De-Risking Grid Automation
Novel Robotic State Engine™ (RSE) and Robotic Adaptive Collaboration™ (RAC) technologies results in a new, breakthrough paradigm: Hybrid Cognition™
Hybrid Cognition elevates the human/machine interface (HMI) into an equal partnership between people and computers, and provides incredible capabilities for Intelligent Infrastructure.
- Industrial-era hub-and-spoke control is fused with emerging IoT/Edge Computing technologies
- Legacy and modern infrastructure control incumbents are disrupted with synergistic, cross-domain capabilities
- A new multi-trillion-dollar market is unlocked by a multi-generational, open-architecture product pathway that begins with the Electric Power Grid
Robotic Hypervisor Virtualizes and Converges RAC Framework Elements into Powerful Hybrid Cognition De-Risking Grid Automation
Edge-State Across Domains
Process Across Networks
Resource Across Humans and Computers
The Electric Power Grid Problem
The U.S. electric power grid transmits high-voltage, bulk power from central generation sources to over 70 thousand substations which distribute medium-voltage power into 140 million low-voltage customer meters, and then into 1.5 billion building circuits.
Our legacy electric power grid cannot keep pace with residential, commercial, industrial, digital, and transportation demand growth that is projected to triple by 2050, while at the same time transitioning from away fossil fuels into renewable energy resources.
GridPathway RSE with RAC Framework provides a risk-mitigated pathway to an incremental transformation that will allow the grid to keep pace with both growth and transitioning requirements.
Hybrid Cognition Enabled Grid Modernization
GridPathway provides the best practice pathway to Intelligent Infrastructure.
- Business Architecture: Provides required foundation to inform, guide and direct technology and stakeholder architecture development.
- Technology Architecture: Provides viable power grid substation digitization that will encapsulate multiple upstream legacy controls, converging them into a downstream wireless edge control network with end-state integrity that is robotically enforced.
- Stakeholder Architecture: Provides an incremental modernization framework that will drive a public /private partnership, leveraging a new high-tech open-platform provider whose success will be enhanced through strong alliances with labor and government.
What We Offer
Robotic Adaptive Collaboration Framework
Enables transparent Distributed Resource (DR) insertion by automating the integration process in an industry-standard, open-architecture framework.
Introduces wide area situational awareness that will utilize look-ahead perception to launch an anticipatory response that is enhanced by distributed machine learning resources.
Introduces “fly-by-wire” (FBW) robotic operability where closed-loop edge automation will improve productivity while remaining under tiered levels of human guidance and over ride.
Introduces network integration framework that will manage state integrity between legacy grid, emerging open grid, emerging IoT-based edge device, and other desired control paradigms.
Intelligent Grid Infrastructure
RSE/RAC leverages Hybrid Cognition to accelerate the transition from centralized to needed intelligent distributed control, creating a more reliable, affordable, sustainable, agile, secure, and resilient power grid.
Increases grid reliability and stability by enabling operators to transparently manage the distributed system integration of a spiraling renewable energy and transportation electrification installed base.
Provides wide area situational awareness across operating domains, launching anticipatory responses to assure end-to-end state management integrity (i.e., reducing unplanned outages, shaving peak loads, repelling threats from nature or bad actors, enabling micro-islanding, staying ahead of market conditions, and related mission critical activities).
Elevates function-based operator input commands into high-level “fly-by-wire” goal commands (similar to autonomous driving) where the robotic system seamlessly interworks between domains, generating low-level function commands optimized by tiered error mitigation.
Ft. Lauderdale, FL
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