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Photovoltaic Resource Modeling for Long-term Simulation

Photovoltaic Resource Modeling for Long-term Simulation. Rajesh Bhana Power and Energy Systems Research Group. Photovoltaic production and load. Sources: ERCOT and NREL. Probabilistic simulation framework. Nature of the problem.

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Photovoltaic Resource Modeling for Long-term Simulation

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  1. Photovoltaic Resource Modeling for Long-term Simulation Rajesh Bhana Power and Energy Systems Research Group

  2. Photovoltaic production and load Sources: ERCOT and NREL

  3. Probabilistic simulation framework

  4. Nature of the problem • Incorporate photovoltaic (PV) production in a probabilistic simulation framework • Identify patterns of PV production • Capture the variable, intermittent and time-dependent characteristics of PV production

  5. Instantaneous PV production model

  6. Time-dependent input

  7. Input and output

  8. Variability of output

  9. Variability of output

  10. Benchmark

  11. Benchmark input and output

  12. Variability of the benchmark

  13. Drivers for scaling • Ability to effectively compare data of different magnitudes and durations • Computational tractability in the representation of the time-dependent, variable and intermittent PV output data

  14. Benchmark

  15. Benchmark magnitude and duration

  16. Scaled output

  17. Time grid

  18. Scaled output averaged over time grid

  19. Two days with a similar pattern

  20. Output over a year

  21. Pattern identification • Separate data into four seasons • Groups (clusters) with associated probabilities • K-means clustering

  22. Euclidean distance • Distance between two output characterizations • Sum of the square of the differences

  23. Summer pattern identification

  24. Summer pattern centroids

  25. Sensitivity of error to K

  26. Summer pattern classification

  27. Summer classification centroids

  28. Pattern sample output

  29. Rescaling

  30. Hourly time grid

  31. Output averaged over each hour

  32. Summary of scaling and rescaling

  33. Multiple sites

  34. Incorporation of probabilistic model into framework • Load conditioned over each hour • PV production subtracted from the load • ‘Controllable load’ • Representative weeks cover shorter durations • Framework applied to obtain reliability, economic and environmental effects

  35. Future Work • Determine a more accurate ‘clear-day’ • Develop and apply a model to represent distributed PV sources • Apply a solar thermal model • Capture storage capability

  36. Radiation measurement devices Pyranometer Pyrheliometer http://www.volker-quaschning.de/fotos/messung/index_e.php

  37. PV production model

  38. Model variability

  39. K-means clustering • K clusters • Result dependent on initial centroid • Euclidean distance • Iteratively: • Characterizations are grouped in clusters • Cluster centroids are recalculated • Process continues until there is no change in the membership of the clusters.

  40. k-means clustering example • Four data points, two clusters • Data points: {1, 3, 8, 10} • Initial centroids: 1 and 3 • Allocation: 1 {1} and 3 {3, 8, 10} • New centroids 1 and 7 • Allocation: 1 {1, 3} and 7 {8, 10} • New centroids: 2 and 9 • Allocation: 2 {1, 3} and 9 {8, 10} • No change in allocation => Complete

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