Maximize Value

with PEAK IQ© 

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

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How PEAK IQ Works

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.

Applications for PEAK IQ

PEAK IQ co-optimizes millions of datapoints to determine an optimal dispatch strategy to maximize value across several categories including:

  • Energy Arbitrage
  • Capacity (Local & System)
  • Solar / Renewables Firming
  • Infrastructure Deferral (Non-Wires Alternative Program Compliance)
  • Ancillary Services (Frequency Regulation, Operating Reserves)
  • Coincident Peak (CP) Demand Charge Avoidance (“Peak Hitting”)
  • Non-Coincident Peak (NCP) Demand Charge Avoidance (“Load Management”)

The PEAK IQ Customer Experience

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:

  • Total Savings
  • Real-Time Asset Operations
  • System State of Charge
  • ITC & REC Compliance
  • Carbon & Emissions Reductions
  • Battery Health Statistics

Who is Leading Peak IQ?

Chris Streeter

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.

David Nie

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.

Avital Szulc

Vice President, Data Science and Machine Learning

Avital is responsible for leading the development and optimization of Convergent’s real-time asset dispatch algorithms and their software integration.
Prior to leading Convergent’s data science and machine learning efforts, Avital led the development of Convergent’s Asset Management Platform, including its design, engineering, testing, and deployment.

Before joining Convergent, Avital worked as a product manager at A.F. Mensah, a distributed energy technology and development company. While at A.F. Mensah, Avital was responsible for the development of its asset management platform and prototyping of optimal asset dispatch algorithms.

Avital holds an M.S.E in Operations Research from Cornell University (Cornell Tech) and a B.S.E in Chemical Engineering from the University of Pennsylvania.

Optimize asset performance with PEAK IQ

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