Assessment of complementarity between wind power and photovoltaic - - PowerPoint PPT Presentation

▶
assessment of complementarity between wind power and
SMART_READER_LITE
LIVE PREVIEW

Assessment of complementarity between wind power and photovoltaic - - PowerPoint PPT Presentation

Oct 03, 2022 530 likes •747 views

Cross border energy infrastructure - future design for a changing region Assessment of complementarity between wind power and photovoltaic installations to supply residential electric demand in Germany and Czech Republic Felix Nitsch 1,2 , Luis

slide-1
SLIDE 1

www.crossenergy.eu Cross border energy infrastructure - future design for a changing region

Assessment of complementarity between wind power and photovoltaic installations to supply residential electric demand in Germany and Czech Republic

Felix Nitsch1,2, Luis Ramirez Camargo1, Katharina Gruber1,2, Wolfgang Dorner1

1 Institute for Applied Informatics [IaI]

Technische Hochschule Deggendorf, Germany

2 Institute for Sustainable Economic Development

University of Natural Resources and Life Sciences Vienna, Austria

slide-2
SLIDE 2

www.crossenergy.eu 2

Motivation

CrossEnergy

  • Research infrastructure for energy

systems in Czech-Bavarian border region in 2050

  • Forecasting, Operating, Planning
  • Three universities (UWB, THD, OTH)

Which challenges and opportunities arise from urbanization trends? What are the effects of technological trends

  • n the electricity grid?

How do regulatory policies influence the design of the energy infrastructure?

Figure 1: Changing population from 2010 to 2050 in the Czech- Bavarian border region from the LUISA platform [1].

slide-3
SLIDE 3

www.crossenergy.eu 3

  • Energy self-sufficiency for single-family houses (SFH) is technically possible
  • New technologies, improved efficiencies and reduced prices make hybrid

systems attractive

  • Optimal sizing of system components is crucial

Motivation

Figure 2: First self-sufficient solar house in Freiburg 1996 [2]. Figure 4: Hybrid electricity system with micro- generation wind turbine and roof-top PV [4]. Figure 3: ”Energy Plus” single- family house with roof-top PV [3].

slide-4
SLIDE 4

www.crossenergy.eu 4

Model overview

COSMO-REA6 wind data COSMO- REA6 irradiation data COSMO-REA6 temperature data clusters of SFH micro- generation wind turbines (10.5 kW) PV modules (two tilts) battery storage

Hybrid electricity system

Figure 5: Schematic model overview.

slide-5
SLIDE 5

www.crossenergy.eu 5

Data – COSMO-REA

Source Type of product Provider Spatial resolution Temporal resolution Units Data format COSMO-REA6 Wind velocity at ten meters height in u direction (U_10M) DWD 6 km x 6 km 1 hour m/s GRIB COSMO-REA6 Wind velocity at ten meters height in v direction (V_10M) DWD 6 km x 6 km 1 hour m/s GRIB COSMO-REA6 Downward diffuse short wave radiation flux at surface (SWDIFDS_RAD) DWD 6 km x 6 km 1 hour W/m2 GRIB COSMO-REA6 Downward direct short wave radiation flux at surface (SWDIRS_RAD) DWD 6 km x 6 km 1 hour W/m2 GRIB COSMO-REA6 Ambient temperature at two meter height (T2M) DWD 6 km x 6 km 1 hour K GRIB Satellite images MSG Snow cover (SC) LSA-SAF 3 km x 3km at nadir 15 min

  • int. values

from 0 to 5 HDF5

Table 1: Overview of the spatiotemporal data used for the PV and wind power potential estimation.

slide-6
SLIDE 6

www.crossenergy.eu 6

Methodology – PV

  • Input:

§ SWDIFDS_RAD and SWDIRS_RAD (both COSMO-REA6) used to calculate GHI using PVLIB § T2M (temperature data at 2 m height) § SC (Snow cover data)

  • Model:

§ GHI, temperature from COSMO-REA6 § PV panel efficiency § Temperature correction factor § Reduction factor due to installation type

  • Output:

§ Hourly output for “optimal” and 70° tilt 1 kWp PV modules

  • Three data sets used for modelling:

§ 2003 (most irradiance) § 2010 (least irradiance) § 1995-2015 (mean values)

Figure 6: Hourly PV power generation calculated using COSMO-REA6 reanalysis data [kWh/m2].

slide-7
SLIDE 7

www.crossenergy.eu 7

Methodology – Wind

  • Input:

§ U_10M and V_10M (both COSMO-REA6) used to calculate resulting wind speeds

  • Model:

§ Power curve of micro-generation wind turbine is used to calculate wind power output

  • Output:

§ Hourly output for a single 10.5 kW wind turbine

  • Three data sets used for modelling:

§ 2003 (most irradiance) § 2010 (least irradiance) § 1995-2015 (mean values)

Figure 7: Hourly wind power generation of a micro- generation wind turbine with 10.5 kW calculated using COSMO-REA6 reanalysis data [kWh].

slide-8
SLIDE 8

www.crossenergy.eu 8

Methodology – Model Parameters

Parameter Assumed value

PV total installation cost [!"#$%&] 2,100 [EUR/kWp] PV panel efficiency ['()] 21% Temperature correction factor [*(+((]

  • 0.0045 [%/°C]

Reduction factor due to installation type [,-] 0.05 [°C /(W/m2)] Nominal operating temperature [./ ] 25 [°C] Wind turbine total installation cost [0123#$%&] 56,000 [EUR/10.5 kW] Electric energy storage [4%#$%&] 1,000 [EUR/kWh] Storage system (round trip) efficiency 75% Hourly self-discharge ration of the storage [45&$671%89:6;4] 0.01% Replacement rate of the storage [4%<4!=:84] 2 Table 2: Overview of the model parameters and their assumed values.

slide-9
SLIDE 9

www.crossenergy.eu 9

  • LUISA population data
  • Focus on “intermediate density areas” and

“thinly populated areas” in Germany and Czech Republic (less than 1,500 inhabitants/km2)

  • Standard load profiles for SFH
  • Yearly demand:

Germany 3,079 kWh/a Czech Republic 3,064 kWh/a

Area of application

Figure 8: Population data (top) [1] and SFH standard load profile (bottom) [5,6].

kWh

slide-10
SLIDE 10

www.crossenergy.eu 10

Optimization model

Main objective: Minimizing the system costs (PV + wind turbine + battery)

! "#$ %&'()*+ ∗ -

.

&'/#01. + 3#$4()*+ ∗ windSize + 1*/#01 ∗ 1*()*+ ∗ 1*<1&=>?1 1@1A>$4B = 1DEF*1B + 1G#$4F*1B + 1/+)H@#*?ℎ>HJ1B ∗ 1/+)H@#*?ℎ>HJ1KLL, ∀+

Main conditions: I) Parity between energy supply and demand

  • .

(3#$4/#01.∗ 3#$4PQ+&Q+B,.) = 1G#$4F*1B + 1G#$4/+)H

B + 3#$4/QH&=Q*B, ∀+

III) Balancing wind energy production

1/P(BST = 1/+)H#$JKLL ∗ 1/P(B + 1/+)H(ℎ>HJ1KLL ∗ (1DE/+)H1BST+ 1G#$4/+)H1BST) − 1/+)H@#*?ℎ>HJ1BST , ∀+

IV) State of charge of the electric storage system

  • .

(&'/#01. ∗ &'PQ+&Q+B,.) = 1DEF*1B + 1DE/+)H1B + &'/QH&=Q*B, ∀+

II) Balancing PV energy production

slide-11
SLIDE 11

www.crossenergy.eu 11

  • Reference scenario:

Minimizing cost scenario in a year with the lowest solar radiation (2010) § Sizing two types of PV generation: PV1 (optimal inclination in summer) and PV2 (70° inclination, optimal in winter) § Calculating the optimal number of micro-generation wind turbines § Sizing the energy storage system

  • Additional scenarios modifying:

§ Optimization objectives (min PV, min battery) § Weather data (highest/lowest solar radiation) § Consideration of snow cover

Scenarios

slide-12
SLIDE 12

www.crossenergy.eu 12

Results – 10 SFHs

Figure 9: The reference scenario for minimizing system cost without considering snow cover (top) and with snow cover (bottom) for clusters of ten SFHs.

slide-13
SLIDE 13

www.crossenergy.eu 13

Results – 10 SFHs

Figure 10: The reference scenario for minimizing battery size without considering snow cover (top) and with snow cover (bottom) for clusters of ten SFHs.

slide-14
SLIDE 14

www.crossenergy.eu 14

Results – !"#$%

Figure 11: Time series of !"#$% of the battery and total PV

  • utput

in a PV/battery system for November and December 2010 for a single pixel close to Dresden, Germany. Figure 12: Time series of !"#$%

  • f the battery, total PV
  • utput and total wind power output in a PV/wind/battery

hybrid system for November and December 2010 for a single pixel close to Dresden, Germany.

slide-15
SLIDE 15

www.crossenergy.eu 15

  • PV, wind and battery system sizes for electricity self-sufficient SFHs

are estimated using high resolution regional reanalysis data for two decades

  • Battery sizes range between 78.1 and 333.2 kWh in a cost optimal

scenario for ten SFHs

  • PV modules of up to 269.1 kWp are necessary in a cost optimal

scenario with and without snow cover

  • Up to six micro-generation wind turbines are installed
  • System sizes change almost linear when the number of SFHs in a

cluster is altered

  • Assessment provides scientifically based information for a topic

vaguely treated by the industry

Conclusion

slide-16
SLIDE 16

www.crossenergy.eu Cross border energy infrastructure - future design for a changing region

Thank you for your attention!

Felix Nitsch felix.nitsch@stud.th-deg.de Luis Ramirez Camargo luis.ramirez-camargo@th-deg.de Katharina Gruber katharina.gruber@stud.th-deg.de Wolfgang Dorner wolfgang.dorner@th-deg.de Technische Hochschule Deggendorf Technologie Campus Freyung Grafenauer Str. 22 94078 Freyung

slide-17
SLIDE 17

www.crossenergy.eu 17

[1] Lavalle, C. (2014). OUTPUT - Population distribution (LUISA Platform REF2014). European Commission, Joint Research Centre, http://data.jrc.ec.europa.eu/dataset/jrc-luisa- population-ref-2014. [2] Voss K, Goetzberger A, Bopp G, Häberle A, Heinzel A, Lehmberg H (1996). The self-sufficient solar house in Freiburg—Results of 3 years of operation. Sol Energy 1996;58:17–23. doi:10.1016/0038-092X(96)00046-1. [3] Schlagmann Poroton (2018). Effizienzhaus Plus – Monitoring. http://schlagmann.de/de/Haeuser/Forschungsprojekt-Effizienzhaus-Plus/Monitoring [4] Photo by Rob Cardillo. [5] Zeising H-J. Energieverbrauch in Deutschland im Jahr 2015 2016. [6] World Energy Council. Electricity use per capita, World Electricity level & trends 2016. https://wec-indicators.enerdata.net/electricity-use-per-capita.html (accessed March 19, 2018).

References

slide-18
SLIDE 18

www.crossenergy.eu 18

Luis Ramirez Camargo, Felix Nitsch, Katharina Gruber, Wolfgang Dorner (2018): Electricity self-sufficiency of single- family houses in Germany and the Czech Republic, Applied Energy, Volume 228, 2018, Pages 902-915.

https://doi.org/10.1016/j.apenergy.2018.06.118

Appendix I: Reference paper PV/battery systems

slide-19
SLIDE 19

www.crossenergy.eu 19

!"#$%!$%&,((*) = * ∗ ./0 ∗ [2 + 4/5//((6&

789 + :6*/<) − 6>)]

@ = GHI computed with PV_LIB from COSMO-REA6 data [W/m2] ABC = photovoltaic panel efficiency, in this case 0.21 [%] DBEBB = temperature correction factor, in this case -0.0045 [%/°C] F&

(GH = ambient air temperature from COSMO-REA6 data [°C]

IJ = reduction factor due to installation type, in this case 0.05 [°C /(W/m2)] K = PV plant area [m2] FL = nominal operating temperature, in this case 25 [°C]

Appendix II: PV power generation

slide-20
SLIDE 20

www.crossenergy.eu 20

Appendix III: Wind power generation

Manufacturer Model

Height [m] Capacity [kW] Schachner SW 10 10 10 winDual A1 K 5,5 1 winDual A1 K 8,2 1 Windspot 7.5Kw 12 7,5 Windspot 7.5Kw 14 7,5 MAN Aeroman 11/11 10 11 ecotècnica ECO 12/30 14 30 Hummer H8.16-10KW 10 10 Synthetic Synthetic 10 10.5

5 10 15 20 25 30 5 10 15 20 25 30 35 40 45 50

  • utput [kW]

wind speed [m/s]

Schachner SW10 winDual A1 K Windspot 7.5Kw MAN Aeroman 11/11 ecoècnica ECO 12/30 Hummer H8.16-10KW MW Hummer Schachner MAN

Figure A.I: Power curves of micro-generation wind turbines, the dark red represents the synthetic model used in this assessment. Table A.I: Specifications of micro-generation wind turbines, the dark red represents the synthetic model used in this assessment.