General Lab Information

Energy Systems Division

Renewables and Grid Modernization

Advanced Grid Modeling (AGM) Research

Brookhaven Lab supports the Department of Energy/OE AGM program through the following projects.

Active Projects

Grid of the Near Future: Development of a New Framework for Modeling, Stability Analysis, and Control

This project will investigate and understand some of the fundamental issues the transitioning bulk power grid of the near future is facing and develop a new framework for modeling, stability analysis, and control to address these issues. The grid of the future is expected to be dominated by converter interfaced generation (CIGs) with some of the grid connected synchronous generators (SGs). Details...

Hierarchical Machine Learning-based Optimal Parameterization Scheme for WECC Composite Load Model under All Disturbances

The purpose of the study is to develop an optimal parameterization scheme for the WECC CLM based on a simulation approach assisted by a unique combination of state-of-the-art machine learning (ML) techniques including imitation learning (IL) and reinforcement learning (RL). Details...

Practical Quantum Analytics for Ultra-Efficient and Resilient Bulk Power Systems Operations

This project aims to develop practical and scalable quantum grid analytics (QGrid Analytics) to enable ultra-resilient bulk power system operations. An open-source QGrid Analytics Toolbox will be developed, which consists of efficient linear and non-linear quantum solvers and quantum consensus-based distributed algorithms for grid analytics.

Completed Projects

Formal Analysis for Dynamic Stability Assessment

Brookhaven Lab has developed an innovative and tractable method for assessing the stability of both transmission and distribution systems in the power grid under uncertainties from heterogeneous sources, such as Distributed Energy Resources. Details...

A Deep Learning Based Online Platform for Critical Anomaly Detection and Emergency Control to Enhance Grid Reliability and Resiliency

The objective of this study is to develop a data-driven, deep learning-based solution to prevent the propagation of cascading failures when the grid is challenged by unexpected contingencies or combinational contingencies under uncertain environments. The proposed end-to-end technology will be an online platform capable of evaluating and predicting grid conditions and selecting emergency control actions focused on load shedding strategies and determination of timing and boundaries for splitting the grid into self-sustained islands, as needed, to mitigate the propagation of cascading failures. Details...

Risk-Informed Decision-Making Capability using Electric Power Industry Standard Planning Tools as a Platform

This project demonstrated the usefulness of probabilistic electric grid reliability metrics and the complementary enhancement of the deterministic counterpart considering various uncertainties, including those from renewable generation in the emerging probabilistic contingency analysis (PCA) for a large utility scale system. Details...

Stochastic Sizing and Operation of Grid-Level Energy Storage Systems under Intermittent Renewable Generation and Increasing Load Forecasting Uncertainties

Brookhaven Lab has developed probabilistic sizing techniques for Energy Storage Systems (ESS) based on grid-inertia responses under high penetration of renewables, along with stochastic operation optimization techniques for ESSs that account for the intermittence of renewable generation. Details...

About DOE AGM Research

The electric power industry has undergone extensive changes over the past several decades and become substantially more complex, dynamic, and uncertain, as new market rules, regulatory policies, and technologies have been adopted. As the electric delivery system continues to evolve, the availability of more detailed data about system conditions from devices such as phasor measurement units (PMUs) used for wide area visibility and advanced meter infrastructure (AMI) used for dynamic pricing and demand response will help improve the system’s reliability and flexibility.

Working with the large volume and variety of data to make it more relevant and actionable to grid operators and utilities, however, poses significant challenges. Continuing to shift operational data analytics from a traditionally off-line environment to further expand real-time situational awareness of grid conditions and measurement-based, faster control will require significant advancements in algorithms and computational approaches.

More about the AGM program on the DOE Office of Electricity website.

To address these challenges, DOE’s Office of Electricity (OE) Advanced Grid Modeling (AGM) Program sponsors research to:

  • Support the transformation of data to enable preventative actions rather than reactive responses to changes in grid conditions;
  • Direct the research and development of advanced computational and control technologies to improve the reliability, resiliency, security, and flexibility of the nation’s electricity system;
  •  Help system operators and utilities prevent blackouts and improve reliability by expanding wide-area real-time visibility into the conditions of the grid
  •  Support improvement of the performance of modeling tools and computations that are basis of the grid operations and planning; and
  • Support the tracking and expansion of the use of quantitative risk and uncertainty methods by federal and state level energy system decision makers regarding energy infrastructure investments.