Fortune 500 company uses MATLAB to address grid uncertainty from DERs.
MathWorks is providing probabilistic load flow (PLF) capabilities to enhance system planning solutions for Eversource Energy, New England’s largest energy utility. The rapid green energy transition, marked by the rise of electric vehicles, heat pumps, and solar panels, has added uncertainty to electric distribution planning. Eversource built a probabilistic power flow system that allows the Fortune 500 company to process and prioritize millions of grid scenarios by likelihood and risk.
Integrating renewable generation capacity into the grid is a key challenge for the US energy transition. As an example, the Energy Information Administration (EIA) projects the addition of 26 gigawatts (GW) of solar capacity in 2025 and 22 GW in 2026. Eversource found traditional model scenarios inadequate for future system planning. Instead, the power company integrated PLF automation into its power system analysis. Using PLF automation allows Eversource to simulate numerous scenarios to improve its distribution modeling capabilities and support investments in enhancing necessary data analytics solutions.
MATLAB was the primary environment used to develop and implement the PLF system at Eversource. The programming and numeric computing platform provided the necessary tools for numerical parallel processing, enabling efficient process distribution across multi-core CPUs and GPU banks. This capability helped Eversource to handle large-scale simulations and data analysis effectively. The integration was achieved using the MATLAB ActiveX server, allowing direct communication between MATLAB and DNV Synergi Electric Solver, without the need for additional programming languages.
MATLAB was also used to implement Monte Carlo simulations, which are essential for probabilistic modeling. These simulations helped Eversource to evaluate numerous scenarios by randomly selecting values from probability distributions of input parameters. Additionally, the data visualization tools in MATLAB were crucial for interpreting PLF simulation results, enabling the power company to create visual representations of the grid’s performance under different conditions and identify potential issues and areas for improvement.
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