5th Generation District Heating: A novel design approach based on mathematical optimization

Abstract This paper presents a novel design methodology based on Linear Programming for designing and evaluating distributed energy systems with bidirectional low temperature networks (BLTNs). The mathematical model determines the optimal selection and sizing of all energy conversion units in buildings and energy hubs connected to the BLTN while minimizing total annualized costs. The optimization superstructure of building energy systems comprises heat pumps, compression chillers, heat exchangers for direct cooling, cooling towers and thermal energy storages. The design approach is applied to a real-world use case in Germany and the BLTN performance is compared to a reference case with individual HVAC systems. The BLTN concept shows a cost reduction of 42% and causes 56% less CO2 emissions compared to individual HVAC systems.

[1]  Viktor Dorer,et al.  Optimisation of a district energy system with a low temperature network , 2017 .

[2]  Wandong Zheng,et al.  Hydraulic performance optimization of meshed district heating network with multiple heat sources , 2017 .

[3]  Qiong Wu,et al.  Modeling and optimization of distributed energy supply network with power and hot water interchanges , 2016 .

[4]  André Bardow,et al.  Robust multi-objective optimization for sustainable design of distributed energy supply systems , 2017, Comput. Chem. Eng..

[5]  Markus Bohlayer,et al.  Low-grade waste heat integration in distributed energy generation systems - An economic optimization approach , 2018 .

[6]  Neven Duić,et al.  Future district heating systems and technologies: On the role of smart energy systems and 4th generation district heating , 2018, Energy.

[7]  Yun Yang,et al.  Optimal design of distributed energy resource systems coupled with energy distribution networks , 2015 .

[8]  André Bardow,et al.  An adaptive discretization MINLP algorithm for optimal synthesis of decentralized energy supply systems , 2016, Comput. Chem. Eng..

[9]  Jan Carmeliet,et al.  A Study of District Heating Systems with Solar Thermal Based Prosumers , 2018, Energy Procedia.

[10]  Masaki Nakao,et al.  Thermal grid system and its field test in multiple buildings with individual heating and cooling facility , 2018, Energy Procedia.

[11]  韩再生,et al.  我国地热资源中长期战略研讨（2020，2030，2050） , 2009 .

[12]  George Mavrotas,et al.  Multi-objective optimization and comparison framework for the design of Distributed Energy Systems , 2019, Energy Conversion and Management.

[13]  Lazaros G. Papageorgiou,et al.  Optimal design and operation of distributed energy systems: Application to Greek residential sector , 2013 .

[14]  Ryan Rogers,et al.  Modeling of Low Temperature Thermal Networks Using Historical Building Data from District Energy Systems , 2019, Modelica.

[15]  Matthias Sulzer,et al.  "Dynamic analysis of the low-temperature district network ""Suurstoffi"" through monitoring" , 2015 .

[16]  D. Stolten,et al.  Time series aggregation for energy system design: Modeling seasonal storage , 2017, 1710.07593.

[17]  Jan Carmeliet,et al.  Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout , 2016 .

[18]  Ibrahim Dincer,et al.  Efficiency analysis of a cogeneration and district energy system , 2005 .

[19]  Mary Ann Piette,et al.  A thermodynamic analysis of a novel bidirectional district heating and cooling network , 2018 .

[20]  Roberto Fedrizzi,et al.  5th generation district heating and cooling systems: A review of existing cases in Europe , 2019, Renewable and Sustainable Energy Reviews.

[21]  Torben Schmidt Ommen,et al.  Heat pump COP, part 2: generalized COP estimation of heat pump processes. , 2018 .

[22]  Alexandra M. Newman,et al.  Evaluating shortfalls in mixed-integer programming approaches for the optimal design and dispatch of distributed generation systems , 2013 .

[23]  Eric S. Fraga,et al.  An energy integrated, multi-microgrid, MILP (mixed-integer linear programming) approach for residential distributed energy system planning – A South Australian case-study , 2015 .

[24]  A. Boies,et al.  Distributed energy resource system optimisation using mixed integer linear programming , 2013 .

[25]  Masaki Nakao,et al.  Estimation of the energy-saving effect of introducing a heat source water network system with single-loop piping utilizing hot spring heat , 2018, Energy Procedia.

[26]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[27]  M. Stadler,et al.  A mixed integer linear programming approach for optimal DER portfolio, sizing, and placement in multi-energy microgrids , 2017 .

[28]  Thomas Schütz,et al.  Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis , 2018, Renewable Energy.

[29]  William Ewart Henley,et al.  OPERATION , 1973, ISO 22301:2019 and business continuity management – Understand how to plan, implement and enhance a business continuity management system (BCMS).

[30]  Matthias Rommel,et al.  Pumping power prediction in low temperature district heating networks , 2015 .

[31]  Svend Svendsen,et al.  The status of 4th generation district heating: Research and results , 2018, Energy.

[32]  S. Dekker,et al.  5th generation district heating and cooling systems as a solution for renewable urban thermal energy supply , 2019, Advances in Geosciences.

[33]  André Bardow,et al.  Optimal (n-1)-reliable design of distributed energy supply systems , 2019, Comput. Chem. Eng..

[34]  Fariborz Haghighat,et al.  Optimization approaches in district heating and cooling thermal network , 2017 .

[35]  Jose Manuel Cejudo-Lopez,et al.  Selection of typical demand days for CHP optimization , 2011 .

[36]  J. Olsen,et al.  The European Commission , 2020, The European Union.

[37]  Dirk Müller,et al.  Bidirectional low temperature district energy systems with agent-based control: Performance comparison and operation optimization , 2018 .

[38]  Edoardo Amaldi,et al.  A detailed MILP optimization model for combined cooling, heat and power system operation planning , 2014 .

[39]  Louis Lamarche,et al.  A new modeling approach for improved ground temperature profile determination , 2016 .

[40]  Rita Streblow,et al.  MIP approach for designing heating systems in residential buildings and neighbourhoods , 2016 .

[41]  N. Woudstra,et al.  Understanding the exergy of cold: theory and practical examples , 2010 .

[42]  Viktor Dorer,et al.  Modelling the Suurstoffi district based on monitored data to analyse future scenarios for energy self-sufficiency , 2016 .

[43]  Patrick Lauenburg,et al.  Prosumers in district heating networks – A Swedish case study , 2016 .

[44]  Marco Mazzotti,et al.  Optimal design of multi-energy systems with seasonal storage , 2017, Applied Energy.

[45]  Marco Pellegrini,et al.  The Innovative Concept of Cold District Heating Networks: A Literature Review , 2018 .

[46]  Brian Vad Mathiesen,et al.  The role of district heating in future renewable energy systems , 2010 .

[47]  Brian Vad Mathiesen,et al.  4th Generation District Heating (4GDH) Integrating smart thermal grids into future sustainable energy systems , 2014 .