A battery needs to be actively managed—and monitored—to deliver optimal performance.
Convergent’s PEAK IQ asset management platform is creating maximum value for our customers. We’ve saved our customers over $50M by using state-of-the-art machine learning, artificial intelligence, and advanced analytics to make data-driven decisions about when and how to dispatch energy storage for optimal value creation.
Human Ingenuity + AI + Machine Learning = PEAK IQ
PEAK IQ is the result of nearly 10 years of research, development, and operation.
The decision about when to discharge an asset is determined by both intrinsic and extrinsic factors (e.g., customer load characteristics, utility rate tariffs, wholesale market pricing, grid peak times, weather forecasts). PEAK IQ optimizes the dispatch of the asset through a combination of human ingenuity, artificial intelligence, and machine learning.
PEAK IQ co-optimizes millions of datapoints to determine an optimal dispatch strategy to maximize value across several categories including:
The predictive power of PEAK IQ has been integrated into a single proprietary control platform that allows for seamless, remote operation. PEAK IQ’s user-friendly interface provides data on:
Chief Information Officer + Chief Risk Officer
Chris is responsible for the quantitative analysis of energy markets and grid infrastructure, regulatory and compliance activities, and the design, production, deployment, and maintenance of software systems to optimize storage asset operations and financial returns.
Prior to joining Convergent, Chris spent 8 years as a strategy consultant, both as an independent contractor and as a member of the firm AltshulerGray, LLC, focusing on complex data analysis and developing/implementing evaluation frameworks to meet profit maximization and compliance targets.
Chris graduated with honors from Harvard University with an A.B. in Biology.
Senior Vice President, Asset Management
David is responsible for economic and financial evaluation of potential project opportunities, general quantitative sales support, wholesale and retail tariff evaluation, dispatch and algorithm development, and managing real-time operations.
Prior to joining Convergent, David worked at EnerNOC on the demand response team managing their portfolio in the PJM region. He has experience in day-to-day operations and long-term strategy execution in capacity and energy markets.
David holds a BA from Dartmouth in Mathematics and Economics.
Data Scientist, Data Science and Machine Learning
Nico leads the development, optimization, and deployment of Convergent’s real-time asset dispatch algorithms, and designs data pipelines for machine learning experimentation, analysis, and platform integration.
Prior to joining Convergent, Nico worked as a Senior Data Analyst at Enel X, where he led development of an asset dispatch co-optimization engine that was relied upon to grow the burgeoning distributed energy business within the company.
Nico holds an MS in Applied Urban Sciences from New York University (CUSP) and a BA in Physics from Boston University.
