7. Agent based model of energy consumption in households
7.3. Simulation Runs
7.3. Simulation Runs et al., 2010)). In theory, it would be possible to compare the aggregated load curves of different city quarters produced by the model to measured data of the same quarter, as the simulation is able to model specific geographical regions and aggregated load curves can be obtained from the respective power transformers feeding this quarter. In practice, however, such data could unfortunately not be obtained in the run of the project, which rules out the comparison on the level of specific regions or city quarters. The only available data suitable for comparison that could be found are the synthetic household load profiles (H0) provided by BDEW and data of the european electricity measurement campaign REMODECE (ENERTECH, 2008). Using these data only a comparison at the topmost level of aggregation is possible, meaning that the similarity of the load curves over all household types – where the behavior and appliance provision levels of each group is modeled with their specific distributions and the share of each group in the model is defined by their share in the survey – serves as an indicator that the different types of load curves and their composition is plausible.
Comparing the aggregated simulated load curves of 1 000 Stuttgart households with the BDEW-H0 standard load profile weighted to the consumption of the sim-ulated households reveals a big similarity of the shape of both load profiles (see Figure 7.2). Both curves are correlated at r = 0.90 and the simulation repro-duces the noon and evening peaks of the H0 profile. The biggest difference lies in the lower values of the simulated curve in the morning hours and the higher values during the night. Due to the lack of specific measured data for Lyon and Stuttgart, it is unfortunately not able to be sure in how far this differences are caused by real differences of Stuttgart households compared to the H0-profile, or by differences between real Stuttgart households and the simulated households.
Since the simulation environment does not yet incorporate electric water heating – which is supposedly used plenty in the morning hours, while and after showers have been taken – this could explain the lower values of the simulated load curves in the morning. Regarding its higher values in the night, one possible explana-tion could be that the Stuttgart households had reported a rather high number of cooling devices such as refrigerators, combined coolers-freezers and freezers, which are higher than the national share (see chapter 8). These devices produce in sum
a almost steady load also during the night, which seems to be the reason for this difference.
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Figure 7.2.: Comparison of simulated load curve of 1 000 Stuttgart households with the BDEW-H0 standard load profile
For France, there is no standard household load profile available for comparison.
Comparing the simulated load curve of 1 000 Lyon households with the BDEW profile (see Figure 7.3), shows a slightly weaker correlation of r = 0.88. Here the main differences to be found are a higher peak in the evening and a similar devia-tion in the morning hours as the simulated curve of the Stuttgart households. For the latter, electric warm water heating could be the reason as with the differences of the simulated load curves of the Stuttgart household. The higher peak during the evening hours can be explained with the much bigger share of french house-holds that prepare a warm dinner (see Section 6.9.6). Having a lower number of cooling devices, the simulated load curve of the Lyon households show a smaller
7.3. Simulation Runs deviance to the standard load profile during the night than the one of the Stuttgart households.
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Figure 7.3.: Comparison of simulated load curve of 1 000 Lyon households with the BDEW-H0 standard load profile
Since the standard load profile is to be weighted to the consumption of the households, it can only give information if the shape of the curve is plausible.
Regarding the height of the load curve further data has to be used. The most suitable publicly available data for such a comparison is provided by the euro-pean measurement campaign REMODECE1, where the electricity consumption of 100 households each in 12 european countries has been measured and aggregated load curves for specific household tasks are reported (ENERTECH, 2008) (see Fig-ure 7.4). Aggregating the simulation results in the same way (FigFig-ures 7.5 and 7.6)
1Residential Monitoring to Decrease Energy Use and Carbon Emissions in Europe http://remodece.isr.uc.pt/
enables a comparison of both data. It shows that the load curve of both samples produced by lighting is very similar to the measured load curve and that the load produced by refrigeration is similar in the Stuttgart case and somewhat lower for the Lyon sample when compared to the measured data. The peaks produced by lunch and dinner preparation are steeper in the simulated load curves, which might be due to the fact, that the REMODECE data averages over all days of a week and 12 countries, while the simulations refer to week-days and single countries which supposedly have a smaller variance regarding meal times than a sample over 12 countries. The load produced by televisions, personal computers, and washing and drying is more shifted towards the evening hours in the measurements than in the simulations and also seems to be higher overall. For one this is probably caused by the fact that the simulation distributes appliance use randomly over the time when people are at home while there seems to be a tendency to perform these tasks in the evening hours. Secondly the share of appliances with an energy efficiency label lower than A is substantially higher in the REMODECE sample than in the Stuttgart and Lyon sample. Finally, the difference could be caused by the fact that people underestimate their appliance use.
Unfortunately, measured load curves of Lyon and Stuttgart could not be ob-tained, these would have enabled a much better comparison of the simulation data. Nevertheless, the comparison of the simulated load curves with the H0-BDEW load profile and the REMODECE data show that the simulated households reproduce the macro phenomena of the aggregated load curves with a big simi-larity. The shape of the simulated load curves is highly correlated to the BDEW standard load profile and the dissimilarities that are found between the simulated load curves and the H0 profile and the REMODECE data can be explained very plausible. This shows that the bottom up approach of simulating household load curves with survey data can yield plausible results that can reproduce a real world phenomena. Keeping in mind that the simulated load curves are not produced by households that all have the similar probabilities for behavior patterns and appli-ance ownership, but by different groups of households with different behavior and ownership rates cumulated regarding to their share in the survey, it seems that the underlying differences in the household agents are plausible also to appear in
7.3. Simulation Runs
Source:(ENERTECH, 2008)
Figure 7.4.: Electricity Consumption for the Average Day for a Typical Household in Europe
the real world and that an agent based simulation might be a suitable approach in order to understand differences in household load curves. It would, of course, be better to compare also the simulated load curves of specific subsamples (like lifestyle groups or single person households) to measured data of the same group, but such data is not available – which was also one of the reasons for the approach chosen. Therefore, the effect of different household composition or lifestyles on the residential load-curve can only be evaluated by simulation results.
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Figure 7.5.: Simulated average electricity consumption by household task for Stuttgart households
7.3. Simulation Runs
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Figure 7.6.: Simulated average electricity consumption by household task for Lyon households