A simulation-based evaluation of substation models for network flexibility characterisation in district heating networks

Abstract To aid in the integration of renewable and residual energy sources in the energy system, energy flexibility is required. By charging and discharging energy storage, energy flexibility can be created and heat demand and heat generation can be matched in time. One possible source of energy flexibility is the thermal capacity of the water in district heating network pipes. Effective use of this thermal energy storage requires efficient techniques to determine the available flexibility. The goal of this paper is to determine the required level of detail of a substation model to characterise network flexibility through simulation. The substation models differ in the assumptions that are made and range from a detailed, non-linear model to a simple, linear model. To analyse the results, we identify different phases occurring during a network flexibility activation. By determining if reduced models are as effective in reproducing important flexibility characteristics as more detailed and computationally expensive models, network flexibility characterisation can be simplified and sped up. Results show that the network flexibility can be adequately characterised even with very simple models, provided correct assumptions are made.

[1]  Sven Werner District Heating and Cooling , 2013 .

[2]  Henrik Lund,et al.  Renewable heating strategies and their consequences for storage and grid infrastructures comparing a smart grid to a smart energy systems approach , 2018 .

[3]  Lieve Helsen,et al.  Integrated Optimal Design and Control of Fourth Generation District Heating Networks with Thermal Energy Storage , 2019, Energies.

[4]  Sven Werner,et al.  Heat Roadmap Europe: Identifying strategic heat synergy regions , 2014 .

[5]  Roland Baviere,et al.  Optimal Control of District Heating Systems using Dynamic Simulation and Mixed Integer Linear Programming , 2017, Modelica.

[6]  Dirk Müller,et al.  Dynamic equation-based thermo-hydraulic pipe model for district heating and cooling systems , 2017 .

[7]  Finn Haugen The Good Gain method for simple experimental tuning of PI controllers , 2012 .

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

[9]  Jerker Delsing,et al.  Improved district heating substation efficiency with a new control strategy , 2010 .

[10]  Enso Ikonen,et al.  Short term optimization of district heating network supply temperatures , 2014, 2014 IEEE International Energy Conference (ENERGYCON).

[11]  Leo Laakkonen,et al.  Predictive supply temperature optimization of district heating networks , 2016 .

[12]  Dirk Saelens,et al.  Energy flexible buildings: an evaluation of definitions and quantification methodologies applied to thermal storage , 2018 .

[13]  Wang Jun,et al.  Optimal operation for integrated energy system considering thermal inertia of district heating network and buildings , 2017 .

[14]  Svend Svendsen,et al.  Decentralized substations for low-temperature district heating with no Legionella risk, and low return temperatures , 2016 .

[15]  Kamil Futyma,et al.  Operational optimization in district heating systems with the use of thermal energy storage , 2018, Energy.

[16]  Dirk Saelens,et al.  Aggregating set-point temperature profiles for archetype-based: simulations of the space heat demand within residential districts , 2020 .

[17]  Thomas Nuytten,et al.  Flexibility of a combined heat and power system with thermal energy storage for district heating , 2013 .

[18]  Jianzhong Wu,et al.  Quantification of Operational Flexibility from a Heating Network , 2018, Energy Procedia.

[19]  Dirk Müller,et al.  Iea Ebc Annex 60 Modelica Library – An International Collaboration to Develop A Free Open-Source Model Library for Buildings And Community Energy Systems , 2015, Building Simulation Conference Proceedings.

[20]  Elisa Guelpa,et al.  Demand side management in district heating networks: A real application , 2019, Energy.

[21]  Henrik Madsen,et al.  Characterizing the energy flexibility of buildings and districts , 2018, Applied Energy.

[22]  J. Delsing,et al.  Thermodynamic Simulation of a Detached House with District Heating Subcentral , 2008, 2008 2nd Annual IEEE Systems Conference.

[23]  Ivan Bajsić,et al.  Modelling and experimental validation of a hot water supply substation , 2006 .

[24]  Svend Svendsen,et al.  Numerical modelling and experimental measurements for a low-temperature district heating substation for instantaneous preparation of DHW with respect to service pipes , 2012 .

[25]  Gerald Schweiger,et al.  District heating and cooling systems – Framework for Modelica-based simulation and dynamic optimization , 2017 .

[26]  Dirk Saelens,et al.  Sources of Energy Flexibility in District Heating Networks: Building Thermal Inertia Versus Thermal Energy Storage in the Network Pipes , 2018 .

[27]  Hanne Kauko,et al.  Dynamic modeling of local district heating grids with prosumers: A case study for Norway , 2018 .

[28]  Mohammad Shahidehpour,et al.  Combined Heat and Power Dispatch Considering Pipeline Energy Storage of District Heating Network , 2016, IEEE Transactions on Sustainable Energy.

[29]  Sebastian Stinner,et al.  Quantifying the operational flexibility of building energy systems with thermal energy storages , 2016 .

[30]  Lieve Helsen,et al.  Controlling district heating and cooling networks to unlock flexibility: A review , 2018 .

[31]  Lieve Helsen,et al.  Quantification of flexibility in buildings by cost curves – Methodology and application , 2016 .

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