1. Breakout Session on Smart Grid Data Analytics
Bert Claessens, Nicolas Gast, Christoph Goebel, Mario Paolone, Anthony Papavasiliou, Jose Rivera, Joachim Sokol, Andreas Veit, Holger Ziekow

2. Managed wholesale electricity markets
Business Level
Managed wholesale electricity markets
Retailer
Conventional genco
Industry
Aggregator
Renewablegenco
TSO
DSO
Information Systems Level
Physical Level

3. Dimensions Stakeholders Data sources Applications Data characteristics
State of the art
Opportunities
Data characteristics
Volume, granularity, velocity, structure

4. Transmission Grid Operators
Data sources:
RTUs
PMUs
Weather data
Historical values and derived system states
Current applications:
Real-time state estimation
Contingency analysis (n-1, critical events)
Model adjustment (Y-Matrix)
Visualization using outcome of 1. and 2.
Operational decision support based on 1. and 2.: switching, reserve activation, redispatch
Long-term planning
High volume (TBs per day?), high velocity (up to 10kHz)

5. Transmission Grid Operators
Opportunities:
Deployment of more PMUs  more data to store and process
More frequent state estimation  up to 30 Hz?
Probabilistic analysis of system trajectory based on forecasted input values  Better modeling based on available data and data available in the future
Advanced planning (impact of renewables etc.)

6. Distribution System Operators
Low volume, low velocity (1 Hz)?
Data sources:
RTUs in substations
Smart meters
Current applications
Voltage regulation (tap transformers)
Switching
Long-term capacity planning (transformers, lines, etc.; trade-off between control and capacity expansion)

7. Distribution System Operators
Opportunities:
More monitoring and control to improve operations, maybe even PMUs deployed in distribution grid
Advanced voltage control using voltage regulators, energy storage, etc.?
Advanced capacity planning using data from smart meters, solar PV inverters, etc.?

8. Low volume (few resources, power only), low velocity (< 1Hz)
Aggregators
Data sources:
Resource state
Smart meters
Resource models
Market data (prices, bids, etc.)
Current applications:
Market participation (secondary reserve, spot market)
Low volume (few resources, power only), low velocity (< 1Hz)

9. Aggregators Opportunities:
Use resource meta and monitoring data to fit advanced resource capability models
Advanced predictive resource assessment via correlation of resource capability with user behavior and environmental data (occupancy, weather, etc.)
From industrial loads and bio gas plants to homes, EVs, etc.

10. Low volume (few resources), velocity?
Renewable Gencos
Data sources:
Monitoring data from generators
Current applications:
Configuration (e.g., operational modes of wind turbines)
Health monitoring
Low volume (few resources), velocity?

11. Renewable Gencos Opportunities:
Advanced control of generators (maximum power point tracking, etc.)
Output forecasting for full market participation
Predictive maintenance (mechanical and power electronics parts)

12. Low volume (many customers, but few data points), low velocity
Utilities / Retailers
Data sources:
Meter data
Customer data
Current applications:
Billing
“Business intelligence” for marketing and sales
Low volume (many customers, but few data points), low velocity

13. Low volume (few metrics), low velocity
Prosumers
Data sources:
Smart meters
“Personal sensors”, e.g., cell phones, smart home devices
Current applications:
Consumption feedback  energy efficiency
Bill savings via time of use tariffs
Low volume (few metrics), low velocity

14. Prosumers Opportunities:
Turn buildings into microgrids by adding local generation and storage
PV prices already lower than electricity end user prices
Battery storage cost declining
Develop systems for optimal control by taking relevant metrics into account (weather, occupancy, desired comfort levels, schedules, etc.)

15. Summary Data volume Data velocity TSOs DSOs Aggregators
Renewable Gencos
Data volume
DSOs
Prosumers
TSOs
Renewable Gencos
Aggregators
Retailers
Prosumers
Data velocity