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Power Supply of Greenhouses by Using Volatile Electricity Grid with High Proportion of Renewables

Authors:

Abstract

Currently, about 33% of Germany’s annual electricity is generated from renewable sources like wind and sun. Until 2030, the German government wants to increase this proportion to 50%. Other EU countries have similar plans. Predictive models indicate that this aim will lead to an increase in volatility of the energy supply and will also affect electricity pricing. Already in 2015, a temporary oversupply of renewable energies in Germany (e.g. at strong wind conditions) resulted in a negative spot price of electricity at 126 annual hours. Otherwise, the number of annual hours with high pricing (e.g. at insufficient solar radiation or windspeed) reached 63 hours. A permanent balance between electricity generation and demand is an important precondition for a stable utility frequency. Therefore, flexible capacities as control reserve (also known as balancing power) are required for energy storage, as well as consumption at any given time.
Contact:
ingo.schuch@agrar.hu-berlin.de
www.ingo-schuch.eu
Project grant:
This feasibility study (BLE reference number 28RZ5-023) is part of the joint research project ELGEVOS and supported by funds of
the German Government’s Special Purpose Fund held at Landwirtschaftliche Rentenbank (LR) with administrative assistance of the
Federal Office for Agriculture and Food (BLE).
Power Supply of Greenhouses by Using Volatile Electricity Grid
with High Proportion of Renewables
I. SCHUCH, D. DANNEHL, U. SCHMIDT
Division Biosystems Engineering, Humboldt-Universität zu Berlin
Albrecht-Thaer-Weg 3, 14195 Berlin, Germany
INTRODUTION
SIMULATIONMODELING
RESULTS
-100
-80
-60
-40
-20
0
20
40
60
80
100
120
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Power price [ EUR/MWh ]
Day-ahead power prices Annual average Low price level High price level
Fig. 1: Hourly power exchange prices for Germany at the spot-market in 2015
Fig. 2: Simulated water temperature of seasonal thermal energy storage
referring to a solar collector greenhouse with/without using control reserve (CR)
Fig. 4: Simulated efficiency of an electric heat pump system for cooling/heating
referring to a solar collector greenhouse with/without using control reserve (CR)
CONCLUSION
Currently, about 33%of Germanys annual electricity is generated from renewable sources
like wind and sun. Until 2030, the German government wants to increase this proportion to 50%.
Other EU countries have similar plans. Predictive models indicate that this aim will lead to an
increase in volatility of the energy supply and will also affect electricity pricing. Already in 2015, a
temporary oversupply of renewable energies in Germany (e.g. at strong wind conditions) resulted
in a negative spot price of electricity at 126 annual hours (Fig. 1; green dashed line). Otherwise,
the number of annual hours with high pricing (e.g. at insufficient solar radiation or wind speed)
reached 63 hours (Fig. 1; red dashed line; in case of doubled annual average). A permanent
balance between electricity generation and demand is an important precondition for a stable utility
frequency. Therefore, flexible capacities as control reserve (also known as balancing power) are
required for energy storage, as well as consumption at any given time.
From an energetic point of view, providing of balancing power referring to greenhouses with electric heat pumps and large-scaled
thermal energy storages (additionally equipped with electrical storage heating) can be worthwhile. In particular, the use of negative
control reserve (electrical loads activated) has the potential to reduce the consumption of fossil fuels for greenhouse heating. This
requires that the demand of control reserve is still increasing. In further investigations, the relation of energy-saving potential and
economic feasibility should be tested. However, a practical test under consideration of modern greenhouses (e.g. with lamps, heat
pumps and thermal energy storages) requires a high investment due to the technical features of such greenhouses.
Scenario P
arameters
Model Value
Unit
Control
reserve (neg./pos.)
126/63
(Regarding to Figure 1); 20/1
h/a;
MWel
Greenhouse
location
Northeastern
Germany
-
Greenhouse
ground area
10
ha
Greenhouse
Ucs-value (day)
0,3216x
(Wind speed) + 3,5849
W
th/m²K
Greenhouse
Ucs-value (night)
0,0605x
(Wind speed) + 1,7117
°
C
Heating
setpoint (day/night)
20/18
(Jan-Mar); 19/17(Apr-Nov); 10/10(Dec)
°
C
Sensible
heat flux at greenhouse
90
(Jan); …; 45(Jun-Oct); …; 95(Dec)
%
Percentage
of collector area
25
%
Collector
efficiency
0
(Jan); …; 58(Sep); …; 0(Dec)
%
COP
heat pump heating
0,0973x
(Storage temperature) + 2,0779
-
COP
heat pump heating+(overall)
0,0759
x(Storage temperature) + 1,143
-
COP
heat pump cooling
-
0,1468x(Storage temperature) + 7,9644
-
COP
heat pump cooling+(overall)
-
0,0661x(Storage temperature) + 4,9699
-
Therma
l storage volume
150.000
m³ H
2O
Thermal
storage Ucs-value
0,9
W
th/m²K
Storage temperature (min./max.)
7
/44
°
C
Efficiency
of electric storage heater
90
%
The operation of modern greenhouses offers different ways to adapt
the energy demand (esp. for cooling, heating, lighting) in order to provide
control reserve (CR). In case of a negative control reserve, electrical loads
are activated (switched on). Instead of that, they are deactivated for the
provision of positive control reserve. For this approach, a computer model
(15 min resolution) was developed for energy simulation of greenhouses.
The energy modeling allows to conduct a scenario analysis to investigate
different assumptions about the technical conditions. Referring to this, a
greenhouse as solar collector with an electrically-driven heat pump system
for cooling, as well as heating and solar energy storage with additional
electrical storage heating was tested under a volatile scenario (with CR)
and compared with a baseline scenario (without CR). The main scenario
parameters (partially empirically determined) are given in the Table 1. The
simulation results refer to seasonal thermal energy storage, reduction of
fossil fuels and efficiency of heat pump system (Fig. 2-4).
Fig. 3: Simulated renewable fraction for heating a solar collector
greenhouse with/without using control reserve (CR)
Tab. 1: Main parameters for energy modeling and simulation of a greenhouse at given scenario
0
5
10
15
20
25
30
35
40
45
50
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mean storage temperature [ °C ]
Storage temperature (with CR) Storage temperature (without CR)
4,4
4,1
3,0
3,2
4,4
4,2
3,0
3,2
0
1
2
3
4
5
Heat pump heating Heat pump cooling Heat pump heating+ Heat pump cooling+
Seasonal performance factor [ SPF/a ]
Seasonal performance factor (with CR) Seasonal performance factor (without CR)
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