Abstract
A data set of heat load measurements for 197 buildings from different building categories (apartment blocks, hotels, nursing homes, offices and schools), have been analysed to evaluate the potential for peak shaving. A moving average filter is applied to investigate how smoothening of the load profiles can reduce the peak loads. It is shown that for short term peak shaving, apartment blocks and hotels have the highest potential (around 8% for a two hour period), while for longer term peak shaving, the results are more even for all the building categories. Schools stand out, with a large difference in heat consumption between inside and outside opening hours, leading to a large flexibility potential on daily basis, but this would require a large amount of stored energy. As it is difficult to control, and thereby reduce the peaks for unknown loads, a prediction model is applied to the data, to analyse the predictability. It is shown that the peak shaving potential is reduced 90-30 % when analysing only the predictable loads. The biggest difference is in the short-term potential for apartment blocks and hotels, while difference is smaller in the long-term (24-hour) peak shaving potential.
Highlights
The Clean Energy Package of the European Union highlights the importance of utilizing demand side flexibility to support the de-carbonisation of the energy system [1]
With a large difference in heat consumption between inside and outside opening hours, leading to a large flexibility potential on daily basis, but this would require a large amount of stored energy
The histogram shows the share of buildings with the respective flexibility potential (Pflex) for each building category and for 2, 6- and 24-hour moving averaging periods
Summary
The Clean Energy Package of the European Union highlights the importance of utilizing demand side flexibility to support the de-carbonisation of the energy system [1]. Demand side flexibility is of interest because it can reduce the need for costly energy sources typically applied during peak hours. Energy-efficient heat supply systems, such as ground source heat pumps (GSHP), are usually installed as base load units, whereas the peak load is covered by a less energy-efficient unit, e.g. an electric boiler [5]. Reducing both peak power and energy demands during peak periods, will lead to an increased utilization of the base load system and decrease the total operation cost of the system [6]. This works seeks to find a simplified way of doing this
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