In the chaos of a large-scale power outage, all other infrastructures and services, in all sectors, will shut down. Restoring and restarting electric grids and other lifeline infrastructures in such a scenario is a particularly complex, manual, labor-intensive effort requiring weeks or months to resolve. And before restoration can begin, assessment and repair of relevant equipment and facilities will be essential, for both the power grid and for other grid-critical infrastructures, suppliers, and customers. Without mission-optimized guidance, the assessment, repair, and restoration process will likely take place far too late to sustain the lives of affected populations.
With both extreme natural hazards and manmade threats increasing, electric grids, microgrids, and their supply chains are increasingly vulnerable to attack and disruption, making preplanned resilience critical to recovery. Yet in today’s reality of closely coupled, interconnected infrastructures, resilience will depend on a wide array of interdependent resources, services, and supply chains (e.g., communications, fuel and rework equipment resupply, transport, etc.). This makes it nearly impossible for operators to project the primary, secondary, and tertiary consequences of repair or restoration choices they make, as they navigate the chaos of an extreme, large-scale power cut.
A true, real-time “digital twin” of the operating systems of interdependent grids/microgrids and their suppliers could be used to resolve this concern, with a computerized system racing through that digital duplicate to recommend mission-prioritized actions. However, extraordinary complexity, fast-changing configurations, and security and proprietary issues have stalled progress in developing a massive, real-time digital duplicate of the operating systems of interdependent infrastructures and suppliers.
Enter GINOM situational awareness system.
A true, real-time digital duplicate of interdependent infrastructures and suppliers would give operators in all sectors the situational awareness and decision support they’ll need to find a successful path among the myriad dead ends in chaotic, highly disrupted scenarios. But if developing and maintaining a massive “mega-model” of many fast-changing, proprietary corporations is impractical, what can be done?
The GINOM situational awareness system takes a revolutionary, out-of-the-box approach to resolving this critical problem. Rather than attempting a massive digital duplicate of all interdependent stakeholders, GINOM simply provides a hosting framework for all players to functionally interconnect and then data-mines that interconnected, functional (non-proprietary) network for both situational awareness and decision support. This, alongside black sky resilience exercise programs, can be the key to securing our future.
In the chaotic environment of a large-scale power cut, situational awareness and AI-optimized decision support will be essential enablers for power grid blackstart and FOB microgrid restoration, making it possible for grid-critical interdependent stakeholders to orchestrate and prioritize their efforts – in advance and in real-time – as part of the complex repair and restoration process. These capabilities will be particularly critical to support rapid recovery, and meet population sustainment restoration timelines.
The GINOM situational awareness platform, currently at a prototype development level, is structured to enable precisely these capabilities.
GINOM is an innovative, disruptive chaos management platform, often referred to as a Multicorporate simulation Operating System (MOS). Structured to operate as a fully interactive digital twin, the platform offers both ahead-of-time grid/microgrid disruption planning, training and exercise support, and AI-guided decision support for power grid/microgrid operators and their counterparts in all sectors in complex catastrophes. A unique critical infrastructure simulation operating system, GINOM is a first-of-its-kind MOS. Under development at EIS Council for six years, it currently operates at a prototype level, based on competitive grants awarded by the Israel Ministry of Energy and multiple US foundations.
As a critical feature, GINOM provides a true, real-time “digital twin” of the operating systems or simulations of many interdependent organizations, which may also be integrated with threat or resource simulations and related models. These features, critical to the resilience of a power grid and other interdependent infrastructures, are made possible by a completely different approach from earlier, unsuccessful efforts at multi-infrastructure digital twin development – i.e., developing computationally massive, compound duplications of all interdependent infrastructures.
Recognizing the inherent complexity, severe scalability limits, and proprietary issues with this earlier approach, GINOM is designed to operate by guided functional integration of an essentially unlimited number of organizations’ operating systems or simulations, rather than by attempting to digitally duplicate them. By utilizing exclusively high-level functional information, there is no need for proprietary or sensitive information sharing in its operation.
GINOM is a large-scale, network-native, multi-sector, critical infrastructure simulation operating system based on techniques from agent-based simulation and discrete-event simulation. It is designed to run on EMP-hardened hardware with data routed over dark fiber or ad hoc mesh networks.
* Screenshot of multi-infrastructure operator interface from current GINOM Prototype.
Three features are particularly fundamental to the platform’s performance, scalability, and – critically important – its feasibility:
Together, these unique, disruptive architectural and design features give GINOM essentially unlimited scalability, with no need for unique computer assets. This will be key to the platform’s continuing utility, and to meeting the goals of DIANA’s Energy Challenge, enabling the system to continue to evolve and grow to integrate an increasing number of interdependent infrastructures, suppliers, and other stakeholder organizations and scenario models, and to address a growing range and scale of energy infrastructure disaster scenarios.
Functional integration is fundamental to the success of GINOM’s architecture, enabling functional integration of an unlimited number of organizations’ operating systems or simulations into a single environment. These operating systems or simulations can run on an organization’s own secure, audited hardware while still participating in a larger-scale GINOM simulation. Only high-level functional information required by the simulation scenario is shared, with no need to exchange low-level details or data that might be sensitive, proprietary, or classified.
In preparation for use with multiple corporate operating systems or simulations, GINOM® currently incorporates notional simulation applications for seven infrastructure systems: electric, natural gas, emergency fuel, health care, water, wastewater, population center, transport, and a basic pandemic simulation. In each case, it accounts for geographic layout and interdependencies, allowing for the simulation of complex phenomena such as cascading failures in extreme scenarios.
One feature that sets GINOM® apart from other infrastructure simulations is that its agent-based structure does not just rely on historical data, and projecting such data into the future. The platform is GIS-based and data-informed. In contrast to data-driven techniques (dashboards) or data-hungry machine learning methods, it takes account of the relationships among different facilities and their dynamic behavior, allowing it to simulate and predict events that have never occurred.
This is particularly important for decision support in complex catastrophe scenarios. Since it is impossible to train a data-driven AI or machine learning model to predict massive, cascading infrastructure failures that have never happened before there is no data to use to train such models. GINOM can capture such phenomena. Not only, does this secure our chances of preparing for catastrophic events, but also ensures our black sky recovery efforts are unmatched.
The GINOM situational awareness system is designed to ultimately function as a “digital twin.” It can incorporate and integrate real-time data from functioning systems, SCADA systems, and other sensing or control systems together with simulated data generated by simulation apps. Crucially, the simulations can respond to this real-time data, allowing GINOM® to combine “situational awareness” with predictive modeling into a powerful in-advance or real-time decision support tool.
Technologically speaking, GINOM’s backend, which is responsible for simulating facilities and entities, is written in the Scala programming language, which runs on the Java Virtual Machine (JVM) or can be compiled into native code. The backend service can be scaled up as server hardware capabilities are increased or can scale out as more servers are added to the compute pool.
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