AutoGrid is Turning the World’s Energy Data into Power

AutoGrid is using predictive data analytics to optimize and manage the ever-evolving energy grid of the 21st Century

The electricity grid of the 21st century is fundamentally transforming, and AutoGrid is using predictive data analytics to help optimize the grid of the future. Historically, electricity was generated by several large power plants (coal, natural gas, nuclear, etc). In contrast, today’s grid is also powered by large scale solar and wind assets, as well as small on-site solar and storage, electric vehicle batteries, demand response programs, etc. These new energy flows are distributed, multi-directional and increasingly complex, presenting a major challenge for utilities trying to manage their grids. The data flowing across the network has become equally important as the electrons flowing through the transmission lines. Autogrid’s mission is to harness this energy data to optimize the grid and turn the world’s energy data into additional power.

Value Creation through Predictive Data Analytics: AutoGrid’s algorithms process petabytes of data coming from millions of connected energy-related assets and devices. The data is organized and analyzed to predict, optimize, and control these energy resources. The algorithms can forecast energy supply and demand and dispatch assets efficiently and automatically, helping energy providers balance their decentralized and complex energy grids. This ensures grid stability and reliability and creates value by carefully optimizing each individual asset.

Value Capture: AutoGrid’s primary customers are those responsible for managing the flow of electricity through the grid (utilities and large energy developers). AutoGrid captures value by selling their products with a B2B SaaS revenue model.  The utility’s customers (energy consumers) are an important secondary customer because there is increasing pressure across the utility industry to provide superior customer service. AutoGrid sells a customer engagement tool that provides consumers real-time energy data helps optimize assets so that consumers can get the best return on their connected devices, thereby reducing the utility bill. The customer engagement tool is also sold to the utility and offered to the end consumer through their utility.  There is no direct B2C business model, which would complicate the business model.

Challenges Overcome

  • Energy grids are mission critical to maintaining a functioning society and economy. As such, the utilities responsible for managing the grid and providing electricity are generally risk adverse and wary of new technologies. Therefore, it is challenging for new software companies to establish win early contracts and establish a track record of success that can catalyze future contracts.  AutoGrid has won contracts with major energy developers and utilities across the U.S, Europe, and Asia.
  • Managing an electricity grid is both incredibly complex and high stakes. The supply and demand for electrons flowing through the system must always match perfectly. This becoming even more complex multi-directional, distributed energy flows. It is a HUGE real-time data and controls challenge to create an algorithm that can accurately manage the grid. Stakes are high, and mistakes are not permissible. AutoGrid appears to be one of the few companies capable of taking on this challenge.

Differentiation:

  1.  AutoGrid’s competitors offer software solutions that are tied to a single, specific type of hardware. In contrast, AutoGrid’s software is hardware agnostic, such that their software can be used to optimize all the connected hardware devices that the utility needs to manage. This streamlined optimization significantly increases the customer value proposition versus that of competitors.
  2. Most traditional data management requires the data to be standardized and structured. However, Autogrid accepts unstructured data. This means that is can collect data from a wide variety of different sources, organize the data, and then make sense of it.
  3. AutoGrid’s competitors are analyzing retroactive data and informing their customers about historical data. This does provide value to customers, who do not have easy visibility into energy use. However, AutoGrid not only assesses historical data, but also incorporates real-time data, as well as environmental information such as weather forecasts and voltage conditions. AutoGrid’s algorithms use all of these inputs to predict future energy use, helping customers optimize the grid and reduce risk.

Currently, AutoGrid has the most sophisticated predictive data analytics tools in the energy industry and is successfully executing on their vision to optimize the flow of energy. However, the energy ecosystem is in a time of significant transformation. Therefore, AutoGrid must be able to adapt to rapidly changing energy technologies, business models, and regulations.

Sources: 

  1. www.autogrid.com
  2. Gray, Dan. 2014. Autogrid – Turning Big Data Into Power: Interview with Sandra Kwak, Director of Marketing and Ecosystem Partnerships . Dataconomy. http://dataconomy.com/2014/05/turning-big-data-power-autogrid/
  3. St. John, Jeff. 2012. AutoGrid, Universal Big-Data-Plus-Apps Platform for the Smart Grid. GreenTech Media. https://www.greentechmedia.com/articles/read/autogrid-universal-big-data-plus-apps-platform-for-the-smart-grid#gs.GOpqg=g
  4. Warner, Chris. 2018. Unlocking the Distributed Grid With Flexibility Management Software. GreenTech Media. https://www.greentechmedia.com/articles/read/unlocking-the-distributed-grid-with-flexibility-management-software#gs.=PG=ckI

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2 thoughts on “AutoGrid is Turning the World’s Energy Data into Power

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  2. Wonderful post. Thank you for sharing. As I read it, I’m wondering about security. With consumers potentially going toward this direction, I wondering what it would look like in this age of computer hacks and such. While there is definitely a lot of potential here, there is also a significant amount of risk. Maybe they could partner with current utilities/tech firms to ensure both the integrity of the system as well as its efficiency.

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