Re-Invisioning the Role and Goals for NOCs

Centralized facilities to monitor and control various assets, often called Network Operating Centers (NOCs) or Remote Operating Centers (ROCs) provide critical 24/7 control and management of a vast array of assets. Given increasing volatility in markets, weather, and market pressures, today’s value for a NOC is materially greater than monitoring asset health and managing identification and prioritization of control, operations, or routine maintenance — to proactive support for “connecting” decisions/actions to requisite outcomes driven by market, fiduciary, and risk postures! But with increased importance of actions, frequently NOCs struggle to deploy OT/IT solutions that can address the full range of needs. Seemingly, there is a never-ending stream of data available – from multiple disparate sources – to analyze and make sense of; the greater the population of assets or greater number of issues/actions to be contemplated, and more work required – by humans or AI systems – to generate insights and actions at increasingly higher levels of complexity and intensity. The temptation is to double down on proven tools and methods – to leverage advanced models and AI frameworks to assess state-of-condition, identify potential areas of concern, and characterize source/criticality of the issue. BUT, such systems tend to be inherently disconnected from market or business contexts, often leaving the quantification of an increasingly important factor – criticality – to the conjecture of the system expert, or via math based on some set of assumptions. If we flip the script, so to speak, and break the paradigm by examining criticality of asset features based on business and markets, we can dramatically alter workflows, focus AI horsepower on exposing higher business value! Our goal is to enable NOC as the “brain” of systems capable of spanning near real-time, tactical and strategic decision realms and assets under management and to efficiently bring context needed to optimize decisions. Let’s begin with tools and methods to truly characterize market and its nuances: seasonal, asset-mix, nature of participation, demand profiles — to clarify precisely where performance/reliability (or lack of it) is most costly or, positively, where there may be significant rewards for committing assets toward bright opportunities. Outcome-Driven: Dynamic assessment or quantification of forward positions for asset to deliver energy or other services – derived from market insights – allows one to understand impacts of performance or maintenance actions and their timing on overall ROI. Focused: Matching areas of opportunity to concern via AI provides critical focus on areas where attention is most warranted. In other words, assure that the efforts expended by experts and traditional AI/diagnostics are naturally prioritized based on our understanding of criticality, ROI, and asset value Opportunity Aware: the ability to influence the magnitude of opportunity? Better performance, efficiency, capacity, assurance of asset starts, reliability/availability, … against a dynamic ROI model that takes costs to support additional capabilities. So back to the focus of this article … the school of hard NOC’s … to take our lessons-learned conventional tools and methods but, to flip the script, to drive additional value via reshaping the nature of the analytics stack for NOCs; in essence, to be more agile, responsive to market while retaining and building on the immense and valuable knowledge and analytics systems.

Approach

Ground truth is critical! Characterization of asset-based outcomes (and risks) associated with state-of-condition, actual weather conditions, alternative operational regimes markets … continues to drive our understanding of asset condition, risks, performance and reliability remedies. Leveraging the plethora of sources of data available from combination of sensor, SCADA, environmental (air/water) management systems, and similar Industrial Internet of Things (IIoT) sources. There is also substantial data available from digital sources that addresses the environment the asset is participating (market interfaces, weather and weather forecasting systems, other exchanges (fuel, emission credits, …). Value generation through structuring/organizing the data and adding additional, valuable context. o Conventional monitoring and diagnostics platforms and digital twins are an ideal source of additional intelligence about the state of condition or performance of an asset or systems. o Asset health monitoring (vibration, oil monitoring, air effluent monitoring systems) also feature specialized analytics to translate raw data to key factors of interest: efficiency, life expended/remaining life, capacity, emission rates, etc. o Fully leveraging the rich legacy and evolving toolbox from original equipment manufacturers (OEMs) and others that focus on providing additional intelligence: monitoring/diagnostics, performance packages, and add-ons to Computer Maintenance Management Systems (CMMS), Enterprise Asset Management (EAM), Computer Aided Facility Management (CAFM), Integrated Workplace Management Systems (IWMS), and Financial Information Systems (FIS). New features and capabilities we add are focused on developing and analyzing a myriad of forward views driven by differing sets of assumptions and commissioned via robust simulation space to examine potential decisions or actions or policies; we leverage scenario exploration and optimization frameworks to identify the most profitable or set of actions – operations, utilization of assets, nature of investment, timing – to consider: Different objectives that will focus on different time dimensions and different resources yet all service the greater mission; this includes short-term or tactical actions as well as operational or capital-prioritized replace/repair decisions. How priorities or objectives may change in importance, in composition, or both Must be calibrated or re-calibrated to changes in external factors influencing the nature and timing of asset value that can be realized. As a result, analytics are able to consider and explore changes in strategies, objectives, and requirements, stakeholder needs and expectations across a range of possible outcomes. The emergence of scalable Generative AI technologies, specifically Generative Pre-training Transformers (GPT) and Reinforcement Learning (RL), provides unprecedented ability to deploy embedded targeted solutions to characterize and optimize future actions and timing/sequence of such actions. GPT offers exciting if not unprecedented ability to apply AI-enabled advanced analytics to identify patterns in data that: Mathematically describe relationships between different aspects of operations, maintenance, capital expenditures, and behaviors against performance outcomes Sponsor powerful models that can leverage these insights and predict how specific aspects of asset performance and reliability will be transformed under different operations and maintenance and utilization scenarios. Relate asset value and its contribution to value-based objectives at location over time using dynamic market-based models that can take weather, market, technology, and services profiles. Can be leveraged to derive expected outcomes from a very broad set of factors, based on ground truth. The value of GPT is additive and incremental. GPT-driven analytics has successfully identified time-critical features of the market to optimize market participation and bid strategies; for NOCs, we extend this GPT fabric to incrementally address asset condition/health, and performance/utilization features of the underlying assets.