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Heat distribution and the future competitiveness of district heating
Applied Energy 88 (2011) 568–576
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Heat distribution and the future competitiveness of district heating Urban Persson ⇑, Sven Werner School of Business and Engineering, Halmstad University, PO Box 823, SE-30118 Halmstad, Sweden
a r t i c l e
i n f o
Article history: Received 4 June 2010 Received in revised form 17 September 2010 Accepted 21 September 2010 Available online 16 October 2010 Keywords: District heating Distribution capital cost Heat density Waste heat Effective width Plot ratio
a b s t r a c t The competitiveness of present and future district heating systems can be at risk when residential and service sector heat demands are expected to decrease in the future. In this study, the future competitiveness of district heating has been examined by an in depth analysis of the distribution capital cost at various city characteristics, city sizes, and heat demands. Hereby, this study explores an important market condition often neglected or badly recognised in traditional comparisons between centralised and decentralised heat supply. By a new theoretical approach, the traditional and empirical expression for linear heat density is transformed into an analytical expression that allows modelling of future distribution capital cost levels also in areas where no district heating exists today. The independent variables in this new analytical expression are population density, specific building space, specific heat demand and effective width. Model input data has primarily been collected from national and European statistical sources on heat use, city populations, city districts and residential living areas. Study objects were 83 cities in Belgium, Germany, France, and the Netherlands. The average heat market share for district heat within these cities was 21% during 2006. The main conclusion is that the future estimated capital costs for district heat distribution in the study cities are rather low, since the cities are very dense. At the current situation, a market share of 60% can be reached with a marginal distribution capital cost of only 2.1 €/GJ, corresponding to an average distribution capital cost of 1.6 €/GJ. The most favourable conditions appear in large cities and in inner city areas. In the future, there is a lower risk for reduced competitiveness due to reduced heat demands in these areas, since the increased distribution capital cost is low compared to the typical prices of district heat and competing heat supply. However, district heating will lose competitiveness in low heat density areas. Hence, reduced heat demands in high heat density areas are not a general barrier for district heating in the future. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction The main additional cost for a district heating system compared to a local heat generation alternative is the unavoidable cost of heat distribution. This cost includes both annual payback of original network investment costs and additional operational costs to compensate for temperature and pressure losses associated with heat distribution. In order to stay competitive, the total cost of district heat must be lower than the cost of any local heat generation alternative. This simple cost comparison is illustrated in Fig. 1.1. While fuel oil and natural gas are the traditional energy sources predominantly used for local heat generation in central European countries, the fundamental idea of district heating is recycling of heat that otherwise would be wasted [1]. By utilising five major base heat supplies (combined heat and power (CHP), waste-to-energy
⇑ Corresponding author. Tel.: +46 (0)35 167405; fax: +46 (0)35 167500. E-mail address: [email protected] (U. Persson). 0306-2619/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2010.09.020
(WTE) plants, excess heat from energy-intensive industrial processes, direct use of biomass fuels and geothermal heat), district heating systems does not use the same energy supply as the traditional local heat-only generation alternatives. As a consequence, district heating systems hereby contribute to improved total energy system efficiency by avoiding the use of high exergy fuels for pure heat generation and offers distribution possibilities for excess heat. Since district heating is mainly based on heat recycling, the total cost for district heating minus the heat distribution cost can be regarded as the payability for recycled heat. With a low value on recycled heat, higher distribution costs can be accepted. Nowadays, local heat generation with fossil fuels or with electricity is furthermore often taxed with consumption or carbon dioxide taxes, while heat recycling normally is untaxed [2]. This additional taxation gives the possibility to accept even higher distribution costs in the competition between district heating and local heating alternatives. Hence, the total future competitiveness of district heating is a combination of two major cost components: Firstly the cost
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parameters influencing the distribution capital cost. Hence, future supply-side fuel and technology options, future excess heat process optimisation analyses or future socio-political considerations have not been elaborated in this context.
2. Method The total cost of district heat distribution consists basically of four different categories of costs [9]. The first and main cost is the distribution capital cost, Cd, which represents annual repayments of investment capital for the construction of the district heating network. Hence, the distribution capital cost depends primarily on the network construction cost, which in turn is influenced by the linear heat density (Qs/L) of the settlement at hand. In general terms, the distribution capital cost is estimated to constitute more than half of the total distribution cost for typical district heating networks. According to [9], the distribution capital cost can be estimated as: Fig. 1.1. General cost structure comparison between local heat generation and district heating, with respect to the heat generation cost and the distribution cost.
difference between centralised and decentralised heat supply and secondly the heat distribution cost. It is easy to find journal articles focusing on the first component [3–7], but it is impossible to find journal articles about the second component. Outside the academic community, some German district heating engineers have paid systematic attention to the distribution cost problem, but without giving a general solution for proper analyses. Some of these engineering articles are used as references in Section 2. The World Bank has also recognised the distribution cost component in rehabilitation projects concerning district heating systems in the former planned economies, as reported on page 18 in [8]. Known as heat density, the total heat demand of a land area, city or settlement is often used to describe and evaluate the conditions for implementation of district heating, since the sellable amount of heat over a certain area dictates the feasibility of any district heating system [1]. As will be investigated in this study, the heat density of a populated area is not a static value over time. On the contrary, due partly to global warming and partly to energy efficiency measures being enforced in European buildings, the heat densities of European cities may very well be lowered in the future. One important condition for this study is that when heat densities decrease, it is primarily the specific distribution cost that increases. Although the total heat cost will decrease at lower heat demands, the total cost decrease is not direct proportional to the heat demand reduction. Especially the distribution cost has some base costs which are not depending on the heat demand level. Hereby, a district heating system has a border for its competitiveness with respect to heat density. As the heat use in European buildings is likely to be reduced in the future, the following questions arise; What are the current distribution capital cost levels and the possible district heat market shares in European cities, with respect to general conditions, area characteristics, and city sizes? How much will distribution capital costs increase when future heat demands decrease? How will this increase influence the future heat market shares for district heating? Consequently, this study focuses solely on the heat distribution aspect with regard to the future competitiveness of district heating. This aim is fulfilled by analysing the demand-side
Cd ¼
a I a ðC 1 þ C 2 da Þ ¼ Qs Qs L
ð€=GJÞ
ð1Þ
a is the annuity, from the chosen interest rate and the investment lifetime, I the total network investment cost (€), Qs the heat annually sold (GJ/a), C1 the construction cost constant (€/m), C2 the construction cost coefficient (€/m2), da the average pipe diameter (m), L the total trench length (m) and Qs/L the linear heat density (GJ/m,a). Secondly, the annual cost covering distribution heat losses is related partly to the distribution capital cost in the sense that low linear heat densities also generate high heat losses (the annual share of heat losses depends on the distribution temperatures used, the average pipe diameter, and the heat resistance of the pipe insulation [9]), and partly to local district heating business conditions according to what value is being paid for heat recycled to the network. Thirdly, the cost covering distribution pressure losses and, fourthly, service and maintenance costs, can be estimated to constitute a minor part of the total distribution cost. Since the focus of this study is explicitly the first category cost, the distribution capital cost, these three latter costs are neglected in this analysis. The concept of linear heat density was introduced and used in the 1930s by the German district heating engineer Schulz [10] and was later developed in [11,12]. In these pioneer works, the values for linear heat densities were assessed solely on the basis of empirical evidence and hence only attained for already existing district heating systems. The challenge at hand in this study is to estimate linear heat densities before district heating is established in a city district. As seen above, linear heat density is, in its traditional form, the quota of heat annually sold (Qs) and the total trench length of the district heating pipe system (L). Offering, in this form, no entrance for estimations of future district heating systems, since none of the two quantities can be known for yet not built systems, the basic theoretical work of this study has been to reformulate the linear heat density expression by decomposition into other – in regard of data, existing and attainable – quantities. As it turns out, it is possible to create a bridging between the linear heat density parameter in Eq. (1) and demographic quantities such as population density (p), specific building space (a) and specific heat demand (q). To complete this bridging, the concept of effective width (w) is introduced. The methodology of the study thus centres on the reformulation of the traditional expression for linear heat density into four attainable independent variables that will allow estimations of future district heating system heat demands. Complemented finally with statistically derived values for construction costs and average grid pipe diameters,
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along with assumed annuity, model projections of future district heat distribution capital costs are made possible. By conducting this reformulation of the linear heat density concept and completing the bridging procedure described above, the annual distribution capital cost can be rewritten as:
Cd ¼
a I a ðC 1 þ C 2 da Þ ¼ Qs paqw
ð€=GJÞ
ð2Þ
The four new parameters in the denominator are defined as:
p ¼ P=AL
ðnumber=m2 Þ
ð3Þ
2
a ¼ AB =P ðm =capitaÞ
ð4Þ
ðGJ=m2 aÞ
q ¼ Q s =AB
ð5Þ
w ¼ AL =L ðmÞ
ð6Þ
where P is the total population (number), AL the total land area (m2) and AB is the total building space area (m2). Each of the four denominator parameters has its own purport. Firstly, the population density is a measure of the population living in the land area to be analysed. Secondly, the specific building space is the area measure of the amount of building space available in the area. This parameter includes all buildings to be heated, mostly residential buildings but also service sector buildings such as offices, shops and hospitals. Thirdly, the specific heat demand contains information about the amount of heat needed in order to provide space heating and domestic hot water in these buildings. Fourthly, the effective width provides information about the length of district heat pipes required to heat the buildings in the area. The first two parameters in the denominator express together the plot ratio (e), a city planning parameter describing the fraction between building space area and corresponding land area;
e¼pa¼
P AB AL P
ðÞ
ð7Þ
Plot ratio expresses the building density within a city area and is used within the model as an intermediate parameter in order to estimate both the construction cost levels and the effective widths. According to traditional Swedish city planning [13], plot ratio values are established for typical city categories such as inner city areas (e 0.5–2.0), outer city areas with multi-stories residential houses (e 0.3–0.5) and heat sparse areas with mainly one-family houses (e 0–0.3). Hence, it is possible to estimate future district heat distribution capital costs from city district population densities together with experienced estimates of specific building spaces, specific heat demands and effective widths.
3. Gathered data Due primarily to the availability of key model data, four European countries were chosen for the study: Germany, France, Belgium and the Netherlands. This selection was also made partly on strategic considerations, since the first two of the included countries have been identified, together with United Kingdom, as having half of the short term expansion market for district heating within the European Union [14]. Information about the cities examined is provided in Table 3.1. Out of a total population of 170 million in the four countries studied, population coverage of 35 million people, or 21%, was reached within the study. Population densities (p) by city districts were directly gathered from the Eurostat Urban Audit 2001 database [15]. Although present at Eurostat database, data from 2004 were not used due to considerable share of unavailable information on population densities. This quantity is considered to be the most unique parameter for the analysis, giving the possibility to estimate the spread of distribution capital costs within each city. The Eurostat Urban Audit database contains totally 7746 city districts for the year 2001, which would allow further extension of the study scope. Specific building spaces (a) consist both of residential and service sector building spaces. Specific residential building spaces by cities were gathered from the Eurostat Urban Audit 2001 database [16]. Note that these data were only available on city level giving city averages. For a total of 11 out of 83 cities, four French and seven Dutch, data on specific residential building spaces were not available in the original data gathering. For these 11 cities, country averages for the other cities included in the study have been applied, see Table 3.2. These city values are slightly lower than country averages normally reported, since smaller flats are more common within cities. Generally, statistical values for service sector building spaces are insufficient, as has been put forward in [2]. However, service sector areas per capita have been statistically secured for three of the four study countries (Germany, France and the Netherlands) in [2]. Based on these figures an overall assessment within the modelling of this study has been to upgrade all collected specific residential building spaces data by a factor 1.4, in order to estimate total specific building spaces. One important circumstance regarding service sector buildings is that these normally are concentrated to cities, giving proportionally higher heat demands compared to rural areas. Specific heat demands (q) were estimated by country averages of specific residential heat demands obtained in Table 3.3. Hence, a study assumption is made that current specific service sector heat demands equal current specific residential heat demands. This strong simplification is introduced due to the problem [14] of
Table 3.1 Information about the study cities by country. Country
Number of cities
Belgium Germany
4 38
France
Number of city districts
Population in these cities (millions)
Cities included
84 632
1.8 17.3
31
826
13.2
Netherlands
10
161
3.0
Brussels, Antwerp, Gent, Charleroi Berlin, Hamburg, Munich, Köln, Frankfurt am Main, Essen, Leipzig, Dresden, Dortmund, Düsseldorf, Bremen, Hannover, Nurnberg, Bochum, Wuppertal, Bielefeld, Halle an der Saale, Magdeburg, Wiesbaden, Gottingen, Mulheim a.d.Ruhr, Darmstadt, Trier, Freiburg im Breisgau, Regensburg, Frankfurt (Oder), Weimar, Schwerin, Erfurt, Augsburg, Bonn, Karlsruhe, Monchengladbach, Mainz, Kiel, Saarbrucken, Potsdam, Koblenz Paris, Lyon, Toulouse, Strasbourg, Bordeaux, Nantes, Lille, Montpellier, Saint-Etienne, Le Havre, Rennes, Amiens, Rouen, Nancy, Metz, Reims, Orleans, Dijon, Poitiers, Clermont-Ferrand, Caen, Limoges, Besancon, Grenoble, Ajaccio, Saint Denis, Pointe-a-Pitre, Fort-de-France, Cayenne, Marseille, Nice s’ Gravenhage, Amsterdam, Rotterdam, Utrecht, Eindhoven, Tilburg, Groningen, Enschede, Arnhem, Heerlen
Total
83
1703
35.3
U. Persson, S. Werner / Applied Energy 88 (2011) 568–576 Table 3.2 Specific building spaces, averages for the study cities by country. Country
Belgium Germany France Netherlands
Specific residential building space, city averages (m2/capita), obtained from [16]
Estimated specific building space, city averages (m2/capita)
35.3 39.3 36.2 39.7
49.4 55.0 50.6 55.6
Table 3.3 Estimated specific residential heat demands by country. Country
Current residential heat demand (PJ/a), estimated from [17]
Current residential building floor space (million m2), obtained from [18]
Current specific residential heat demand (GJ/m2a)
Belgium Germany France Netherlands
220 1900 1250 280
392 3461 2715 667
0.56 0.55 0.46 0.42
Total
3650
7235
0.50
Table 3.4 Characteristic plot ratio levels and corresponding cost constant and cost coefficient in Eq. (1). Area characteristics
Plot ratio (e)
C1 (€/m)
C2 (€/m2)
Inner city areas (A) Outer city areas (B) Park areas (C)
e P 0.5 0.3 6 e < 0.5 0 6 e < 0.3
286 214 151
2022 1725 1378
obtaining verified aggregated specific heat demands for the service sectors in the four countries. Effective widths (w) are generally not available up to this date. The concept of effective width itself is rather new, since it was introduced in 1997 [19], and previously not elaborated. One preliminary and important pre-study [20] initiated by the authors delivered crucial data for the completion of the study. This prestudy combined previous results from a study of 39 detached house districts heating schemes in Sweden [21] with own collected data from 34 multi-family housing district heating systems in the
571
Swedish cities of Halmstad and Gothenburg. On the basis of these results, effective widths within the modelling of this study were estimated by use of the power function in Eq. (8). Note that (e) refers to the plot ratio value, not to the natural logarithm base.
w ¼ 61:8 e0:15
ðmÞ
ð8Þ
Construction costs for pairs of district heating pipes buried into the ground follow a linear equation with the pipe diameter as independent variable, as in Eq. (1). Hence, the average construction cost by trench length consists of a size-independent base cost (C1) and an additional cost coefficient (C2) directly proportional to the pipe diameter. Depending on country experiences, the cost levels are different. The lowest construction costs are found in mature district heating countries, as verified in older comparisons between district heating systems in various countries [22,23]. In this study, Swedish cost experiences have been used for three different area characteristics; Inner city areas (A), Outer city areas (B) and Park areas (C), according to original data presented in [24]. With use of a currency exchange rate of 10.8 SEK/€ [25], the estimated values for the cost constant and the cost coefficient were calculated and are presented in Table 3.4. The intervals of plot ratio chosen for the three different area characteristics were based on traditional classification within Swedish city planning [13]. Average pipe diameters were estimated through linear heat density model values, by use of the following model function;
da ¼ 0:0486 lnðQ s =LÞ þ 0:0007 ðmÞ
ð9Þ
As can be seen in Fig. 3.1, this logarithmic function reflects the relationship between linear heat densities and average pipe diameters in 134 observations concerning whole or parts of Swedish district heating systems [9]. Annuity (a), for estimating the annual heat distribution capital cost, was chosen to reflect a long term investment strategy in order to obtain the benefits of district heating in the future. This approach has rendered a model use of a real interest rate of 3%, along with a 30 year investment lifetime for district heating networks. 4. Extent and some city characteristics Plot ratios for the 1703 city districts are presented in Fig. 4.1. A majority of the study city districts can be described as low or semidense populations, with some exceptionally low plot ratio values for scarcely populated large land area districts. In 678 city districts
Fig. 3.1. Relationship between linear heat densities and average pipe diameters in 134 Swedish district heating systems and city districts.
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Fig. 4.1. Combinations of populations and plot ratios for the 1703 study city districts.
Fig. 4.2. Combinations of total city populations and population densities for the 1703 study city districts and for the 83 study cities.
the plot ratios were below 0.15 (40%). Most frequented are city districts with populations in the interval of 8000 to 35,000 inhabitants, with typical plot ratio values between 0.1 and 0.6. Within the high density inner city areas of larger cities, plot ratios may very well escalate to values above 2.0. Population densities, presented in Fig. 4.2, typically average at values from 700 to 2000 inhabitants per square kilometre for total city populations of 100,000–700,000 inhabitants. For larger cities with more than one million inhabitants, population densities from 2000 to well above 10,000 inhabitants per square kilometre are found within the study. The highest city population density was found in Paris with an average population density of 20,183 inhabitants per square kilometre. Distributed quite differently within the four countries, population densities are generally higher in French cities and lower in Belgian cities. Population densities above 10,000 inhabitants per square kilometre represent less than 10% of total city areas of the survey, while an estimated 50% of the Netherlands and the Belgian city areas are populated by less than 2000 inhabitants per square kilometre.
Table 4.1 Aggregated district heat market shares in the study cities by country. Country
Current district heat sales (PJ/a) in the study cities, according to [26– 28]
Model estimated heat demands in the study cities (PJ/a)
Estimated present heat market shares for district heat in the study cities (%)
Belgium Germany France Netherlands
0.1 153 34 14
51 523 307 70
0 29 11 21
Total
201
951
21
Estimations of present district heat market shares in the studied cities are presented in Table 4.1. On the basis of model calculated total heat demands along with externally collected data on current district heat sales within the 83 study cities, an average district heat market share of 21% is estimated. Currently, 69 of the 83 study
U. Persson, S. Werner / Applied Energy 88 (2011) 568–576
cities have district heating systems in operation. Expansion of present district heating systems is a major possibility within these cities, while 14 study cities require the establishment of entirely new systems. For 10 of the study cities, the current district heating market share is above 40%. 5. Results The primary study result indicators are the combinations of marginal distribution capital costs and the corresponding heat market shares. These combinations have been consecutively sorted from the lowest to the highest marginal distribution capital cost. Model calculations were executed on the basis of the methodology, input data and assumptions described above. Model calculations were further based on a general assumption of a 100% connection rate to district heating networks. The intention of expressing the competitiveness of future district heating systems by use of the marginal distribution capital
573
cost is to identify competitive cost levels of network extensions. However, the actual distribution capital cost may be lower than what is outlined by the marginal distribution capital cost, typically in high heat density inner city areas. The uncertainties in the estimated marginal distribution capital costs come from possible deviations in the estimated input values and assumptions. Major deviations can be: District heating companies having planned rate of returns higher than the level represented by the assumed annuity. The construction cost levels used come from a mature district heating country (Sweden), while the cost levels can be higher in novel district heating countries. Lower construction cost levels from future alternative pipe materials and network technologies (4th generation of district heating networks). Other future city shapes than represented by the 2001 Urban audit database.
Fig. 5.1. Current distribution capital cost levels and the corresponding district heat market shares in the study city districts.
Fig. 5.2. Current marginal distribution capital cost levels and the corresponding district heat market shares in the study city districts at various city area characteristics: A for inner city areas, B for outer city areas, and C for park areas.
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The uncertainty from lower heat demands is regarded as a prime interest and is therefore one of the three research questions in this analysis.
Table 5.1 City districts by area characteristics with average distribution capital costs, total heat demands and corresponding investments. Area characteristics
5.1. General conditions The current distribution capital cost levels and the corresponding possible district heat market shares in the study city districts are presented in Fig. 5.1. To enhance the distinction between marginal and average distribution capital cost further, the dotted line in Fig. 5.1 illustrates the average distribution capital cost, while the solid line shows the marginal distribution capital cost. In the following result graphs, the grand total curve for marginal distribution capital cost is always included for reference. It is noticeable that the grand total curve for the marginal distribution capital cost levels out to create a relative plateau for heat market shares in the interval of 10–60%, where market expansion can occur with only slight cost increases. For heat market shares within this interval there are remarkably favourable conditions for extensions of present and establishment of new district heating systems. Since the estimated present district heat market share is 21% in the study city districts, this circumstance suggests a viable threefold expansion possibility for district heating systems within these city districts in the coming future. At the current situation, a district heat market share of 60% can be reached with an estimated marginal distribution capital cost of only 2.1 €/GJ, corresponding to an average distribution capital cost of 1.6 €/GJ. Since only small differences appeared in the four studied countries, all located in the same geographical region, these results are considered general for all four countries. Hence, a heat market share of 60% can be considered as an indicative threshold market share in European urban areas, where district heat is directly feasible at current heat market conditions. This conclusion comes from the fact that the corresponding marginal distribution capital cost is only slightly higher than the marginal distribution capital cost associated with the current heat market share of 21% in the study cities. However, in countries like Denmark and Sweden, with heavy taxation of fossil fuels giving a very high cost difference between district heat and alternative heat supply, the threshold market shares are higher than 60%. At some level of decreasing heat densities, as the area characteristic shifts from inner city areas to park areas, it is evident that the cost of network establishments will cease to be economically viable. Heat market shares above 60% are anticipated to be associated with increased marginal distribution capital costs, since this market segment is characterised by low heat density areas. Hence, an indicative threshold plot ratio value of about 0.15–0.20 has been identified for directly feasible district heating in the studied cities, corresponding to a marginal distribution capital cost of 2.1 €/GJ. In order to understand the magnitude of the distribution capital cost, it has to be compared with the total district heat price levels in the study countries. A short review gave the interval of 13–17 €/ GJ excluding VAT during 2007, similar to the current costs of alternative local heat supply. Consequently, an average distribution capital cost of 1.6 €/GJ represents about 10% of the full price level. Hence, the distribution capital cost must increase considerably in order to reduce the competitiveness of district heat. To add further perspective on the distribution capital cost level, a comparison with a hypothetical carbon dioxide emission cost scenario can be performed. If natural gas, used for space heating in European buildings, were to be part of the emission trading system (ETS), an emission price of 20 €/ton CO2 would correspond to an additional heat cost of 1.3 €/GJ. At an emission price of 40 €/ ton CO2, the equivalent additional heat cost would be 2.6 €/GJ. Hence, the investment cost for establishing viable district heating
Number of city districts
Average distribution capital cost, Cd,a (€/GJ)
Model estimated heat demands, Qs (PJ/a)
Required investments in heat distribution networks, I (G€)
Inner city areas (A) Outer city areas (B) Park areas (C), feasible
317
1.2
182 (19%)
4.3
296
1.6
160 (17%)
5.1
355
1.8
236 (25%)
8.2
Total direct feasible Park areas (C), less feasible
968
1.6
578 (61%)
17.6
735
4.5
373 (39%)
32.9
1703
2.7
951 (100%)
50.5
Total
networks in dense and semi-dense European cities, is within the same magnitude as the emission prohibition costs that eventually could be added to the major local heating fuel alternative. Since recycling of excess heat generally is not taxed, the acceptance of even higher distribution capital costs could further strengthen the future possibilities for district heating in competition with local heating alternatives. The general finding is then that the magnitude of the distribution capital cost is not a current barrier for expanding district heating in the study cities, since this cost is a rather small part of the total cost level and comparable with the current carbon dioxide prices, if applied for heating. An indicative threshold heat market share has been found for direct feasible district heating together with a corresponding indicative threshold plot ratio level. 5.2. Area characteristics In Fig. 5.2, the heat market share curves are presented for each area characteristic category at various marginal distribution capital costs. Presented in this way, the beneficial conditions for district heating in high population density areas are further enhanced. Although the cost for network establishments is higher in inner city areas, the concentration of heat demands in these areas compensate for this cost addition, rendering better conditions for district heat distribution. Model estimations indicate possible district heat market shares of 100% in inner city areas (A), at a marginal distribution capital cost level of 2.0 €/GJ, and in outer city areas (B) at 2.2 €/GJ. In park areas (C), heat market shares of 100% are not reached within the limited 8.0 €/GJ ordinate, but a 90% district heat market share is estimated at a marginal distribution capital cost level of 5.8 €/GJ. The average distribution capital cost levels for the study city districts by area characteristics are presented in Table 5.1. Above the indicative feasibility threshold, corresponding to a marginal distribution cost of 2.1 €/GJ, the model calculations suggest a total investment volume of 17.6 G€ in order to annually distribute 578 PJ of heat. 5.3. City sizes Reflecting well the general increase of distribution capital cost levels in scarcely populated areas, the presentation of study results divided into four city size categories clearly indicates this basic circumstance (Fig. 5.3). At the indicative threshold conditions, possi-
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Fig. 5.3. Current marginal distribution capital cost levels and the corresponding district heat market shares in the study city districts at various city sizes.
Fig. 5.4. Marginal distribution capital cost levels and the corresponding district heat market shares in the study city districts at various heat demand levels: Grand total for the 83 cities at the current situation, 20% and 50% reduction scenarios, respectively.
ble heat market coverage of 79% is estimated for cities larger than one million people. For cities with 500,000–1,000,000 inhabitants, the corresponding value is anticipated to 62%. In smaller cities, the equivalent heat market share is 47% (150,000–500,000 inhabitants) and 24% (60,000–150,000 inhabitants). As expected, large cities provide the most beneficial conditions for district heat distribution, since the high population densities within these cities renders high heat densities. 5.4. Heat demand reductions In order to answer the second introductory question of how much the distribution capital costs will increase when heat demands decrease, the grand total curve for the 83 study cities at current conditions were used as a foundation for two additional heat demand projections. The first projection relates to the European Union energy efficiency target for 2020, which implies a 20% overall reduction of primary energy use [29]. In the second projection, a 50% heat demand reduction was elaborated to inves-
tigate the consequences of halved residential and service sector heat demands in a more distant future. With use of correspondingly altered specific heat demands, recalculation of model data generated the resulting graphs in Fig. 5.4. A horizontal observation of the grand total curve in Fig. 5.4 reveals an enhanced sensitivity for heat demand reductions within the heat market interval of 10–60%, due to the flat curve characteristics of this market segment. If considering the case where 2.1 €/GJ is regarded as an indicative feasible marginal distribution capital cost level, the possible market share for district heat would be reduced from 60% down to about 45% at the 20% reduction scenario, and down to about 10% at the 50% reduction scenario. However, if this would be the case, a strong pressure would appear for reduced payability for recycled heat according to the introductory discussion based on Fig. 1.1. A vertical observation in Fig. 5.4 reveals that in order to persist at a current district heat market share of 60%, acceptance of marginal distribution capital costs of estimated 2.4 €/GJ would be required at the 20% reduction scenario. At the 50% reduction
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scenario, the corresponding marginal distribution cost level is anticipated to 3.6 €/GJ. The additional cost for maintaining a given heat market share is then not proportional to the magnitude of the heat demand decrease itself, and it is also depending on the present district heat market share. At a district heat market share of 60%, heat demand reductions of 20% renders an increase of distribution capital cost levels of anticipated 20%, while reductions of 50% induce a 70% cost increase. This circumstance can be explained by the fact that pipe diameters in new networks can be reduced, since less heat is to be distributed. Hence, reduced heat demands will increase the current marginal overall district heat cost level with 2% and 7%, respectively, since the distribution capital cost constitutes about 10% of the current total price level. The combined conclusion from these horizontal and vertical observations would be that lower heat demands will give pressures for somewhat higher customer prices and somewhat lower payability for recycled heat into district heating systems. 6. Conclusions The main conclusion is that the future estimated capital costs for district heat distribution in the study cities are rather low, since the cities are very dense. Even at ambitious heat reduction targets, district heating is expected to remain competitive in high heat density city districts and large city areas. In low heat density areas, local heating alternatives are expected to dominate. The future competitiveness of district heating in Europe is depending on a mixture of boundary conditions and counteracting factors. Three determining factors emerge as critical: the future competition on the heat market, the current use of district heat, and the future city shapes. The future competition on the heat market will be based on renewables, more efficient use of fossil fuels, district heating, and other energy efficiency measures such as heat pumps. If natural gas and fuel oil used for heating also would be part of the emission trading system (ETS), the customer costs for heating would be further increased. As estimated in this study, this cost increase will have the same magnitude as the current district heat distribution capital cost levels, indicating a possibility to accept even higher distribution capital cost levels for future district heating systems. Hereby, the future competitiveness of district heating will always depend on the combination of distribution cost and the cost difference between district heat supply and alternative local heat supply. The current use of district heat is also a key factor. Market share increases can be facilitated by expansions of already existing network systems and by utilisation of already existing organisations and business models. Further, the key ability of district heating networks to receive and distribute excess heat is a central counteracting factor in support of future establishments of competitive district heating systems. The European potential for recycling of excess heat, and the capacity for waste incineration, remains poorly utilised up to this date. As introductory described in this study, the distribution capital cost of district heating outlines the payability for excess heat. Hence, an extended utilisation of abundant European excess heat would presuppose the establishments of many new district heating systems in the future. Added values would of course be local and regional environmental benefits, customer comfort and the resource optimisation quality of recycled excess heat that otherwise would be unutilised. Finally, future city shapes is also a counteracting factor, by the continued raise of population densities within the European cities. After decades of urban sprawl, a revival of compact cities could act as a major climate change mitigation measure. As this study has shown, compact cities have better conditions for district heating (and cooling) than sparse cities, since the plot ratios are higher,
and therefore the future competitiveness of district heating additionally involves socio-political decisions and demographic considerations as well. Acknowledgements This analysis was performed by financial support from the Swedish Energy Agency through the Swedish System Solutions Project and from Fjärrsyn, the Swedish district heating research programme, through the District Heating System Technology Project. References [1] Werner S. District heating and cooling. Encyclopedia of Energy 2004;1:841–8. [2] Werner S. The European heat market, WP1 of the Ecoheatcool project. Euroheat & Power, Brussels 2005. Available at. [3] Holmgren K. The role of district heating networks as a user of waste heat supply from various sources – the case of Göteborg. Appl Energy 2006;83: 1351–67. [4] Karlsson M, Gebremedhin A, Klugman S, Henning D, Moshfegh B. Regional energy system optimization – potential for a regional heat market. Appl Energy 2009;86:441–51. [5] Egeskog A, Hansson J, Berndes G, Werner S. Co-generation of biofuels for transportation and heat for district heating systems – an assessment of national possibilities in the EU. Energy Policy 2009;37:5260–72. [6] Lund H, Möller B, Mathiesen BV, Dyrelund A. The role of district heating in future renewable energy systems. Energy 2010;35:1381–90. [7] Möller B, Lund H. Conversion of individual natural gas to district heating: geographical studies of supply costs and consequences for the Danish energy system. Appl Energy 2010;87:1846–57. [8] Lampietti JA, Meyer AS. Coping with the cold. World Bank Technical Paper no. 529, Washington; 2003. [9] Frederiksen S. Werner S, Fjärrvärme – teori, teknik och funktion (District heating – theory, technology and function). Studentlitteratur, Lund; 1993. [10] Schulz E. Öffentliche Heizkraftwerke und Elekrizitätswirtschaft in Städten. Berlin: Verlag von Julius Springer; 1933. [11] Simon W. Spezifische Fernheiznetz-Anlagekosten bei verschiedenen Siedlungsarten und Wohndichten. Elektrizitätswirtschaft 1950;49(10):292–5. [12] Brachetti HE. Sind Wärmeverteilkosten von der Wärmedichte abhängig? Fernwärme International 1984;13(4):201–5. [13] Statens Planverk. Värmeförsörjning och Bebyggelseplanering (Heat supply and city planning). Statens Planverk Rapport 71:1985. Stockholm 1985. [14] Werner S. Possibilities with more district heating in Europe, WP4 of the Ecoheatcool project. Euroheat & Power, Brussels 2006. Available at http:// www.ecoheatcool.org. [15] Eurostat. Urban Audit 2001 Database. Luxemburg; 2009. . [Derived indicators for sub-city districts, Date of extraction: 9 Jun 09. Extracted categories; de1001i, en5003i, en5101i and en5012i]. [16] Eurostat. Urban Audit 2001 Database. Luxemburg; 2009. . [Data collected for core cities, Date of extraction: 10 Jun 09. Extracted categories; sa1022v]. [17] IEA. Energy balances for OECD countries 2006. Paris; 2008. [18] Italian Ministry of Infrastructure, Housing statistics in the European Union; 2005/2006. [19] Werner S. Fjärrvärme till småhus – värmeförluster och distributionskostnader (District heating for one-family houses – heat losses and distribution costs). The Swedish district heating association, report FVF 1997:11. Stockholm 1997. [20] Netterberg H, Isaksson I. District heating in slough. BSc thesis from Halmstad University, Halmstad; 2009. [21] Larsson L, Andersson S, Werner S. Status for district heating in areas with low heat density (published in Swedish: Nuläge Värmegles Fjärrvärme). The Swedish district heating association, research report FoU 2002:74. Stockholm; 2002. [22] IEA. District heating, cost analysis of district heating networks. National energy administration, report 1986:R10. Stockholm; 1986. [23] Euroheat & Power. Construction costs comparison of preinsulated bonded district heating pipes. Study committee for heat transport and distribution. Jahrbuch Euroheat & Power – Fernwärme International; 1999. p. 54–62. [24] Svensk Fjärrvärme. Kulvertkostnadskatalog (The district heating pipe cost catalogue). The Swedish district heating association, report 2007:1. Stockholm; 2007. [25] Sveriges Riksbank. Accumulated average trading currency SEK/€ jan–aug 2009. . 2009-09-14. [26] Via Seva. L’anuaire des reseaux de chaleur et de froid. Lyon; 2008. [27] AGFW. Hauptbericht der Fernwärmeversorgung 2006. Arbeitsgemeinschaft für Wärme und Heizkraftwirtschaft, Frankfurt am Main; 2007. [28] CE Delft, Warmtenetten in Nederland – Overzicht van grootschalige en kleinschalige warmtenetten in Nederland, Delft; 2009. [29] EC. Action plan for energy efficiency: realising the potential. European Commission, COM; 2006. p. 545.
Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Heat distribution and the future competitiveness of district heating Urban Persson ⇑, Sven Werner School of Business and Engineering, Halmstad University, PO Box 823, SE-30118 Halmstad, Sweden
a r t i c l e
i n f o
Article history: Received 4 June 2010 Received in revised form 17 September 2010 Accepted 21 September 2010 Available online 16 October 2010 Keywords: District heating Distribution capital cost Heat density Waste heat Effective width Plot ratio
a b s t r a c t The competitiveness of present and future district heating systems can be at risk when residential and service sector heat demands are expected to decrease in the future. In this study, the future competitiveness of district heating has been examined by an in depth analysis of the distribution capital cost at various city characteristics, city sizes, and heat demands. Hereby, this study explores an important market condition often neglected or badly recognised in traditional comparisons between centralised and decentralised heat supply. By a new theoretical approach, the traditional and empirical expression for linear heat density is transformed into an analytical expression that allows modelling of future distribution capital cost levels also in areas where no district heating exists today. The independent variables in this new analytical expression are population density, specific building space, specific heat demand and effective width. Model input data has primarily been collected from national and European statistical sources on heat use, city populations, city districts and residential living areas. Study objects were 83 cities in Belgium, Germany, France, and the Netherlands. The average heat market share for district heat within these cities was 21% during 2006. The main conclusion is that the future estimated capital costs for district heat distribution in the study cities are rather low, since the cities are very dense. At the current situation, a market share of 60% can be reached with a marginal distribution capital cost of only 2.1 €/GJ, corresponding to an average distribution capital cost of 1.6 €/GJ. The most favourable conditions appear in large cities and in inner city areas. In the future, there is a lower risk for reduced competitiveness due to reduced heat demands in these areas, since the increased distribution capital cost is low compared to the typical prices of district heat and competing heat supply. However, district heating will lose competitiveness in low heat density areas. Hence, reduced heat demands in high heat density areas are not a general barrier for district heating in the future. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction The main additional cost for a district heating system compared to a local heat generation alternative is the unavoidable cost of heat distribution. This cost includes both annual payback of original network investment costs and additional operational costs to compensate for temperature and pressure losses associated with heat distribution. In order to stay competitive, the total cost of district heat must be lower than the cost of any local heat generation alternative. This simple cost comparison is illustrated in Fig. 1.1. While fuel oil and natural gas are the traditional energy sources predominantly used for local heat generation in central European countries, the fundamental idea of district heating is recycling of heat that otherwise would be wasted [1]. By utilising five major base heat supplies (combined heat and power (CHP), waste-to-energy
⇑ Corresponding author. Tel.: +46 (0)35 167405; fax: +46 (0)35 167500. E-mail address: [email protected] (U. Persson). 0306-2619/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2010.09.020
(WTE) plants, excess heat from energy-intensive industrial processes, direct use of biomass fuels and geothermal heat), district heating systems does not use the same energy supply as the traditional local heat-only generation alternatives. As a consequence, district heating systems hereby contribute to improved total energy system efficiency by avoiding the use of high exergy fuels for pure heat generation and offers distribution possibilities for excess heat. Since district heating is mainly based on heat recycling, the total cost for district heating minus the heat distribution cost can be regarded as the payability for recycled heat. With a low value on recycled heat, higher distribution costs can be accepted. Nowadays, local heat generation with fossil fuels or with electricity is furthermore often taxed with consumption or carbon dioxide taxes, while heat recycling normally is untaxed [2]. This additional taxation gives the possibility to accept even higher distribution costs in the competition between district heating and local heating alternatives. Hence, the total future competitiveness of district heating is a combination of two major cost components: Firstly the cost
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parameters influencing the distribution capital cost. Hence, future supply-side fuel and technology options, future excess heat process optimisation analyses or future socio-political considerations have not been elaborated in this context.
2. Method The total cost of district heat distribution consists basically of four different categories of costs [9]. The first and main cost is the distribution capital cost, Cd, which represents annual repayments of investment capital for the construction of the district heating network. Hence, the distribution capital cost depends primarily on the network construction cost, which in turn is influenced by the linear heat density (Qs/L) of the settlement at hand. In general terms, the distribution capital cost is estimated to constitute more than half of the total distribution cost for typical district heating networks. According to [9], the distribution capital cost can be estimated as: Fig. 1.1. General cost structure comparison between local heat generation and district heating, with respect to the heat generation cost and the distribution cost.
difference between centralised and decentralised heat supply and secondly the heat distribution cost. It is easy to find journal articles focusing on the first component [3–7], but it is impossible to find journal articles about the second component. Outside the academic community, some German district heating engineers have paid systematic attention to the distribution cost problem, but without giving a general solution for proper analyses. Some of these engineering articles are used as references in Section 2. The World Bank has also recognised the distribution cost component in rehabilitation projects concerning district heating systems in the former planned economies, as reported on page 18 in [8]. Known as heat density, the total heat demand of a land area, city or settlement is often used to describe and evaluate the conditions for implementation of district heating, since the sellable amount of heat over a certain area dictates the feasibility of any district heating system [1]. As will be investigated in this study, the heat density of a populated area is not a static value over time. On the contrary, due partly to global warming and partly to energy efficiency measures being enforced in European buildings, the heat densities of European cities may very well be lowered in the future. One important condition for this study is that when heat densities decrease, it is primarily the specific distribution cost that increases. Although the total heat cost will decrease at lower heat demands, the total cost decrease is not direct proportional to the heat demand reduction. Especially the distribution cost has some base costs which are not depending on the heat demand level. Hereby, a district heating system has a border for its competitiveness with respect to heat density. As the heat use in European buildings is likely to be reduced in the future, the following questions arise; What are the current distribution capital cost levels and the possible district heat market shares in European cities, with respect to general conditions, area characteristics, and city sizes? How much will distribution capital costs increase when future heat demands decrease? How will this increase influence the future heat market shares for district heating? Consequently, this study focuses solely on the heat distribution aspect with regard to the future competitiveness of district heating. This aim is fulfilled by analysing the demand-side
Cd ¼
a I a ðC 1 þ C 2 da Þ ¼ Qs Qs L
ð€=GJÞ
ð1Þ
a is the annuity, from the chosen interest rate and the investment lifetime, I the total network investment cost (€), Qs the heat annually sold (GJ/a), C1 the construction cost constant (€/m), C2 the construction cost coefficient (€/m2), da the average pipe diameter (m), L the total trench length (m) and Qs/L the linear heat density (GJ/m,a). Secondly, the annual cost covering distribution heat losses is related partly to the distribution capital cost in the sense that low linear heat densities also generate high heat losses (the annual share of heat losses depends on the distribution temperatures used, the average pipe diameter, and the heat resistance of the pipe insulation [9]), and partly to local district heating business conditions according to what value is being paid for heat recycled to the network. Thirdly, the cost covering distribution pressure losses and, fourthly, service and maintenance costs, can be estimated to constitute a minor part of the total distribution cost. Since the focus of this study is explicitly the first category cost, the distribution capital cost, these three latter costs are neglected in this analysis. The concept of linear heat density was introduced and used in the 1930s by the German district heating engineer Schulz [10] and was later developed in [11,12]. In these pioneer works, the values for linear heat densities were assessed solely on the basis of empirical evidence and hence only attained for already existing district heating systems. The challenge at hand in this study is to estimate linear heat densities before district heating is established in a city district. As seen above, linear heat density is, in its traditional form, the quota of heat annually sold (Qs) and the total trench length of the district heating pipe system (L). Offering, in this form, no entrance for estimations of future district heating systems, since none of the two quantities can be known for yet not built systems, the basic theoretical work of this study has been to reformulate the linear heat density expression by decomposition into other – in regard of data, existing and attainable – quantities. As it turns out, it is possible to create a bridging between the linear heat density parameter in Eq. (1) and demographic quantities such as population density (p), specific building space (a) and specific heat demand (q). To complete this bridging, the concept of effective width (w) is introduced. The methodology of the study thus centres on the reformulation of the traditional expression for linear heat density into four attainable independent variables that will allow estimations of future district heating system heat demands. Complemented finally with statistically derived values for construction costs and average grid pipe diameters,
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along with assumed annuity, model projections of future district heat distribution capital costs are made possible. By conducting this reformulation of the linear heat density concept and completing the bridging procedure described above, the annual distribution capital cost can be rewritten as:
Cd ¼
a I a ðC 1 þ C 2 da Þ ¼ Qs paqw
ð€=GJÞ
ð2Þ
The four new parameters in the denominator are defined as:
p ¼ P=AL
ðnumber=m2 Þ
ð3Þ
2
a ¼ AB =P ðm =capitaÞ
ð4Þ
ðGJ=m2 aÞ
q ¼ Q s =AB
ð5Þ
w ¼ AL =L ðmÞ
ð6Þ
where P is the total population (number), AL the total land area (m2) and AB is the total building space area (m2). Each of the four denominator parameters has its own purport. Firstly, the population density is a measure of the population living in the land area to be analysed. Secondly, the specific building space is the area measure of the amount of building space available in the area. This parameter includes all buildings to be heated, mostly residential buildings but also service sector buildings such as offices, shops and hospitals. Thirdly, the specific heat demand contains information about the amount of heat needed in order to provide space heating and domestic hot water in these buildings. Fourthly, the effective width provides information about the length of district heat pipes required to heat the buildings in the area. The first two parameters in the denominator express together the plot ratio (e), a city planning parameter describing the fraction between building space area and corresponding land area;
e¼pa¼
P AB AL P
ðÞ
ð7Þ
Plot ratio expresses the building density within a city area and is used within the model as an intermediate parameter in order to estimate both the construction cost levels and the effective widths. According to traditional Swedish city planning [13], plot ratio values are established for typical city categories such as inner city areas (e 0.5–2.0), outer city areas with multi-stories residential houses (e 0.3–0.5) and heat sparse areas with mainly one-family houses (e 0–0.3). Hence, it is possible to estimate future district heat distribution capital costs from city district population densities together with experienced estimates of specific building spaces, specific heat demands and effective widths.
3. Gathered data Due primarily to the availability of key model data, four European countries were chosen for the study: Germany, France, Belgium and the Netherlands. This selection was also made partly on strategic considerations, since the first two of the included countries have been identified, together with United Kingdom, as having half of the short term expansion market for district heating within the European Union [14]. Information about the cities examined is provided in Table 3.1. Out of a total population of 170 million in the four countries studied, population coverage of 35 million people, or 21%, was reached within the study. Population densities (p) by city districts were directly gathered from the Eurostat Urban Audit 2001 database [15]. Although present at Eurostat database, data from 2004 were not used due to considerable share of unavailable information on population densities. This quantity is considered to be the most unique parameter for the analysis, giving the possibility to estimate the spread of distribution capital costs within each city. The Eurostat Urban Audit database contains totally 7746 city districts for the year 2001, which would allow further extension of the study scope. Specific building spaces (a) consist both of residential and service sector building spaces. Specific residential building spaces by cities were gathered from the Eurostat Urban Audit 2001 database [16]. Note that these data were only available on city level giving city averages. For a total of 11 out of 83 cities, four French and seven Dutch, data on specific residential building spaces were not available in the original data gathering. For these 11 cities, country averages for the other cities included in the study have been applied, see Table 3.2. These city values are slightly lower than country averages normally reported, since smaller flats are more common within cities. Generally, statistical values for service sector building spaces are insufficient, as has been put forward in [2]. However, service sector areas per capita have been statistically secured for three of the four study countries (Germany, France and the Netherlands) in [2]. Based on these figures an overall assessment within the modelling of this study has been to upgrade all collected specific residential building spaces data by a factor 1.4, in order to estimate total specific building spaces. One important circumstance regarding service sector buildings is that these normally are concentrated to cities, giving proportionally higher heat demands compared to rural areas. Specific heat demands (q) were estimated by country averages of specific residential heat demands obtained in Table 3.3. Hence, a study assumption is made that current specific service sector heat demands equal current specific residential heat demands. This strong simplification is introduced due to the problem [14] of
Table 3.1 Information about the study cities by country. Country
Number of cities
Belgium Germany
4 38
France
Number of city districts
Population in these cities (millions)
Cities included
84 632
1.8 17.3
31
826
13.2
Netherlands
10
161
3.0
Brussels, Antwerp, Gent, Charleroi Berlin, Hamburg, Munich, Köln, Frankfurt am Main, Essen, Leipzig, Dresden, Dortmund, Düsseldorf, Bremen, Hannover, Nurnberg, Bochum, Wuppertal, Bielefeld, Halle an der Saale, Magdeburg, Wiesbaden, Gottingen, Mulheim a.d.Ruhr, Darmstadt, Trier, Freiburg im Breisgau, Regensburg, Frankfurt (Oder), Weimar, Schwerin, Erfurt, Augsburg, Bonn, Karlsruhe, Monchengladbach, Mainz, Kiel, Saarbrucken, Potsdam, Koblenz Paris, Lyon, Toulouse, Strasbourg, Bordeaux, Nantes, Lille, Montpellier, Saint-Etienne, Le Havre, Rennes, Amiens, Rouen, Nancy, Metz, Reims, Orleans, Dijon, Poitiers, Clermont-Ferrand, Caen, Limoges, Besancon, Grenoble, Ajaccio, Saint Denis, Pointe-a-Pitre, Fort-de-France, Cayenne, Marseille, Nice s’ Gravenhage, Amsterdam, Rotterdam, Utrecht, Eindhoven, Tilburg, Groningen, Enschede, Arnhem, Heerlen
Total
83
1703
35.3
U. Persson, S. Werner / Applied Energy 88 (2011) 568–576 Table 3.2 Specific building spaces, averages for the study cities by country. Country
Belgium Germany France Netherlands
Specific residential building space, city averages (m2/capita), obtained from [16]
Estimated specific building space, city averages (m2/capita)
35.3 39.3 36.2 39.7
49.4 55.0 50.6 55.6
Table 3.3 Estimated specific residential heat demands by country. Country
Current residential heat demand (PJ/a), estimated from [17]
Current residential building floor space (million m2), obtained from [18]
Current specific residential heat demand (GJ/m2a)
Belgium Germany France Netherlands
220 1900 1250 280
392 3461 2715 667
0.56 0.55 0.46 0.42
Total
3650
7235
0.50
Table 3.4 Characteristic plot ratio levels and corresponding cost constant and cost coefficient in Eq. (1). Area characteristics
Plot ratio (e)
C1 (€/m)
C2 (€/m2)
Inner city areas (A) Outer city areas (B) Park areas (C)
e P 0.5 0.3 6 e < 0.5 0 6 e < 0.3
286 214 151
2022 1725 1378
obtaining verified aggregated specific heat demands for the service sectors in the four countries. Effective widths (w) are generally not available up to this date. The concept of effective width itself is rather new, since it was introduced in 1997 [19], and previously not elaborated. One preliminary and important pre-study [20] initiated by the authors delivered crucial data for the completion of the study. This prestudy combined previous results from a study of 39 detached house districts heating schemes in Sweden [21] with own collected data from 34 multi-family housing district heating systems in the
571
Swedish cities of Halmstad and Gothenburg. On the basis of these results, effective widths within the modelling of this study were estimated by use of the power function in Eq. (8). Note that (e) refers to the plot ratio value, not to the natural logarithm base.
w ¼ 61:8 e0:15
ðmÞ
ð8Þ
Construction costs for pairs of district heating pipes buried into the ground follow a linear equation with the pipe diameter as independent variable, as in Eq. (1). Hence, the average construction cost by trench length consists of a size-independent base cost (C1) and an additional cost coefficient (C2) directly proportional to the pipe diameter. Depending on country experiences, the cost levels are different. The lowest construction costs are found in mature district heating countries, as verified in older comparisons between district heating systems in various countries [22,23]. In this study, Swedish cost experiences have been used for three different area characteristics; Inner city areas (A), Outer city areas (B) and Park areas (C), according to original data presented in [24]. With use of a currency exchange rate of 10.8 SEK/€ [25], the estimated values for the cost constant and the cost coefficient were calculated and are presented in Table 3.4. The intervals of plot ratio chosen for the three different area characteristics were based on traditional classification within Swedish city planning [13]. Average pipe diameters were estimated through linear heat density model values, by use of the following model function;
da ¼ 0:0486 lnðQ s =LÞ þ 0:0007 ðmÞ
ð9Þ
As can be seen in Fig. 3.1, this logarithmic function reflects the relationship between linear heat densities and average pipe diameters in 134 observations concerning whole or parts of Swedish district heating systems [9]. Annuity (a), for estimating the annual heat distribution capital cost, was chosen to reflect a long term investment strategy in order to obtain the benefits of district heating in the future. This approach has rendered a model use of a real interest rate of 3%, along with a 30 year investment lifetime for district heating networks. 4. Extent and some city characteristics Plot ratios for the 1703 city districts are presented in Fig. 4.1. A majority of the study city districts can be described as low or semidense populations, with some exceptionally low plot ratio values for scarcely populated large land area districts. In 678 city districts
Fig. 3.1. Relationship between linear heat densities and average pipe diameters in 134 Swedish district heating systems and city districts.
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Fig. 4.1. Combinations of populations and plot ratios for the 1703 study city districts.
Fig. 4.2. Combinations of total city populations and population densities for the 1703 study city districts and for the 83 study cities.
the plot ratios were below 0.15 (40%). Most frequented are city districts with populations in the interval of 8000 to 35,000 inhabitants, with typical plot ratio values between 0.1 and 0.6. Within the high density inner city areas of larger cities, plot ratios may very well escalate to values above 2.0. Population densities, presented in Fig. 4.2, typically average at values from 700 to 2000 inhabitants per square kilometre for total city populations of 100,000–700,000 inhabitants. For larger cities with more than one million inhabitants, population densities from 2000 to well above 10,000 inhabitants per square kilometre are found within the study. The highest city population density was found in Paris with an average population density of 20,183 inhabitants per square kilometre. Distributed quite differently within the four countries, population densities are generally higher in French cities and lower in Belgian cities. Population densities above 10,000 inhabitants per square kilometre represent less than 10% of total city areas of the survey, while an estimated 50% of the Netherlands and the Belgian city areas are populated by less than 2000 inhabitants per square kilometre.
Table 4.1 Aggregated district heat market shares in the study cities by country. Country
Current district heat sales (PJ/a) in the study cities, according to [26– 28]
Model estimated heat demands in the study cities (PJ/a)
Estimated present heat market shares for district heat in the study cities (%)
Belgium Germany France Netherlands
0.1 153 34 14
51 523 307 70
0 29 11 21
Total
201
951
21
Estimations of present district heat market shares in the studied cities are presented in Table 4.1. On the basis of model calculated total heat demands along with externally collected data on current district heat sales within the 83 study cities, an average district heat market share of 21% is estimated. Currently, 69 of the 83 study
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cities have district heating systems in operation. Expansion of present district heating systems is a major possibility within these cities, while 14 study cities require the establishment of entirely new systems. For 10 of the study cities, the current district heating market share is above 40%. 5. Results The primary study result indicators are the combinations of marginal distribution capital costs and the corresponding heat market shares. These combinations have been consecutively sorted from the lowest to the highest marginal distribution capital cost. Model calculations were executed on the basis of the methodology, input data and assumptions described above. Model calculations were further based on a general assumption of a 100% connection rate to district heating networks. The intention of expressing the competitiveness of future district heating systems by use of the marginal distribution capital
573
cost is to identify competitive cost levels of network extensions. However, the actual distribution capital cost may be lower than what is outlined by the marginal distribution capital cost, typically in high heat density inner city areas. The uncertainties in the estimated marginal distribution capital costs come from possible deviations in the estimated input values and assumptions. Major deviations can be: District heating companies having planned rate of returns higher than the level represented by the assumed annuity. The construction cost levels used come from a mature district heating country (Sweden), while the cost levels can be higher in novel district heating countries. Lower construction cost levels from future alternative pipe materials and network technologies (4th generation of district heating networks). Other future city shapes than represented by the 2001 Urban audit database.
Fig. 5.1. Current distribution capital cost levels and the corresponding district heat market shares in the study city districts.
Fig. 5.2. Current marginal distribution capital cost levels and the corresponding district heat market shares in the study city districts at various city area characteristics: A for inner city areas, B for outer city areas, and C for park areas.
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The uncertainty from lower heat demands is regarded as a prime interest and is therefore one of the three research questions in this analysis.
Table 5.1 City districts by area characteristics with average distribution capital costs, total heat demands and corresponding investments. Area characteristics
5.1. General conditions The current distribution capital cost levels and the corresponding possible district heat market shares in the study city districts are presented in Fig. 5.1. To enhance the distinction between marginal and average distribution capital cost further, the dotted line in Fig. 5.1 illustrates the average distribution capital cost, while the solid line shows the marginal distribution capital cost. In the following result graphs, the grand total curve for marginal distribution capital cost is always included for reference. It is noticeable that the grand total curve for the marginal distribution capital cost levels out to create a relative plateau for heat market shares in the interval of 10–60%, where market expansion can occur with only slight cost increases. For heat market shares within this interval there are remarkably favourable conditions for extensions of present and establishment of new district heating systems. Since the estimated present district heat market share is 21% in the study city districts, this circumstance suggests a viable threefold expansion possibility for district heating systems within these city districts in the coming future. At the current situation, a district heat market share of 60% can be reached with an estimated marginal distribution capital cost of only 2.1 €/GJ, corresponding to an average distribution capital cost of 1.6 €/GJ. Since only small differences appeared in the four studied countries, all located in the same geographical region, these results are considered general for all four countries. Hence, a heat market share of 60% can be considered as an indicative threshold market share in European urban areas, where district heat is directly feasible at current heat market conditions. This conclusion comes from the fact that the corresponding marginal distribution capital cost is only slightly higher than the marginal distribution capital cost associated with the current heat market share of 21% in the study cities. However, in countries like Denmark and Sweden, with heavy taxation of fossil fuels giving a very high cost difference between district heat and alternative heat supply, the threshold market shares are higher than 60%. At some level of decreasing heat densities, as the area characteristic shifts from inner city areas to park areas, it is evident that the cost of network establishments will cease to be economically viable. Heat market shares above 60% are anticipated to be associated with increased marginal distribution capital costs, since this market segment is characterised by low heat density areas. Hence, an indicative threshold plot ratio value of about 0.15–0.20 has been identified for directly feasible district heating in the studied cities, corresponding to a marginal distribution capital cost of 2.1 €/GJ. In order to understand the magnitude of the distribution capital cost, it has to be compared with the total district heat price levels in the study countries. A short review gave the interval of 13–17 €/ GJ excluding VAT during 2007, similar to the current costs of alternative local heat supply. Consequently, an average distribution capital cost of 1.6 €/GJ represents about 10% of the full price level. Hence, the distribution capital cost must increase considerably in order to reduce the competitiveness of district heat. To add further perspective on the distribution capital cost level, a comparison with a hypothetical carbon dioxide emission cost scenario can be performed. If natural gas, used for space heating in European buildings, were to be part of the emission trading system (ETS), an emission price of 20 €/ton CO2 would correspond to an additional heat cost of 1.3 €/GJ. At an emission price of 40 €/ ton CO2, the equivalent additional heat cost would be 2.6 €/GJ. Hence, the investment cost for establishing viable district heating
Number of city districts
Average distribution capital cost, Cd,a (€/GJ)
Model estimated heat demands, Qs (PJ/a)
Required investments in heat distribution networks, I (G€)
Inner city areas (A) Outer city areas (B) Park areas (C), feasible
317
1.2
182 (19%)
4.3
296
1.6
160 (17%)
5.1
355
1.8
236 (25%)
8.2
Total direct feasible Park areas (C), less feasible
968
1.6
578 (61%)
17.6
735
4.5
373 (39%)
32.9
1703
2.7
951 (100%)
50.5
Total
networks in dense and semi-dense European cities, is within the same magnitude as the emission prohibition costs that eventually could be added to the major local heating fuel alternative. Since recycling of excess heat generally is not taxed, the acceptance of even higher distribution capital costs could further strengthen the future possibilities for district heating in competition with local heating alternatives. The general finding is then that the magnitude of the distribution capital cost is not a current barrier for expanding district heating in the study cities, since this cost is a rather small part of the total cost level and comparable with the current carbon dioxide prices, if applied for heating. An indicative threshold heat market share has been found for direct feasible district heating together with a corresponding indicative threshold plot ratio level. 5.2. Area characteristics In Fig. 5.2, the heat market share curves are presented for each area characteristic category at various marginal distribution capital costs. Presented in this way, the beneficial conditions for district heating in high population density areas are further enhanced. Although the cost for network establishments is higher in inner city areas, the concentration of heat demands in these areas compensate for this cost addition, rendering better conditions for district heat distribution. Model estimations indicate possible district heat market shares of 100% in inner city areas (A), at a marginal distribution capital cost level of 2.0 €/GJ, and in outer city areas (B) at 2.2 €/GJ. In park areas (C), heat market shares of 100% are not reached within the limited 8.0 €/GJ ordinate, but a 90% district heat market share is estimated at a marginal distribution capital cost level of 5.8 €/GJ. The average distribution capital cost levels for the study city districts by area characteristics are presented in Table 5.1. Above the indicative feasibility threshold, corresponding to a marginal distribution cost of 2.1 €/GJ, the model calculations suggest a total investment volume of 17.6 G€ in order to annually distribute 578 PJ of heat. 5.3. City sizes Reflecting well the general increase of distribution capital cost levels in scarcely populated areas, the presentation of study results divided into four city size categories clearly indicates this basic circumstance (Fig. 5.3). At the indicative threshold conditions, possi-
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Fig. 5.3. Current marginal distribution capital cost levels and the corresponding district heat market shares in the study city districts at various city sizes.
Fig. 5.4. Marginal distribution capital cost levels and the corresponding district heat market shares in the study city districts at various heat demand levels: Grand total for the 83 cities at the current situation, 20% and 50% reduction scenarios, respectively.
ble heat market coverage of 79% is estimated for cities larger than one million people. For cities with 500,000–1,000,000 inhabitants, the corresponding value is anticipated to 62%. In smaller cities, the equivalent heat market share is 47% (150,000–500,000 inhabitants) and 24% (60,000–150,000 inhabitants). As expected, large cities provide the most beneficial conditions for district heat distribution, since the high population densities within these cities renders high heat densities. 5.4. Heat demand reductions In order to answer the second introductory question of how much the distribution capital costs will increase when heat demands decrease, the grand total curve for the 83 study cities at current conditions were used as a foundation for two additional heat demand projections. The first projection relates to the European Union energy efficiency target for 2020, which implies a 20% overall reduction of primary energy use [29]. In the second projection, a 50% heat demand reduction was elaborated to inves-
tigate the consequences of halved residential and service sector heat demands in a more distant future. With use of correspondingly altered specific heat demands, recalculation of model data generated the resulting graphs in Fig. 5.4. A horizontal observation of the grand total curve in Fig. 5.4 reveals an enhanced sensitivity for heat demand reductions within the heat market interval of 10–60%, due to the flat curve characteristics of this market segment. If considering the case where 2.1 €/GJ is regarded as an indicative feasible marginal distribution capital cost level, the possible market share for district heat would be reduced from 60% down to about 45% at the 20% reduction scenario, and down to about 10% at the 50% reduction scenario. However, if this would be the case, a strong pressure would appear for reduced payability for recycled heat according to the introductory discussion based on Fig. 1.1. A vertical observation in Fig. 5.4 reveals that in order to persist at a current district heat market share of 60%, acceptance of marginal distribution capital costs of estimated 2.4 €/GJ would be required at the 20% reduction scenario. At the 50% reduction
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scenario, the corresponding marginal distribution cost level is anticipated to 3.6 €/GJ. The additional cost for maintaining a given heat market share is then not proportional to the magnitude of the heat demand decrease itself, and it is also depending on the present district heat market share. At a district heat market share of 60%, heat demand reductions of 20% renders an increase of distribution capital cost levels of anticipated 20%, while reductions of 50% induce a 70% cost increase. This circumstance can be explained by the fact that pipe diameters in new networks can be reduced, since less heat is to be distributed. Hence, reduced heat demands will increase the current marginal overall district heat cost level with 2% and 7%, respectively, since the distribution capital cost constitutes about 10% of the current total price level. The combined conclusion from these horizontal and vertical observations would be that lower heat demands will give pressures for somewhat higher customer prices and somewhat lower payability for recycled heat into district heating systems. 6. Conclusions The main conclusion is that the future estimated capital costs for district heat distribution in the study cities are rather low, since the cities are very dense. Even at ambitious heat reduction targets, district heating is expected to remain competitive in high heat density city districts and large city areas. In low heat density areas, local heating alternatives are expected to dominate. The future competitiveness of district heating in Europe is depending on a mixture of boundary conditions and counteracting factors. Three determining factors emerge as critical: the future competition on the heat market, the current use of district heat, and the future city shapes. The future competition on the heat market will be based on renewables, more efficient use of fossil fuels, district heating, and other energy efficiency measures such as heat pumps. If natural gas and fuel oil used for heating also would be part of the emission trading system (ETS), the customer costs for heating would be further increased. As estimated in this study, this cost increase will have the same magnitude as the current district heat distribution capital cost levels, indicating a possibility to accept even higher distribution capital cost levels for future district heating systems. Hereby, the future competitiveness of district heating will always depend on the combination of distribution cost and the cost difference between district heat supply and alternative local heat supply. The current use of district heat is also a key factor. Market share increases can be facilitated by expansions of already existing network systems and by utilisation of already existing organisations and business models. Further, the key ability of district heating networks to receive and distribute excess heat is a central counteracting factor in support of future establishments of competitive district heating systems. The European potential for recycling of excess heat, and the capacity for waste incineration, remains poorly utilised up to this date. As introductory described in this study, the distribution capital cost of district heating outlines the payability for excess heat. Hence, an extended utilisation of abundant European excess heat would presuppose the establishments of many new district heating systems in the future. Added values would of course be local and regional environmental benefits, customer comfort and the resource optimisation quality of recycled excess heat that otherwise would be unutilised. Finally, future city shapes is also a counteracting factor, by the continued raise of population densities within the European cities. After decades of urban sprawl, a revival of compact cities could act as a major climate change mitigation measure. As this study has shown, compact cities have better conditions for district heating (and cooling) than sparse cities, since the plot ratios are higher,
and therefore the future competitiveness of district heating additionally involves socio-political decisions and demographic considerations as well. Acknowledgements This analysis was performed by financial support from the Swedish Energy Agency through the Swedish System Solutions Project and from Fjärrsyn, the Swedish district heating research programme, through the District Heating System Technology Project. References [1] Werner S. District heating and cooling. Encyclopedia of Energy 2004;1:841–8. [2] Werner S. The European heat market, WP1 of the Ecoheatcool project. Euroheat & Power, Brussels 2005. Available at