Using Gurobi to Optimize Distributed Energy Storage Assets

Many organizations use optimization within some part of their business. Some companies, such as Advanced Microgrid Solution (AMS), use optimization as an integral part of their solution. AMS provides a software-as-a-service product that optimizes the operation of a clients’ energy storage assets in order to reduce their electric bill, while also meeting contractual obligation to provide capacity to the electric utility. AMS aggregates a number of distributed assets to provide the utility with a virtual power plant that can be used to reduce system peak load. Given the dynamic nature of the electric grid, the problem needs to be solved every 5-10 minutes, requiring a robust, reliable, and scalable solution to manage continual growth.

This 40-minute recording will focus on how AMS integrates Gurobi into their software solutions, including:

  • The business case from a high level
  • The problem formulation as a Mixed Integer Program using Gurobi’s Python API
  • Use of Gurobi server to allow for scaling
  • Tips and tricks for building production level models

This topic is presented by Andrew Martinez from AMS. Andrew is a member of the Data Science and Optimization Team at Advanced Microgrid Solutions (AMS). Prior to joining AMS, he was building optimization models to size generation equipment for campus-scale microgrids as well as large-scale optimization models to answer energy policy questions. He holds a MS in Mechanical Engineering from Stanford University and a BS in Mechanical Engineering from Purdue University.

You can download the materials associated with this webinar here.

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