luca Urbanucci & Francesco D’Ettorre & Daniele Testi, 2019. "A Comprehensive Methodology for the Integrated Optimal Sizing and Operation of Cogeneration Systems with Thermal Energy Storage," Energies, MDPI, vol. 12(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:875-:d:211506
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    References listed on IDEAS

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    2. Andrea Lazzaretto & Andrea Toffolo, 2019. "Optimum Choice of Energy System Configuration and Storages for a Proper Match between Energy Conversion and Demands," Energies, MDPI, vol. 12(20), pages 1-6, October.
    3. Nakama, Caroline S.M. & Knudsen, Brage R. & Tysland, Agnes C. & Jäschke, Johannes, 2023. "A simple dynamic optimization-based approach for sizing thermal energy storage using process data," Energy, Elsevier, vol. 268(C).
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    5. Lucas Schmeling & Patrik Schönfeldt & Peter Klement & Steffen Wehkamp & Benedikt Hanke & Carsten Agert, 2020. "Development of a Decision-Making Framework for Distributed Energy Systems in a German District," Energies, MDPI, vol. 13(3), pages 1-22, January.
    6. Wojciech Kosman & Andrzej Rusin, 2020. "The Application of Molten Salt Energy Storage to Advance the Transition from Coal to Green Energy Power Systems," Energies, MDPI, vol. 13(9), pages 1-18, May.
    7. Emanuele Guerrazzi & Dimitri Thomopulos & Davide Fioriti & Ivan Mariuzzo & Eva Schito & Davide Poli & Marco Raugi, 2023. "Design of Energy Communities and Data-Sharing: Format and Open Data," Energies, MDPI, vol. 16(17), pages 1-26, August.
    8. Attila R. Imre & Sindu Daniarta & Przemysław Błasiak & Piotr Kolasiński, 2023. "Design, Integration, and Control of Organic Rankine Cycles with Thermal Energy Storage and Two-Phase Expansion System Utilizing Intermittent and Fluctuating Heat Sources—A Review," Energies, MDPI, vol. 16(16), pages 1-25, August.
    9. Muschick, D. & Zlabinger, S. & Moser, A. & Lichtenegger, K. & Gölles, M., 2022. "A multi-layer model of stratified thermal storage for MILP-based energy management systems," Applied Energy, Elsevier, vol. 314(C).
    10. Schmeling, Lucas & Schönfeldt, Patrik & Klement, Peter & Vorspel, Lena & Hanke, Benedikt & von Maydell, Karsten & Agert, Carsten, 2022. "A generalised optimal design methodology for distributed energy systems," Renewable Energy, Elsevier, vol. 200(C), pages 1223-1239.
    11. Pierpaolo Garavaso & Fabio Bignucolo & Jacopo Vivian & Giulia Alessio & Michele De Carli, 2021. "Optimal Planning and Operation of a Residential Energy Community under Shared Electricity Incentives," Energies, MDPI, vol. 14(8), pages 1-24, April.
    12. Rohde, Daniel & Knudsen, Brage Rugstad & Andresen, Trond & Nord, Natasa, 2020. "Dynamic optimization of control setpoints for an integrated heating and cooling system with thermal energy storages," Energy, Elsevier, vol. 193(C).

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