The global electric utility system is in the midst of an historic clean energy transition away from traditional fossil fuels, making the management of an unprecedented volume of new and dynamic grid asset data from varying sources imperative to maintaining grid reliability.

For decades the power grid operated as a hub-and-spoke model with large fossil fuel power plants dispatching power to consumers. This provided remarkable reliability and was hailed as the pinnacle of the 20^th century’s innovations by the National Academies of Sciences, Engineering and Medicine.

However, the focus to reduce greenhouse gas emissions generated by these solutions, coupled with the transition to renewables and distributed energy resources (DERs), is posing significant challenges to conventional methods of grid management. There is a mountain of data associated with this transition to sort through and stay ahead of.

 

The Data Deluge

In its first ever DER outlook in 2020, Wood Mackenzie projected staggering growth in capacity to the United States cumulative DER to 387 gigawatts by 2025, necessitating significant investments in DERs and the electric grid. Depending on the diversity of the individual DER, the data generated by these solutions can reach petabytes. Types of data include:

• DER Asset Data- This includes data on DER assets such as solar PV systems, wind turbines, energy storage systems and electric vehicle chargers.
  
  
• Grid Monitoring Data- Real-time data from grid monitoring devices such as smart meters, sensors and SCADA systems provide information on grid conditions, load demand, voltage levels and frequency.
  
  
• Weather and Environmental Data- Utilities integrating renewable energy resources like solar and wind often rely on weather forecasts, historical weather data and environmental conditions to predict renewable energy generation and optimize grid operations.
  
  
• Customer Data- Utilities may perform analysis based on customer data to tailor services, implement demand response programs and engage customers in DER adoption initiatives.
  
  
• Market and Regulatory Data- Utilities need to track regulatory requirements, market rules, energy tariffs, incentive programs and other market-related data to comply with regulations.

 

^{The DER grid needs constant access to vast amounts of data from various sources for proper distribution and to adapt to outages}

 

This data deluge presents both opportunities and challenges for utilities looking to maintain a reliable grid at scale. It enables predictive maintenance, fault detection and precise localization, all crucial for preventing outages and ensuring rapid response to grid problems. Data-driven insights enabled by data historian solutions are also key to support decision-making, optimizing asset management and facilitating progress towards sustainability goals. The types of data handled by these solutions include:

 

• Data Collection and Integration- Data historian software can collect and integrate data from various sources, including sensors, meters, SCADA systems and IoT devices deployed across a distributed energy network.
  
  
• Real-time Monitoring and Control- By continuously collecting and processing data, data historian software enables real-time monitoring of DERs. Operators can track energy generation, consumption, grid stability and other relevant metrics to ensure optimal performance and respond promptly to unplanned downtime or outages.
  
  
• Historical Data Analysis- Data historians store historical data over extended periods, enabling trend analysis, performance evaluation and predictive modeling.
  
  
• Integration with Energy Management Systems (EMS) and SCADA- This allows for coordinated control of DERs, optimization of energy flows and improved grid stability.
  
  
• Compliance and Reporting- Data historians facilitate compliance with regulatory requirements by documenting data related to energy production, consumption and environmental impact.
  
  
• Scalability and Flexibility- Distributed energy systems are dynamic and often expand or evolve over time. Data historian software solutions are designed to be scalable and adaptable, accommodating changes in the DER infrastructure and supporting the integration of new technologies and resources.

 

By now, I hope you see what I mean by a “data deluge.” However, by identifying patterns or extracting insights from large volumes of structured and unstructured data (known as “Big Data”), utilities can ensure secure, detailed and real-time data access necessary for operating resilient and reliable grids.

 

 

For example, the adaptive approach of AspenTech OSI CHRONUS™ offers scalability, data integrity and resiliency. At the IT level, data is ingested by AspenTech Inmation™, providing context and aggregation for the CHRONUS data necessary for enterprise analytics and exploratory analysis. By providing reliable data for monitoring progress and identifying cost-efficient decarbonization strategies, operators are provided support for key performance metrics. It also integrates with existing infrastructure to ensure a smooth transition, safeguarding legacy data while future-proofing grid management.

The clean energy transition presents unprecedented challenges and opportunities for the electric power utility system. Renewables and DERs offer a myriad of benefits, from emissions reduction to providing customers revolutionary revenue streams through virtual power plants (VPP) and enabling participation in demand response. However, they also introduce complexities, such as bidirectional power flow and the processing of enormous amounts of grid data. Navigating this transformation requires a modernized approach to data management. Visibility into these vital components of the grid empowers utilities to embrace the future of grid management with confidence and resilience.