IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v9y2016i8p573-d74629.html
   My bibliography  Save this article

Optimal Cooling Load Sharing Strategies for Different Types of Absorption Chillers in Trigeneration Plants

Author

Listed:
  • Benedetto Conte

    (Department of Mechanical Engineering, University Rovira i Virgili, CREVER-Research Group on Applied Thermal Engineering, Avda. Països Catalans 26, Tarragona 43007, Spain)

  • Joan Carles Bruno

    (Department of Mechanical Engineering, University Rovira i Virgili, CREVER-Research Group on Applied Thermal Engineering, Avda. Països Catalans 26, Tarragona 43007, Spain)

  • Alberto Coronas

    (Department of Mechanical Engineering, University Rovira i Virgili, CREVER-Research Group on Applied Thermal Engineering, Avda. Països Catalans 26, Tarragona 43007, Spain)

Abstract

Trigeneration plants can use different types of chillers in the same plant, typically single effect and double effect absorption chillers, vapour compression chillers and also cooling storage systems. The highly variable cooling demand of the buildings connected to a district heating and cooling (DHC) network has to be distributed among these chillers to achieve lower operating costs and higher energy efficiencies. This problem is difficult to solve due to the different partial load behaviour of each chiller and the different chiller combinations that can cover a certain cooling demand using an appropriate sizing of the cooling storage. The objective of this paper is to optimize the daily plant operation of an existing trigeneration plant based on cogeneration engines and to study the optimal cooling load sharing between different types of absorption chillers using a mixed integer linear programming (MILP) model. Real data from a trigeneration plant connected to a DHC close to Barcelona (Spain) is used for the development of this model. The cooling load distribution among the different units is heavily influenced by the price of the electricity sold to the grid which rules the duration of the operation time of the engines. The main parameter to compare load distribution configurations is the primary energy saving indicator. Cooling load distribution among the different chillers changes also with the load of the whole plant because the chiller performance changes with load.

Suggested Citation

  • Benedetto Conte & Joan Carles Bruno & Alberto Coronas, 2016. "Optimal Cooling Load Sharing Strategies for Different Types of Absorption Chillers in Trigeneration Plants," Energies, MDPI, vol. 9(8), pages 1-16, July.
  • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:573-:d:74629
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/9/8/573/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/9/8/573/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jing, Z.X. & Jiang, X.S. & Wu, Q.H. & Tang, W.H. & Hua, B., 2014. "Modelling and optimal operation of a small-scale integrated energy based district heating and cooling system," Energy, Elsevier, vol. 73(C), pages 399-415.
    2. Roque Díaz, P. & Benito, Y.R. & Parise, J.A.R., 2010. "Thermoeconomic assessment of a multi-engine, multi-heat-pump CCHP (combined cooling, heating and power generation) system – A case study," Energy, Elsevier, vol. 35(9), pages 3540-3550.
    3. Rezaie, Behnaz & Rosen, Marc A., 2012. "District heating and cooling: Review of technology and potential enhancements," Applied Energy, Elsevier, vol. 93(C), pages 2-10.
    4. Voll, Philip & Klaffke, Carsten & Hennen, Maike & Bardow, André, 2013. "Automated superstructure-based synthesis and optimization of distributed energy supply systems," Energy, Elsevier, vol. 50(C), pages 374-388.
    5. Anna, Petrenko, 2016. "Мaркування готової продукції як складова частина інформаційного забезпечення маркетингової діяльності підприємств овочепродуктового підкомплексу," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 2(1), March.
    6. Abou-Ziyan, Hosny Z. & Alajmi, Ali F., 2014. "Effect of load-sharing operation strategy on the aggregate performance of existed multiple-chiller systems," Applied Energy, Elsevier, vol. 135(C), pages 329-338.
    7. Chicco, Gianfranco & Mancarella, Pierluigi, 2009. "Matrix modelling of small-scale trigeneration systems and application to operational optimization," Energy, Elsevier, vol. 34(3), pages 261-273.
    8. Chris Underwood & Bobo Ng & Francis Yik, 2015. "Scheduling of Multiple Chillers in Trigeneration Plants," Energies, MDPI, vol. 8(10), pages 1-25, October.
    9. Cho, Heejin & Smith, Amanda D. & Mago, Pedro, 2014. "Combined cooling, heating and power: A review of performance improvement and optimization," Applied Energy, Elsevier, vol. 136(C), pages 168-185.
    10. Carvalho, Monica & Serra, Luis Maria & Lozano, Miguel Angel, 2011. "Optimal synthesis of trigeneration systems subject to environmental constraints," Energy, Elsevier, vol. 36(6), pages 3779-3790.
    11. Piacentino, Antonio & Barbaro, Chiara, 2013. "A comprehensive tool for efficient design and operation of polygeneration-based energy μgrids serving a cluster of buildings. Part II: Analysis of the applicative potential," Applied Energy, Elsevier, vol. 111(C), pages 1222-1238.
    12. Mirko M. Stojiljković & Mladen M. Stojiljković & Bratislav D. Blagojević, 2014. "Multi-Objective Combinatorial Optimization of Trigeneration Plants Based on Metaheuristics," Energies, MDPI, vol. 7(12), pages 1-28, December.
    13. Piacentino, Antonio & Barbaro, Chiara & Cardona, Fabio & Gallea, Roberto & Cardona, Ennio, 2013. "A comprehensive tool for efficient design and operation of polygeneration-based energy μgrids serving a cluster of buildings. Part I: Description of the method," Applied Energy, Elsevier, vol. 111(C), pages 1204-1221.
    14. Entchev, E. & Yang, L. & Ghorab, M. & Lee, E.J., 2013. "Simulation of hybrid renewable microgeneration systems in load sharing applications," Energy, Elsevier, vol. 50(C), pages 252-261.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Francesco Calise & Massimo Dentice D’Accadia, 2016. "Simulation of Polygeneration Systems," Energies, MDPI, vol. 9(11), pages 1-9, November.
    2. Marco Pellegrini & Augusto Bianchini, 2018. "The Innovative Concept of Cold District Heating Networks: A Literature Review," Energies, MDPI, vol. 11(1), pages 1-16, January.
    3. Urbanucci, Luca & Bruno, Joan Carles & Testi, Daniele, 2019. "Thermodynamic and economic analysis of the integration of high-temperature heat pumps in trigeneration systems," Applied Energy, Elsevier, vol. 238(C), pages 516-533.
    4. Calise, Francesco & de Notaristefani di Vastogirardi, Giulio & Dentice d'Accadia, Massimo & Vicidomini, Maria, 2018. "Simulation of polygeneration systems," Energy, Elsevier, vol. 163(C), pages 290-337.
    5. Afzali, Sayyed Faridoddin & Mahalec, Vladimir, 2017. "Optimal design, operation and analytical criteria for determining optimal operating modes of a CCHP with fired HRSG, boiler, electric chiller and absorption chiller," Energy, Elsevier, vol. 139(C), pages 1052-1065.
    6. Sheykhi, Mohammad & Chahartaghi, Mahmood & Safaei Pirooz, Amir Ali & Flay, Richard G.J., 2020. "Investigation of the effects of operating parameters of an internal combustion engine on the performance and fuel consumption of a CCHP system," Energy, Elsevier, vol. 211(C).
    7. Ryszard Bartnik & Zbigniew Buryn & Anna Hnydiuk-Stefan & Waldemar Skomudek & Aleksandra Otawa, 2020. "Thermodynamic and Economic Analysis of Trigeneration System Comprising a Hierarchical Gas-Gas Engine for Production of Electricity, Heat and Cold," Energies, MDPI, vol. 13(4), pages 1-33, February.

    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. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    2. Yokoyama, Ryohei & Tokunaga, Akira & Wakui, Tetsuya, 2018. "Robust optimal design of energy supply systems under uncertain energy demands based on a mixed-integer linear model," Energy, Elsevier, vol. 153(C), pages 159-169.
    3. Yokoyama, Ryohei & Shinano, Yuji & Taniguchi, Syusuke & Wakui, Tetsuya, 2019. "Search for K-best solutions in optimal design of energy supply systems by an extended MILP hierarchical branch and bound method," Energy, Elsevier, vol. 184(C), pages 45-57.
    4. Yokoyama, Ryohei & Shinano, Yuji & Wakayama, Yuki & Wakui, Tetsuya, 2019. "Model reduction by time aggregation for optimal design of energy supply systems by an MILP hierarchical branch and bound method," Energy, Elsevier, vol. 181(C), pages 782-792.
    5. Yokoyama, Ryohei & Takeuchi, Kotaro & Shinano, Yuji & Wakui, Tetsuya, 2021. "Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method," Energy, Elsevier, vol. 228(C).
    6. Calise, Francesco & Cipollina, Andrea & Dentice d’Accadia, Massimo & Piacentino, Antonio, 2014. "A novel renewable polygeneration system for a small Mediterranean volcanic island for the combined production of energy and water: Dynamic simulation and economic assessment," Applied Energy, Elsevier, vol. 135(C), pages 675-693.
    7. Colmenar-Santos, Antonio & Rosales-Asensio, Enrique & Borge-Diez, David & Collado-Fernández, Eduardo, 2016. "Evaluation of the cost of using power plant reject heat in low-temperature district heating and cooling networks," Applied Energy, Elsevier, vol. 162(C), pages 892-907.
    8. Giaouris, Damian & Papadopoulos, Athanasios I. & Ziogou, Chrysovalantou & Ipsakis, Dimitris & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos & Stergiopoulos, Fotis & Elmasides, Costas, 2013. "Performance investigation of a hybrid renewable power generation and storage system using systemic power management models," Energy, Elsevier, vol. 61(C), pages 621-635.
    9. Lythcke-Jørgensen, Christoffer & Ensinas, Adriano Viana & Münster, Marie & Haglind, Fredrik, 2016. "A methodology for designing flexible multi-generation systems," Energy, Elsevier, vol. 110(C), pages 34-54.
    10. Mancarella, Pierluigi & Chicco, Gianfranco & Capuder, Tomislav, 2018. "Arbitrage opportunities for distributed multi-energy systems in providing power system ancillary services," Energy, Elsevier, vol. 161(C), pages 381-395.
    11. Sayegh, M.A. & Danielewicz, J. & Nannou, T. & Miniewicz, M. & Jadwiszczak, P. & Piekarska, K. & Jouhara, H., 2017. "Trends of European research and development in district heating technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1183-1192.
    12. Valerie Eveloy & Dereje S. Ayou, 2019. "Sustainable District Cooling Systems: Status, Challenges, and Future Opportunities, with Emphasis on Cooling-Dominated Regions," Energies, MDPI, vol. 12(2), pages 1-64, January.
    13. Rismanchi, B., 2017. "District energy network (DEN), current global status and future development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 571-579.
    14. Guozheng Li & Rui Wang & Tao Zhang & Mengjun Ming, 2018. "Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g," Energies, MDPI, vol. 11(4), pages 1-26, March.
    15. Haiyan Meng & Yakai Lu & Zhe Tian & Xiangbei Jiang & Zhongqing Han & Jide Niu, 2023. "Performance Evaluation Method of Day-Ahead Load Prediction Models in a District Heating and Cooling System: A Case Study," Energies, MDPI, vol. 16(14), pages 1-19, July.
    16. Suberu, Mohammed Yekini & Mustafa, Mohd Wazir & Bashir, Nouruddeen & Muhamad, Nor Asiah & Mokhtar, Ahmad Safawi, 2013. "Power sector renewable energy integration for expanding access to electricity in sub-Saharan Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 630-642.
    17. Zheng, Xuyue & Wu, Guoce & Qiu, Yuwei & Zhan, Xiangyan & Shah, Nilay & Li, Ning & Zhao, Yingru, 2018. "A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China," Applied Energy, Elsevier, vol. 210(C), pages 1126-1140.
    18. Pietro Catrini & Tancredi Testasecca & Alessandro Buscemi & Antonio Piacentino, 2022. "Exergoeconomics as a Cost-Accounting Method in Thermal Grids with the Presence of Renewable Energy Producers," Sustainability, MDPI, vol. 14(7), pages 1-27, March.
    19. Sara Ghaem Sigarchian & Anders Malmquist & Viktoria Martin, 2018. "Design Optimization of a Small-Scale Polygeneration Energy System in Different Climate Zones in Iran," Energies, MDPI, vol. 11(5), pages 1-19, May.
    20. Francesco Calise & Massimo Dentice d’Accadia & Maria Vicidomini, 2022. "Integrated Solar Thermal Systems," Energies, MDPI, vol. 15(10), pages 1-8, May.

    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:gam:jeners:v:9:y:2016:i:8:p:573-:d:74629. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.