Third Annual SERI Conference Demonstrations

The following analytics applications demonstrated in the IBM Research THINKLab, during the SERI Conference, address a wide range of pressing problems for energy utilities across five research tracks: outage planning optimization, asset management optimization, wide-area situational awareness, integration of renewables and distributed energy resources, and the participatory network. Please contact Amy Schneider if you are interested in further information about these projects.

Asset Risk Management and optimized Repair-Rehab-Replace (ARMOR3) demo

Asset Risk Management and Optimized Repair-Rehab-Replace (ARMOR3)

ARMOR3 applies predictive and prescriptive analytics on big data to identify, quantify and ultimately optimize infrastructure maintenance and planning for all electrical assets including transformers, cables, poles, circuits. ARMOR3 converts data into information, insight and foresight with the aim of providing decision support across the complete electric infrastructure.

ARMOR3 provides the ability to run a broad set of scenarios on the same detailed data, prioritizing across multiple teams / groups. It offers predictive maintenance to identify and fix the next failure before it happens, and generates asset risk and investment profiles to enable 100% utilization (useful life) of the asset while taking into account resource constraints.

Connectivity models demo

Connectivity Models

Using advanced analytics on meter measurements, the Connectivity Models application infers customer phase and customer-to-transformer connectivity, which is generally inaccurate or unknown.

An accurate and sustainable connectivity model is a key enabler of capabilities needed to improve the reliability and efficiency of the distribution grid. Utility efforts to build and verify their connectivity models are labor and resource intensive. The analytics approach will help to radically lower the cost of such processes.

Customer Intelligence Demo

Customer Intelligence

Through data-driven analytics, Customer Intelligence provides advanced customer segmentation capabilities for utilities to better understand their customers and the impact on utility operations.

Such customer insights will help a utility transform the relationship with customers, improving:

  • The effectiveness of campaigns and pilot programs by smarter targeting
  • Grid stability by understanding changes in customer dynamics such as Demand Response Behavior, Adoption of Renewables and Plug-in Vehicles
  • Revenue protection by more accurately detecting energy theft
Outage Prediction and Response Optimization (OPRO)

Outage Prediction and Response Optimization (OPRO)

OPRO uses advanced weather prediction, predictive damage estimates, and optimized crew positioning and response planning to improve a utility's preparation for and response to weather-related power outages.

With more than $14B in total annual lost value of service due to storms in the U.S. alone, improvements in outage restoration and reduction in operational costs would lead to significant value for the utility, in terms of both economic value and improved customer satisfaction.

Transactive Energy Management

Transactive Energy

Transactive Energy Management is the use of economic and control mechanisms that allow the dynamic balance of supply and demand across the entire electrical infrastructure using "value" as a key operational parameter. All business and operational objectives and constraints can be assigned positive or negative values and be incorporated into these transactive signals.

Vermont Renewable Energy Integration

Vermont Renewable Energy Integration

The initial application of the Vermont Weather Analytics Center (VTWAC) focuses on integration of renewable power (wind and solar). The system is composed of several components, which use an advanced weather prediction capability as a foundation. This physics-based weather model is coupled to data-driven models of electricity demand, wind power and solar power. The outputs of these probabilistic models are used to assess the uncertainty in the predictions and to drive a stochastic engine to assist the utility in avoiding congestion and improving the stability of the transmission network.

Demand forecasting by means of data-driven technique
RACE (Risk Analytics for Critical Energy)

Risk Analytics for Critical Energy (RACE)

RACE enables a reliable operation of the critical infrastructure by providing holistic analytics capabilities including leak detection and condition based maintenance:

Wide-Area Situational Awareness (WASA)

Wide-Area Situational Awareness (WASA)

WASA uses descriptive and prescriptive analytics to interpret and summarize electrical events in the transmission system, and provide insights during post-event analysis. It also uses predictive analytics to provide early warning indicators of complex events that could affect grid stability and operations.

WASA seeks to identify grid anomalies and alert operators to act before disturbances such as geomagnetically induced current (GIC) events lead to grid collapse. The application also provides low-latency and high-throughput monitoring, archiving, reporting, advanced querying, and visualization of the grid state.