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LBNL Tracking the Sun

Description

Berkeley Lab’s Tracking the Sun report series is dedicated to summarizing installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The present report, the 11th edition in the series, focuses on systems installed through year-end 2017, with preliminary trends for the first half of 2018. As in years past, the primary emphasis is on describing changes in installed prices over time and variation in pricing across projects based on location, project ownership, system design, and other attributes. New to this year, however, is an expanded discussion of other project characteristics in the large underlying data sample. Future editions will include more of such material, beyond the report’s traditional focus on installed pricing.

The trends described in this report derive primarily from project-level data reported to state agencies and utilities that administer PV incentive programs, solar renewable energy credit (SREC) registration systems, or interconnection processes. In total, data were collected and cleaned for more than 1.3 million individual PV systems, representing 81% of U.S. residential and non-residential PV systems installed through 2017. The analysis of installed pricing trends is based on a subset of roughly 770,000 systems with available installed price data.

A technical summary of the dataset is as follows:

Focuses on projects installed through 2018 with preliminary data for the first half of 2019:

  • Describes and analyzes trends related to Project characteristics, including system size and design, ownership, customer segmentation, and other attributes
  • National median installed prices, both long-term and recent trends, focusing on host-owned systems
  • Variability in pricing across projects according to system size, state, installer, module efficiency, inverter technology, residential new construction vs. retrofit, tax-exempt vs. commercial site hosts, and mounting configuration
  • Distributed PV for the purpose of this report, includes residential and non-residential systems that are roof-mounted (of any size) or groundmounted up to 5 MWAC

Tracking the Sun relies on project-level data:

  • Provided by state agencies and utilities that administer PV incentive programs, renewable energy credit registration (REC) systems, or interconnection processes
  • Some of these data already exist in the public domain (e.g., California’s Currently Interconnected Dataset), though LBNL may receive supplementary fields, in some cases covered under non-disclosure agreements
  • 67 entities spanning 30 states contributed data to this year’s report (Table A-1 in the Appendix of the report )

Customer Segments

  • Residential: Single-family and, depending on the data provider, may also include multi-family
  • Small Non-Residential: Non-residential systems ≤100 kWDC
  • Large Non-Residential: Non-residential systems >100 kWDC (and ≤5,000 kWAC if ground-mounted) * Independent of whether connected to the customer- or utility-side of the meter

Units

  • Real 2018 dollars
  • Direct current (DC) Watts (W), unless otherwise noted

Installed Price: Up-front $/W price paid by the PV system owner, prior to incentives

Sample Frames and Data cleaning Full sample: (Used to describe system characteristics, the basis for the public dataset)

  1. Remove systems with missing size or install date
  2. Standardize installer, module, inverter names
  3. Integrate equipment spec sheet data – Module efficiency and technology type – Flag microinverters or DC optimizers
  4. Convert dollar and kW values to appropriate units, and compute other derived fields

Installed-Price Sample: (Used in analysis of installed prices) 5. Remove systems if: – Missing installed price data – Third-party owned (TPO) – Battery storage included – Self-installed

Directory Structure

The Tracking the Sun Dataset is made available in Parquet format on AWS and is partitioned by state in AWS Glue and Athena. The schema may change across dataset years on S3.

  • s3://oedi-data-lake/tracking-the-sun/2019/

python Connection examples

import pandas as pd
from pyathena import connect

conn = connect(
    s3_staging_dir='s3://<user-defined>/tracking-the-sun', ##user defined staging directory
    region_name='us-west-2',
    work_group='<USER SPECIFIC WORKGROUP>'  ##specify workgroup if exists
)

df = pd.read_sql("SELECT * FROM oedi_tracking_the_sun_2019 limit 8;", conn)

For jupyter notebook example see our notebook which includes partitions and data dictionary: examples repository

Data Dictionary for 2019 Dataset:

Available States
1. AR
2. AZ
3. CA
4. CO
5. CT
6. DC
7. DE
8. FL
9. IL
10. KS
11. MA
12. MD
13. ME
14. MN
15. MO
16. NH
17. NJ
18. NM
19. NY
20. OH
21. OR
22. PA
23. RI
24. TX
25. UT
26. VT
27. WI
28. WA

data_provider string
system_id_from_first_data_provider string
system_id_from_second_data_provider_if_applicable string
system_id_tracking_the_sun string
installation_date date
system_size double
total_installed_price double
appraised_value_flag boolean
sales_tax_cost double
rebate_or_grant double
performance_based_incentive_annual_payment double
performance_based_incentives_duration int
feed_in_tariff_annual_payment double
feed_in_tariff_duration int
customer_segment string
new_construction int
tracking int
ground_mounted int
battery_system int
zip_code string
city string
utility_service_territory string
third_party_owned int
installer_name string
self_installed int
azimuth_1 double
azimuth_2 double
azimuth_3 double
tilt_1 double
tilt_2 double
tilt_3 double
module_manufacturer_1 string
module_model_1 string
module_manufacturer_2 string
module_model_2 string
module_manufacturer_3 string
module_model_3 string
additional_module_model int
module_technology_1 string
module_technology_2 string
module_technology_3 string
bipv_module_1 int
bipv_module_2 int
bipv_module_3 int
module_efficiency_1 double
module_efficiency_2 double
module_efficiency_3 double
inverter_manufacturer_1 string
inverter_manufacturer_2 string
inverter_manufacturer_3 string
inverter_model_1 string
inverter_model_2 string
inverter_model_3 string
microinverter_1 int
microinverter_2 int
microinverter_3 int
system_inverter_capacity double
dc_optimizer int
inverter_loading_ratio double
state string\

References

https://emp.lbl.gov/sites/default/files/tracking_the_sun_2019_report.pdf

https://emp.lbl.gov/sites/default/files/tracking_the_sun_2019_slide_deck_summary_0.pdf

https://emp.lbl.gov/tracking-sun-tool

Disclaimer and Attribution

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