Performance assessment of a multi-source heat production system with storage for district heating

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Energy (2018) 000–000 390–399 EnergyProcedia Procedia149 00 (2017) www.elsevier.com/locate/procedia

16th 16th International International Symposium Symposium on on District District Heating Heating and and Cooling, Cooling, DHC2018, DHC2018, 9–12 September 2018, Hamburg, Germany 9–12 September 2018, Hamburg, Germany

Performance of aa multi-source production The assessment 15th International on Districtheat Heating and Cooling system Performance assessment ofSymposium multi-source heat production system with with storage storage for for district district heating heating Assessing the feasibility of using the heat demand-outdoor Descamps, G.long-term Leoncini, M. Vallée*, C. M.N. Descamps, Leoncini, M.district Vallée*, heat C. Paulus Paulus temperature M.N. function for aG. demand forecast a,b,c

I. Andrić

CEA, Grenoble, France France CEA, LITEN, LITEN, 17 17 rue rue des des Martyrs, Martyrs, F-38054 F-38054 Grenoble,

*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

a

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Abstract Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract

This This work work contributes contributes to to the the development development of of aa multi-vector multi-vector flexibility flexibility management management platform, platform, combining combining electric, electric, heat heat and and gas gas optimization optimization at at district district level. level. The The multi-vector multi-vector flexibility flexibility management management platform platform will will be be validated validated both both experimentally experimentally and and by by Abstract on simulation, simulation, on aa set set of of demonstration demonstration scenarios. scenarios. Each Each scenario scenario refers refers to to an an eco-district eco-district topology topology with with aa given given distribution distribution network network of and aa multi-source multi-source heat heat production production plant, plant, consisting consisting of of aa gas gas boiler, boiler, aa solar solar collector, collector, and and aa heat heat pump. pump. of energy, energy, aa consumer consumer side side and District heating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the In addition, a thermal storage in the form of a water tank is connected to the network. A key aspect is the ability to optimize such In addition, a thermal storage in the form of a water tank is connected to the network. A key aspect is the ability to optimize such emissions sector.of require high investments are returned through theand heat system at at gas district level, from with the thebuilding performance ofThese the systems individual components dependingwhich on operating operating temperatures and aa greenhouse system district level, with the performance the individual components depending on temperatures sales. Due toconditions, the changed and building renovation heat demand in with the aafuture could decrease, environmental conditions, andclimate varyingconditions primary energy energy prices. By By simulatingpolicies, the distribution distribution network with dynamic model, the environmental and varying primary prices. simulating the network dynamic model, the prolonging the investment return period. non-linear influence of various parameters on the system can be investigated. non-linear influence of various parameters on the system can be investigated. main scope of this paper to assess the heat demand – outdoor temperature for heat demand InThe particular, the current current studyisfocuses focuses on the thefeasibility operation of of using the multi-source multi-source heat production production system and and function thermal storage. storage. A 1D 1D In particular, the study on the operation of the heat system thermal A forecast. Themulti-source district of Alvalade, located in Lisbon (Portugal), wasthe as a storage, case study. district is consisted of 665 model of the the multi-source heat production production (gas, solar and and heat pump), pump), theused thermal storage, and aaThe global consumer is performed performed model of heat (gas, solar heat thermal and global consumer is buildings that vary in both constructionlanguage period and typology. Three scenarios (low, medium, high)model and three using the equation-based equation-based object-oriented language Modelica along withweather the simulation simulation platform Dymola. The The model is run rundistrict with using the object-oriented Modelica along with the platform Dymola. is with renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were standard controls from district heating provider, i.e. constant or linear controls. For a given consumer load, a set of key performance standard controls from district heating provider, i.e. constant or linear controls. For a given consumer load, a set of key performance compared with results from a dynamic heat demand model, previously developed and validated by the authors. indicators (KPI) are used to assess the performance of the system, e.g. energy share from renewables and storage utilization rate. indicators (KPI) are used to assess the performance of the system, e.g. energy share from renewables and storage utilization rate. Thesensitivity results showed wheninput onlyis is The considered, the be margin of reference error could acceptable someschemes applications The sensitivity to the the that model’s input isweather analyzedchange as well. well. The results can can be used as as reference to be apply optimal for control schemes and The to model’s analyzed as results used to apply optimal control and (the error in annual was lowerKPIs. than 20% for all weather scenarios considered). However, after introducing renovation study the influence influence ondemand the corresponding corresponding KPIs. study the on the scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). ©The 2018 The Authors. Elsevier Ltd.average within the range of 3.8% up to 8% per decade, that corresponds to the value slope Published coefficientby increased © 2018 The of Authors. Published by Elsevier on Ltd. This is an open access article under the CC BY-NC-ND BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) decrease in the number of heating hours of 22-139h during heating season (depending on the combination of weather and This is is an an open open access access article article under under the the CC licensethe (https://creativecommons.org/licenses/by-nc-nd/4.0/) This CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee the 16th International Symposium District Heating renovation scenarios considered). On the other hand, function interceptofincreased for 7.8-12.7% per decadeon(depending on the and Cooling, DHC2018. coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and ** Corresponding Corresponding author. author. Cooling. E-mail address: E-mail address: [email protected] [email protected] Keywords: Heat demand; Forecast; Climatebychange 1876-6102 1876-6102 © © 2018 2018 The The Authors. Authors. Published Published by Elsevier Elsevier Ltd. Ltd. This is an open access article under the CC BY-NC-ND This is an open access article under the CC BY-NC-ND license license (https://creativecommons.org/licenses/by-nc-nd/4.0/) (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection Selection and and peer-review peer-review under under responsibility responsibility of of the the scientific scientific committee committee of of the the 16th 16th International International Symposium Symposium on on District District Heating Heating and and Cooling, Cooling, DHC2018. DHC2018. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. 1876-6102 © 2018 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of the 16th International Symposium on District Heating and Cooling, DHC2018. 10.1016/j.egypro.2018.08.203

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of Descamps the scientific of the 16th International Selection and peer-review under responsibility M.N. et al.committee / Energy Procedia 149 (2018) 390–399Symposium on District Heating 391 and Cooling, DHC2018.

Keywords: Hybrid District Heating; Power to Heat; Dynamic Simulation

1. Introduction District heating networks (DHN) have long been coupled to electricity and gas networks via conversion units such as heat pumps for instance. However the trend is to have an even higher integration of the energy networks, leading to the concept of Hybrid Energy Networks (HEN). Advanced control strategies are required to manage the energy generation, storage and consumption with a high share of renewable energy sources, whilst ensuring cost efficiency. In this context, planning tools based on optimization algorithms are used. The performance of such tools is assessed by comparing an optimal situation to a reference situation. This can only be done by means of numerical simulations, which can generate reproducible and controllable conditions for the operation of a DHN. It should be noted that it can be challenging to validate and calibrate the components of a DHN simulation, due to the lack of high quality measured data [1]. Therefore the purpose of the current study is to develop a methodology for DHN simulations calibrated on experimental data. The DHN simulations will later be linked to an optimization module for the flexible management of a HEN. The experimental data is collected from the micro DHN facility of CEA-INES, which can be used for the validation of DHN simulation as well as the implementation of optimal control strategies in real time. The proposed methodology relies on a dynamic simulation of the heating network with the equation-based object oriented language Modelica. A set of KPIs are defined and evaluated. The present work aims more specifically at defining a reference setup, and therefore focuses on targeted KPIs for a non-optimal energy management strategy. This strategy is implemented via a set of logical rules applied at each time step, and tested for the satisfaction of a prescribed building heat load for a year. Different scenarios are taken into account to study the influence of the multisource heat plant sizing. In a subsequent work, the flexible management of the system will be demonstrated by comparing an optimal strategy to the current one. Nomenclature COP DHN DHW GB HP HEN mflow KPI SH Tmin, Tmax, T_stor_top T_stor_bot cp P_stor_demand P_stor_max

Coefficient Of Performance District Heating Network Domestic Hot Water Gas Boiler Heat Pump Hybrid Energy Network Mass flow rate (kg/s) Key Performance Indicator Space Heating Minimum and maximum temperature in the thermal storage tank ( C) Maximum and maximum temperature in the thermal storage tank ( C) Temperature at the top of the storage tank ( C) Temperature at the bottom of the storage tank ( C) Specific heat capacity of water (J/kg/K) Heat load on the thermal storage tank applied by the consumer and the solar field (kW) Thermal storage tank critical power above which the gas boiler is used (kW)

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2. Case study 2.1. Heating network

Fig. 1: Heating network considered in the present study

A small scale heating network system is considered as a case study. The benefit of this approach is that it allows to use the micro district heating network of CEA-INES [2] to partially validate the heating network model. The focus is on the management of the energy production, with flexibility through storage at district level, as opposed to e.g. demand side management, which is not considered here. A simplified network layout is used (Fig. 1), whereby all heat production units run in parallel, and the consumer side is represented by a single heat exchanger which aggregates all the heat loads coming from virtual buildings. A water tank is used as thermal storage for this system. The heat is distributed to the consumer via the distribution pipes, through which heat loss with the environment occur. The case study will be sized to comply with the scale of the micro district heating network of CEA-INES. In particular, the maximum heat load applied on the consumer side will be limited to 50 kW in this study. Despite its simplicity and scale, this case study is suitable to assess some key concepts of district heating systems, such as the integration of renewable energy sources, the coupling between the heating network and electricity grid, or the control strategy to reach an optimum operation of the network. The outcome of the study can be up-scaled or adapted to existing eco-districts. 2.2. Control strategy Each heat generator (gas boiler and heat pump) operates at a fixed supply temperature (i.e. set point) and variable mass flow rate. The consumer supply temperature set point is satisfied by using a three-way valve, which acts as a bypass between the thermal storage and the consumer (see Fig. 1). The storage temperature should therefore be larger than the consumer supply temperature. The solar thermal system is used at fixed flow operational mode, which means the feed-in pump of the solar field operates at constant volumetric flow for a solar irradiation above a given threshold. For the purpose of this study, the detail hydraulics of the solar field is not required. Instead, the output power of the solar field is used. The overall control strategy combines the requirement to satisfy the consumer demand to the constraints on the thermal storage tank. The following rules are listed:  The power coming from the solar panels is not a control variable (passive components from the operator point of view),

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 The heat pump is used for the base load, therefore has priority over the gas boiler to satisfy the demand, up to its maximum power,

Fig. 2: Reduced functional heating network layout considered

 The gas boiler is used for peak load, i.e. to provide the remaining power when the heat pump is at maximum power and the demand is not satisfied,  The temperature at the top of the storage tank should stay within a range [|Tmin, Tmax]. By aggregating the heat load coming from the solar field and the buildings, a reduced functional model of the case study can be proposed (Fig. 2) It can be seen that the thermal storage tank is equivalent to a heat consumer with its own demand, which corresponds the building heat load from which the solar field production is subtracted. Consequently, the heat load demand used in the control strategy is the thermal storage power demand, calculated as 𝑃𝑃_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠_𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑 = (𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚_𝐺𝐺𝐺𝐺 + 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚_𝐻𝐻𝐻𝐻) ∗ 𝑐𝑐𝑝𝑝 ∗ (𝑇𝑇_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠_𝑡𝑡𝑡𝑡𝑡𝑡 − 𝑇𝑇_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠_𝑏𝑏𝑏𝑏𝑏𝑏)

(1)

In addition, the heat plant is required to provide power whenever the temperature in the storage falls below Tmin. In this case, the heat pump operates by default at its maximum capacity. The gas boiler operates to provide some extra power which is scaled to the maximum flow rate allowable as follows: 𝑃𝑃_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠_𝑚𝑚𝑚𝑚𝑚𝑚 = max_flowrate ∗ 𝑐𝑐𝑝𝑝 ∗ (𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 − 𝑇𝑇_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠_𝑡𝑡𝑡𝑡𝑡𝑡)

(2)

In other words, when the temperature of the storage tank is too low, the control strategy maximizes the power provided by the heat plant to bring it back above Tmin, without necessarily using both the heat pump and the gas boiler at maximum capacity. Indeed, preliminary tests have shown that if both the heat pump and gas boiler operate at their maximum power to fulfill the minimum storage temperature constraint, then in practice the heat plant operates at maximum capacity for a large part of the year, which is not efficient.

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A flow chart representing the control strategy is displayed in Fig. 3. The strategy is only based on instantaneous inputs. The implementation of the control strategy is carried out using smooth operator functions instead of logical relation

Fig. 3: Control strategy for the management of the multi-source heat plant and storage

events. The benefit of this approach is that it avoids event triggering processes which are not computationally efficient. As a reminder, the heating network simulator will run alongside other modules such as an electric simulator, an optimizer, a prediction module, in cycles of 15 min. Therefore, it is critical to achieve a good performance of each module. The drawback of the use of smooth operator function is that the priority rules need to be implemented with care, especially in the acausal environment chosen for the model (presented in section 3.1). 2.3. Scenarios The energy produced by the multi-source heat plant is consumed by a set of residential buildings, all identical. In the following, the multi-source heat plant and thermal storage are sized according to different scenarios using available data [3], [4], [5]. A summary of the reference system is shown in Table 1. Table 1: Reference building characteristics System parameters

Value

Two storey single family building Total floor area

140 m2

SH load at design outdoor temperature [3]

8400 kWh/a

SH peak load estimation [6]

4.2 kW

DHW peak load with diversity factor [5]

3 kW

Design inlet temperature of the heating system [3]

40 C

Design outlet temperature of the heating system [3]

35 C

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The reference building has a peak load of DHW and SH of 7.2 kW, which includes the diversity factor, i.e. the fact that DHW draw-off peaks are not synchronized among several houses. In order to match the heat load limit mentioned in section 2.1, it is assumed that 7 reference buildings are connected to the district heating network, accounting for approximately 50 kW of consumer peak load. With these figures, the multi-source heat plant is designed to provide a maximum of 60 kW, which should satisfy the SH and the DHW at district level, i.e. taking account of the pipe heat loss. The distribution pipe length, insulation and diameter are representative of what is installed on the experimental facility. Different scenarios are investigated with respect to the sizing of the heat generators and thermal storage ( Table 2). Table 2: Sizing of the multi-source heat plant for different scenarios Component

Scenario 0

Scenario 1

Scenario 2

Scenario 3

Gas boiler

45 kW

35 kW

45 kW

45 kW

Heat pump heating capacity

15 kW

25 kW

15 kW

15 kW

Water tank

15 m3

15 m3

10 m3

15 m3

Solar field

50 m2

50 m2

50 m2

25 m2

It should be noted that because cooling loads are not considered here, a careful sizing of the solar field and thermal storage should be undertaken. The risk is that in the summer months, the combination of a low consumer demand and high solar production leads to temperatures above the operational constraint inside the thermal storage tank. For the scenarios proposed, it has been verified that the operational constraints are respected. Interestingly, the methodology developed in the current study can also be used to design and size a multi-source production heat plant. The chosen set point temperatures for the scenarios are shown in Table 3. Table 3: Set point and critical temperature for the scenarios Temperature

Value (C)

Gas boiler supply

80

Heat pump supply

80

Consumer supply

60

Storage top max

85

Storage top min

70

3. Methodology 3.1. Dynamic distribution network model A dynamic network model based on the layout shown in Fig. 1 is built using the equation-based object-oriented language Modelica along the Dymola simulation platform and an in-house model library [7]. On the production side, the solar field is represented by a component which computes the solar heat gain based on ASHRAE Standard 93. The gas boiler is represented by a heat generator component which models heat and momentum balances. It takes as input the supply temperature downstream of the heat generator and outputs the power. The heat pump is represented by a component with a performance curve based on Carnot efficiency. The water from the cold source enters the evaporator at 12 C. The cold water circuit is not modelled in this work. The distribution pipes are represented by a component which implements the method of characteristics to integrate the transport equation along a fluid particle’s path. The water tank is represented by a vessel component with perfect temperature stratification. The insulation properties are taken from the experimental facility specifications. From the heat production side, the hot water enters

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the tank from the top, and leaves colder from the bottom. On the consumer side, the hot water leaves the top of the tank, and returns colder at the bottom of the tank. The consumer is modelled by prescribing a heat flux from a time-series table. The 3 way valve that sets the consumer supply temperature is adjusted via a Proportional Integral controller. 3.2. Validation of the model using experimental data The tank and gas boiler components are validated using experimental data from the micro district heating network of CEA-INES. The micro-DHN consists in a two-tube district heating network of about 200 m long, supplying heat to real buildings (offices and clean room) and to an emulated building. The main production unit of this micro-DHN is a condensing gas boiler of 280 kW. A solar field of 300 m² (about 210 kW) with various thermal solar panels technologies is also supplying heat to the network either in a centralized or in a decentralized manner. The network is also equipped with a hot storage tank of 40 m3.

Fig. 4: Response to a sudden increase of set point of the gas boiler at t=6h and comparison between experimental data and simulation using Modelica (Left) Output power of the gas boiler (Right) Temperature at the top of the storage tank

The data used for the validation corresponds to a sudden increase of temperature inside the water tank following the activation of the gas boiler. The inputs to the simulation are:  The maximum power of the boiler;  The gas boiler set point temperature profile, which is in the range 72 to 76 C;  The experimental heat load applied to the thermal storage tank by the solar field and an external heat consumer. Fig. 4 shows that the dynamics of the system is reasonably well captured. The simulated gas boiler power increases suddenly to its maximum value, then decreases along the same rate as the experimental data, although with a delay of 1h. This could be due to some unaccounted heat load on the experimental set. Furthermore, the simulated temperature in the storage tank is in good agreement with the experimental data. An extensive set of data can be used to calibrate or validate other components of the heating network simulation, which will be carried out in a future work. 3.3. Key performance indicators In the current study, several KPIs are defined, with a focus on the heating network (Table 4). The electrical energy used for the compressor of the heat pump is calculated based on the COP of the heat pump, which is an output of the simulation. It should be noted that in the calculation of the primary energy consumption, a primary energy factor of 2.5 is included for the electricity [8], and the gas boiler is assumed ideal with an efficiency of 1. The maximum accumulated energy in the storage tank is calculated for a temperature range between the zero enthalpy temperature of 0 C and 100 C as a maximum temperature. Since the results from the current study are used as a reference to compare with alternative control strategies in a subsequent work, the KPI’s listed in Table 4 are absolute quantifiers, i.e. they are not relative to a reference case.

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Table 4 : Key performance indicators KPI

Description

Thermal energy share (HP, GB, solar)

Ratio of the annual thermal energy produced by the heat pump, gas boiler and solar field to the total thermal energy produced

Primary energy ratio

Ratio of the annual energy demand to annual primary energy production (electricity and gas)

Storage average capacity

Annual average ratio of the energy accumulated in the storage to the maximum energy capacity of the storage

4. Results and discussion The heat loads for SH and DHW are generated prior to the network simulation using the methodology developed in IEA SHC – Task 32 [3]. SH load profile of the reference building (section 2.3) is generated using TRNSYS transient system simulation software with a weather file input corresponding to Chambéry (France). As part of this simulation, the average DHW energy in kWh is calculated for the cold water temperature of the building using the average DHW load profile mentioned above. The simulation is run for 1 year, using a time step of 15 min. The heat load curve and heat load diagram for one house are shown in Fig. 5. The consumer peak load is about 5 kW for one house, or 35 kW for the 7 houses, which is less than the design value, hence the multi-source heat plant is slightly over-sized.

Fig. 5: Annual heat load (space heating and domestic hot water) for one residential buildings. (a) Time series; (b) Heat load diagram

The heat production profile and diagram for scenario 0 is shown in Fig. 6. As prescribed in the control strategy, the heat pump provides a base load, and the gas boiler is used to provide the excess power during peak demand. The configuration chosen, with the thermal storage acting as a buffer between the heat plant and the consumer, means that the production and consumption peaks are not synchronized. As a consequence, there is a mismatch of the heat load diagrams between the production (heat pump, gas boiler, and solar) and the DHN demand (Fig. 6 bottom). However the energy balance over the year is respected within 2%. The annual energy at district level is around 99000 kWh for scenario 0, which is 14140 kWh per house for SH and DHW including distribution heat loss. The temperature at the top of the storage tank remains above 70 C, whereas the consumer supply temperature is fluctuating around 60 C, with higher temperature in the summer months, as shown in Fig. 7.This could be improved

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by tuning the setting of the PI controller and reviewing the thermal storage control (e.g adding schedule constraints for the summer months).

Fig. 6: Heat plant load profile for scenario 0

The KPIs are shown in Table 5 for the different scenarios. When the size of the storage tank is decreased, its average capacity increases, which means it is easier to charge it to full capacity (scenarios 0 and 2). However there is no impact on the energy share of the generators, which is a sign that the control strategy is not able to leverage the thermal storage. The energy share of the HP increases when its nominal power increases, as expected (scenarios 0 and 1), and it also increases when the solar field surface is decreased (scenarios 0 and 3), because the HP is then used more often to keep the storage tank at a sufficient temperature in the absence of sufficient solar power. The primary energy ratio is quite low for all scenarios, which is mainly due to the low COP of the heat pump (around 2.1 for temperature levels of the study). Overall, what the KPI values of Table 5 reflect is the absence of a predictive strategy in the energy management. Therefore, the current set of results can be used as a reference to compare against an optimal control strategy which would take full advantage of the storage for demand peak shaving.

Fig. 7: Temperature profiles for scenario 0

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Table 5 : Calculated KPIs for the different scenarios Key performance indicator

Scenario 0

Scenario 1

Scenario 2

Scenario 3

Storage capacity

47%

47%

82%

46%

Energy share HP

59%

73%

59%

69%

Energy share GB

18%

4%

18%

19%

Energy share solar

23%

23%

23%

12%

Primary energy ratio

0.35

0.30

0.35

0.30

5. Conclusion and future work This study proposes a methodology to simulate a heating network and evaluate KPIs, in order to contribute to the development of a HEN flexibility management platform. A thermal storage tank and a multi-source heat plant consisting of a heat pump, a gas boiler and a solar field are specified for a small scale district heating network. A dynamic network simulation was run on a set of scenarios for a control strategy based on logical rules. The analysis of the KPIs confirm that in the absence of any predictive feature, the control strategy is non-optimal. The study can be replicated on other district configurations for a range of scenarios. In a future work, the DHN simulation will be linked to an optimization module to compare the results to the nonoptimal case. In this perspective; the heating network simulation will be modified to include machine learning models, using historical data from the solar field of CEA-INES to train and test the models. The price of primary energy will be included in the analysis. Furthermore, the experimental micro DHN of CEA-INES will be used to test in live conditions the optimal control strategy. Acknowledgements The authors gratefully acknowledge the financial support of the funded PENTAGON project by the European Commission, granted in the Horizon 2020 no.731125. References [1] D. Coakley, P. Raftery et M. Keane, «A review of methods to match building energy simulation models to measured data,» Renewable and Sustainable Energy Reviews, vol. 37, pp. 123-141, 2014. [2] N. Lamaison, C. Tantolin et C. Paulus, «Experimental Solar District Heating Network Operation at CEA INES,» in poster of the 5th International Solar District Heating Conference, Graz, Austria, 2018. [3] R. Heimrath et M. Haller, «Project report A2 of subtask A : the reference heating system, the template solar system,» IEA SHC Task 32, 2007. [4] U. Jordan et K. Vajen, «Realistic domestic hot-water profiles,» IEA SHC Task 26, 2001. [5] U. Jordan et K. Vajen, «DHWcalc : Program to generate domestic hot water profiles with statistical means for user defined conditions,» chez ISES Solar World Congress, Orlando, US, 2005. [6] M. Airaksinen et M. Vuolle, «Heating energy and peak-power demand in a standard and low energy building,» Energies, vol. 6, pp. 235250, 2013. [7] L. Giraud, R. Bavière, M. Vallée et C. Paulus, «Presentation, Validation, and Application of the DistrictHeating Modelica Library,» in Proceedings of the 11th International Modelica Conference, Versailles, France, 2015. [8] International Institute for Sustainability Analysis and Strategy, «Development of the primary energy factor of electricity generation in the EU-28 from 2010-2013,» Darmstadt, 2015, available at : http://www.iinas.org/tl_files/iinas/downloads/GEMIS/2015_PEF_EU28_Electricity_2010-2013.pdf (accessed July 2018)