Wind solar energy_modeling_analysis
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The presentation models and analyzes wind and solar energy resources for four US locations: Charlotte NC, Boston MA, Boulder CO, and Tucson AZ. Data on wind speed, solar irradiance, and other factors are used to calculate the energy output and net capacity factor for each location. Boston generally has the highest wind energy potential while Tucson has the greatest solar energy potential. Further work could involve modeling renewable energy backed by energy storage systems.
Wind solar energy_modeling_analysis
- 1. Wind and Solar Energy Resources Modeling and Analysis Technical Presentation By Mohamed Abuella The University of North Carolina at Charlotte August 9th, 2019
- 2. 2 Presentation Outline Wind Energy Resources Modeling Solar Energy Resources Modeling 8/9/2019
- 3. Four U.S. Locations for Comparison of Renewable Energy Modeling and Analysis Charlotte NC, Boston MA, Boulder CO, Tucson AZ. 3 For Different Locations in the U.S. Data are retrieved from NREL’s Developer Network: https://developer.nrel.gov/ Wind and Solar Energy Resources Modeling and Analysis 8/9/2019
- 4. Charlotte, NC 4 Boston, MA Boulder, CO Tucson, AZ Time series of wind speed at height 100m (m/s) 𝑃𝑤 = 1 2 𝜌𝐴𝒗 𝟑 Wind Energy Resources Modeling 8/9/2019
- 5. Charlotte, NC 5 Boston, MA Boulder, CO Tucson, AZ Boxplots of monthly distribution of wind speed Wind Energy Resources Modeling 8/9/2019
- 6. Charlotte, NC 6 Boston, MA Boulder, CO Tucson, AZ Wind Roses of Wind Speed Distribution of wind direction and speed Wind Energy Resources Modeling 8/9/2019
- 7. Charlotte, NC 7 Boston, MA Boulder, CO Tucson, AZ Probability density distribution of wind speed Wind Energy Resources Modeling 8/9/2019
- 8. Charlotte, NC 8 Boston, MA Boulder, CO Tucson, AZ Wind turbine GE 1.5SLE 77m is utilized for modeling The wind power curve for GE 1.5SLE 77m to covert the wind speed to wind power http://www.wind-power-program.com/Downloads/Databasepowercurves(May2017).zip 𝑃𝑤 = 1 2 𝜌𝐴𝑣3 Wind Energy Resources Modeling 8/9/2019
- 9. Charlotte, NC 9 Boston, MA Boulder, CO Tucson, AZ Probability density distribution of wind power 𝑃𝑤 = 1 2 𝜌𝐴𝑣3Considering other parameters such as air pressure, temperature and density at the given height=100m Wind Energy Resources Modeling 8/9/2019
- 10. Charlotte, NC 10 Boston, MA Boulder, CO Tucson, AZ Month0 MWh NCF 1 467.945 41.90% 2 584.361 58.00% 3 510.499 45.70% 4 512.191 47.40% 5 420.662 37.70% 6 239.808 22.20% 7 354.663 31.80% 8 285.923 25.60% 9 396.328 36.70% 10 504.488 45.20% 11 471.683 43.70% 12 691.553 62.00% Month MWh NCF 1 387.574 34.70% 2 455.725 45.20% 3 410.236 36.80% 4 456.256 42.20% 5 381.352 34.20% 6 193.475 17.90% 7 230.690 20.70% 8 141.379 12.70% 9 197.738 18.30% 10 310.630 27.80% 11 355.663 32.90% 12 298.279 26.70% Month MWh NCF 1 413.396 37.00% 2 288.217 28.60% 3 382.094 34.20% 4 339.568 31.40% 5 222.077 19.90% 6 187.928 17.40% 7 216.100 19.40% 8 215.737 19.30% 9 268.648 24.90% 10 206.966 18.50% 11 224.898 20.80% 12 312.770 28.00% Month0 MWh NCF 1 262.764 23.50% 2 247.664 24.60% 3 259.804 23.30% 4 378.228 35.00% 5 184.679 16.50% 6 188.630 17.50% 7 111.523 10.00% 8 143.448 12.90% 9 223.078 20.70% 10 258.261 23.10% 11 236.885 21.90% 12 274.958 24.60% Wind Energy Modeling in 2009 Calculating the net capacity factor (NCF) for each month, then over the entire year NCF= 𝑇ℎ𝑒 𝑎𝑐𝑡𝑢𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑇ℎ𝑒 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑒𝑛𝑒𝑟𝑔𝑦 𝑡ℎ𝑎𝑡 𝑐𝑜𝑢𝑙𝑑 ℎ𝑎𝑣𝑒 𝑏𝑒𝑒𝑛 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 NCF= 𝑇ℎ𝑒 𝑎𝑐𝑡𝑢𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 (𝑀𝑊ℎ) 𝑇ℎ𝑒 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 ∗𝑡𝑖𝑚𝑒 (𝑀𝑊ℎ) Wind Energy Resources Modeling 8/9/2019
- 11. 11 Wind Energy Modeling in 2009 and 2010 2009 MWh NCF Charlotte 3818.9 29.1% Boulder 3278.4 24.9% Boston 5440.1 41.4% Tucson 2769.9 21.1% 2010 MWh NCF Charlotte 3544.6 27.0% Boulder 2676.9 20.4% Boston 6107.7 46.5% Tucson 2969.9 22.6% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Charlotte Boulder Boston Tucson NCF Yearly Net Capacity Factor 2009 2010 0 1000 2000 3000 4000 5000 6000 7000 Charlotte Boulder Boston Tucson MWh Yearly Yeild of Wind Energy 2009 2010 Wind Energy Resources Modeling 8/9/2019
- 12. 128/9/2019 Solar Energy Resources Modeling Charlotte, NC Boston, MA Boulder, CO Tucson, AZ Time series of Global Horizontal Solar Irradiance data (W/m2)
- 13. 138/9/2019 Solar Energy Resources Modeling Charlotte, NC Boston, MA Boulder, CO Tucson, AZ Boxplots of monthly distribution of Global Horizontal Solar Irradiance (GHI)
- 14. 14 Time series of components of solar irradiance, GHI, DNI, DHI (W/m2) Solar Energy Resources Modeling 8/9/2019
- 15. 15 Global Horizontal Irradiance (GHI) at the plane of array (POA) Solar Energy Resources Modeling 8/9/2019
- 16. 16 Pvlib Toolbox from Sandia and NREL’s SAM package and Weather Data from GFS Global Model https://pvlib-python.readthedocs.io/en/latest/introexamples.html https://pvlib-python.readthedocs.io/en/latest/forecasts.htm To convert the solar irradiance to solar PV power, besides the cloud cover and radiative transfer model, other parameters are considered, such as air temperature at the plane of array, module orientation and efficiency 𝜂 𝑚𝑝𝑝. 𝑃 𝑠𝑜𝑙 ≅ 𝜂 𝑚𝑝𝑝 𝐺𝐻𝐼 𝑃𝑂𝐴 , 𝑇𝑚 𝐺𝐻𝐼 𝑃𝑂𝐴 ∗ 𝐴 Convert GHI at the POA to Solar PV Power PV Module CS5P-220M Manufacturer: Canadian Solar Type: Polycrystalline Cells Power: 220 W (Maximum) Length: 63.1in (1,602mm) Width: 41.8in (1,061mm) Depth: 1.6in (40mm) https://www.solarover.com/panels/cs5p.pdf Solar Energy Resources Modeling 8/9/2019
- 17. 17 Time series of Solar Power (W), for a solar plant with 15*300 PV modules (220W for each) Solar Energy Resources Modeling 8/9/2019
- 18. Charlotte, NC 18 Boston, MA Boulder, CO Tucson, AZ Month0 Wh NCF 1 20036.95 12.24% 2 24185.77 16.36% 3 28973.17 17.70% 4 30208.65 19.07% 5 34770.35 21.24% 6 32713.24 20.65% 7 35418.34 21.64% 8 33992.69 20.77% 9 29731.42 18.77% 10 25504.76 15.58% 11 19138.34 12.08% 12 17866.74 10.93% Month Wh NCF 1 24215.85 14.79% 2 23356.93 15.80% 3 32597.21 19.92% 4 34440.42 21.74% 5 34155.32 20.87% 6 34034.79 21.49% 7 33878.82 20.70% 8 32241.97 19.70% 9 29538.82 18.65% 10 32026.31 19.57% 11 24730.09 15.61% 12 23873.06 14.60% Month Wh NCF 1 26432.06 16.15% 2 26847.92 18.16% 3 34451.63 21.05% 4 36840.41 23.26% 5 38157.38 23.31% 6 35819.85 22.61% 7 35061.05 21.42% 8 34979.76 21.37% 9 33776.53 21.32% 10 31482.96 19.23% 11 25915.03 16.36% 12 25576.46 15.65% Month0 Wh NCF 1 32631.67 19.94% 2 31464.61 21.28% 3 38343.02 23.43% 4 41304.64 26.08% 5 41329.6 25.25% 6 38467.5 24.29% 7 35637.61 21.77% 8 36685.47 22.41% 9 36643.06 23.13% 10 38215.38 23.35% 11 35144.2 22.19% 12 32264.5 19.74% Solar Energy Modeling Typical Meteorological Year (TMY) Calculating the net capacity factor (NCF) for each month, then over the entire year NCF= 𝑇ℎ𝑒 𝑎𝑐𝑡𝑢𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑇ℎ𝑒 𝑝𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑒𝑛𝑒𝑟𝑔𝑦 𝑡ℎ𝑎𝑡 𝑐𝑜𝑢𝑙𝑑 ℎ𝑎𝑣𝑒 𝑏𝑒𝑒𝑛 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 NCF= 𝑇ℎ𝑒 𝑎𝑐𝑡𝑢𝑎𝑙 𝑒𝑛𝑒𝑟𝑔𝑦 (𝑊ℎ) 𝑇ℎ𝑒 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 ∗𝑡𝑖𝑚𝑒 (𝑊ℎ) Solar Energy Resources Modeling For 1 PV module (220W, PV Module CS5P-220M) 8/9/2019
- 19. 19 TMY KWh NCF Charlotte 359.0896 18.63% Boulder 385.341 19.99% Boston 332.5404 17.26% Tucson 438.1313 22.73% Solar Energy Resources Modeling For 1 PV module (220W, PV Module CS5P-220M) Calculating the total energy and net capacity factor, as it is done in the wind energy modeling 0 50 100 150 200 250 300 350 400 450 500 Charlotte Boulder Boston Tucson KWh Yearly Yeild of Solar Energy TMY TMY: Typical Meteorological Year, which means it assumes the same variability of solar output for other years. 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% Charlotte Boulder Boston Tucson Yearly Net Capacity Factor TMY 8/9/2019
- 20. 20 2009 NCF Wind Energy Solar Energy Charlotte 29.10% 18.63% Boulder 24.90% 19.99% Boston 41.40% 17.26% Tucson 21.10% 22.73% 2010 NCF Wind Energy Solar Energy Charlotte 27.00% 18.63% Boulder 20.40% 19.99% Boston 46.50% 17.26% Tucson 22.60% 22.73% Comparison of NCF for Resources of Wind & Solar Energy Wind and Solar Energy Resources Modeling 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% 45.00% Charlotte Boulder Boston Tucson NCF 2009 Net Capacity Factor Wind Energy Solar Energy 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% 40.00% 45.00% 50.00% Charlotte Boulder Boston Tucson NCF 2010 Net Capacity Factor Wind Energy Solar Energy 8/9/2019
- 21. Wind and Solar Energy Resources Modeling 21 Conclusion The performance of wind and solar energy resources depends significantly on their location and weather conditions. Further Work Modeling and evaluate the wind and solar resources backed up by energy storage systems. References 1.https://www.r-bloggers.com/time-series-analysis-with-wind-resource- assessment-in-r/ 2.https://github.com/mhdella/AWEA_WRA_Working_Group/blob/master/Example_Wind_Resource_Assess ment_Using_R.md 3.https://pvlib-python.readthedocs.io/en/latest/introexamples.html 4.Stein, J. S., Holmgren, W. F., Forbess, J., & Hansen, C. W. (2016, June). PVLIB: Open source photovoltaic performance modeling functions for Matlab and Python. In 2016 ieee 43rd photovoltaic specialists conference (pvsc) (pp. 3425-3430). IEEE. 5.Blair, N., Dobos, A. P., Freeman, J., Neises, T., Wagner, M., Ferguson, T., ... & Janzou, S. (2014). System advisor model, sam 2014.1. 14: General description (No. NREL/TP-6A20-61019). National Renewable Energy Lab.(NREL), Golden, CO (United States). 8/9/2019
- 22. Thanks for Listening Any Question? http://epic.uncc.edu/ Mohamed Abuella https://mohamedabuella.github.io Energy Production and Infrastructure Center University of North Carolina at Charlotte