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 Ramirez Camargo 1 , Katharina Gruber 1,2 , Wolfgang Dorner 1 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 www.crossenergy.eu
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 on 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]. www.crossenergy.eu 2
Motivation Figure 3: ”Energy Plus” single- Figure 2: First self-sufficient solar Figure 4: Hybrid electricity family house with roof-top PV [3]. house in Freiburg 1996 [2]. system with micro- generation wind turbine and roof-top PV [4]. • 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 www.crossenergy.eu 3
Model overview Hybrid electricity system COSMO- micro- REA6 generation irradiation wind turbines data (10.5 kW) clusters of SFH PV modules (two tilts) COSMO-REA6 wind data COSMO-REA6 temperature data battery storage Figure 5: Schematic model overview. www.crossenergy.eu 4
Data – COSMO-REA Table 1: Overview of the spatiotemporal data used for the PV and wind power potential estimation. Spatial Temporal Source Type of product Provider Units Data format resolution resolution Wind velocity at ten meters COSMO-REA6 DWD 6 km x 6 km 1 hour m/s GRIB height in u direction (U_10M) Wind velocity at ten meters COSMO-REA6 DWD 6 km x 6 km 1 hour m/s GRIB height in v direction (V_10M) Downward diffuse short wave COSMO-REA6 radiation flux at surface DWD 6 km x 6 km 1 hour W/m 2 GRIB (SWDIFDS_RAD) Downward direct short wave radiation flux at surface COSMO-REA6 DWD 6 km x 6 km 1 hour W/m 2 GRIB (SWDIRS_RAD) Ambient temperature at two COSMO-REA6 DWD 6 km x 6 km 1 hour K GRIB meter height (T2M) Satellite 3 km x 3km int. values Snow cover (SC) LSA-SAF 15 min HDF5 images MSG at nadir from 0 to 5 www.crossenergy.eu 5
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) Figure 6: Hourly PV power generation calculated using COSMO-REA6 reanalysis data [kWh/m 2 ]. § 1995-2015 (mean values) www.crossenergy.eu 6
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]. www.crossenergy.eu 7
Methodology – Model Parameters Table 2: Overview of the model parameters and their assumed values. 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&$671%89:6;4 ] 0.01% Replacement rate of the storage [ 4%<4!=:84 ] 2 www.crossenergy.eu 8
Area of application • LUISA population data • Focus on “intermediate density areas” and “thinly populated areas” in Germany and Czech Republic (less than 1,500 inhabitants/km 2 ) • Standard load profiles for SFH • Yearly demand: Germany 3,079 kWh/a Czech Republic kWh 3,064 kWh/a Figure 8: Population data (top) [1] and SFH standard load profile (bottom) [5,6]. www.crossenergy.eu 9
Optimization model Main objective: Minimizing the system costs "#$ %&'()*+ ∗ - &'/#01 . + 3#$4()*+ ∗ windSize + 1*/#01 ∗ 1*()*+ ∗ 1*<1&=>?1 ! (PV + wind turbine + battery) . Main conditions: I) Parity between energy 1@1A>$4 B = 1DEF*1 B + 1G#$4F*1 B + 1/+)H@#*?ℎ>HJ1 B ∗ 1/+)H@#*?ℎ>HJ1KLL, ∀+ supply and demand II) Balancing PV energy - (&'/#01 . ∗ &'PQ+&Q+ B,. ) = 1DEF*1 B + 1DE/+)H1 B + &'/QH&=Q* B , ∀+ production . III) Balancing wind - (3#$4/#01 . ∗ 3#$4PQ+&Q+ B,. ) = 1G#$4F*1 B + 1G#$4/+)H B + 3#$4/QH&=Q* B , ∀+ energy production . IV) State of charge of the 1/P( BST = 1/+)H#$JKLL ∗ 1/P( B + 1/+)H(ℎ>HJ1KLL electric storage system ∗ (1DE/+)H1 BST + 1G#$4/+)H1 BST ) − 1/+)H@#*?ℎ>HJ1 BST , ∀+ www.crossenergy.eu 10
Scenarios • Reference scenario: Minimizing cost scenario in a year with the lowest solar radiation (2010) § Sizing two types of PV generation: PV1 (optimal inclination in summer) and PV2 (70° inclination, optimal in winter) § Calculating the optimal number of micro-generation wind turbines § Sizing the energy storage system • Additional scenarios modifying: § Optimization objectives (min PV, min battery) § Weather data (highest/lowest solar radiation) § Consideration of snow cover www.crossenergy.eu 11
Results – 10 SFHs Figure 9: The reference scenario for minimizing system cost without considering snow cover (top) and with snow cover (bottom) for clusters of ten SFHs. www.crossenergy.eu 12
Results – 10 SFHs Figure 10: The reference scenario for minimizing battery size without considering snow cover (top) and with snow cover (bottom) for clusters of ten SFHs. www.crossenergy.eu 13
Results – !"#$ % Figure 11: Time series of !"#$ % of the battery and total PV Figure 12: Time series of !"#$ % of the battery, total PV output in a PV/battery system for November and output and total wind power output in a PV/wind/battery December 2010 for a single pixel close to Dresden, hybrid system for November and December 2010 for a Germany. single pixel close to Dresden, Germany. www.crossenergy.eu 14
Conclusion • 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 www.crossenergy.eu 15
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 www.crossenergy.eu
References [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). www.crossenergy.eu 17
Appendix I: Reference paper PV/battery systems 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 www.crossenergy.eu 18
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