IEEE Power & Energy Magazine - January/February 2017 - 30

The cost of storing thermal energy in water tanks
decreases rapidly with larger tank size (Figure 6). Longer
time constants and, consequently, more flexibility could be
achieved if the hot-water tank were oversized. Sharing the
tank between several buildings would, in turn, decrease the

US$/kWh (∆ 50 K)

60
50
40
30
20
10
0
0

5

10
m3
(a)

15

20

US$/kWh (∆ 50 K)

10
8

Forecasting

6
4
2
0
0

5,000

10,000

15,000

m3
(b)

figure 6. The cost of hot water tanks per unit of storable
heat in relation to the storage tank size. (Small tank sizes
are based on market data; large tank sizes are from the
European Commission Joint Research Centre's 2012 report.)

Normalized Load (-)

16 January 2015 08:00
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0

24

48

72

96

Horizon (Hours)

figure 7. The quality of heat forecasts 0-96 hours ahead for
the Sønderborg district heating system in Denmark (the blue
line). The 5% and 95% percentiles are shown in red, and the
actual load as it was observed later is shown in black. The
plot is generated using the adaptive heat-load forecasting
system PRESS (www.enfor.dk/products/press.aspx).
30

specific costs. District heating systems already often utilize
large tanks to make their operation more flexible.
Thermal electricity end uses such as water heating,
space heating/cooling, and refrigerator/freezers are a
suitable source for flexibility due to their discretionary
nature, inherent thermal inertia, and large volumes. Large
thermal inertia means that these loads can be switched
off for a while without affecting consumer comfort. Furthermore, because the flexible loads consist of a number
of appliances dispersed across the system, reliability can
be statistically greater compared to an individual conventional power plant.
The building envelope itself also provides thermal inertia depending on the insulation level and thermal mass of
the building. In a well-insulated house, electric load can be
shifted (2-12 hours depending on building), while consumer
comfort is still met. Preheating or precooling increases flexibility but typically also increases energy usage (depending
on the insulation level). Thermal storage and building preheating enable considerable demand-shifting potential at a
comparably low cost.

ieee power & energy magazine

Thermal-load forecasting is often used in district heating systems and for estimating electric heating loads. The uncertainty
of heat-load forecasts is important when trying to optimize
heating or cooling. Forecasting failures can lead to unwarranted costs or uncomfortable inside temperatures. For example, when a heat-load forecast error persists in one direction
for muliple hours, heat storage may become emptied or filled,
after which it is not useful any more. When uncertainty is not
considered, optimizing the use of heat storage is too easy, and
model results or control strategies are too optimistic. Figure 7
demonstrates the quality of heat forecasts 0-96 hours ahead
for the Sønderborg district heating system in Denmark.
If heating and cooling loads are to be controlled in a
manner that provides flexibility to the power system, accurate forecasts will be important. Good forecasts will need
to include climatic variables, building characteristics, and
often predictions about user behavior. Building characteristics can be considered either through direct modeling or past
behavior. Occupancy behavior varies from family to family:
some families have a very systematic and easily predicted
load pattern, whereas others seem to have a very random and
less predictive load profile.
Similar to electric loads, it has been shown that the
aggregation of individual loads can decrease relative forecast errors and smooth out rapid variations. However,
when heating/cooling takes place in individual buildings,
the control algorithm cannot aggregate. On the other hand,
when several buildings are connected to a district heating
system, the forecasts for control can be aggregated and
relative forecast errors decrease. This is different from just
forecasting loads without control, where aggregation can
take place in all cases.
january/february 2017


http://www.enfor.dk/products/press.aspx

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - January/February 2017

IEEE Power & Energy Magazine - January/February 2017 - Cover1
IEEE Power & Energy Magazine - January/February 2017 - Cover2
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IEEE Power & Energy Magazine - January/February 2017 - Cover3
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