Analysis of Building Parameter Uncertainty in District Heating for Optimal Control of Network Flexibility

Network flexibility is the use of the thermal capacity of water that is contained in the district heating network pipes to store energy and shift the heat load in time. Through optimal control, this network flexibility can aid in applications such as peak shaving and operational heat pump optimisation. Yet, optimal control requires perfect predictions and complete knowledge of the system characteristics. In reality, this is not the case and uncertainties exist. To obtain insight into the importance of these uncertainties, this paper studies the influence of imperfect knowledge of building parameters on the optimal network flexibility activation and its performance. It is found that for the optimisation of heat pump operation, building parameter uncertainties do not present large risks. For peak shaving, a more robust result can be achieved by activating more network flexibility than may be required.

[1]  Martin Mönnigmann,et al.  Numerical solution approaches for robust nonlinear optimal control problems , 2008, Comput. Chem. Eng..

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

[3]  E. Rosenblueth.  Point estimates for probability moments. , 1975, Proceedings of the National Academy of Sciences of the United States of America.

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

[5]  Dirk Saelens,et al.  A Probabilistic building characterization method for district energy simulations , 2021 .

[6]  Raul Borsche,et al.  A local time stepping method for thermal energy transport in district heating networks , 2019, Appl. Math. Comput..

[7]  Wandong Zheng,et al.  Thermal transient prediction of district heating pipeline: Optimal selection of the time and spatial steps for fast and accurate calculation , 2017 .

[8]  Risto Lahdelma,et al.  Analysis of the location for peak heating in CHP based combined district heating systems , 2015 .

[9]  Bernd Möller,et al.  Heat Roadmap Europe: Combining district heating with heat savings to decarbonise the EU energy system , 2014 .

[10]  R. Bavière,et al.  Optimal Temperature Control of Large Scale District Heating Networks , 2018, Energy Procedia.

[11]  Dirk Saelens,et al.  Impact of building geometry description within district energy simulations , 2018, Energy.

[12]  Christoph F. Reinhart,et al.  Urban building energy modeling – A review of a nascent field , 2015 .

[13]  Li Guo,et al.  Robust scheduling of building energy system under uncertainty , 2016 .

[14]  Dirk Müller,et al.  TEASER: an open tool for urban energy modelling of building stocks , 2018 .

[15]  Ruchi Choudhary,et al.  DELORES – an open-source tool for stochastic prediction of occupant services demand , 2015 .

[16]  JiGuan G. Lin.  On min-norm and min-max methods of multi-objective optimization , 2005, Math. Program..

[17]  John Lygeros,et al.  Improved day ahead heating demand forecasting by online correction methods , 2020 .

[18]  Manuel Betancourt Schwarz,et al.  Modified finite volumes method for the simulation of dynamic district heating networks , 2019, Energy.

[19]  Guillermo Escrivá-Escrivá,et al.  Statistical methodology to assess changes in the electrical consumption profile of buildings , 2018 .

[20]  Libor Kudela,et al.  Performant and Simple Numerical Modeling of District Heating Pipes with Heat Accumulation , 2019, Energies.

[21]  William D'haeseleer,et al.  Short-term demand response of flexible electric heating systems: The need for integrated simulations , 2013, 2013 10th International Conference on the European Energy Market (EEM).

[22]  Kevin Sartor,et al.  A comparative study for simulating heat transport in large district heating networks , 2018 .

[23]  Dirk Saelens,et al.  Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour , 2016 .

[24]  Taher Niknam,et al.  Investigation of Carrier Demand Response Uncertainty on Energy Flow of Renewable-Based Integrated Electricity–Gas–Heat Systems , 2018, IEEE Transactions on Industrial Informatics.

[25]  Axel Klar,et al.  Optimal control of district heating networks using a reduced order model , 2019, Optimal Control Applications and Methods.

[26]  Lieve Helsen,et al.  Practical implementation and evaluation of model predictive control for an office building in Brussels , 2016 .

[27]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

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

[29]  Xinping Liu,et al.  A Deep Peak Regulation Auxiliary Service Bidding Strategy for CHP Units Based on a Risk-Averse Model and District Heating Network Energy Storage , 2019, Energies.

[30]  Yongjun Sun,et al.  A robust demand response control of commercial buildings for smart grid under load prediction uncertainty , 2015 .

[31]  Bram van der Heijde,et al.  A simulation-based evaluation of substation models for network flexibility characterisation in district heating networks , 2020 .

[32]  Elisa Guelpa,et al.  Thermo-fluid dynamic model of large district heating networks for the analysis of primary energy savings , 2017, Energy.

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

[34]  Dejan Mumovic,et al.  A review of bottom-up building stock models for energy consumption in the residential sector , 2010 .

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

[36]  Henrik Madsen,et al.  Implementing flexibility into energy planning models: Soft-linking of a high-level energy planning model and a short-term operational model , 2020 .

[37]  P. Mancarella,et al.  Stochastic control and real options valuation of thermal storage-enabled demand response from flexible district energy systems , 2015 .

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

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

[40]  Sean Hay Kim,et al.  An evaluation of robust controls for passive building thermal mass and mechanical thermal energy storage under uncertainty , 2013 .

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

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

[43]  Benny Bøhm,et al.  Operational optimization in a district heating system , 1995 .