Energy Domain challenges

1. Energy Domain challenges Autonomous, Resilient, Sustainable, Integrated Energy Infrastructure • Self-healing at scale • Reliable Renewable aggregation • National-scale dispatch optimization • Accurate weather models and RE impact • Autonomous End-User • Energy Infrastructure Interdependencies • Scalable Markets AI Challenges • Real-time Intelligence from Disparate Data sources for Situational Awareness towards resilience/Self-healing • Autonomous Control and restoration of energy infrastructure with high penetration of renewables • Dynamic dispatch , virtual power plant, virtual inertia • Resilience of interdependent infrastructure

2. Real-time Intelligence from Disparate Data sources for Situational Awareness towards Resilience/Self-healing • Domain: Energy Generation & Distribution • Time Frame: 3-5 years • Description of the potential breakthrough  • 100% self-healing resilience grid • System health monitoring – predictive analytics – fault detection and isolation • Resilience of aging infrastructure in the presence of new DERs and cyber-physical security concerns • Diverse data sources (time & space, may be missing data), protected by utilities • Indication of the performance challenges with current approaches: Current approaches to FLISR are largely reactive • An indication of AI approaches that might be applicable: Information fusion (electrical data, social media data, local events), real-time analysis and decision support

3. Autonomous Control and restoration of energy infrastructure with high penetration of renewables • Domain: Energy Generation & Distribution • Category: Monitoring Controls • Time Frame: 10-15 years • Description of the potential breakthrough  • Need for advanced control to optimally utilize these resources in tandem with the bulk electric system (BES), and to respond to and recover from events (e.g. faults or extreme weather) to reduce their impact • automated black-start restoration under uncertainty, network state estimation with sparse data, and automated islanding and operation of microgrids • Real-time scalable operation of networked microgrids • Indication of the performance challenges with current approaches: • Currently, distribution grids are largely designed for robustness with respect to the BES and are operated by much smaller operations teams relative to transmission operations, leading to potential inefficiencies and losses • Centralized and not scalable for real-time applications • An indication of AI approaches that might be applicable: A combination of machine learning (ML) and novel global optimization approaches (MINLP) will be needed to address this technical challenge • Special care will be needed to design ML algorithms to make them robust and secure • Infer models

4. Energy Generation and Distribution BreakoutORNL AI Town Hall Co-Leads:  Tara Pandya, ORNL TejaKuruganti, ORNL Mike Sprague, NREL August 20, 2019

5. Energy Domain challenges • Autonomous, Resilient, Sustainable, Integrated Energy Infrastructure • Self-healing at scale • Reliable Renewable aggregation • National-scale dispatch optimization • Accurate weather models and RE impact • Autonomous End-User • Energy Infrastructure Interdependencies • Scalable Markets AI Challenges • Real-time Intelligence from Disparate Data sources for Situational Awareness towards resilience/Self-healing • Autonomous Control and restoration of energy infrastructure with high penetration of renewables • Dynamic dispatch , virtual power plant, virtual inertia • Resilience of interdependent infrastructure

6. Real-time Intelligence from Disparate Data sources for Situational Awareness towards Resilience/Self-healing • Domain: Energy Generation & Distribution • Time Frame: 3-5 years • Potential breakthrough:  • 100% self-healing resilience grid • System health monitoring – predictive analytics – fault detection and isolation • Resilience of aging infrastructure in the presence of new DERs and cyber-physical security concerns • Diverse data sources (time & space, may be missing data), protected by utilities • Performance challenges: Current approaches to FLISR are largely reactive  • AI approaches: Information fusion (electrical data, social media data, local events), real-time analysis and decision support 

7. Autonomous Control and restoration of energy infrastructure with high penetration of renewables • Domain: Energy Generation & Distribution • Category: Monitoring Controls • Time Frame: 10-15 years • Description of the potential breakthrough  • Need for advanced control to optimally utilize these resources in tandem with the bulk electric system (BES), and to respond to and recover from events (e.g. faults or extreme weather) to reduce their impact • automated black-start restoration under uncertainty, network state estimation with sparse data, and automated islanding and operation of microgrids • Real-time scalable operation of networked microgrids • Indication of the performance challenges with current approaches: • Currently, distribution grids are largely designed for robustness with respect to the BES and are operated by much smaller operations teams relative to transmission operations, leading to potential inefficiencies and losses • Centralized and not scalable for real-time applications • An indication of AI approaches that might be applicable: A combination of machine learning (ML) and novel global optimization approaches (MINLP) will be needed to address this technical challenge • Special care will be needed to design ML algorithms to make them robust and secure • Infer models

8. Enabling Predictive Design Tools for Carbon-Free Energy Generation • Domain: Energy Generation & Distribution • Category: Predictive Modeling & Simulation  • Time Frame: 10-15 years • Enable exploration of unconstrained design • How to achieve faster than real time, accurate, predictive models? • Cost effective efficient at scale • Experimental data is sparse and uncertain; simulated data is sparse and sensitive • Challenges with current approaches: • State-of-the art models are not predictive in application • "Best models are too slow and fast models are too bad" • AI approaches that might be applicable: • Surrogate models, closure models, acceleration of high-fidelity models and solvers

9. Enabling Predictive Design Tools for Carbon-Free Energy Generation • Domain: Energy Generation & Distribution • Category: Predictive Modeling & Simulation  • Time Frame: 10-15 years • Enable exploration of unconstrained design • How to achieve faster than real time, accurate, predictive models? • Cost effective efficient at scale • Experimental data is sparse and uncertain; simulated data is sparse and sensitive • Challenges with current approaches: • State-of-the art models are not predictive in application • "Best models are too slow and fast models are too bad" • AI approaches that might be applicable: • Surrogate models, closure models, acceleration of high-fidelity models and solvers