4.1. District Heating Network Design and Operations
shows the results of the network layout design, whereby (a) shows the identified connection points of all buildings in ArcGIS and (b) shows the final design of the network layout. Red nodes represent connecting nodes from buildings to the network, black nodes represent mixer or diverter nodes and node 1 (in green) is the central location where central technologies such as TES and GB are installed. As a design criterion, total piping length is minimized while following the street layout.
The design of the network layout is the same for all scenarios; only the total network length varies among the three different district sizes. The resulting network lengths are 438, 2190 and 4380 m respectively. The resulting linear heat load densities are 5.53, 1.10 and 0.55 MWh/km for the cases without building retrofitting, and 2.83, 0.57 and 0.28 MWh/km for cases with building retrofitting. Detailed pipe diameter designs corresponding to all scenarios are given in Table A5
in Appendix C
. For the investment costs of the network, both the piping costs (including pipes and trench costs) and pump costs are taken into account. Total investment costs for the 6+six district Scenarios are shown in Figure 9
It is shown that costs for pumps are relatively small, compared to the piping costs, amounting for less than 5% of the total network costs. The total network investment costs increase with the growth of district area—4.7 times and 9.3 times higher than district x 1, which are close, but not strictly proportional to the increases in size by 5 and 10 times. This is due to the fact that the detailed model considers pipe design per segment, and the cost are not linear for network length, but also account for the designed pipe diameter. Results show that for the scenarios with building retrofitting where total annual heating demand reduced by half, the network investment costs did not change significantly. This is due to the fact that building retrofitting does not significantly make an impact on the peak heating demand.
shows thermal losses of the network and pumping energy as a percentage of the total heating demand for all DHS scenarios.
When evaluating the networks’ energy performance, pumping energy was found to play a less significant role compared to the heat loss in the total energy consumption of the DHS scenarios. Depending on the size of the district, heat loss increased from less than 2% to around 12.3%. Additionally, pumping energy increased significantly from around 0.6% to 5.7%. Similarly to the network investment costs, energy consumption is not linearly proportional to the size increases of the district. For the scenarios with retrofitting, both heat loss and pumping energy were reduced compared to the non-retrofitted scenario. Additionally, the reduction was much more significant for larger districts.
4.2. Economic, Energy and Environmental Analyses of Scenarios
This section shows the results of the economic, energy and environmental analyses for the DHS and IHS scenarios. As mentioned earlier, the operational performance of a solar-based heating system is highly dependent on the TES capacity relative to the installed STC area. The volume of TES is varied as the solar area ratio (VAR) from 1 to 10 in the scenario analysis. Figure 11
displays a detailed breakdown of system investment costs for both heating system scenarios (DHS and IHS), which includes costs for the network (evaluated in the previous section), gas boiler (GB), solar thermal collectors (STC) and thermal storage tank (TES) for the 10 different VAR scenarios. The error bars in the figure illustrate the ranges between the lowest and highest costs.
The results show that for all VAR scenarios, total investment costs for the IHS were higher than in DHS scenarios, even for the biggest district case scenario (DHS_10). This is mainly due to higher investment costs of smaller GB and TES, if they are installed at the individual building level, which overtakes the extra investment costs for the network in the DHS scenarios. As shown by the cost curve in Figure A1
in Appendix A
, the cost of TES is strongly dependent on economy of scale. From the data which reflect the least impact from economy of scale (bottom curve in Figure A1
) shown in the lower bar, it is observed that IHS is much more costly than DHS. For the retrofitted building results, the same observations apply in terms of investment costs, since peak demands, which are used for system sizing, do not vary significantly based on whether a building is retrofitted or not. It is worth mentioning that retrofitting costs for buildings were not included in this analysis, since the focus was on the system scenarios.
shows the annual operational costs for the same set of scenarios.
In the non-retrofitted scenarios, a decreasing trajectory of operational costs appeared with larger VARs until hitting a minimal point, and then it started rising again. This minimal point (highlighted with bold edges in Figure 12
) occurred at different VAR values depending on the case. With increasing storage capacity for larger VARs, all surplus solar energy can be stored in the summer, until the turning point, whereat the capacity is bigger than it needs to be, which results in higher thermal losses compared to a properly sized, smaller storage tank. After that point, operational costs rise again and the additional storage volume remains unused, which demonstrates the trade-offs between increased investments in storage size and the potential to reduce operational costs. The same effect can be observed for retrofitted buildings—less pronounced, however.
For the original district size, operational costs of DHS were lower than for the IHS scenario for all VARs. This means that gains from the thermal networks, such as higher utilization rates of energy, surpass the additional distribution costs of the thermal network due to additional thermal losses and pumping energy for smaller districts. For bigger districts (district x5 and x10), the operational costs for DHS were higher than IHS if VAR was small (as shown in Figure 12
). However, with a properly sized storage tank (VAR above 6), DHS_5 still outperformed IHS, whereas operational costs for DHS_10 remained higher for all VARs in the non-retrofitted scenario.
For the retrofitted case, operational costs decreased significantly (as shown in Figure 12
on the bottom), but the overall trend was similar. In this case, DHS scenarios can be attractive for district x10 if the storage tank is properly sized (starting from VAR 6).
To summarize, the operational costs of solar-based district heating network solutions with storage outperform individual solar-based heating system solutions in many cases, if the storage volume to solar area ratio is properly sized.
shows the annual specific GHG emissions per heated area for all district and system scenarios for the different VARs.
First of all, GHG emissions reduce almost half from the non-retrofitted to the retrofitted case. As a general trend, GHG emissions reduce with increased TES size for all system scenarios. However, similarly to operational costs, if the storage volume is oversized, emissions increase again (as highlighted in the graph with bold edges). For districts DHS_1 and DHS_5, DHS outperforms IHS for all storage volumes in terms of emissions. Only when comparing DHS_10 with IHS, does IHS have lower emissions for most VAR scenarios (VAR < 7). Furthermore, for the case with retrofitting, DHS performs better for almost all district sizes with VAR > 5. Only when a smaller storage volume is chosen, is IHS more advantageous compared to DHS_5 and DHS_10.
To summarize, GHG emissions of small scale retrofitted districts are typically lower for DHS than IHS cases. IHS only performs better if the storage tank is undersized, which means that available solar potential is not fully utilized.
shows solar fractions for all district case and system scenarios for different VARs.
It was observed that the solar fraction increases significantly with TES volume, and stagnates after reaching its maximum (highlighted with bold edges), and then decreases gradually. For the non-retrofitted DHS scenarios, the maximum SF for district sizes x1, x5 and x10 were 49.8%, 45.0% and 40.0% respectively. The maximum SF for IHS was 35.2%. For the retrofitted scenario, the overall SF was much higher than in the non-retrofitted case, with a maximum above 63%. It is important to mention that the improved SF resulted in a lower heating demand and therefore in a higher surplus of solar energy; the district heating network contributed to a shift in diurnal mismatch between buildings. Among the different district sizes, it is clear that IHS only outperforms DHS for bigger districts (e.g., size x5 and x10) if the storage is significantly undersized (with VAR < 3).
Finally, for all scenarios the VARs with the lowest carbon emissions are selected. The solar fraction, equivalent annual cost and annual emissions respectively, among each system, are compared and shown in Figure 15
. Hatched lines represent the results for retrofitting scenarios.
When comparing the carbon optimal solutions, it is clear that for all district case scenarios, whether with retrofitting or not, DHS outperforms IHS in terms of energy (SF), economic (costs) and environmental (emissions) performance indicators.
From an energy viewpoint, DHS resulted in higher solar fractions compared to IHS for all districts. However, a higher load density leads typically to better results than sparsely populated districts due to higher distribution losses in the network. This effect is even more pronounced for retrofitted buildings.
Economically, district heating solutions with central thermal storage and distributed solar thermal collectors benefit significantly from lower investment costs compared to IHS. Only in terms of operational costs do results differ depending on district size and retrofitting conditions. The operational costs of IHS are higher than those of DHS for high load density districts (district x1), but are generally lower than DHS for low density districts (x5 and x10). Similar observations apply to the retrofitted cases, with minor differences in absolute values.
From an environmental viewpoint, DHS also outperforms IHS. The benefits are much more significant for high-load-density districts than low-load-density districts. With retrofitting measures, the total emissions are significantly reduced by more than half compared to the non-retrofitted case.
To summarize, despite differences in performances among the different scenarios, DHS is typically a more attractive solution compared to IHS in regard to energy, economic and environmental performance if systems are properly sized. Even though this methodology can be applied to multiple district cases and system scenarios, the optimal solution in terms of economic or environmental performance is strongly dependent on input data, which require reliable data sources. Conclusions were drawn based on specific datasets for a typical Swiss district. However, the sensitivity analysis showed that even with varying costs for storage and the network, the conclusions are still valid. Moreover, the framework can be applied to districts in other countries, with different input data and climate conditions. A current limitation of this work lies in a single combination of technologies, including solar thermal collectors with a simple thermal storage tank model. For more robust system operation in real practice, other more sophisticated thermal storage models (e.g., stratified water tank model) could be investigated. In addition, the framework could be combined with non-linear optimization algorithms (e.g., genetic algorithms) for identifying the optimal selections of design and location for systems and technologies.