energycioinsights

Integral Analytics: Optimizing Electrical Grid for Distributed Resources

Tom Osterhus, PhD, CEO, Integral AnalyticsTom Osterhus, PhD, CEO
Historically, electric power was a “one-way” street, where large, multi-generational infrastructure investments sent power from a franchised monopoly producer downstream to residential, commercial and industrial consumers. Today, with the propagation of customer-sited assets, like solar, batteries or curtailment assets, the customer has become a counterpart to the utility, with the potential to displace or sell back energy and create a bilateral relationship. Within this evolution and due to the proliferation of devices governing the grid edge, there is a growing requirement to harness vast amounts of performance data which is essential to maintaining reliability at lowest cost. Integral Analytics’ (IA) software was built as a productized database and analytics platform to manage these transactions and help deploy the flexible grid of the future.

The company’s software products have been assembled to leverage proliferation of distributed resources and their streams of data along with the desire to transact with each other and the utility. CEO, Tom Osterhus states, “We built our software applications to ingest the terabytes of historical customer and grid performance data that inform forecasts of future use and optimal location and value of distributed resources.” IA’s software provides a multi-year ‘blueprint’ for growth on the grid, by location, for planning and capital deployment to efficiently match future needs by corresponding the expected attributes of customer need with known characteristics of potential production assets.

As the energy industry evolves, utilities want dynamic products that they can configure and grow with their needs to forecast load, prices and execute programs to manage their distribution systems better. IA offers two primary products; LoadSEER for the distribution planning and resource integration groups and IDROP, for near-real time operations of grid edge portfolios, to manage these emerging needs.

We built our software applications to ingest the terabytes of historical customer and grid performance data that inform forecasts of future use and optimal location and value of distributed resources


LoadSEER provides a comprehensive T&D spatial load forecasting application that accommodates risk analysis and power flow modeling and integrates resource planning with demand-side management (DSM) measures. In addition, IDROP has been created to dynamically manage the distributed resources on the grid to find lowest cost performance within given reliability constraints. IDROP ingests data, calculates the optimal provisioning of services by assets within the system and publishes that “script” to any governance system employed by the utility to execute the system balancing.

One of IA’s clients, Pacific Gas & Electric (PG&E) in California—a combined natural gas and electric utility has been a dedicated user of IA’s LoadSEER software. Several dozen engineers in various areas of the utility leverage IA’s scenario analysis capabilities to “stress test” sections of the grid, providing stakeholders and regulators the value and capacity to host higher penetrations of solar assets on the system. PG&E has adopted LoadSEER as core to its planning environment and uses the software to understand the impacts of a “multi-owner” energy portfolio.

Applied data science within scale data architecture is the only way to ensure that utilities and other participants in the distributed energy markets may make capital and system decisions based on the most granular sets of data and understand the interaction between utility and customer. “The complexity of these interactions at the edge require a platform approach that may house and compute against a multitude of scenarios; we will continue providing the proven software on which utilities and others may rely to embrace a complex future,” concludes Osterhus.