Transmission-Level Operations

NLR works at the leading edge of transmission operations, helping utilities and operators overcome instabilities, oscillations, and dynamics arising from a mix of generation types.

As operators face an electric grid with a wider range of energy resources and operational timescales, they need new tools to assess grid strength, intelligently dispatch generation, and identify instabilities. NLR is an innovator on all fronts, with solutions stemming from artificial intelligence (AI), real-time analysis, and stability assessment that have been proven on live power systems.

Capabilities

Inertia Estimation

As the electric grid becomes more reliant on power electronics, operators must reassess their sources of grid strength and inertia. Grid-forming inverters are a necessary solution to improve inertia, but determining where and how to deploy grid-forming controls or other stabilizing resources requires operators to know their system inertia.

NLR has developed and validated tools for inertia estimation using:

• Ambient data: A nonintrusive, low-cost solution that leverages existing monitoring infrastructure to estimate system inertia using naturally occurring power and frequency fluctuations.
• Probing signal: A solution that uses a battery and inverter to inject active power pulses to estimate grid inertia and fast frequency response. It relies on retrofitting existing inverters and does not require new hardware.

NLR's tools have been demonstrated on live power systems, showing that they can help utilities and operators to map grid stability, find weaknesses, select optimal plant settings, schedule stability services, and site new energy resources.

Grid Strength Evaluation Tool

The Grid Strength Evaluation Tool is an NLR-developed, Python-based tool for both online and offline grid-strength assessment and visualization. It integrates a suite of traditional and advanced grid-strength metrics—including short circuit ratio (SCR), node system SCR, and generalized SCR—by leveraging Power System Simulation for Engineering's built-in short-circuit analysis module. The Grid Strength Evaluation Tool provides a practical approach for grid planners and operators to evaluate system strength in terms of short-circuit capacity and system-strength contribution.

Oscillation Detection

Grid oscillations have been increasing on power systems around the world, but their source and propagation can be difficult to study. NLR created the Grid Oscillation Analyzer (GOAL) to diagnose the oscillatory dynamics affecting any utility's power grid. GOAL has been validated on Kauai's power system, where it helped the local electric cooperative identify and prevent multiple electric oscillations.

GOAL uses the following framework:

1. Event overview with utilities and vendors
2. Field data collection
3. Oscillation source identification
4. Root mean square and electromagnetic transient modeling
5. Small-signal modeling and analysis
6. Mitigation methods and validation.

Grid Model Validation

NLR's Grid Model Validation Tool (GVaT) is an online/offline model validation tool for inverter-based resources to improve the accuracy for grid simulations. GVaT validates models against real event data, improving the fidelity of vendor-provided electromagnetic transient models.

Multi-Timescale Integrated Dynamic and Scheduling

The new operational environment on the grid requires real-time dispatch of both generation and stability services. The Multi-Timescale Integrated Dynamic and Scheduling (MIDAS) tool is an all-in-one modeling tool for operators to optimize both economics and reliability in their day-to-day system dispatch.

Control Room of the Future

NLR is pioneering the future control room of power systems: AI-assisted, multi-timescale, and highly predictive. The central tool in this effort is Real-Time Analytics for Bulk Grid (RTAG).

RTAG is a control room operation simulator that mimics power system operations on a full Western Interconnect bulk energy system for reliability and resilience assessment under real-time and near-term planning horizons. It simulates extreme large system events, variable penetration scenarios, and bulk energy system-natural gas interdependencies across steady-state, dynamic, and transient time domains.

RTAG-based AI and machine learning will support next-generation control room functions. Deeper models will create awareness in:

• Load forecasting
• Distributed energy resource management and virtual operator assistants
• System restoration from blackouts
• Predictive cascading analysis
• Collaborative and analytic visualization.

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