IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v265y2023ics0360544222031632.html
   My bibliography  Save this article

Portfolio optimization in district heating: Merit order or mixed integer linear programming?

Author

Listed:
  • Gonzalez-Salazar, Miguel
  • Klossek, Julia
  • Dubucq, Pascal
  • Punde, Thomas

Abstract

Long-term portfolio optimization is commonly used to find the most cost-effective design and operation of a district heating system, subject to technical, financial, and environmental restrictions. Optimizing a district heating system is not trivial and demands high accuracy and high computational speed. However, existing methods addressing this problem offer one or the other but not both at the same time. The state-of-the-art method for portfolio optimization is mixed integer linear programming (MILP), which is extensively used in industry and academia but can be computing and resource-intensive for large portfolio models. This limitation has motivated the development of various options to reduce the computation time while maintaining the accuracy to a large extent. An alternative method to MILP is the merit order (MO) method, which has been used especially for power generation applications due to its simplicity and faster computation but somewhat reduced accuracy. The aim of this paper is to investigate the potential advantages and disadvantages of MO models compared to MILP models in the context of optimizing the portfolio of assets supplying a district heating network. As a study case, we analyze a large portion of the district heating network in Berlin. Four MO model variants with different levels of complexity are proposed and compared to a reference MILP model. Results suggest that MO models variants including heat storage and describing CHP plants with significant detail have the potential to reduce calculation time by nearly three orders of magnitude compared to the reference MILP model, without significantly sacrificing accuracy. In fact, differences in heat generation and net present value (NPV) between the most accurate MO model and the reference MILP model account for ±4% and −6%, respectively. Moreover, results show that combining MO and MILP models is advantageous and offers high computational speed and at the same time high accuracy, especially when a large number of runs might be necessary. MO models could thus be used prior to MILP models to perform a pre-evaluation, an exploration of sensitivities, or for downsizing the initial optimization problem. Combining MO and MILP models could result in faster and more robust decision-making, which could otherwise not be attained with any of the two options individually.

Suggested Citation

  • Gonzalez-Salazar, Miguel & Klossek, Julia & Dubucq, Pascal & Punde, Thomas, 2023. "Portfolio optimization in district heating: Merit order or mixed integer linear programming?," Energy, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s0360544222031632
    DOI: 10.1016/j.energy.2022.126277
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222031632
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.126277?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rikkas, Rebecka & Lahdelma, Risto, 2021. "Energy supply and storage optimization for mixed-type buildings," Energy, Elsevier, vol. 231(C).
    2. Ioannou, Anastasia & Angus, Andrew & Brennan, Feargal, 2017. "Risk-based methods for sustainable energy system planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 602-615.
    3. Kouhia, Mikko & Laukkanen, Timo & Holmberg, Henrik & Ahtila, Pekka, 2019. "Evaluation of design objectives in district heating system design," Energy, Elsevier, vol. 167(C), pages 369-378.
    4. Moser, Simon & Puschnigg, Stefan & Rodin, Valerie, 2020. "Designing the Heat Merit Order to determine the value of industrial waste heat for district heating systems," Energy, Elsevier, vol. 200(C).
    5. Miguel Gonzalez-Salazar & Thomas Langrock & Christoph Koch & Jana Spieß & Alexander Noack & Markus Witt & Michael Ritzau & Armin Michels, 2020. "Evaluation of Energy Transition Pathways to Phase out Coal for District Heating in Berlin," Energies, MDPI, vol. 13(23), pages 1-27, December.
    6. Billette de Villemeur, Etienne & Pineau, Pierre-Olivier, 2012. "Regulation and electricity market integration: When trade introduces inefficiencies," Energy Economics, Elsevier, vol. 34(2), pages 529-535.
    7. Delmastro, C. & Martinsson, F. & Dulac, J. & Corgnati, S.P., 2017. "Sustainable urban heat strategies: Perspectives from integrated district energy choices and energy conservation in buildings. Case studies in Torino and Stockholm," Energy, Elsevier, vol. 138(C), pages 1209-1220.
    8. Leśko, Michał & Bujalski, Wojciech & Futyma, Kamil, 2018. "Operational optimization in district heating systems with the use of thermal energy storage," Energy, Elsevier, vol. 165(PA), pages 902-915.
    9. Hansol Shin & Tae Hyun Kim & Kyuhyeong Kwag & Wook Kim, 2021. "A Comparative Study of Pricing Mechanisms to Reduce Side-Payments in the Electricity Market: A Case Study for South Korea," Energies, MDPI, vol. 14(12), pages 1-19, June.
    10. Andreas Willeke, 1998. "Risikoanalyse in der Energiewirtschaft," Schmalenbach Journal of Business Research, Springer, vol. 50(12), pages 1146-1164, December.
    11. Maier, Sebastian & Pflug, Georg C. & Polak, John W., 2020. "Valuing portfolios of interdependent real options under exogenous and endogenous uncertainties," European Journal of Operational Research, Elsevier, vol. 285(1), pages 133-147.
    12. Casisi, M. & Pinamonti, P. & Reini, M., 2009. "Optimal lay-out and operation of combined heat & power (CHP) distributed generation systems," Energy, Elsevier, vol. 34(12), pages 2175-2183.
    13. Lamaison, Nicolas & Collette, Simon & Vallée, Mathieu & Bavière, Roland, 2019. "Storage influence in a combined biomass and power-to-heat district heating production plant," Energy, Elsevier, vol. 186(C).
    14. Elsido, Cristina & Bischi, Aldo & Silva, Paolo & Martelli, Emanuele, 2017. "Two-stage MINLP algorithm for the optimal synthesis and design of networks of CHP units," Energy, Elsevier, vol. 121(C), pages 403-426.
    15. Benalcazar, Pablo, 2021. "Optimal sizing of thermal energy storage systems for CHP plants considering specific investment costs: A case study," Energy, Elsevier, vol. 234(C).
    16. Capuder, Tomislav & Mancarella, Pierluigi, 2014. "Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options," Energy, Elsevier, vol. 71(C), pages 516-533.
    17. 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.
    18. Rieder, Andreas & Christidis, Andreas & Tsatsaronis, George, 2014. "Multi criteria dynamic design optimization of a small scale distributed energy system," Energy, Elsevier, vol. 74(C), pages 230-239.
    19. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout," Energy, Elsevier, vol. 116(P1), pages 619-636.
    20. Liu, Wen & Klip, Diederik & Zappa, William & Jelles, Sytse & Kramer, Gert Jan & van den Broek, Machteld, 2019. "The marginal-cost pricing for a competitive wholesale district heating market: A case study in the Netherlands," Energy, Elsevier, vol. 189(C).
    21. Bracco, Stefano & Dentici, Gabriele & Siri, Silvia, 2013. "Economic and environmental optimization model for the design and the operation of a combined heat and power distributed generation system in an urban area," Energy, Elsevier, vol. 55(C), pages 1014-1024.
    22. deLlano-Paz, Fernando & Calvo-Silvosa, Anxo & Antelo, Susana Iglesias & Soares, Isabel, 2017. "Energy planning and modern portfolio theory: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 636-651.
    23. Hofmeister, Markus & Mosbach, Sebastian & Hammacher, Jörg & Blum, Martin & Röhrig, Gerd & Dörr, Christoph & Flegel, Volker & Bhave, Amit & Kraft, Markus, 2022. "Resource-optimised generation dispatch strategy for district heating systems using dynamic hierarchical optimisation," Applied Energy, Elsevier, vol. 305(C).
    24. Arcuri, P. & Beraldi, P. & Florio, G. & Fragiacomo, P., 2015. "Optimal design of a small size trigeneration plant in civil users: A MINLP (Mixed Integer Non Linear Programming Model)," Energy, Elsevier, vol. 80(C), pages 628-641.
    25. Pérez Odeh, Rodrigo & Watts, David & Flores, Yarela, 2018. "Planning in a changing environment: Applications of portfolio optimisation to deal with risk in the electricity sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3808-3823.
    26. Pavičević, Matija & Novosel, Tomislav & Pukšec, Tomislav & Duić, Neven, 2017. "Hourly optimization and sizing of district heating systems considering building refurbishment – Case study for the city of Zagreb," Energy, Elsevier, vol. 137(C), pages 1264-1276.
    27. Beiron, Johanna & Montañés, Rubén M. & Normann, Fredrik & Johnsson, Filip, 2020. "Flexible operation of a combined cycle cogeneration plant – A techno-economic assessment," Applied Energy, Elsevier, vol. 278(C).
    28. Delangle, Axelle & Lambert, Romain S.C. & Shah, Nilay & Acha, Salvador & Markides, Christos N., 2017. "Modelling and optimising the marginal expansion of an existing district heating network," Energy, Elsevier, vol. 140(P1), pages 209-223.
    29. Li, Can & Conejo, Antonio J. & Liu, Peng & Omell, Benjamin P. & Siirola, John D. & Grossmann, Ignacio E., 2022. "Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1071-1082.
    30. Ward, K.R. & Green, R. & Staffell, I., 2019. "Getting prices right in structural electricity market models," Energy Policy, Elsevier, vol. 129(C), pages 1190-1206.
    31. Sjödin, Jörgen & Henning, Dag, 2004. "Calculating the marginal costs of a district-heating utility," Applied Energy, Elsevier, vol. 78(1), pages 1-18, May.
    32. Li, Hailong & Sun, Qie & Zhang, Qi & Wallin, Fredrik, 2015. "A review of the pricing mechanisms for district heating systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 56-65.
    33. Rech, Sergio & Toffolo, Andrea & Lazzaretto, Andrea, 2012. "TSO-STO: A two-step approach to the optimal operation of heat storage systems with variable temperature tanks," Energy, Elsevier, vol. 45(1), pages 366-374.
    34. Ghilardi, Lavinia Marina Paola & Castelli, Alessandro Francesco & Moretti, Luca & Morini, Mirko & Martelli, Emanuele, 2021. "Co-optimization of multi-energy system operation, district heating/cooling network and thermal comfort management for buildings," Applied Energy, Elsevier, vol. 302(C).
    35. Dominković, Dominik Franjo & Wahlroos, Mikko & Syri, Sanna & Pedersen, Allan Schrøder, 2018. "Influence of different technologies on dynamic pricing in district heating systems: Comparative case studies," Energy, Elsevier, vol. 153(C), pages 136-148.
    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. Hasanien, Hany M. & Alsaleh, Ibrahim & Tostado-Véliz, Marcos & Zhang, Miao & Alateeq, Ayoob & Jurado, Francisco & Alassaf, Abdullah, 2024. "Hybrid particle swarm and sea horse optimization algorithm-based optimal reactive power dispatch of power systems comprising electric vehicles," Energy, Elsevier, vol. 286(C).
    2. Vilén, Karl & Ahlgren, Erik O., 2023. "Linear or mixed integer programming in long-term energy systems modeling – A comparative analysis for a local expanding heating system," Energy, Elsevier, vol. 283(C).

    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. Dorotić, Hrvoje & Ban, Marko & Pukšec, Tomislav & Duić, Neven, 2020. "Impact of wind penetration in electricity markets on optimal power-to-heat capacities in a local district heating system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    2. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
    3. Pina, Eduardo A. & Lozano, Miguel A. & Serra, Luis M., 2017. "Optimal operation and marginal costs in simple trigeneration systems including thermal energy storage," Energy, Elsevier, vol. 135(C), pages 788-798.
    4. Pina, Eduardo A. & Lozano, Miguel A. & Serra, Luis M., 2018. "Thermoeconomic cost allocation in simple trigeneration systems including thermal energy storage," Energy, Elsevier, vol. 153(C), pages 170-184.
    5. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2021. "Multi-objective optimization of district energy systems with demand response," Energy, Elsevier, vol. 227(C).
    6. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    7. Wakui, Tetsuya & Hashiguchi, Moe & Yokoyama, Ryohei, 2021. "Structural design of distributed energy networks by a hierarchical combination of variable- and constraint-based decomposition methods," Energy, Elsevier, vol. 224(C).
    8. Wakui, Tetsuya & Yokoyama, Ryohei, 2014. "Optimal structural design of residential cogeneration systems in consideration of their operating restrictions," Energy, Elsevier, vol. 64(C), pages 719-733.
    9. 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.
    10. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing, 2016. "Multi-objective optimization of a distributed energy network integrated with heating interchange," Energy, Elsevier, vol. 109(C), pages 353-364.
    11. Liu, Wen & Klip, Diederik & Zappa, William & Jelles, Sytse & Kramer, Gert Jan & van den Broek, Machteld, 2019. "The marginal-cost pricing for a competitive wholesale district heating market: A case study in the Netherlands," Energy, Elsevier, vol. 189(C).
    12. Stojiljković, Mirko M. & Ignjatović, Marko G. & Vučković, Goran D., 2015. "Greenhouse gases emission assessment in residential sector through buildings simulations and operation optimization," Energy, Elsevier, vol. 92(P3), pages 420-434.
    13. Stojiljković, Mirko M., 2017. "Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics," Energy, Elsevier, vol. 137(C), pages 1231-1251.
    14. Ronelly De Souza & Emanuele Nadalon & Melchiorre Casisi & Mauro Reini, 2022. "Optimal Sharing Electricity and Thermal Energy Integration for an Energy Community in the Perspective of 100% RES Scenario," Sustainability, MDPI, vol. 14(16), pages 1-39, August.
    15. Benalcazar, Pablo, 2021. "Optimal sizing of thermal energy storage systems for CHP plants considering specific investment costs: A case study," Energy, Elsevier, vol. 234(C).
    16. Kotzur, Leander & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "Time series aggregation for energy system design: Modeling seasonal storage," Applied Energy, Elsevier, vol. 213(C), pages 123-135.
    17. Wissocq, Thibaut & Ghazouani, Sami & Le Bourdiec, Solène, 2019. "A methodology for designing thermodynamic energy conversion systems in industrial mass/heat integration problems based on MILP models," Energy, Elsevier, vol. 185(C), pages 121-135.
    18. Dominković, Dominik Franjo & Stunjek, Goran & Blanco, Ignacio & Madsen, Henrik & Krajačić, Goran, 2020. "Technical, economic and environmental optimization of district heating expansion in an urban agglomeration," Energy, Elsevier, vol. 197(C).
    19. Cai, Hanmin & Ziras, Charalampos & You, Shi & Li, Rongling & Honoré, Kristian & Bindner, Henrik W., 2018. "Demand side management in urban district heating networks," Applied Energy, Elsevier, vol. 230(C), pages 506-518.
    20. Dorotić, Hrvoje & Pukšec, Tomislav & Schneider, Daniel Rolph & Duić, Neven, 2021. "Evaluation of district heating with regard to individual systems – Importance of carbon and cost allocation in cogeneration units," Energy, Elsevier, vol. 221(C).

    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:eee:energy:v:265:y:2023:i:c:s0360544222031632. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.