Smarter Energy Research Institute (SERI)

2013 Inaugural Conference

The Smarter Energy Research Institute

The inaugural SERI Conference was held at the T.J. Watson Research Center, June 11-12, with the founding utility members (DTE Energy, Alliander, and Hydro-Québec), as well as other large electric and gas utilities.

The inaugural Smarter Energy Research Institute (SERI) Conference, held June 11-12, 2013, launched the next generation of analytics for the energy and utilities industry. The founding SERI members and IBM Research staff co-led the unveiling of seven new applications across the institute's innovation tracks.

Master inventor Dean Kamen gave the keynote address about his work in bringing clean water and electricity to the developing world. Kamen also encouraged the technology and utility leaders in the audience to share their passions with the next generation of young scientists and engineers and to participate in Dean’s not-for-profit program, FIRST.

Dean Kamen

Dean Kamen

IBM's Vijay Arya and DTE Energy's Richard Mueller describe the Connectivity Models application IBM's Vijay Arya and DTE Energy's Richard Mueller describe the Connectivity Models application.
IBM's Jinjun Xiong and Alliander's  Jeroen Schuddebeurs describe the OPAMCI application IBM's Jinjun Xiong and Alliander's Jeroen Schuddebeurs describe the OPAMCI application
Outage Prediction and Response Optimization (OPRO) demo screenshot

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.

Wide-Area Situational Awareness (WASA)

Wide-Area Situational Awareness (WASA)

WASA seeks to identify grid anomalies and alert operators to act before cascading failures that lead to massive black outs. The application will also provide low latency and high throughput monitoring, archiving, reporting, advanced querying and visualization of the grid state.

WASA offers significant potential value in helping to avert large-scale blackouts through greater grid state awareness. To provide an example of the magnitude of such events, the 2003 North American blackout had an estimated total economic cost of more than $6 billion.

Wind and Hydro Integrated Stochastic Engine (WhISE)

Wind and Hydro Integrated Stochastic Engine (WhISE)

WhISE is an energy generation planning solution that enables a high percentage of renewable integration. It models the uncertainty of renewables and helps trade off the impact of demand mismatch with the cost of generation unit commitments.

Current practices rely on high levels of reserves to ensure power availability across all reasonable scenarios. The WhISE approach allows for significant reductions in reserves with better demand matching resulting in cost savings.

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
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.

Complementary Applications in Asset Health Optimization

Utilities are among the most capital intensive industries in the world, and even modest improvements in asset maintenance can lead to very large cost savings. Asset health optimization employs advanced analytics to identify the most vulnerable assets and to optimize maintenance and capital investment planning. The following applications are pursuing complementary approaches to asset health.

Optimized Planned Asset Maintenance and Capital Investment demo

Optimized Planned Asset Maintenance and Capital Investment (OPAMCI)

OPAMCI improves visibility into utility asset health conditions based on existing partial instrumentation results and power flow simulation, enabling better asset maintenance, capital investment optimization, and deferment of instrumentation rollout.

OPAMCI aims to reduce the outage cost associated with asset failure by more than 10% through optimizing asset maintenance and replacement schedules. It also aims to reduce the need for expensive instrumentation as an alternative path to such asset health insight.

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.