Performance monitoring of rural district heating systems

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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 monitoring of district heating systems Performance monitoring of rural rural heating The 15th International Symposium on district District Heating andsystems Cooling a, b a Dominikus Dominikus Bücker Bückera,*, *, Peter Peter Jell Jellb,, Rafael Rafael Botsch Botscha

Assessing the Hochschule feasibility of using the heat demand-outdoor Hochschule Rosenheim, Rosenheim, Hochschulstr. Hochschulstr. 1, 1, 83024 83024 Rosenheim, Rosenheim, Germany Germany Seestrasse 31, 83254 Breitbrunn, Germany 31, 83254 Breitbrunn,district Germany temperature function forSeestrasse a long-term heat demand forecast a a

Abstract Abstract

b b

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

a

IN+ Center forsix Innovation, Technology and Policy Research -in Instituto Superiorhas Técnico, Av. Roviscoover Paisa 1, 1049-001 period Lisbon, Portugal The district heating (DHS) Upper been monitored b The performance performance of of six small small scale scale district heating&systems systems (DHS) Upper Bavaria Bavaria has been 78520 monitored over a 12 12 months’ months’ period with with the the aim aim Veolia Recherche Innovation, 291inAvenue Dreyfous Daniel, Limay, France to identify typical optimization potentials and to develop a standardized approach to performance monitoring. Extensive operational data were to identify typical optimization potentials and to develop a standardized approach to performance monitoring. Extensive operational data were c Département Systèmes Énergétiques et Environnement IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France analyzed analyzed and and Key Key Performance Performance Indicators Indicators (KPIs) (KPIs) were were assessed. assessed. KPIs KPIs show show strong strong fluctuation fluctuation and and variation variation between between different different DHS. DHS. Main Main potentials potentials were were found found in in the the control control of of the the DHS, DHS, component component sizing, sizing, and and grid grid temperatures. temperatures. Further Further standardization standardization of of performance performance monitoring monitoring is required required and and will will be be addressed addressed in in aa follow-up follow-up study study which which was was started started in in January January 2018. 2018. is

©Abstract 2018 The Authors. Published by Elsevier Elsevier Ltd. Ltd. © 2018The TheAuthors. Authors. Published by Elsevier © 2018 Published by Ltd. This is isisan an open access article under the CC CC BY-NC-ND licenselicense (https://creativecommons.org/licenses/by-nc-nd/4.0/) This anopen openaccess access article under the BY-NC-ND CC BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) This article under the license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility ofofthe the scientific committee of the 16th International Symposium on on District Heating and District and heating networks are responsibility commonly addressed in thecommittee literature asof one of the most effective solutions for decreasing the Selection and peer-review peer-review under responsibility thescientific scientific committee the16th 16th International Symposium District Heating Selection under of of the International Symposium on District Heating and Cooling, DHC2018. and Cooling, DHC2018. greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat Cooling, DHC2018. sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, Keywords: performance monitoring; district heating; Keywords: performance monitoring; districtperiod. heating; optimization; optimization; efficiency efficiency prolonging the investment return The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand forecast. The district of Alvalade, located in Lisbon (Portugal), was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and * author. +49-8031-805-2652; fax: +49-8031-805-2688. * Corresponding Corresponding author. Tel.: Tel.: +49-8031-805-2652; +49-8031-805-2688. renovation scenarios considered). On thefax: other hand, function intercept increased for 7.8-12.7% per decade (depending on the E-mail E-mail address: address: [email protected] [email protected] 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 Cooling. Keywords: Heat demand; Forecast; Climate change

1876-6102 2018 © 2017 The Authors. Published by Elsevier Ltd. 1876-6102 The Authors. Published by Elsevier 1876-6102© 2018 Authors. Published by Ltd. Elsevier Ltd. 1876-6102 ©©2018 TheThe Authors. Published Elsevier Ltd. Peer-review under responsibility ofCC thebyScientific Committee of The 15th International Symposium on District Heating and Cooling. This open access article under license Thisis open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This isisan anan open access article under the the CC BY-NC-ND BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection peer-review under responsibility of the Symposium on Heating Cooling, DHC2018. Selectionand and peer-review responsibility of thecommittee scientificof of the 16th International Symposium District Heating Selection and peer-review underunder responsibility of the the scientific scientific committee ofcommittee the 16th 16th International International Symposium on District District Heating and and on Cooling, DHC2018.

and Cooling, DHC2018. 10.1016/j.egypro.2018.08.164

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1. Introduction The operation of district heating systems (DHS) is based on a complex interaction of different heat sources with multiple heat consumers. Many parameters need to be managed dynamically within the overall operational strategy to achieve optimum DHS performance and reliability. Prioritization of heat sources, control of main and distribution pumps, operation of heat storage systems as well as monitoring and control of the system temperatures are only a few examples for operational aspects that have a direct influence on performance of the DHS and the durability of its components. Therefore, knowledge of the dynamic behavior of the DHS and close monitoring of the key operational indicators is vital for efficient operation of the system. In today’s DHS, many operational parameters are being recorded already. However, these values provide only little insight into the operating conditions when they are analyzed separately. In addition, many parameters are recorded as accumulated or time-averaged values only. In these cases, unsatisfying annual key figures might indicate weaknesses, but may not reveal the real cause and optimization potential. Systematic and continuous processing and evaluation of recorded data and a standard operating procedure to identify weaknesses and optimization potentials are missing in most large and virtually all smaller DHS. A survey amongst the operators of the DHS participating in the present study, revealed the following main reasons for insufficient performance monitoring of small scale DHS:  DHS have grown over time and may have changed in structure both on the heat production and heat consumption side, making operation of the DHS more and more complex  Data acquisition may be insufficient to ensure effective monitoring of DHS operation  Staff may be lacking to perform required data acquisition and data analysis tasks  Know-how for data analysis may be missing  Standard operating procedures for an effective performance monitoring may be missing Monitoring the relevant operating parameters with a sufficient frequency and the use of standard operating procedures for analyzing the recorded operational data is key to identify optimization potentials. Subsequently, suitable measures can be developed to permanently improve the performance of the system. In view of this, the MoNa project (“MoNa – Monitoring von Nahwärmenetzen”) was initiated at the University of Applied Sciences Rosenheim in 2014. The main goal of the study was to develop a standardized approach towards performance monitoring and thus to facilitate performance monitoring of small scale DHS. Six rural district heating systems in Upper Bavaria have been investigated, monitoring their performance over a 12 months’ period. To this end, a set of key performance indicators (KPI) were assembled to compare the performance of the systems and minimum requirements for measurement and data acquisition were defined to assess the KPIs. During the monitoring period, extensive operational data were recorded using the equipment present in the DHS. The data were then validated and analyzed, the KPIs were calculated over different time intervals and further operational data were taken into account to identify further optimization potentials. These potentials were then categorized to identify typical optimization potentials of small scale rural DHS. 2. Investigated DHS The six DHS considered in the study are located in communities of 1,000 to 20,000 inhabitants in mostly rural areas in Upper Bavaria. All DHS provide heat to a mix of residential, commercial, and sometimes public customers. Four of the DHS use wood chips as a main source of energy, three of the DHS have combined heat and power (CHP) plants. Table 1 gives an overview of essential characteristics of the investigated DHS. Table 1. Characteristics of the investigated DHS. DHS

Network length [km]

Installed thermal capacity [MW]

Energy sources

CHP

A

3.2

3.7

Wood chips, fuel oil

No

B

7.4

3.7

Wood chips, fuel oil

No

C

1.6

1.7

Wood chips, fuel oil, natural gas, solar heat

No

D

1.0

2.2

Wood chips, palm oil, fuel oil

Yes

E

11.4

4.0

Natural gas, biomethane, biogas, power-to-heat

Yes

F

2.8

1.5

Natural gas, biogas

Yes



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3. Key performance indicators A set of key performance indicators was assembled to assess performance of the DHS. The set includes parameters indicating efficiencies as well as criteria describing environmental impact and security of supply. The following performance indicators were assessed:  Primary Energy Factor (PEF), indicating the ratio of primary energy used to heat delivered according to AGFW 309-1 [1],  Efficiencies of heat distribution and heat generation,  Heat consumption,  Auxiliary power consumption,  Reliability of main heat generators (in terms of hours of unplanned outages),  Labor input (man-hours per year for operation and maintenance),  Regional origin of energy sources (distance between sourcing and consumption of the energy sources),  Degree of fulfilment of the German Renewable Energies Heating Act (EEWärmeG),  CO2 Emission. While all of the above-mentioned criteria appear to be important for the assessment of the performance of DHS, reliable values could not be established for some of them. “Labor input”, for instance is difficult to be determined in a standardized way and values for this KPI may not be established through metering data. Other criteria, like the degree of fulfilment of the EEWärmeG may have a local importance but may not be relevant on an international scale. In the following sections, exemplary results for some of the most important KPIs will be presented, with the chosen time scale being monthly values over a 12-month period.Certainly, in future standardized monitoring campaigns, many more KPIs will be assessed in varying time scales. In this respect, this work is only a first step towards the formulation of requirements for monitoring campaigns. A larger follow-up project, “Nemo” [2], was started by the University of Applied Sciences Rosenheim and the AGFW – der Energieeffizienzverband für Wärme, Kälte und KWK e. V. (AGFW) in 2018 with the goal to establish detailed standards and requirements for performance monitoring of DHS, see also [3]. 4. Measurement Requirements In order to evaluate the KPIs and to show their fluctuations over time, metering data need to be recorded and analyzed during the monitoring campaign. The required measurement equipment has been defined by “minimum measurement requirements” including metering points, measured variables, and metering intervals. In general, more metering points, higher metering frequencies, and higher accuracies of measurement will lead to better results in the evaluation of the DHS performance, identification of weaknesses and optimization potentials. For this reason, all available data and all existing metering points should be included in the monitoring campaign. Nevertheless, technology levels of existing DHS with respect to metering equipment vary greatly and effective monitoring is also possible with limited metering equipment. Table 2 gives an overview of the most important metering points in a monitoring campaign. Metering points which are crucial for the energy balance from heat source to grid injection are considered as “essential”. In some cases, certain metering points can be replaced by smart balancing. In addition, differential pressure gauges across the main grid and subnets, and the main electricity meter are considered essential because they are important for understanding the overall operational strategy and the consumption of auxiliary electric energy. Metering points which give important information on the performance of the DHS and its components, but which are not necessary for setting up the basic balances are considered as “recommended”. In many cases, some of these metering points will not exist or at least not be accessible by the monitoring system. Table 2. Most important metering points in a monitoring campaign. Component

Essential

Heat source (heat generator)

Heat meter (incl. temperature and flow)

Recommended Fuel meter Status signals

Thermal energy storage

Temperature gauge

Heat meter (incl. temperature and flow)

Main grid / Subnet

Differential pressure gauge

Index circuit measurement (differential pressure)

Heat meter (incl. temperature and flow)

Status signals

Network /subnet pump

Power meter

Customer

Heat meter (incl. temperature and flow) Differential pressure (primary site)

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To determine the required frequency of measurement, the monitoring objective needs to be defined. If only yearly or monthly values of the KPIs are investigated, the frequency of measurement can be chosen accordingly. This, however, will not facilitate detection of events taking place on shorter time levels, such as charging/discharging the thermal energy storage, fluctuation of temperatures and flows, intermittent operation of components etc. To enable this kind of event detection, the rate of change of the KPIs needs to be assessed in more detail. The highest rates of change can be expected for KPIs directly reacting on control commands of the DHS and the substations. Considering the KPIs discussed in this report, these are the primary energy factor, efficiencies of generation, heat consumption, auxiliary power consumption, and CO2 emissions. To recognize all significant changes in these parameters, a 15-minute interval was chosen for the measurement of all parameters relevant for these KPIs. This interval has proven to enable detection of most of the events relevant during operation of the DHS. However, in subsequent analysis, even higher frequencies of measurements have shown to be required. One example of such analysis is the evaluation of charging and discharging rates of a thermal energy storage (TES) based on temperature measurements [3]. Additional metering points exceeding the parameters listed in Table 1 are not necessary for a minimum standard data acquisition concept, but may help to gain better results on the operating conditions and optimization potentials of the DHS and thus improve the quality of the monitoring campaign. 5. Monitoring results Table 3 reports yearly values of key parameters of the investigated DHS calculated from metering data monitored in the current campaign. With the yearly heat consumption ranging from 2,600 to 11,300 MWh, the investigated DHS are significantly smaller than the average of 46,605 MWh per DHS and year that is reported by AGFW for the 1.405 German DHS considered in the AGWF 2016 survey [4]. The same holds true for the heat consumption density, with an average value of 3,140 kWh/(m∙a) reported by AGFW and values ranging from 636 to 3,030 kWh/(m∙a) for the DHS investigated in this study. The main reason for the low heat consumption density is the rural character of the DHS. The heat loss per network length of most of the investigated DHS is well below the average of 53.1 W/m which can be derived from the values reported by AGFW. With most of the investigated systems having been commissioned after 2000, the technology level and condition of the piping can be assumed to be above average. Combining the low heat consumption density with small heat losses per network length yields values of the relative heat loss which correspond very well to the values reported by AGFW. Empirical data for small DHS are scarce. Nevertheless, the values given in Table 3 appear to correspond well with other published data, see [5-10]. CO2 emission data were calculated according to [11] with the allocation of CO2 between heat and power production based on their respective exergetic value. Table 3. Yearly values of key parameters of the investigated DHS monitored in the current campaign. Negative calculated values of primary energy factors are the result of the underlying allocation method for CHP plants. These values are set to zero according to [11] DHS

Heat production [MWh]

Heat consumption [MWh]

Heat consumption density [kWh/(m∙a)]

Relative heat loss [% of production]

Heat loss per network length [W/m]

Primary energy factor [-]

CO2 emission per heat consumption [g/kWh]

A

6,651

5,696

1,798

14.4

34.4

0.33

5.5

B

5,491

4,708

636

14.3

12.1

0.36

28.5

C

2,946

2,618

1,689

11.1

24.2

0.82

119.0

D

3,430

2,882

3,030

16.0

65.8

0 (-1.01)

15.0

E

13,452

11,293

994

16.0

21.7

0.49

107.0

F

4,405

3,822

1,362

13.2

23.7

0.41

264.0

5.1. Heat consumption As mentioned above, heat consumption densities of the considered DHS represent typical values for smaller rural DHS. Fig. 1 shows monthly totals of heat consumption, normalized on the maximum monthly heat consumption of the respective DHS. The heat consumption curves for DHS A, B, C, and E are very similar, showing typical time progressions for systems dominated by space heating demand. Maxima are reached in January or February and minima are reached in July or August with minimum values ranging from 10 % to 20 % of the peak demand. DHS D and F, however, show different seasonal progressions of heat demand, caused by atypical consumers like open-air public pools (DHS F) and process heat (DHS D). It is obvious, that atypical consumers will have a significant effect on the seasonal progression of heat demand in smaller DHS.



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With regard to a future standardization of performance monitoring, the following values should be considered:  Temporal location of maximum and minimum monthly and daily heat loads  Ratio of maximum to minimum monthly heat consumption. While these parameters will give a good indication on the structure of heat demand, changing them is difficult from an operator’s perspective. Seasonal levelling of heat demand requires either large seasonal thermal energy storage systems or the integration of atypical heat consumers whose availability is outside the operator’s sphere of influence. On a smaller time scale, however, optimising the DHS may be easier. For example, ratios of minimum and maximum daily heat consumption in a week’s turn may indicate the necessity of a TES and may even give a first estimate on the required storage capacity.

Fig. 1. Monthly totals of heat consumption, normalized on maximum monthly heat consumption of the respective DHS. July 2014 to June 2015.

5.2. Primary energy factor The German Renewable Energies Act (EEG) introduces the Primary Energy Factor (PEF) as a means to compare the specific consumption of (non-renewable) primary energy of different energy systems. A high value of the PEF indicates a high specific consumption of non-renewable primary energy. The PEF not only accounts for the non-renewable part of the primary energy source itself but also for the consumption of non-renewable primary energy in the upstream processes. As an example, the PEF of natural gas or fuel oil is 1.1, while the PEF of wood-based fuels is 0.2. For heat supplied by a district heating system, the PEF may be calculated according to AGF 309-1 [1]. The value depends basically on the mix of energy sources used and the efficiencies of heat generation and distribution. In the case of CHP production, the fuel consumption of the plant has to be allocated to heat and power production, respectively, using a prescribed allocation method. According to legislation, the PEF has to be calculated as annual value. In DHS with a high contribution of heat from CHPs fuelled by renewable energy sources such as biogas, biomethane or palm oil, the PEF will be very low or may even turn negative as a result of the allocation method. In these cases, the PEF is set to zero. Fig. 2 shows the PEF of the six DHS as monthly values, keeping the negative values where applicable. The DHS using CHP (D, E, F) show very low values of the PEF, but also considerable variations over time. The reason for the high variations for DHS E and F is the use of fossil fuel based boilers for peak demand and back-up, making the actual dispatch of the CHP plant and boiler a crucial criterion for the system’s performance with respect to primary energy efficiency. For DHS D, the relatively high value of the PEF in Summer is a consequence of the detrimental part load operation of the CHP in times of low heat demand. DHS A and B, using wood as base load energy source, show relatively low values of the PEF with only little variations over the course of the year. The wood-chip fuelled boiler of DHS B is severely oversized, leading to detrimental intermittent operation in spring and fall. The effect of this behaviour shows as a small peak in March. However, to reliably detect these intermittencies, higher resolutions in time are required, see [3].

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Fig. 2. Monthly and annual values of the primary energy factor, calculated based on [1]. July 2014 to June 2015. According to [1], official PEF have to be calculated as annual values and negative annual values have to be set to zero. DHS D, E, and F use CHP.

DHS C shows rather high values of the PEF, especially when considering that it uses wood chips as energy source for the main heat generator and additionally integrates large rooftop solar thermal heat installations. The reasons for this are manifold, with two long unplanned outage periods of the wood-chip burner in November and May being one part of the problem and the complex hydraulic set-up of the DHS with a partly inefficient integration of solar thermal heat being another, see section 5.4.2 for details. With respect to a standardized monitoring campaign, primary energy efficiency is an important criterion for the performance of DHS and should therefore be monitored closely. However, obtaining short term values of this parameter may be difficult since it requires knowledge of the fuel consumption of all heat generators as well as heat consumption and – if applicable – power production, all of these values determined for every period in consideration. Besides their effect on the PEF in DHS C, unplanned outages of the main components are also a crucial criterion for the performance of DHS and should always be considered in a monitoring campaign. 5.3. Heat distribution efficiency and thermal losses

Fig. 3. Monthly and annual values of the heat distribution efficiency. July 2014 to June 2015.



Dominikus Bücker et al. / Energy Procedia 149 (2018) 5–14 Author name / Energy Procedia 00 (2018) 000–000

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Heat distribution efficiency is defined as ratio of total heat consumption of the consumers to heat supplied to the grid by the heat generators. Heat losses, on the other hand, are calculated as the difference between heat supplied to the grid and total heat consumed. Both KPIs are important criteria for judging on the quality of heat distribution. As shown in Fig. 3, annual average heat distribution efficiencies of the investigated DHS are high, ranging from 84 % to 89 %. As expected, efficiencies are highest in winter when the heat load is high. The systems D and F, facing smaller seasonal variation in heat demand, can retain better efficiencies even in summer. Fig. 4 shows heat losses of the DHS, given as percentage of the heat supplied by the heat generators and divided by the network length. The result is the specific relative heat loss of the DHS. This diagram gives a better view on the technology level of the piping used. DHS D, using old pipes with poor thermal insulation shows high specific heat losses. However, since the total Network length is small and heat consumption density is high, the heat distribution efficiency is still good, as can be seen from Figure 3. DHS E, however, due to its high network length, needs very low specific heat losses to reach acceptable heat distribution efficiencies. Ranking of different DHS with respect to their heat distribution efficiencies and heat losses is ambiguous, however, since parameters like supply, return, and ambient temperatures, network length and pipe diameter, heat consumption density and linear heat density are decisive boundary conditions and need to be accounted for, see also [12].

Fig. 4. Monthly and annual values of the relative specific heat loss as percentage of heat supplied to the grid by the heat generators, divided by the network length. July 2014 to June 2015.

Annual values of heat losses can be calculated based on data used for financial settlement. Many heat meters log monthly values so that monthly heat losses can be calculated when collecting these data from all substations and comparing them to metering data from the heat generators. This procedure is obviously very time-consuming so that it should be applied only if there is a particular interest in knowing the seasonal variation of heat losses. The calculation of shorter term values for heat losses requires that all substations be equipped with online metering. In this case, calculation of heat losses is easy and should be part of the monitoring campaign. The results of this analysis can be used, for instance, to optimize heat losses by adapting the grid temperatures in particular seasons or in times of low demand. 5.4. Typical optimization potentials In the course of the study, a total of 46 optimization potentials have been identified by analysing the extensive metering data, with the individual number identified potentials for each DHS ranging from five to thirteen. The potentials were assigned to four different categories and a total of eleven sub-categories and were rated as either “high potential / urgent need for action” and “medium potential” depending on their impact on performance of the DHS or their potential to shorten lifetime of critical components. A “high potential / urgent need for action” rating was assigned to 



Components operating at efficiencies at least 30 % below best practice for more than three days. Examples: o Fixed speed pumps, o Wood-chip boilers with high intermitting frequencies, o Solar heating systems with very little or no feed-in during solar radiation, Thermal energy systems utilizing less than 50 % of their nominal capacity for more than three months,

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  

Flow temperatures below contractual value or return temperatures more than 10 K above contractual value, or temperature spreads below 10 K for more than three days, Significantly increased slag production in wood burners, Failure of supply.

A “medium” potential was assigned to components or operational states with a significant lack of efficiency compared to best practice that were not rated as “high potential / urgent need for action”. Multiple optimization potentials could be identified for each of the DHS. An overview of the identified potentials is shown in Fig. 5. Each red block represents a DHS with a high optimization potential in the respective sub-category, while each yellow block represents a DHS with medium potential. Green blocks represent DHS with no potential in the corresponding sub-category. If a certain category is not relevant for all DHS, the according spaces are left blank.

Fig. 5. Incidence of optimization potentials according to categories. Red blocks represent DHS with high optimization potential, yellow blocks represent DHS with medium optimization potential. Green blocks represent DHS without optimization potential in a specific category. If a category is not relevant for all of the DHS, spaces are left blank.

As expected, component sizing is an issue in small scale DHS, were there is often just one dedicated base load heat generator and one or two central pumps. Also, both flow and return temperatures will always play a central role in optimizing DHS. Surprisingly, however, most of the optimization potentials with a significant impact on the performance of the DHS were identified in the “control and dynamics” category. Four DHS were either using inefficient or even ineffective control mechanisms for the network differential pressure, and all DHS using decentralized heat generation had problems with their hydraulic integration. In DHS with more than two heat generators, their coordination was sometimes found to be inefficient, leading to increased cost of heat generation. Also, controlling the thermal energy storage system to effectively support efficient operation is not an easy task and may cause difficulties for the operator. Many of these specific optimization potentials may not be captured by analyzing annual values of the KPIs only, but may easily be identified in a continuous monitoring campaign were the time progression of the metering data is tracked and analyzed systematically. 5.4.1. Newer DHS with wood-chip heat generator and gas-fueled backup boiler Many small scale DHS combine a wood-chip heat generator as base load heat source with a gas-fueled boiler for peak demand and backup. Two of the systems investigated in this study (A, B) use this setup. Both DHS were commissioned within the last ten years. The base load heat generator of DHS B is oversized due to false estimation of total heat demand of the DHS. In combination with an inefficient operating regime of the thermal energy storage, this leads to a detrimental intermittent operation, especially in fall and spring. In consequence, the boiler efficiency is poor and molten slag is formed in the combustion chamber, increasing the cost for maintenance and repair. Detection of this effect requires metering frequencies of a few minutes or smaller. In preparation of the follow-up project Nemo, new KPIs are defined that can be used to identify this optimization potential [3]. No other weaknesses with “high potential” or “urgent need for action” were detected in these two DHS, indicating that this setup can work efficiently if current technology is used and the system is designed properly. 5.4.2. DHS with wood-chip heat generator, gas-fueled backup boiler, and a significant contribution of solar heat DHS C comprises a wood-chip heat generator for base load supply and a gas boiler for peak demand and backup. In addition, rooftop solar thermal systems with a total collector area of 716 m2 are integrated. The grid is operated with the flow temperature fluctuating between 25 °C and 95 °C. Decentral heat pumps are used to raise the temperature to the level required by the individual buildings. The aim of this complex setup is to maximize contribution from solar heat. The system has been monitored



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in great detail from 2012 to 2015 in a research project funded by the German Federal Ministry of Economic Affairs and Technology (“Monitoring NES”, funding code 0327400V) [13]. In this period, major optimization potentials of the DHS could be detected. The contribution of solar heat was very low, reaching only 25 % of the expected value. A main problem was caused by the undersized pumps of the solar energy systems being unable to overcome the differential pressure necessary for direct injection to the grid. In addition, at times of low heat demand and high solar radiation, increasing flow temperatures caused the distribution grid flow to virtually stop, preventing the injection of solar heat. The wood chip boiler was also facing different problems. In the beginning of the Monitoring NES project, the boiler showed intermittent operation with up to 170 starts per day due to fluctuations in heat demand and solar heat injection [13]. This issue could be rectified by adding a TES. Poor hydraulic balancing between wood and gas boiler additionally restrained the operating hours of the wood boiler. With these problems solved, the boiler performance was expected to increase in the monitoring period 2014/2015 which was considered in the MoNa project. Nevertheless, two long periods of unplanned outages occurred. The boiler uses a custom-made configuration combining a combustion chamber initially designed for 400 kW with increased heat transfer surfaces to enable a total heat output of 500 kW. The setup works well with high-quality sieved and dried wood chops or pellets. In 2014 and 2015, however, lower-quality fuel was tested to increase cost-effectiveness. This led to significant problems with melting slag and finally to long outages for repair in November 2014 and in Mai 2015. The effect shows clearly in the respective peaks of the primary energy factor in Fig. 2. Detailed monitoring results for this DHS and conclusions on the design and operation of such complex systems are given in the final report on the Monitoring NES project [13]. 5.4.3. DHS with CHP DHS D, E, and F all use CHP plants as baseload heat generators. System D is a local heat distribution grid supplying a larger facility consisting of a total of 36 heat consumers. It uses two palm-oil-fueled plants, two oil-fueled boilers and a wood-chip boiler. The system has developed from three separate boiler houses that have been connected by a distribution grid and has grown significantly over the last few decades. Some of the components are outdated and no higher-level process control system is in place. Optimization potentials that were detected include fixed speed pumps in the main boiler house, undefined hydraulic pressure and flow distribution in the grid due to decentral unregulated pumps in the auxiliary boiler houses, lack of process control, fluctuating return flow temperature with temperature spreads below 10 K over long periods. In consequence, auxiliary power consumption and heat losses are high while security of supply and component efficiencies are low. Systems E uses a total of nine heat generators, including six CHP plants (2 x biogas, 2 x biomethane, 2 x natural gas), two gas boilers and one power-to-heat module. Both natural-gas-driven CHPs and one boiler are located in an auxiliary boiler house. The piping is at a higher technology level compared to System D. A higher-level control system is missing. Optimization potentials that were detected include oversized fixed speed pumps, outdated gas boilers with low efficiencies, undefined dispatch order of the individual heat generators, lack of process control, insufficient use of the power-to-heat unit, insufficient use of the TES, high return flow temperatures. For both Systems D and E, the optimization potentials that were detected are rooted in the overly complex structure and the partially outdated components, combined with a lack of process control. System F is a smaller DHS using 2 CHPs (one natural gas and one biogas) and two natural gas boilers. The heat consumption is dominated by an open-air public pool and public schools, with a few apartment buildings and single-family homes. Main optimization potentials include excessive drops in the flow temperature during summer, when the public pool starts to heat up the water and a hydraulic short circuit at the end of the main loop caused by a defective volume control valve. 6. Conclusion The MoNa project was completed successfully and should be seen as a first step towards a systematic and standardized performance monitoring of DHS. While the investigated smaller rural DHS show good performance with respect to some of the most important KPIs such as heat distribution efficiency, the investigated systems also show significant optimization potentials. Although small scale DHS appear to be less complex and easier to control than large urban networks, many of the identified optimization potentials belong to the “control and dynamics” category. Lack of high-level control systems, overly complex structures even in presumably small systems, and outdated or badly designed components can be considered as main sources for insufficient performance in the investigated systems. It should be noted that in most cases the operators were aware of a general lack of efficiency and effectiveness of their systems, but could not identify the underlying problems. By analyzing the dynamic operation of the DHS, performance monitoring helps to reveal the causes. Since performance monitoring has proven to be an effective way of identifying optimization potentials of DHS, a further dissemination should be aimed at. To this end, standard operating procedures to ensure quality and usability should be developed and monitoring cost should be reduced further to make this method available for all sizes of DHS. This is addressed in a follow-up project, “Nemo” [2], funded by the German Federal Ministry of Economic Affairs and Technology (funding code 03ET1538A), that was started in January 2018. 15 – 20 DHS will be monitored over a 24-month’s period and all aspects of standardized performance monitoring will be approached systematically with the aim to develop guidelines and standard operating procedures.

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