Estimating the behind-the-meter solar
generation with existing infrastructure
Emre Can Kara,
Michaelangelo Tabone, Sila Kiliccote

AI-at-SLAC
July 11, 2017

Our team
Science Directorate > Applied Energy Program:● Materials for energy● Subsurface Technology● 21st Century Electric Grid

gismo.slac.stanford.edu●●Team of 15 researchers, scientists
and engineers.
Major funding:○ DOE EERE
○ CEC EPIC
○ ARPA-e2

What do we do @GISMo?

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Integrating Renewable Energy

Supply=DemandTransmission Grid

Distribution Grid4

Integrating Renewable Energy
generation

timeSupply=DemandTransmission Grid

Distribution Grid
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VADER: Visualization and Analytics forDistributed Energy Resources
Enabling high penetration of renewables requires:●●

More comprehensive and dynamic situational awareness; andActive control of the distribution network.

Traditional approach/technology is inadequate:●●●

Partial observability
New devices and models
Heterogenous data sources and data streams

6Plenty of data that is not being used!
Interval meters
Synchrophasors
Smart meters
Micro-synchrophasors

Line sensors

B2GPowerSystem
VGI

μgridsBuildings& DERs

Trend logs frombuilding
management
Systems (BMS)V2BMobility
B2G – Building to Gridμgrids – Microgrids
V2B – Vehicle to Building
VGI – Vehicle to Grid

Charging sessions

Driving patterns
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VADER Introduction
Visualization and Analytics of Distributed Energy Resources

Funded by:

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VADER Analytics

VADER key analytics pieces:▪ Solar disaggregation▪ Switch configuration▪ ML-based power flow analysis▪ Network topology detection▪ Load forecasting9

VADER Infrastructure

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VADER Infrastructure●Polyglot persistence:○ Why?

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Introduction▪Increasing solarpenetration– Behind-the-meter– Distribution-level▪Switches maintain aradial structure▪Load is masked▪Visibility intobehind-the-meter solargeneration is limited
How do we gain more visibility into the load and solar generation?

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Introduction▪Traditional approaches include:– Physics-based models:▪ Geometry of the array▪ Nameplate capacity▪ Site-specific irradiance measurements (diffuse horizontal, direct
normal, and global horizontal irradiance)– Costly to obtain DHI and DNI often only GHI is available▪ Plane-of-array (POA) irradiance estimation errors:– Typically~10-13% RMSECan we use existing measurements that are collected by the▪ Satellite-driven estimation of irradianceutilities to estimate behind-the-meter solar in real-time?
– Typically~20%
RMSE these estimates would be?
If so, howaccurate– Monitoring:▪ Could be costly to do in real-time.
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Problem formulation▪Contextually supervised source separation*:– Observing an aggregate measurement of signalsof interest*Wytock, Matt, and J. Zico Kolter.
"Contextually Supervised Source
Separation with Application to Energy
Disaggregation." AAAI. 2014.

– Each signal can be represented as a linear model– Contextual knowledge about signal characteristics– Used in Non-intrusive Load Monitoring
Error between the estimates ofthe signal and what the linear
model would doRegularization

Contextual knowledge
on the shape ofindividual signals
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Problem formulation
real-time SCADA measurements (typically 4 seconds)
AMI: overnight updates(1-min to 15-min sampling rate)

Model15

Problem formulation
Day-ahead training problem (learning the model):InputsAMI dataSparse # of solar sensors+ Outdoor Temp.

=16Outputs
Load and solar at each meter

Problem formulation
Need to be tuned

Aggregate load
is much morepredictable17

Problem formulation
Creation of aggregate load regressors:18

Tuning alphas▪Intuitive choice: the inverse of the expected variance ofmodel's errors▪During nighttime there is no solar generation– Net load = Aggregate Load
– Estimate the error for the AL model▪During day time we can estimate the total error.
▪Assuming the all errors are independent:

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Problem formulation
Real-time estimation problem:

Input
Data from load aggregation point (substation)
Solar from sparse # of sensors
Outdoor Temp.

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Outputs
Solar at each meter

Problem formulation

Observedthrough SCADA

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Dataset
Subset of Pecan Street
dataset:▪ Load and solar
sub-metered at
1-minute resolution▪ 110 households inTX
▪ Total duration 7-days▪ Divided into:– 55 homes with solar– 55 homes withoutsolar– Varying number of
distribution-level solar(used as proxy)22

Results▪ We use coefficient ofvariation for homes
with solar.
▪ We report only
RMSE for homes
without solar.

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Training Results (Untuned vs. Tuned)24Real-time Results (Untuned vs. Tuned)25

Conclusions & Future Work
▪ Useful tool to estimate behind-the-meter solar in real-time.
– Can also be used to select the optimal number of proxies and theirlocations for a guaranteed performance of estimation.

▪ Incorporate losses in the network▪ Expanding the disaggregation strategy to incorporate
storage▪ Running the model on a larger network in SCE’s territory
through our partnership in VADER.

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Thank you!

Michaelangelo Tabone
Post-doctoral Fellow
Stanford University,
SLAC National Accelerator
Laboratory
mtabone@stanford.edu

Emre Can Kara
Associate StaffScientist
SLAC National
Accelerator Laboratory
ekara@stanford.edu

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Sila Kiliccote
Staff Scientist
SLAC National
Accelerator Laboratory
silak@stanford.edu

Additional Slides

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