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

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

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

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)

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.)

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)

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.?

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)

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.

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?

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)

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

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

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.)

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