1. Advanced Analytics to Understand Impact of the Integrated Customer
Sudeshna Pabi. PhD Senior Scientist
Austin Electricity Conference April 13,2018

2. The Integrated Grid

3. Examples of existing activities – EPRI Power Delivery and Utilization
T&D Data Analytics Applications
Asset Management
The Connected Customer
DMD and TMD
Data Repository e.g. AMI
Smart Thermostats, Connected Customer Technology Working Council, Customer Model of the Future
Advanced Electric Communities
EMCB
Renewables
Asset Management – Industry repositories
Advanced Sensors

4. Integrating the Customer
Understanding the customer
Efficiency/Electrification opportunities
Flexibility Characteristics
Customer services
Customer model of the future

5. Bottom Up Modeling approach
Customer Model for each Customer

6. Intelligent efficiency is no longer static but dynamic
Smart Thermostat
Solar & Storage
All dispatchable
Controllable Loads
Smart Heat Pump Water Heater
Combining Intelligent efficiency with electrification, controllable loads, local generation and storage

7. Example 1: Smart homes: impact of customer behavior and choices
20 high performance homes delivering data every minute.
Possible Research questions:
What is the combined impact of these DERs on the load shape? (if not then could be )
What is the impact on distribution systems
What is the energy use impact?
How do homes perform compared to building models
Can we get more insight into deviant behavior using deep data measurements? For example, ca we correlate HVAC consumption differences with set point behavior?
What are the main energy users over the long term? How can we use this data to develop EE programs?
What are possible behavioral adaptation measures that we can provide to impact energy use in high performance homes?
Inside a connected home

8. Behavioral and Device Choices

9. Analysis of peak impacts
Figure details the hours at which the resistive heating elements within the HPWHs turn on, a large load which is again controlled by customer behavior. As an excessive amount of hot water is used, the resistive heating element will turn on, drastically increasing power consumption by 1000s of kW and causing an unexpected spike in usage. By analyzing trends in customer behavior, strategies to shift individual loads, such as water heating, can be planned and tested.

10. Example 2: Decarbonization from Electrification
Scenarios Considered:
Mixed Fuel: Gas Heating and water heating + gas appliances (dryer, cooking, fireplace)
Gas: Electric Heating and water heating + gas appliances (dryer ,cooking, fireplace)
Electric: All electric heating and appliances
Starting Point:
Hourly generation data for California for (source: CAISO)
Collected system CO2 emission per KWh with CAISO heat rate
Calculated building level hourly CO2 emissions taking into account
Electric gas transmission losses (CPUC report, 2017)
Gas Losses from Cottingham et. Al PG&E study (fugitive methane not included)
Example File of Hourly Carbon Emissions

11. Total Generation and Emission Timeseries
---- Total GHG emissions
Disconnect between generation and emissions:
highest emissions rates are in the nighttime in fall
lowest rates are in the daytime in spring.
corresponds to solar PV
Incremental increase in hourly load from ZNE corresponds to bulk emissions rate at that hour
Assuming one single entity such as a ZNE community is not significant in itself to change the bulk grid emissions rate).
So energy use at nighttime in fall increase emissions more in percentage terms vs. energy use during midday in spring.
This does not correspond with the TDV values and has significant implications for the driving statewide goals of reducing carbon emissions in California.
For instance, if an energy storage system is optimized for TDV, then it will charge at night and discharge during the day. However, that same operating algorithm that could enable ZNE could potentially end up increasing carbon

12. Emissions for the 3 scenarios: By Month and by year

13. Ongoing work
Extending the forecasts to future years using Machine Learning, such as recursive neural network
Using Machine Learning for NILMS

14. A Person on a beach flying a kite
How would you characterize this picture?
Within two seconds, Google created a caption describing it as an image of a person on a beach flying a kite.
A Person on a beach flying a kite

15. Traffic Light Hanging from a Wire
Tall Brown Grass
Microsoft referred to the image on the right as a traffic light that’s hanging from a wire. It got the wire right.
Microsoft CaptionBot referred to the image on the right as I think it is a tall brown grass. It got the grass right :); Google cloud vision corrent

16. Deteriorated Wooden Pole Top Corroded Transmission Tower
What is the EPRI Power Vision API could correctly recognize the flaws in the picture emulate the expertise of a line inspector?
We can then do automated line inspections, vegetation management, damage assessment after a storm and many other applications
Deteriorated Wooden Pole Top
Corroded Transmission Tower