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Potential for use of heat rejected from industry in district heating networks, GB perspective
Journal of the Energy Institute xxx (2015) 1e13
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Potential for use of heat rejected from industry in district heating networks, GB perspective Samuel J.G. Cooper a, *, Geoffrey P. Hammond a, b, Jonathan B. Norman a a b
Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK Institute for Sustainable Energy and the Environment, University of Bath, Bath BA2 7AY, UK
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 October 2014 Received in revised form 5 January 2015 Accepted 11 January 2015 Available online xxx
District heating schemes can act as enablers for the use of low carbon heat sources. Heat rejected by industrial sites could potentially be used in supplying such schemes but various constraints limit the extent to which this is likely to occur. This study estimates the quantity of heat that could be supplied to district heating networks from industrial sites in Great Britain (GB), in the context of a range of limiting criteria. An existing spatially disaggregated database of GB's heat demands was combined with previous analysis of the magnitude, temperature and location of the heat that is rejected by industrial sites. The heat that could be supplied from the industrial sites to the heat demands was then calculated with a range of different criteria applied. The criteria which were considered included the maximum allowable distance between the heat sources and demands, the minimum heat density of the demands which was considered feasible, the heat losses that may occur, and the seasonal profile of the heat demands. The potential gains from using absorption heat pumps, driven by high temperature heat to supply lower grade heat were found to be limited. High and low estimates of the heat rejected by the industrial sites were compared. Domestic and non-domestic heat demands were examined. Although the domestic demands have a greater total, they tend to occur at lower densities and so it is likely that both demands will take a significant share of the heat supplied. The seasonality of the heat demands has a greater impact on the need for supplementary heat sources than on the total heat delivered. For the distance and heat demand density criteria considered most appropriate, only half of the heat which is rejected by industry could be utilised by district heating networks. © 2015 Energy Institute. Published by Elsevier Ltd. All rights reserved.
Keywords: District heat network Industry Waste heat Energy efficiency Low carbon heating
1. Introduction 1.1. District heating networks District Heating Networks (DHN) have been suggested as an enabler in the process of decarbonising the provision of space heating [7,20]. At present around 2000 networks serve approximately 200,000 dwellings in the UK, meeting just under 10 TWh/yr or 2% of domestic and non-domestic space heating demands [6]. Despite the relatively modest uptake of DHNs to date, analysis by Ref. [5] suggested that 4.4 to 6.5 million dwellings and 15.8 to 20.7 TWh/yr of non-domestic space heating demand could be supplied by DHNs in the UK (if a 6% financial rate of return is required). They found capital costs to be the main driver of cost competitiveness. The typical heat demand density which they found to correspond to the cost-effective development of DHNs was 3 MW/km2. Subsequent modelling by DECC [17] which used this heat density as the criteria for DHNs concluded that 20% of domestic heat demand is suitable for connection to DHNs. Equivalent analysis of data supplied by Ref. [21] suggested a similar result (18.6% of total space heating and hot water demand, around 63 TWh/yr). Finney et al [9] advocate the matching of heat sources and sinks as a means for identifying feasible connections. They point out that a key benefit of DHNs is that they enable flexibility in terms of the heat sources which are used. Additionally, DHNs introduce the possibility of
* Corresponding author. Tel.: þ44 01225 385366. E-mail address: [email protected] (S.J.G. Cooper). http://dx.doi.org/10.1016/j.joei.2015.01.010 1743-9671/© 2015 Energy Institute. Published by Elsevier Ltd. All rights reserved.
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Fig. 1. Heat loss rates calculated for initial conditions (see Fig. 4).
changing the heat sources at a later date without requiring modifications within each individual building. Possible heat sources for DHNs include combined heat and power (CHP), energy from waste and biomass. Considering the potential use of waste heat from power stations Refs. [15,19] investigated the technically feasible heat demands within a 30 km range of power stations across the UK and at four possible sites in Scotland, respectively. Other studies consider waste heat from industry. For example Ref. [11], has presented a case study considering €teborg, Sweden. Ammar et al. [1] discussed some practical options to expand upon the 115 to 160 MWth supplied by oil refineries in Go issues concerning the recovery of low grade heat from the process industry in the UK. A complementary approach investigated by Ref. [13] is the co-location of large CHP power stations with clusters of industrial demand. They estimated that in this way, CHP could supply 8.4 to 11.9 TWh/yr of industrial heat demand. However, this would not significantly affect the heat subsequently rejected by the industrial processes which would, all things being equal, still be available for further reuse supplying DHNs. The Heat Roadmap Europe project has produced analysis of the European heat demands and sources that might be connected using DHNs. In a broad study considering energy, economic and environmental factors [4], report some of the project's findings; they determined that 20% of relevant heat loads exist in heat demand densities greater than 50 TJ/km2-yr (approximately 1.5 MW/km2) but noted that in many places, far lower heat densities are supplied by DHNs (e.g. 15 TJ/km2-yr in Aarhus, Denmark). Persson et al. [17] provided more detail of the consideration given by the project to the proximity of the heat demands and sources, including a full range of different heat sources across Europe. They presented their analysis in terms of the total heat sources and demands within defined (NUTS3) zones in order to provide guidance on the areas in which DHNs are most likely to be appropriate. It does not appear that the possible connections of each individual heat sources were explicitly analysed. In related work [16], provided a method for improving the spatial modelling of the possible location of DHN lines and applied it to examples in Denmark. McKenna & Norman [15] identified the technically recoverable waste heat available from UK industrial sites included in the EU Emissions Trading System. They concluded that 37 to 73 PJ/yr (10e20 TWh/yr) of such heat is available. Hammond et al. [10] subsequently analysed various options for the use of this waste heat including reuse at lower temperatures, electricity production, upgrading for use at higher temperatures, provision of chilling, and transportation to other industrial sites. The possibility of using the heat to supply DHNs is mentioned but not analysed in detail. Fang, et al. [8] compared the amount of heat rejected by energy-intensive industry and demanded for DHNs in China while [3] conducted a similar study at a greater level of detail for the city of Hamburg, Germany. In both of these studies, it is
Fig. 2. Seasonality of heat demand.
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suggested that the overall ratio won't capture the situations in which the heat cannot be practically used and the need for spatial and temporal considerations is identified. 1.2. Analysis presented The present study extends the work of Ref. [10] by investigating the feasibility of DHNs as a consumer of waste heat that has been rejected by industrial sites. As such, the potential supply of other sources of heat such as waste heat from power stations or from CHP plants is not considered. It is possible that these sources of heat will be significant but the aim of the present study is to consider the potential for DHNs to use waste heat from industry, rather than to assess the total waste heat resource which is potentially available to DHNs. There is therefore an implicit assumption in this analysis that a low marginal cost makes the rejected heat from industry preferable when several sources are available. It should not be inferred that this will always be the case but, rather, that the results presented here relate to this condition. The alternative uses for the waste heat which have been analysed by Ref. [10] are excluded from the present study. Rather, it focusses on the feasibility of using the waste heat in DHNs that supply space heating and sanitary hot water. This feasibility is assessed by analysing the extent to which the heat demands and supplies are compatible. The relatively large heat demand which might be connected to DHNs presents the possibility that most of the heat rejected by industry could be used to supply DHNs but this potential will be limited by the consideration of several criteria. The criteria which are considered here are: the distance between the heat sources and demands, the heat density of the demands, the heat losses that may occur, the temporal profile of heat demands and the potential use of heat pumps to downgrade high temperature heat. 2. Method Heat demand data was made available by the DS4DS project [21]. This includes heat demand from commercial and domestic buildings across GB at a 1 km2 spatial resolution (over 280,000 data points). Both domestic and non-domestic space heating and sanitary hot water demands were included in the analysis. It was assumed that heat networks would be built in areas where they were feasible (defined here as areas with heat demand densities in excess of the criteria). It should be noted that non-domestic demands do not include demands for industrial process heat.
Fig. 3. Map of heat sources and demands.
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Industrial heat rejection data was taken from previous work by Ref. [15]. The data includes high and low estimates of heat rejection at 425 industrial sites (along with their locations). Calculations were repeated with both the high and low estimates in order to compare the effect of this variable. Taking each industrial site, in descending order of heat available (i.e. starting with the heat source with the greatest magnitude), the heat demands within a specified range and above a specified heat density were analysed. For each of these heat demands, a ‘priority factor’ was calculated as the quotient of the heat demand density divided by the distance from the heat source to the demand. These heat demands were then sorted in descending order of ‘priority factor’ (i.e. starting with the heat demand with the greatest ‘priority factor’ relative to that heat source). The heat available from the industrial site was allocated to these demands in this order until the heat was exhausted or no viable heat demands remained. The process was then repeated and the heat supplied was summed across all industrial sites. This method approximates the order in which heat sources and demands might be connected by DHNs. All things being equal, DHNs are likely to connect the largest sources to the nearest demands at the highest heat densities before other connections are made. In reality, all things are not equal and other factors are likely to play a role in determining which heat sources and demands are connected to DHNs first. These factors were considered to be beyond the scope of this overview analysis. It should be noted that this method will not necessarily maximise the heat which is delivered. Within the model, it is possible that a heat source will be exhausted in supplying a demand that could have been supplied by a second source, and therefore unable to supply a heat demand that is out of range of the second source. To assess the maximum effect of this issue, a set of results were prepared for which any heat sources which were within range of each other were assumed
Fig. 4. Heat delivered for different range and density criteria.
Please cite this article in press as: S.J.G. Cooper, et al., Potential for use of heat rejected from industry in district heating networks, GB perspective, Journal of the Energy Institute (2015), http://dx.doi.org/10.1016/j.joei.2015.01.010
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to be interconnected, making the sum of their heat supplies available to any demands within range of either heat source. This method will have overestimated the potential heat transfers as (within the model) some demands which were in range of one of the interconnected heat sources will have been supplied by the other. The results (with the interconnections) were prepared in order to provide an upper limit for comparison purposes. The calculation of the potential heat supplied by the industrial sites was repeated using a range of different criteria. These criteria consisted of maximum heat transmission distances that varied from 1 km to 64 km (i.e. exceeding the maximum range usually assumed) and of minimum heat demand densities that varied from 375 kW/km2 (i.e. including most built up areas) to over 8 MW/km2 (i.e. only high density heat demand). The “baseline criteria” used in cases where results are investigated in more detail consisted of a maximum heat transmission distance of 16 km and a minimum heat demand density for a network to be considered feasible of 3 MW/km2 (consistent with Ref. [5]). Skagestad et al. [18] suggested that heat losses experienced in DHNs of 5% e 20% are typical while the Ref. [12] indicated losses of 4% e 15%. These numbers are consistent with the 17% average losses used in Connolly et al.'s (2014) study and in an example provided by Ref. [2]; in which heat losses averaged around 60 W/m (including flow and return pipes) in a 273/400-mm transmission line near Copenhagen. Geometric considerations indicate that a linear increase in distribution pipe length is likely to be required by a squaring of heat demand e i.e. there should be an approximately inverse relationship between heat demand density and the proportional losses from the distribution network. Given these considerations, the approach taken in this work was to calculate a “losses factor” for each heat demand that resulted in heat losses proportional to the heat supplied. The losses factor consisted of components for transmission and distribution losses. The transmission losses component was scaled linearly with transmission distance and the distribution losses component was scaled inversely
Fig. 5. Heat delivered to domestic and non-domestic sectors.
Please cite this article in press as: S.J.G. Cooper, et al., Potential for use of heat rejected from industry in district heating networks, GB perspective, Journal of the Energy Institute (2015), http://dx.doi.org/10.1016/j.joei.2015.01.010
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Fig. 6. Proportion of heat going to domestic and non-domestic demands.
with heat demand density. Most of the results presented here relate to an assumed condition of 8% transmission losses (normalised to 16 km) and 10% distribution losses (normalised to 3 MW/km2 heat demand density). The heat losses which are calculated are therefore dependent upon the distribution of the distances that heat is transported and the heat densities to which it is supplied. Fig. 1 is provided to enable the reader to compare the losses modelled in this analysis to those observed elsewhere. It shows the mean of the heat losses rates which are simulated in the DHNs for each set of criteria, including those which occur in DHNs with heat densities greater than the minimum required or located nearer to their heat sources than the maximum permitted. Although a DHN with a density of 3 MW/km2, 16 km from a source will exhibit losses of 18% (i.e. 8% þ 10% under the assumptions in this study), the mean losses when these are the minimum density and maximum range criteria were calculated to be 11%, as shown in Fig. 1. Similarly, the mean heat loss rate was 14% if a maximum range of 32 km was considered. These figures are consistent with those found in the literature referenced above. It should be noted that there is some uncertainty over these data; they are not presented as results but rather as an aid for comparing the results of this study to similar works. Reflecting this uncertainty, additional results were prepared for which it was assumed that there were (a) no heat losses and (b) these heat loss factors doubled to 16% and 20% respectively.
Fig. 7. Heat delivered with heat sources interconnected.
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Most of the waste heat sources included in this analysis have a higher temperature than that required for a DHN [14]; considered the largest recoverable waste heat source at each industrial site). It is possible that heat driven heat pumps (e.g. using absorption cycles) could be used to take advantage of this by increasing the flow of heat at the appropriate (lower) temperature. A set of results were calculated in order to explore this possibility. The waste heat source temperatures (from Ref. [14] were used with an assumed ground temperature of 10 C and heat flow temperature of 120 C. It was assumed that the heat pumps could achieve 50% exergy efficiency (i.e. half that which is thermodynamically possible). This is currently achievable but does depend upon appropriate working fluids being matched to each heat source temperature. As the industrial heat sources are less seasonal than the heat demands, it is possible that a straight comparison of annual supply and demand would overestimate the potential transfer which can take place; in some months the supply and demand may not match. To address this, the modelling weights the annual heat demands using monthly average air temperatures to produce monthly heat demands, see Fig. 2. The same seasonal weighting pattern was used for domestic and non-domestic demands. It was assumed that the industrial heat sources are consistent throughout the year. A set of additional results were prepared in order to investigate the effect of removing all seasonality from the heat demand characteristics. 3. Results and discussion To put numbers in context, total industrial heat rejection is estimated at 10.1 TWh/yr (low estimate) to 20.3 TWh/yr (high estimate) by Ref. [10]. Total low temperature heat demand (i.e. domestic and non-domestic) with a density greater than 3 MW/km2 is estimated to be 63.1 TWh/yr by Ref. [21].
Fig. 8. Effect of removing heat losses.
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Fig. 3 illustrates the sources of industrial rejected heat and the heat demands which could be connected if the criteria of a 32 km maximum range and 3 MW/km2 minimum heat demand density are applied (with the high estimate of available heat resulting in a total of almost 10 TWh/yr of heat being supplied). As might be expected, there are clusters around the large population and industrial areas of the country. The map excludes many additional areas in which space heating demand exceeds the minimum density criteria but for which there are no suitable sources of industrial rejected heat to supply them. It is possible that heat networks will be created to supply these additional areas but they will require alternative heat sources. It is also possible that heat from the industrial sites could be used in other applications; as discussed, for example, by Ref. [10]. The heat delivered when a range of different maximum range and minimum heat density criteria are applied is shown in Fig. 4, considering both the high and low estimates of available heat. The graphs in Figs. 4,5,7e11 (excluding 6) follow the same format. In each case, the total heat which would be delivered under the relevant assumptions is shown when a combination of maximum distance and minimum heat density criteria are satisfied. For example, in the top of Fig. 4, 6.82 TWh/yr of heat would be supplied if heat transfers only occurred to heat demands that have a heat density of more than 3 MW/km2 and that are less than 16 km from the heat source. Following the plot relating to the 3 MW/km2 criteria up to the top of the slope (on the right), it can be seen that if heat transfers of up to 64 km were allowed, then the total heat supplied would rise to almost 14 TWh/yr. From that point, following the 64 km plot up to the left (i.e “into the page), it can be seen that relaxing the heat density requirement to 2.1 MW/km2 will increase the heat which could be delivered to around 16 TWh/yr but that relaxing it further will have little effect.
Fig. 9. Effect of doubling heat losses.
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At the points corresponding to the baseline criteria (16 km maximum range, 3 MW/km2 minimum density, labelled), the heat delivered is more sensitive to assumptions regarding the required heat density. However, if lower heat densities become feasible, the range becomes the key sensitivity; the heat which can be used does not significantly increase if heat densities below 1.5 MW/km2 become feasible. Under the baseline criteria, only 34%e40% of the heat which is rejected from industrial sites can be used. In order to use more than 75% of the high estimate heat resource, the heat would need to be transported up to 45 km and include areas with heat demand densities as low as 1.5 MW/ km2. Fig. 5 illustrates the share of heat delivered to the domestic and non-domestic sectors (based upon the high estimate of available heat). Under the baseline criteria, heat is shared almost equally between the two. If the criteria are relaxed to allow longer transmission to lower heat density demands then the domestic demands dominates. However, as shown in Fig. 6, the non-domestic demands form the majority of the heat demands with a heat density greater than 3 MW/km2 or less than 2 km from the heat source. It is possible that these will characterise the demands which are connected first. Fig. 7 shows the heat delivered when hypothetical interconnections are made between heat sources which are within range of each other (as discussed in the method this provides an upper limit on heat delivered). By comparison with Fig. 4, it can be seen that this increases the heat delivered under the baseline criteria by 24%. If a lower maximum range or a higher minimum heat density is required, then the effect of the interconnections is decreased. If the maximum range is increased to around 45 km but only heat demand densities greater than 4.5 MW/ km2 are feasible, then the results show the interconnections having a large effect. However, relatively little increase in the heat delivered is observed as the maximum range is further increased to 64 km. It seems likely, therefore, that the effect of introducing the interconnections
Fig. 10. Effect of using heat pumps.
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is mainly due to interconnected heat sources effectively extending the range which heat can be transferred (in the model) rather than due to any significantly sub-optimal connections being accounted for. Figs. 8 and 9 explore the effect of different assumptions regarding the heat losses from the heat transmission and distribution systems. Removing the heat losses (compare Fig. 8 to Fig. 4) results in an increase of 7% e 8% in the heat delivered under the baseline criteria, but this increases to over 20% when the most relaxed criteria are applied (as these imply longer transmission to less dense demands which would otherwise incur greater losses). Similarly, a relative doubling of the heat loss factors (compare Fig. 9 to Fig. 4) results in a decrease in heat delivered of just over 6% under the baseline criteria, increasing to 13% e 16% when the least restrictive assumptions are made. Fig. 10 shows that the average potential benefit of heat driven heat pumps is somewhat limited. It is possible to increase the heat delivered by just over 5% if extensive use is made of heat pumps. It is possible that heat pumps will be appropriate in some situations but it does not appear that they will make a relatively large contribution in this application. Fig. 11 shows the results when the monthly heat demand weighting is not applied (i.e. annual averages are assumed). Comparison with Fig. 4 reveals that this simplification has little effect on the total heat transfers which occur. However, the nature of some of the heat transfers is different; this is explored further in Fig. 12. Fig. 12 shows the proportion of the heat demand (for the heat demands which are connected) which is satisfied by the waste heat sources. In both graphs, the high estimate of heat availability is used but in the top case the demands are varied seasonally while annual averages are used for the bottom case. The seasonal nature of the demands in the top case means that there are many cases in which the waste heat sources considered here are not able to meet the higher winter time heating loads and would therefore need to be supplemented
Fig. 11. Effect of using annual average demands.
Please cite this article in press as: S.J.G. Cooper, et al., Potential for use of heat rejected from industry in district heating networks, GB perspective, Journal of the Energy Institute (2015), http://dx.doi.org/10.1016/j.joei.2015.01.010
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Fig. 12. Proportion of heat demand (for connected demands) met by heat sources.
with other (more flexible) sources of heating. When demand is constant, the demands which are connected tend to have a greater proportion of their demand satisfied. When this does not occur, the reason is that most of the heat source has been expended in supplying other demands and so the final demand which is connected (in the model) is not fully satisfied. This unmet demand forms a lesser proportion of the total demand supplied when more demands are connected (i.e. when the maximum range and minimum heat density criteria are relaxed). Figs. 4e11 relate to the total heat transfers which occur under each set of criteria and assumptions but they do not give information about the distances this heat is transported or the heat densities to which it is supplied (except that they must satisfy the criteria). Fig. 13 presents the characteristics of the heat transfers which occur in the case that the baseline criteria are applied. The plots represent the cumulative heat transfers which occur at a distance lower than that plotted, to a heat density higher than that plotted (e.g. a total of 6.82 TWh/yr is delivered when all transfers of less than 16 km to heat densities in excess of 3 MW/km2 are included, for the high estimate of available heat). The plots show that although heat demands at very high densities are supplied, over half of transfers occur to heat densities below 4 MW/km2. Similarly, around a third of the heat supply is accounted for by transfers of less than 2 km but transfers of up to 7 km need to be included in order to account for half of the heat supply.
4. Concluding remarks Under standard assumptions, just over a third of the heat rejected by industrial processes could be used in DHNs (6.8 TWh/yr of the 20 TWh/yr available, see Fig. 4), despite the fact that the total heat demands of appropriate heat density, are greater than the heat available. Doubling the maximum range of heat transmission (from 16 km to 32 km) would increase this proportion to around a half under the same assumptions (9.6 TWh/yr). If DHNs are established to make use of heat rejected by industrial sites for space heating, it seems probable that non-domestic demands will form the majority of the heat demands which are initially supplied (see Fig. 6). They are likely to present opportunities that are initially Please cite this article in press as: S.J.G. Cooper, et al., Potential for use of heat rejected from industry in district heating networks, GB perspective, Journal of the Energy Institute (2015), http://dx.doi.org/10.1016/j.joei.2015.01.010
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Fig. 13. Distances and heat densities to which heat transfers occur.
more feasible. However, domestic heat demands are greater overall and so as DHNs develop and mature they represent the majority of the demand which might ultimately be supplied. It is possible (see, for example Ref. [5], that small heat networks will develop where quick wins are available and then be interconnected later (at which point the industrial supply might be connected) but the present study does not address this. If the DHNs primarily supply seasonally variable space heating loads then the industrial waste heat would need to be supplemented with other, more flexible, heat sources. The waste heat which is rejected by industry has many possible uses. This study has investigated one of these applications and found that while it offers promise there are limitations to its potential that should be appreciated.
Acknowledgements The authors' current research in the field of industrial energy demand and carbon emissions reduction is supported by funding from the Engineering and Physical Sciences Research Council (EPSRC) as part of the UK Indemand Research Centre [Grant EP/K011774/1]. Previous research conducted by the second and third author, which fed into this paper was supported by the UK Energy Research Centre (UKERC); Phase II renewed in 2009 [under Grant NE/G007748/1]. The authors also wish to thank the Heat Group of the Energy Technologies Institute (ETI) for encouraging the original research that formed the industrial heat rejection dataset used here. The authors are very grateful to the DS4DS project, and in particular, to Dr. Simon Taylor, for access to the DS4DS heat demand database and assistance in interpreting it. We are grateful to two anonymous reviewers for comments that have helped us to improve both this study and its presentation. Authors' names appear alphabetically. Please cite this article in press as: S.J.G. Cooper, et al., Potential for use of heat rejected from industry in district heating networks, GB perspective, Journal of the Energy Institute (2015), http://dx.doi.org/10.1016/j.joei.2015.01.010
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Potential for use of heat rejected from industry in district heating networks, GB perspective Samuel J.G. Cooper a, *, Geoffrey P. Hammond a, b, Jonathan B. Norman a a b
Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK Institute for Sustainable Energy and the Environment, University of Bath, Bath BA2 7AY, UK
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 October 2014 Received in revised form 5 January 2015 Accepted 11 January 2015 Available online xxx
District heating schemes can act as enablers for the use of low carbon heat sources. Heat rejected by industrial sites could potentially be used in supplying such schemes but various constraints limit the extent to which this is likely to occur. This study estimates the quantity of heat that could be supplied to district heating networks from industrial sites in Great Britain (GB), in the context of a range of limiting criteria. An existing spatially disaggregated database of GB's heat demands was combined with previous analysis of the magnitude, temperature and location of the heat that is rejected by industrial sites. The heat that could be supplied from the industrial sites to the heat demands was then calculated with a range of different criteria applied. The criteria which were considered included the maximum allowable distance between the heat sources and demands, the minimum heat density of the demands which was considered feasible, the heat losses that may occur, and the seasonal profile of the heat demands. The potential gains from using absorption heat pumps, driven by high temperature heat to supply lower grade heat were found to be limited. High and low estimates of the heat rejected by the industrial sites were compared. Domestic and non-domestic heat demands were examined. Although the domestic demands have a greater total, they tend to occur at lower densities and so it is likely that both demands will take a significant share of the heat supplied. The seasonality of the heat demands has a greater impact on the need for supplementary heat sources than on the total heat delivered. For the distance and heat demand density criteria considered most appropriate, only half of the heat which is rejected by industry could be utilised by district heating networks. © 2015 Energy Institute. Published by Elsevier Ltd. All rights reserved.
Keywords: District heat network Industry Waste heat Energy efficiency Low carbon heating
1. Introduction 1.1. District heating networks District Heating Networks (DHN) have been suggested as an enabler in the process of decarbonising the provision of space heating [7,20]. At present around 2000 networks serve approximately 200,000 dwellings in the UK, meeting just under 10 TWh/yr or 2% of domestic and non-domestic space heating demands [6]. Despite the relatively modest uptake of DHNs to date, analysis by Ref. [5] suggested that 4.4 to 6.5 million dwellings and 15.8 to 20.7 TWh/yr of non-domestic space heating demand could be supplied by DHNs in the UK (if a 6% financial rate of return is required). They found capital costs to be the main driver of cost competitiveness. The typical heat demand density which they found to correspond to the cost-effective development of DHNs was 3 MW/km2. Subsequent modelling by DECC [17] which used this heat density as the criteria for DHNs concluded that 20% of domestic heat demand is suitable for connection to DHNs. Equivalent analysis of data supplied by Ref. [21] suggested a similar result (18.6% of total space heating and hot water demand, around 63 TWh/yr). Finney et al [9] advocate the matching of heat sources and sinks as a means for identifying feasible connections. They point out that a key benefit of DHNs is that they enable flexibility in terms of the heat sources which are used. Additionally, DHNs introduce the possibility of
* Corresponding author. Tel.: þ44 01225 385366. E-mail address: [email protected] (S.J.G. Cooper). http://dx.doi.org/10.1016/j.joei.2015.01.010 1743-9671/© 2015 Energy Institute. Published by Elsevier Ltd. All rights reserved.
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Fig. 1. Heat loss rates calculated for initial conditions (see Fig. 4).
changing the heat sources at a later date without requiring modifications within each individual building. Possible heat sources for DHNs include combined heat and power (CHP), energy from waste and biomass. Considering the potential use of waste heat from power stations Refs. [15,19] investigated the technically feasible heat demands within a 30 km range of power stations across the UK and at four possible sites in Scotland, respectively. Other studies consider waste heat from industry. For example Ref. [11], has presented a case study considering €teborg, Sweden. Ammar et al. [1] discussed some practical options to expand upon the 115 to 160 MWth supplied by oil refineries in Go issues concerning the recovery of low grade heat from the process industry in the UK. A complementary approach investigated by Ref. [13] is the co-location of large CHP power stations with clusters of industrial demand. They estimated that in this way, CHP could supply 8.4 to 11.9 TWh/yr of industrial heat demand. However, this would not significantly affect the heat subsequently rejected by the industrial processes which would, all things being equal, still be available for further reuse supplying DHNs. The Heat Roadmap Europe project has produced analysis of the European heat demands and sources that might be connected using DHNs. In a broad study considering energy, economic and environmental factors [4], report some of the project's findings; they determined that 20% of relevant heat loads exist in heat demand densities greater than 50 TJ/km2-yr (approximately 1.5 MW/km2) but noted that in many places, far lower heat densities are supplied by DHNs (e.g. 15 TJ/km2-yr in Aarhus, Denmark). Persson et al. [17] provided more detail of the consideration given by the project to the proximity of the heat demands and sources, including a full range of different heat sources across Europe. They presented their analysis in terms of the total heat sources and demands within defined (NUTS3) zones in order to provide guidance on the areas in which DHNs are most likely to be appropriate. It does not appear that the possible connections of each individual heat sources were explicitly analysed. In related work [16], provided a method for improving the spatial modelling of the possible location of DHN lines and applied it to examples in Denmark. McKenna & Norman [15] identified the technically recoverable waste heat available from UK industrial sites included in the EU Emissions Trading System. They concluded that 37 to 73 PJ/yr (10e20 TWh/yr) of such heat is available. Hammond et al. [10] subsequently analysed various options for the use of this waste heat including reuse at lower temperatures, electricity production, upgrading for use at higher temperatures, provision of chilling, and transportation to other industrial sites. The possibility of using the heat to supply DHNs is mentioned but not analysed in detail. Fang, et al. [8] compared the amount of heat rejected by energy-intensive industry and demanded for DHNs in China while [3] conducted a similar study at a greater level of detail for the city of Hamburg, Germany. In both of these studies, it is
Fig. 2. Seasonality of heat demand.
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suggested that the overall ratio won't capture the situations in which the heat cannot be practically used and the need for spatial and temporal considerations is identified. 1.2. Analysis presented The present study extends the work of Ref. [10] by investigating the feasibility of DHNs as a consumer of waste heat that has been rejected by industrial sites. As such, the potential supply of other sources of heat such as waste heat from power stations or from CHP plants is not considered. It is possible that these sources of heat will be significant but the aim of the present study is to consider the potential for DHNs to use waste heat from industry, rather than to assess the total waste heat resource which is potentially available to DHNs. There is therefore an implicit assumption in this analysis that a low marginal cost makes the rejected heat from industry preferable when several sources are available. It should not be inferred that this will always be the case but, rather, that the results presented here relate to this condition. The alternative uses for the waste heat which have been analysed by Ref. [10] are excluded from the present study. Rather, it focusses on the feasibility of using the waste heat in DHNs that supply space heating and sanitary hot water. This feasibility is assessed by analysing the extent to which the heat demands and supplies are compatible. The relatively large heat demand which might be connected to DHNs presents the possibility that most of the heat rejected by industry could be used to supply DHNs but this potential will be limited by the consideration of several criteria. The criteria which are considered here are: the distance between the heat sources and demands, the heat density of the demands, the heat losses that may occur, the temporal profile of heat demands and the potential use of heat pumps to downgrade high temperature heat. 2. Method Heat demand data was made available by the DS4DS project [21]. This includes heat demand from commercial and domestic buildings across GB at a 1 km2 spatial resolution (over 280,000 data points). Both domestic and non-domestic space heating and sanitary hot water demands were included in the analysis. It was assumed that heat networks would be built in areas where they were feasible (defined here as areas with heat demand densities in excess of the criteria). It should be noted that non-domestic demands do not include demands for industrial process heat.
Fig. 3. Map of heat sources and demands.
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Industrial heat rejection data was taken from previous work by Ref. [15]. The data includes high and low estimates of heat rejection at 425 industrial sites (along with their locations). Calculations were repeated with both the high and low estimates in order to compare the effect of this variable. Taking each industrial site, in descending order of heat available (i.e. starting with the heat source with the greatest magnitude), the heat demands within a specified range and above a specified heat density were analysed. For each of these heat demands, a ‘priority factor’ was calculated as the quotient of the heat demand density divided by the distance from the heat source to the demand. These heat demands were then sorted in descending order of ‘priority factor’ (i.e. starting with the heat demand with the greatest ‘priority factor’ relative to that heat source). The heat available from the industrial site was allocated to these demands in this order until the heat was exhausted or no viable heat demands remained. The process was then repeated and the heat supplied was summed across all industrial sites. This method approximates the order in which heat sources and demands might be connected by DHNs. All things being equal, DHNs are likely to connect the largest sources to the nearest demands at the highest heat densities before other connections are made. In reality, all things are not equal and other factors are likely to play a role in determining which heat sources and demands are connected to DHNs first. These factors were considered to be beyond the scope of this overview analysis. It should be noted that this method will not necessarily maximise the heat which is delivered. Within the model, it is possible that a heat source will be exhausted in supplying a demand that could have been supplied by a second source, and therefore unable to supply a heat demand that is out of range of the second source. To assess the maximum effect of this issue, a set of results were prepared for which any heat sources which were within range of each other were assumed
Fig. 4. Heat delivered for different range and density criteria.
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to be interconnected, making the sum of their heat supplies available to any demands within range of either heat source. This method will have overestimated the potential heat transfers as (within the model) some demands which were in range of one of the interconnected heat sources will have been supplied by the other. The results (with the interconnections) were prepared in order to provide an upper limit for comparison purposes. The calculation of the potential heat supplied by the industrial sites was repeated using a range of different criteria. These criteria consisted of maximum heat transmission distances that varied from 1 km to 64 km (i.e. exceeding the maximum range usually assumed) and of minimum heat demand densities that varied from 375 kW/km2 (i.e. including most built up areas) to over 8 MW/km2 (i.e. only high density heat demand). The “baseline criteria” used in cases where results are investigated in more detail consisted of a maximum heat transmission distance of 16 km and a minimum heat demand density for a network to be considered feasible of 3 MW/km2 (consistent with Ref. [5]). Skagestad et al. [18] suggested that heat losses experienced in DHNs of 5% e 20% are typical while the Ref. [12] indicated losses of 4% e 15%. These numbers are consistent with the 17% average losses used in Connolly et al.'s (2014) study and in an example provided by Ref. [2]; in which heat losses averaged around 60 W/m (including flow and return pipes) in a 273/400-mm transmission line near Copenhagen. Geometric considerations indicate that a linear increase in distribution pipe length is likely to be required by a squaring of heat demand e i.e. there should be an approximately inverse relationship between heat demand density and the proportional losses from the distribution network. Given these considerations, the approach taken in this work was to calculate a “losses factor” for each heat demand that resulted in heat losses proportional to the heat supplied. The losses factor consisted of components for transmission and distribution losses. The transmission losses component was scaled linearly with transmission distance and the distribution losses component was scaled inversely
Fig. 5. Heat delivered to domestic and non-domestic sectors.
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Fig. 6. Proportion of heat going to domestic and non-domestic demands.
with heat demand density. Most of the results presented here relate to an assumed condition of 8% transmission losses (normalised to 16 km) and 10% distribution losses (normalised to 3 MW/km2 heat demand density). The heat losses which are calculated are therefore dependent upon the distribution of the distances that heat is transported and the heat densities to which it is supplied. Fig. 1 is provided to enable the reader to compare the losses modelled in this analysis to those observed elsewhere. It shows the mean of the heat losses rates which are simulated in the DHNs for each set of criteria, including those which occur in DHNs with heat densities greater than the minimum required or located nearer to their heat sources than the maximum permitted. Although a DHN with a density of 3 MW/km2, 16 km from a source will exhibit losses of 18% (i.e. 8% þ 10% under the assumptions in this study), the mean losses when these are the minimum density and maximum range criteria were calculated to be 11%, as shown in Fig. 1. Similarly, the mean heat loss rate was 14% if a maximum range of 32 km was considered. These figures are consistent with those found in the literature referenced above. It should be noted that there is some uncertainty over these data; they are not presented as results but rather as an aid for comparing the results of this study to similar works. Reflecting this uncertainty, additional results were prepared for which it was assumed that there were (a) no heat losses and (b) these heat loss factors doubled to 16% and 20% respectively.
Fig. 7. Heat delivered with heat sources interconnected.
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Most of the waste heat sources included in this analysis have a higher temperature than that required for a DHN [14]; considered the largest recoverable waste heat source at each industrial site). It is possible that heat driven heat pumps (e.g. using absorption cycles) could be used to take advantage of this by increasing the flow of heat at the appropriate (lower) temperature. A set of results were calculated in order to explore this possibility. The waste heat source temperatures (from Ref. [14] were used with an assumed ground temperature of 10 C and heat flow temperature of 120 C. It was assumed that the heat pumps could achieve 50% exergy efficiency (i.e. half that which is thermodynamically possible). This is currently achievable but does depend upon appropriate working fluids being matched to each heat source temperature. As the industrial heat sources are less seasonal than the heat demands, it is possible that a straight comparison of annual supply and demand would overestimate the potential transfer which can take place; in some months the supply and demand may not match. To address this, the modelling weights the annual heat demands using monthly average air temperatures to produce monthly heat demands, see Fig. 2. The same seasonal weighting pattern was used for domestic and non-domestic demands. It was assumed that the industrial heat sources are consistent throughout the year. A set of additional results were prepared in order to investigate the effect of removing all seasonality from the heat demand characteristics. 3. Results and discussion To put numbers in context, total industrial heat rejection is estimated at 10.1 TWh/yr (low estimate) to 20.3 TWh/yr (high estimate) by Ref. [10]. Total low temperature heat demand (i.e. domestic and non-domestic) with a density greater than 3 MW/km2 is estimated to be 63.1 TWh/yr by Ref. [21].
Fig. 8. Effect of removing heat losses.
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Fig. 3 illustrates the sources of industrial rejected heat and the heat demands which could be connected if the criteria of a 32 km maximum range and 3 MW/km2 minimum heat demand density are applied (with the high estimate of available heat resulting in a total of almost 10 TWh/yr of heat being supplied). As might be expected, there are clusters around the large population and industrial areas of the country. The map excludes many additional areas in which space heating demand exceeds the minimum density criteria but for which there are no suitable sources of industrial rejected heat to supply them. It is possible that heat networks will be created to supply these additional areas but they will require alternative heat sources. It is also possible that heat from the industrial sites could be used in other applications; as discussed, for example, by Ref. [10]. The heat delivered when a range of different maximum range and minimum heat density criteria are applied is shown in Fig. 4, considering both the high and low estimates of available heat. The graphs in Figs. 4,5,7e11 (excluding 6) follow the same format. In each case, the total heat which would be delivered under the relevant assumptions is shown when a combination of maximum distance and minimum heat density criteria are satisfied. For example, in the top of Fig. 4, 6.82 TWh/yr of heat would be supplied if heat transfers only occurred to heat demands that have a heat density of more than 3 MW/km2 and that are less than 16 km from the heat source. Following the plot relating to the 3 MW/km2 criteria up to the top of the slope (on the right), it can be seen that if heat transfers of up to 64 km were allowed, then the total heat supplied would rise to almost 14 TWh/yr. From that point, following the 64 km plot up to the left (i.e “into the page), it can be seen that relaxing the heat density requirement to 2.1 MW/km2 will increase the heat which could be delivered to around 16 TWh/yr but that relaxing it further will have little effect.
Fig. 9. Effect of doubling heat losses.
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At the points corresponding to the baseline criteria (16 km maximum range, 3 MW/km2 minimum density, labelled), the heat delivered is more sensitive to assumptions regarding the required heat density. However, if lower heat densities become feasible, the range becomes the key sensitivity; the heat which can be used does not significantly increase if heat densities below 1.5 MW/km2 become feasible. Under the baseline criteria, only 34%e40% of the heat which is rejected from industrial sites can be used. In order to use more than 75% of the high estimate heat resource, the heat would need to be transported up to 45 km and include areas with heat demand densities as low as 1.5 MW/ km2. Fig. 5 illustrates the share of heat delivered to the domestic and non-domestic sectors (based upon the high estimate of available heat). Under the baseline criteria, heat is shared almost equally between the two. If the criteria are relaxed to allow longer transmission to lower heat density demands then the domestic demands dominates. However, as shown in Fig. 6, the non-domestic demands form the majority of the heat demands with a heat density greater than 3 MW/km2 or less than 2 km from the heat source. It is possible that these will characterise the demands which are connected first. Fig. 7 shows the heat delivered when hypothetical interconnections are made between heat sources which are within range of each other (as discussed in the method this provides an upper limit on heat delivered). By comparison with Fig. 4, it can be seen that this increases the heat delivered under the baseline criteria by 24%. If a lower maximum range or a higher minimum heat density is required, then the effect of the interconnections is decreased. If the maximum range is increased to around 45 km but only heat demand densities greater than 4.5 MW/ km2 are feasible, then the results show the interconnections having a large effect. However, relatively little increase in the heat delivered is observed as the maximum range is further increased to 64 km. It seems likely, therefore, that the effect of introducing the interconnections
Fig. 10. Effect of using heat pumps.
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is mainly due to interconnected heat sources effectively extending the range which heat can be transferred (in the model) rather than due to any significantly sub-optimal connections being accounted for. Figs. 8 and 9 explore the effect of different assumptions regarding the heat losses from the heat transmission and distribution systems. Removing the heat losses (compare Fig. 8 to Fig. 4) results in an increase of 7% e 8% in the heat delivered under the baseline criteria, but this increases to over 20% when the most relaxed criteria are applied (as these imply longer transmission to less dense demands which would otherwise incur greater losses). Similarly, a relative doubling of the heat loss factors (compare Fig. 9 to Fig. 4) results in a decrease in heat delivered of just over 6% under the baseline criteria, increasing to 13% e 16% when the least restrictive assumptions are made. Fig. 10 shows that the average potential benefit of heat driven heat pumps is somewhat limited. It is possible to increase the heat delivered by just over 5% if extensive use is made of heat pumps. It is possible that heat pumps will be appropriate in some situations but it does not appear that they will make a relatively large contribution in this application. Fig. 11 shows the results when the monthly heat demand weighting is not applied (i.e. annual averages are assumed). Comparison with Fig. 4 reveals that this simplification has little effect on the total heat transfers which occur. However, the nature of some of the heat transfers is different; this is explored further in Fig. 12. Fig. 12 shows the proportion of the heat demand (for the heat demands which are connected) which is satisfied by the waste heat sources. In both graphs, the high estimate of heat availability is used but in the top case the demands are varied seasonally while annual averages are used for the bottom case. The seasonal nature of the demands in the top case means that there are many cases in which the waste heat sources considered here are not able to meet the higher winter time heating loads and would therefore need to be supplemented
Fig. 11. Effect of using annual average demands.
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Fig. 12. Proportion of heat demand (for connected demands) met by heat sources.
with other (more flexible) sources of heating. When demand is constant, the demands which are connected tend to have a greater proportion of their demand satisfied. When this does not occur, the reason is that most of the heat source has been expended in supplying other demands and so the final demand which is connected (in the model) is not fully satisfied. This unmet demand forms a lesser proportion of the total demand supplied when more demands are connected (i.e. when the maximum range and minimum heat density criteria are relaxed). Figs. 4e11 relate to the total heat transfers which occur under each set of criteria and assumptions but they do not give information about the distances this heat is transported or the heat densities to which it is supplied (except that they must satisfy the criteria). Fig. 13 presents the characteristics of the heat transfers which occur in the case that the baseline criteria are applied. The plots represent the cumulative heat transfers which occur at a distance lower than that plotted, to a heat density higher than that plotted (e.g. a total of 6.82 TWh/yr is delivered when all transfers of less than 16 km to heat densities in excess of 3 MW/km2 are included, for the high estimate of available heat). The plots show that although heat demands at very high densities are supplied, over half of transfers occur to heat densities below 4 MW/km2. Similarly, around a third of the heat supply is accounted for by transfers of less than 2 km but transfers of up to 7 km need to be included in order to account for half of the heat supply.
4. Concluding remarks Under standard assumptions, just over a third of the heat rejected by industrial processes could be used in DHNs (6.8 TWh/yr of the 20 TWh/yr available, see Fig. 4), despite the fact that the total heat demands of appropriate heat density, are greater than the heat available. Doubling the maximum range of heat transmission (from 16 km to 32 km) would increase this proportion to around a half under the same assumptions (9.6 TWh/yr). If DHNs are established to make use of heat rejected by industrial sites for space heating, it seems probable that non-domestic demands will form the majority of the heat demands which are initially supplied (see Fig. 6). They are likely to present opportunities that are initially Please cite this article in press as: S.J.G. Cooper, et al., Potential for use of heat rejected from industry in district heating networks, GB perspective, Journal of the Energy Institute (2015), http://dx.doi.org/10.1016/j.joei.2015.01.010
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Fig. 13. Distances and heat densities to which heat transfers occur.
more feasible. However, domestic heat demands are greater overall and so as DHNs develop and mature they represent the majority of the demand which might ultimately be supplied. It is possible (see, for example Ref. [5], that small heat networks will develop where quick wins are available and then be interconnected later (at which point the industrial supply might be connected) but the present study does not address this. If the DHNs primarily supply seasonally variable space heating loads then the industrial waste heat would need to be supplemented with other, more flexible, heat sources. The waste heat which is rejected by industry has many possible uses. This study has investigated one of these applications and found that while it offers promise there are limitations to its potential that should be appreciated.
Acknowledgements The authors' current research in the field of industrial energy demand and carbon emissions reduction is supported by funding from the Engineering and Physical Sciences Research Council (EPSRC) as part of the UK Indemand Research Centre [Grant EP/K011774/1]. Previous research conducted by the second and third author, which fed into this paper was supported by the UK Energy Research Centre (UKERC); Phase II renewed in 2009 [under Grant NE/G007748/1]. The authors also wish to thank the Heat Group of the Energy Technologies Institute (ETI) for encouraging the original research that formed the industrial heat rejection dataset used here. The authors are very grateful to the DS4DS project, and in particular, to Dr. Simon Taylor, for access to the DS4DS heat demand database and assistance in interpreting it. We are grateful to two anonymous reviewers for comments that have helped us to improve both this study and its presentation. Authors' names appear alphabetically. Please cite this article in press as: S.J.G. Cooper, et al., Potential for use of heat rejected from industry in district heating networks, GB perspective, Journal of the Energy Institute (2015), http://dx.doi.org/10.1016/j.joei.2015.01.010
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Please cite this article in press as: S.J.G. Cooper, et al., Potential for use of heat rejected from industry in district heating networks, GB perspective, Journal of the Energy Institute (2015), http://dx.doi.org/10.1016/j.joei.2015.01.010