With the increasing influx of renewable energy sources like solar and wind into the power grid, the infrastructure of the future must efficiently handle their variability, while keeping costs low and operational risks minimal. 

Professor Santiago Grijalva’s Energy Variability and Electricity Optimization Using Stochastic Operational Envelopes (ENVELOPE) project, now backed by a $3 million grant from the U.S. Department of Energy (DOE), is poised to take the next big leap in addressing this challenge. 

ENVELOPE, funded under the DOE’s OPTIMA program and developed in collaboration with Siemens Energy, Inc., aims to revolutionize the way large-scale electricity systems are managed and optimized. 

“Utility companies decide hour by hour which power plants need to be online and how much they should produce to collectively meet the electricity demand,” Grijalva said. “The surge of renewable energy on the grid is less predictable, so we need to have a mechanism to capture that uncertainty in optimal scheduling decisions.” 

To do this, the project looks to implement the first system for large-scale resource scheduling that uses stochastic decentralized optimization methods. This means that instead of relying on a single, centralized system to make all the decisions, the ENVELOPE platform will use a network of smaller, interconnected computing agents that work together. These agents take into account risk to make decisions, which helps them better handle the unpredictability of renewable energy sources. 

Grijalva, a Southern Company Distinguished Professor in the School of Electrical and Computer Engineering (ECE), is leading the project with the help of Professor Santanu Dey from the H. Milton Stewart School of Industrial and Systems Engineering (ISyE), who is aiding in the design of new distributed algorithms with performance guarantees.

ENVELOPE will incorporate several cutting-edge features that set it apart from existing solutions. These features include detailed models of inverter-based resources (IBRs), comprehensive models for energy storage accounting for non-linear degradation, and advanced modeling of reserves and dynamic reserve margins for grid stability. Additionally, the platform will incorporate advanced microgrids and flexible loads for enhanced flexibility and capabilities for transmission line switching and flow routing to optimize power distribution. 

One of the most innovative aspects of ENVELOPE is its use of artificial intelligence (AI). By running numerous simulations, the AI component learns to schedule resources optimally, minimizing costs and reducing the time required to find solutions. 

“The practical benefits of ENVELOPE are significant,” said Grijalva. “By optimizing resource scheduling, the platform can reduce electricity costs for consumers while enhancing the reliability and resilience of the grid and minimize risk.” 

Additionally, by maximizing the production of renewable energy, ENVELOPE contributes to reducing greenhouse gas emissions, supporting a more sustainable energy future, according to Grijalva 

Looking forward, the project will undergo integration, testing, and demonstration using large-scale, realistic solutions within a real Energy Management System (EMS) environment. This process aims to achieve a high level of technological readiness, ensuring the system is robust and effective in real-world applications.