Co-Optimizing Integrated Energy Systems for Enhancing Thermal-to-Electric Flexibility: Modeling of Heat Flow Directions

Authors

DOI:

https://doi.org/10.52825/isec.v2i.3413

Keywords:

Bidirectional District Heating, Co-Optimization, Integrated Energy Systems, Sector Coupling, Thermal-To-Electric Flexibility Coupling, Thermal-To-Electric Flexibility

Abstract

The growing integration of intermittent renewable energy sources (RESs) increases the need for operational flexibility in power systems. Conventional district heating (DH) networks, constrained by fixed unidirectional flow, offer limited adaptability. This paper presents a novel co-optimization framework that couples electricity and new-generation district heating (NGDH) networks to enhance system-wide flexibility. Mass flow direction is treated as a decision variable, enabling bidirectional operation. By incorporating nonlinear hydraulic and thermal constraints, including pressure drops, temperature-dependent heat losses, and flow-temperature interactions, the proposed formulation captures the physical reality of heat transport more accurately than simplified models. To preserve tractability, the original mixed-integer, nonlinear, and non-convex problem is reformulated using second-order cone programming, McCormick envelopes, and outer approximation methods. Numerical results show that bidirectional thermal flow control can reduce total operational costs and improve renewable integration compared to conventional unidirectional strategies.

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References

[1] European Commission, “Fit for 55”: delivering the EU’s 2030 Climate Target on the way to climate neutrality., (2021). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021DC0550 (accessed December 26, 2024).

[2] Bundesamt für Justiz, Bundestag D. Bundes-Klimaschutzgesetz (KSG)., (2021). https://www.gesetze-im-internet.de/ksg/BJNR251310019.html (accessed December 26, 2024).

[3] H. Lund, S. Werner, R. Wiltshire, S. Svendsen, J. Eric, F. Hvelplund, B. Vad, 4th Generation District Heating ( 4GDH ) Integrating smart thermal grids into future sustainable energy systems, Energy 68 (2014) 1–11. https://doi.org/10.1016/j.energy.2014.02.089.

[4] Euroheat & Power, DHC Market Outlook 2023, (2023). https://www.euroheat.org/data-insights/outlooks/market-outlook-2023 (accessed January 16, 2024).

[5] European Comission, Directive 2012/27/EU of the European Parliament and of the Council of 25 October 2012 on energy efficiency, amending Directives 2009/125/EC and 2010/30/EU and repealing Directives 2004/8/EC and 2006/32/EC, Official Journal of the European Union (2012).

[6] BDEW Bundesverband der Energie- und Wasserwirtschaft e.V., Positionspapier: 10 Thesen zur Sektorkopplung, (2017) 1–17.

[7] H. Lund, Renewable heating strategies and their consequences for storage and grid infrastructures comparing a smart grid to a smart energy systems approach, Energy 151 (2018) 94–102. https://doi.org/10.1016/j.energy.2018.03.010.

[8] C. Bernath, G. Deac, F. Sensfuß, Influence of heat pumps on renewable electricity integration: Germany in a European context, Energy Strategy Reviews 26 (2019). https://doi.org/10.1016/j.esr.2019.100389.

[9] P. Mancarella, MES (multi-energy systems): An overview of concepts and evaluation models, Energy 65 (2014) 1–17. https://doi.org/10.1016/j.energy.2013.10.041.

[10] S. Kuntuarova, T. Licklederer, T. Huynh, D. Zinsmeister, T. Hamacher, V. Perić, Design and simulation of district heating networks: A review of modeling approaches and tools, Energy 305 (2024). https://doi.org/10.1016/j.energy.2024.132189.

[11] K. Gjoka, B. Rismanchi, R.H. Crawford, Fifth-generation district heating and cooling: Opportunities and implementation challenges in a mild climate, Energy 286 (2024). https://doi.org/10.1016/j.energy.2023.129525.

[12] M. Bilardo, F. Sandrone, G. Zanzottera, E. Fabrizio, Modelling a fifth-generation bidirectional low temperature district heating and cooling (5GDHC) network for nearly Zero Energy District (nZED), Energy Reports 7 (2021) 8390–8405. https://doi.org/10.1016/j.egyr.2021.04.054.

[13] M. Sulzer, S. Werner, S. Mennel, M. Wetter, Vocabulary for the fourth generation of district heating and cooling, Smart Energy 1 (2021). https://doi.org/10.1016/j.segy.2021.100003.

[14] J. Lindhe, S. Javed, D. Johansson, H. Bagge, A review of the current status and development of 5GDHC and characterization of a novel shared energy system, Sci. Technol. Built Environ. 28 (2022) 595–609. https://doi.org/10.1080/23744731.2022.2057111.

[15] Z. Li, W. Wu, M. Shahidehpour, J. Wang, B. Zhang, Combined heat and power dispatch considering pipeline energy storage of district heating network, IEEE Trans. Sustain. Energy 7 (2016) 12–22. https://doi.org/10.1109/TSTE.2015.2467383.

[16] W. Gu, J. Wang, S. Lu, Z. Luo, C. Wu, Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings, Appl. Energy 199 (2017) 234–246. https://doi.org/10.1016/j.apenergy.2017.05.004.

[17] X. Liu, Combined Analysis of Electricity and Heat Networks, 2013.

[18] R. Yokoyama, H. Kitano, T. Wakui, Optimal operation of heat supply systems with piping network, Energy 137 (2017) 888–897. https://doi.org/10.1016/j.energy.2017.03.146.

[19] L. Merkert, P.M. Castro, Optimal Scheduling of a District Heat System with a Combined Heat and Power Plant Considering Pipeline Dynamics, Ind. Eng. Chem. Res. 59 (2020) 5969–5984. https://doi.org/10.1021/acs.iecr.9b06971.

[20] S. Huang, W. Tang, Q. Wu, C. Li, Network constrained economic dispatch of integrated heat and electricity systems through mixed integer conic programming, Energy 179 (2019) 464–474. https://doi.org/10.1016/j.energy.2019.05.041.

[21] R.Z. Pass, M. Wetter, M.A. Piette, A thermodynamic analysis of a novel bidirectional district heating and cooling network, Energy 144 (2018) 20–30. https://doi.org/10.1016/j.energy.2017.11.122.

[22] T. Blacha, M. Mans, P. Remmen, D. Müller, Dynamic simulation of bidirectional low-temperature networks -A case study to facilitate the integration of renewable energies, in: Building Simulation Conference Proceedings, International Building Performance Simulation Association, 2019: pp. 3491–3498. https://doi.org/10.26868/25222708.2019.210670.

[23] L. Mitridati, J.A. Taylor, Power Systems Flexibility from District Heating Networks, (2018).

[24] G.O. Brown, The History of the Darcy-Weisbach Equation for Pipe Flow Resistance, 40650 (2002). https://doi.org/10.1061/40650(2003)4.

[25] P.M. Castro, Tightening piecewise McCormick relaxations for bilinear problems, Comput. Chem. Eng. (2014). https://doi.org/10.1016/j.compchemeng.2014.03.025.

[26] G.P. Mccormick, COMPUTABILITY OF GLOBAL SOLUTIONS TO FACTORABLE NONCONVEX PROGRAMS: PART I-CONVEX UNDERESTIMATING PROBLEMS *, North-HoUand Publishing Company, 1976.

[27] H. Hijazi, C. Coffrin, P. Van Hentenryck, Convex quadratic relaxations for mixed-integer nonlinear programs in power systems, Math. Program. Comput. 9 (2017) 321–367. https://doi.org/10.1007/s12532-016-0112-z.

[28] A.M. Duran, I.E. Grossman, An outer-approximation algorithm for a class of mixed-integer nonlinear programs, 36 (1986) 307–339.

[29] Z. Li, W. Wu, M. Shahidehpour, J. Wang, B. Zhang, Combined heat and power dispatch considering pipeline energy storage of district heating network, IEEE Trans. Sustain. Energy 7 (2016) 12–22. https://doi.org/10.1109/TSTE.2015.2467383.

[30] W. Bukhsh, Data for stochastic multiperiod optimal power flowproblem, (n.d.).

[31] D.S. Wicaksono, I.A. Karimi, Piecewise MILP under- and overestimators for global optimization of bilinear programs, AIChE Journal 54 (2008) 991–1008. https://doi.org/10.1002/aic.11425.

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Published

2026-06-03

How to Cite

Kuntuarova, S., Kiani-Moghaddam, M., Peric, V., & Hamacher, T. (2026). Co-Optimizing Integrated Energy Systems for Enhancing Thermal-to-Electric Flexibility: Modeling of Heat Flow Directions . International Sustainable Energy Conference - Proceedings, 2. https://doi.org/10.52825/isec.v2i.3413

Conference Proceedings Volume

Section

Future District Heating and Cooling

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