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

The Integrated Grid

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

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

Bottom Up Modeling approach Customer Model for each Customer

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

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

Behavioral and Device Choices

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.

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 2017 (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. 1989 PG&E study (fugitive methane not included) Example File of Hourly Carbon Emissions

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

Emissions for the 3 scenarios: By Month and by year

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

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

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

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