Framtidens effektprofiler: Hur småhus möter ett elsystem i förändring

Sammanfattning

The objective of this study is to investigate how the power profiles of residential houses in Sweden may look like in the future. Median power profiles for typical summer and winter days were created using real customer data. The customer group analyzed consists of houses in southern Sweden, SE4, equipped with both solar electricity production (PV) and electric vehicles (EVs) with home charging stations. This group was selected to represent a growing segment of residential customers that reflects the characteristics of the houses of tomorrow. Simulations were conducted to assess the potential impact of home batteries, Vehicle-to-Grid (V2G) technology, and smart charging on the established median power profiles. These simulations were based on two scenarios: One were the greatest cost savings is achieved by flattening out the power profile, and one were they are achieved by adapting to price variations. A literature review was also carried out in order to find information that were useful for the simulations as well as to examine how energy efficiency improvements, electricity prices, tariff structures, and policy instruments influence power profiles. The report presents examples of how power profiles may appear under different scenarios. By examining the median profiles, some conclusions could be drawn: The customer group studied will probably see an increased self-use of solar power. Furthermore, solar power production during summer is higher than the winter consumption, suggesting that it will become the dimensioning factor in grid planning. Another observations is that feed-in to the grid tend to be higher on weekdays compared to weekends. The study further indicates that enabling technologies such as V2G, home batteries, and smart charging may be essential to see any kind of change in the profiles, as previous research has shown limited behavioral response to price signals alone. V2G have the potential to change the power profiles drastically with high peaks and low valleys when optimized according to electricity prices. In contrast, if the goal is to smoothen the profiles, home batteries appear more effective. Smart charging has the least impact. However, there is much evidence that the chosen methodology has led to an underestimation of the total charging demand of households, which in turn limits the potential. For some scenarios where optimization is based on electricity prices, extreme fluctuations in the profiles can be observed, which sometimes results in reversed grid flows where electricity is being fed into the grid from homes even during periods without solar radiation. Furthermore, while overall energy use may change, these shifts are likely to be less significant than the ability to shift loads in time. Energy efficiency is not expected to be a major driver of change, except in scenarios with sustained high electricity prices. The power profiles can look very different depending on several factors, such as whether tariffs or an increased variation of electricity prices become the dominant optimization parameter, and which technologies are widely adopted by households. However, a consistent pattern across all scenarios is a reduction in grid power consumption during the evening peak, around the time 18–19, with a corresponding increase in consumption during nighttime and/or daytime hours. Beyond insights into the possible shapes of future power profiles, an important conclusion is that accurate forecasting of these profiles remains a major challenge primarily due to a lack of transparency regarding behind-the-meter activities in households. The lack of information regarding heating systems, home batteries and home charging for each metering point further complicate forecacsting. Although the original plan was to also assess the impact of Home Energy Management Systems (HEMS) on the profiles, this was later reduced to smart charging only due to the unavailability of necessary data. For improved forecasting and analysis, it is crucial that grid operators collect more detailed information about their customers. Motivation for customers to share information could be lower network charges as a consequence of more accurate analyses leading to more efficient network usage.