Research, Education, and Cybersecurity via Open/Remote Access Test-beds Kishan Baheti National Science Foundation Examples of CYber Attacks Superyacht attack, demonstrated July 20132 UAV attack, demonstrated June 20131 Phasor measurement unit attack, demonstrated December 20113 Illustration: John MacNeill Automotive radar also appears vulnerable 1 A.J. Kerns et al. Journal of Field Robotics, 31(4): 617–636, 2014. J.A. Bhatti and T.E. Humphreys. 2014. In preparation. 3 D.P. Shepard, T.E. Humphreys, A.A. Fansler. International Journal of Critical Infrastructure Protection, 2012. 2 Distributed laboratory space with open/Remote access • Develop and use open/Remote access, shared engineering laboratory spaces • Potential laboratories: robotics, power systems, drones, medical systems, etc. • Shared cost and leveraging expertise Images: Vehicle: I. Lee, KOCSEA Workshop 2014; Microgrid: https://der.lbl.gov/ IMU GPS Left Encoder Camera Right Encoder Classroom and research access to remote laboratory experience • “Bare bones” multi-robot laboratory ~ $250k • Price is a major stumbling block for many universities and research groups • Research is done in isolation • Resource competition Image: M. Egerstadt; Ga Tech. White Paper. 2015. Combination of research and education • CPS security is a key requirement of these laboratories • Real-time code verification needs to be developed • Multiple groups of researchers and students can simultaneously work on the systems • Competitive educational games can be played to test security of the systems Energy, Power, Control & Networks (EPCN) • Design and analysis of complex dynamic systems integrating sensing, imaging, control, networking, and computational technologies • Emphasis on electric power systems – generation, transmission, distribution • High power electronics and drives • Regulatory and economic structures Energy, Power, Control & Networks (EPCN) • Energy Harvesting, Storage Devices and Systems • Solar and Wind Energy and Integration of renewables with Grid • Monitoring, Protection and Cyber Security of Power Grid • Advanced Power Electronics and Electrical Machines • Electric and Hybrid Vehicles; Integration with Grid • Policy, Economics, Consumer Behavior and the Power Grid TRUSTWORTHINESS OF SMART GRIDS: U.S. DEPARMENT OF ENERGY SUPPORT FOR THE GRID OF THE FUTURE IEEE Conference on Innovative Smart Grid Technologies Washington, DC February 18, 2015 Gil Bindewald Evolution in the Power Grid Availability of Data is Growing “Smart Grid” data sources enable real-time precision in operations and control to dynamically optimize grid operations to adapt to changing conditions • Real-time data from distribution automation and smart meter systems will significantly advance real-time operations of distribution systems and enable customer engagement through demand response, efficiency etc. • Time-synchronized phasor data, linked with advanced computation and visualization, enable advances in state estimation, real-time contingency analysis, and real-time monitoring of dynamic (oscillatory) behaviors in the system. Vast Quantities of Data Energy Meets Big Data With so much grid state and customer data available in nearreal time, how can operators make sense of it all? • Tools must be developed to leverage the data to assist system operators in effectively predicting, preventing, and responding to emerging system events • Processing real time grid state and customer data is critical in enabling operators to react to grid disturbances • Data architecture needs to address heterogeneity, scale, timeliness, complexity and privacy The DOE Advanced Grid Modeling Program enables predictive capabilities 5 Future Grid Architecture Today's grid is generation that follows load. Tomorrow’s grid is loads that follow generation. • • • • Grid architecture must accommodate more distributed systems Lines between transmission & distribution will be blurred Linkage of EMS and DMS will be important Next Generation EMCS (Energy Management & Control System) Architecture being developed at CURENT Complementary Program Activities: Measurements and Model-Based Analytics To turn real-time system data into actionable information requires an understanding of not only “what is happening” but also “what could happen.” Cyber-Physical ICT Turning Massive Data into Actionable Information • Analytics initiatives are needed to understand and optimize the electricity system Dynamic States • Furthermore, addressing emerging dynamics and uncertainty will require merging rigorous data analysis and modeling techniques for application to power system problems in “real-time” Data cycle Simulation “What could be” Stability Dynamic contingency analysis Look-ahead dynamic simulation Contingency Analysis Dynamics state estimation 1/30 sec 2/30 sec 3/30 sec Optimization 1 min Time State Estimation Topology “What is” Promoting Strong Stakeholder Collaboration “Sustaining aggressive and proactive energy delivery systems security improvements over the long term requires a strong and enduring commitment of resources, clear incentives, and close collaboration among stakeholders. Energy sector collaboration provides the resources and incentives required for facilitating and increasing sector resilience.” 1 Cyber-Physical ICT and Power: Issues and Opportunities Application Context Setting Erich W. Gunther, P.E., IEEE Fellow Chairman and CTO - EnerNex [email protected] 2 Smart Grid Domains, Interfaces and Applications Markets Operations Retailer / Wholesaler RTO/ISO Ops Transmission Ops Service Providers Distribution Ops DMS WAMS Service Integration Energy Market Clearinghouse Demand Response Service Integration CIS Billing Service Integration Transmission SCADA Metering System Distribution SCADA Wide Area Networks Field Area Networks Energy Services Interface Data Collector Substation Integration Substation Controller Substation Device Domain Customer Equipment Meter Field Device Distributed Generation Electric Storage Network Roles and Actors Gateway Role Comms Path Comms Path Changes Owner / Domain Transmission Others Customer EMS Premises Networks Generators Generation Billing Home / Building Manager Internet / e-Business Market Services Interface CIS Retail Energy Provider MDMS Internet / e-Business Plant Control System Third-Party Provider Aggregator RTO SCADA ISO/RTO Participant Asset Mgmt EMS EMS Aggregator Utility Provider Electric Storage Electric Storage Appliances Electric Vehicle Distributed Generation Distribution Distributed Energy Resources Thermostat Customer Smart Grid Command and Control Enterprise Strategy & Operations • Asset Forecasting • Workforce Forecasting • Generation Planning Monitor • • • • • • • • • Situation Awareness Comm Status Environmental Smart Grid Devices Sensors Security Market conditions Maintenance Status GIS • Energy Efficiencies • Total Ownership Cost • Carbon Trading Assess • • • • • • • • • Outages Maintenance Data Demand Generation Capacity Demand Response Capacity Intrusions Trending Actual vs. Planned Data Mining • Customer Trending • Maintenance Forecasting • Automated Outage Mgmt Predict & Plan • • • • • • • • • • Modeling Maintenance Predicted Demand Weather Ecological Renewable Resources Consumer Patterns Market Pricing T&D Constraints Optimization Control • Classic SCADA & System Control • IT System Control • Dist Resource & Load Control • Intelligent Device Mgmt • Authorize Sales • Crew Dispatching • Regulatory Reports • Back office • Purchase Requests Interfaces: Smart Grid Devices, Field Devices & Systems, Sensors, Market Pricing, Weather, RTO Demand Response, Back Office Systems Technology: SOA, C2 Apps, Modeling, Simulation, Data Mining/Correlation/Fusion, Visualization, Business Intelligence, Large-scale Systems Mgmt & Optimization SGCC Architecture Predicted vs. Energy Market Environmental Recommended (Current, Predicted) (e.g., Wx, pollution) Course of Actions Presentation (Operator Stations, Web Displays, Data Walls) Enterprise Status and Diagnostics Demand vs. Generation Enterprise Strategy & Operations Monitor Assess Net Predict & Plan Control Enterprise System Mgmt Information Bus Federated Command Federated Center Command Center Security Grid Comms Networks SOA Infrastructure (Scalable, Open, Standards-Based) Adaptation Training Data Legacy Data COOP Data Enterprise Operations Data Alternate Site Layered Enterprise Integration Platform Operational Decision & Response Times Increased Variability Requiring More Dynamic Operations on Shorter Time Cycles Background - Execution Cycles & Temporal Coordination Slower cycles – 2 sec & over faster cycles - under 2 sec Hour-Ahead Cycle 5 min cycle 1 min cycle Power System Including control, measurement and protection devices 2 sec cycle 1 sec cycle 100 m-sec cycle 10 m-sec cycle Forward plans, schedules, guidelines controls, messages Feedback data, violations, alerts, messages Edge Function Fn Function F2 Function F1 (e.g. Voltage Stability) Intelligent Functional Agent for F1 Intelligent Functional Agent for F1 Intelligent Functional Agent for Fn Intelligent Functional Agent for F1 Intelligent Functional Agent for Fn Intelligent Functional Agent for F1 Intelligent Functional Agent for F1 Actuator Actuator Actuator Intelligent Functional Agent for Fn Intelligent Functional Agent for F2 Actuator Actuator Actuator Intelligent Functional Agent Fn Actuator Actuator Actuator Integrated Messaging/Data Feeder Substation Region Central Distributed Hierarchical Control System Impact of Latency on Control No Latency Slower cycles – 2 sec & over faster cycles - under 2 sec Hour-Ahead Cycle Fixed Latency Random Latency 5 min cycle 1 min cycle 2 sec cycle 1 sec cycle 100 m-sec cycle 10 m-sec cycle Power System Including control, measurement and protection devices Forward plans, schedules, guidelines controls, messages Feedback data, violations, alerts, messages It’s All About the Requirements! Developed under contract for EPRI by EnerNex and GE IECSA Team 11 New Technology Adoption is not Easy • • • • • • • • • • • • Clear Business Objective Technology Selection Impact on Existing Infrastructure Ability of Organization to Adapt Method of Implementation Reliability and Security Impacts Testing and Certification Metrics to Evaluate Implementation Effectiveness Cost Recovery and Other Regulatory Issues Business Risk Assessment and Overall Governance Life Cycle Management End of Life 13 Questions ???? • Erich W. Gunther [email protected] GE Digital Energy ICT and Power: Role of ICT in SG, Data Management, Communications, IoT and Analytics, R&D Opportunities John D. McDonald, P.E. Director Technical Strategy & Policy Development IEEE Fellow IEEE PES Substations Committee Chair 2001-2002 IEEE PES President 2006-2007 IEEE Division VII Director 2008-2009 IEEE-SA Board of Governors 2010-2011 IEEE PES ISGT ToSG Workshop Washington, DC February 18, 2015 Role of ICT in Smart Grid A “Smarter” Grid Enabled Utility Managers ‘New Applications enabled by Additional Infrastructure’ Management “ Applications” Economic Dispatch Energy Optimization Asset Optimization Demand Delivery Optimization Optimization Enabled Consumers Control “How Power Flows” Gen & Trans Mgt. Transmission Automation Dist. Mgt. Sensors Dist. Automation Heavy Metal “ Generate & Deliver Power” Thermal Generation Lines Sub Stations Dist Equipment Old Grid Voltage Control Renewable Generation Adv.Metering System Old Grid Smart Grid Adds Smart Grid • You call when the power goes out. • Utility knows power is out and usually restores it automatically. • Utility pays whatever it takes to meet peak demand. • Utility suppresses demand at peak. Lowers cost. Reduces CAPEX. • Difficult to manage high Wind and Solar penetration • No problem with higher wind and solar penetration. • Cannot manage distributed generation safely. • Can manage distributed generation safely. • ~10% power loss in T&D • Power Loss reduced by 2+%… lowers emissions & customer bills. Smart Grid Technology Roadmap IT – Data Validation and Management Smart Meters/AMI Integration with GIS, OMS and DMS Smart Meters/AMI • Meter Readings • Voltage => DMS • Last Gasp Communication => OMS GIS • Network Model Information => OMS, DMS DMS • Status Changes => OMS Customers • Phone Calls => OMS • Social Media => OMS Enterprise Data Management - Concept Enterprise Data Management – Architecture Distribution State Estimation (SE) What are “States” – State Variables ? States are the minimum set of variables that uniquely determine the grid operation condition, e.g., Node Voltage Magnitudes and Phase Angles, With node Vs and , all other quantities like Branch/Node Ps, Qs, Is can be uniquely calculated Can other sets of variables be state variables? Why do we need State Estimation? There are more known variables than the minimum number of knowns to uniquely find out the unknowns (an over-determined problem) The knowns are conflict and cannot consistently determine the unknowns Different levels of trust to the accuracies of the knowns What are the differences of SE for Distribution Grid vs. Transmission Grid? Distribution SE - Data Validation Usecases of Distribution SE Bad Data Detection for real time and estimated measurements Accurate Voltages and Power Factors for VVC Control in real time Accurate awareness of grid operation condition in real time Differences of SE for Dist. Grid vs. Trans. Grid Unbalanced Gird – Single Phase, 2 – 3 Phases, Grounded, Non-grounded Unbalanced Loads, Unbalanced DG Generations Mainly Radial Grid Configuration With Limited or lack of sufficient measurement redundancy Taking advantage of radial network configuration Available for decoupled SE solution: Sub. By Sub. Or even Feeder by Feeder Communications – Foundational Technology Reliable, Empowered, Clean, Efficient, Productive Complex Event Processing, Analytics, and Visualization Smart Meters Volt/VAR Control Smart Substation Design Tools Microgrid Applications Energy Efficiency DERM Modular Design Monitor & Asset Diagnostics Management Energy Demand EV Charging Storage Response Automation Renewable Workforce Modular Generation Management Substations FDIR/FLSIR Smart Appliances Physical Enterprise Adapters Adapters Business Process Management Domain Services Data Services Electrical Network Model Federated Data Model System Security Services Head End Systems Backhaul Plant LAN Generation System WAN Transmission Substation LAN Substations Field LAN Customer Area Networks Distribution Customer Premise Distributed Generation and Storage Economical, Societal and Environmental Sustainability Security and Privacy Comms Data Services Multiple Applications Platforms NIST- Recognized Standards Release 1.0 Following the April 28-29 Smart Grid Interoperability workshop, NIST deemed that sufficient consensus has been achieved on 16 initial standards On May 8, NIST announced intention to recognize these standards following 30 day comment period NIST’s announcement recognized that some of these standards will require further development and many additional standards will be needed. NIST will recognize additional standards as consensus is achieved Standard Application AMI-SEC System Security Requirements Advanced metering infrastructure (AMI) and Smart Grid end-to-end security ANSI C12.19/MC1219 Revenue metering information model BACnet ANSI ASHRAE 135-2008/ISO 16484-5 Building automation DNP3 Substation and feeder device automation IEC 60870-6 / TASE.2 Inter-control center communications IEC 61850 Substation automation and protection IEC 61968/61970 Application level energy management system interfaces IEC 62351 Parts 1-8 Information security for power system control operations IEEE C37.118 Phasor measurement unit (PMU) communications IEEE 1547 Physical and electrical interconnections between utility and distributed generation (DG) IEEE 1686-2007 Security for intelligent electronic devices (IEDs) NERC CIP 002-009 Cyber security standards for the bulk power system NIST Special Publication (SP) 80053, NIST SP 800-82 Cyber security standards and guidelines for federal information systems, including those for the bulk power system Open Automated Demand Response (Open ADR) Price responsive and direct load control OpenHAN Home Area Network device communication, measurement, and control ZigBee/HomePlug Smart Energy Profile Home Area Network (HAN) Device Communications and Information Model Communication Protocols Control Center to Control Center • IEC 60870-6/TASE.2 – Inter-control Center Communications Protocol (ICCP) Control Center to Field Equipment • IEEE 1815 (DNP3) – North American Suppliers • IEC 60870-5 – European Suppliers • 101 – serial communications • 103 – protection devices • 104 – TCP/IP (network communications) Field Equipment • IEC 61850 – substation automation and protection • IEEE 1815 (DNP3) – substation and feeder device automation IoT and Analytics Internet of Things (IoT) Drive the next productivity revolution by connecting intelligent machines with people at work The “II” Connects… 1. Intelligent Machines Leverage technology & communication to costeffectively connect machines + 2. Big Data & Analytics Combine the power of big data, big analytics, and industry physics + 3. People at Work Connecting people any place, any way, and any time for intelligent operations = A world that works better, faster, safer, cleaner and cheaper Energy Value: Global Energy Capex $1.9T/year The first 1% annual savings equals $300B over 15 years Analytics Meter Insight Outage Insight Reliability Insight Renewables Insight Consumer Insight (in development) (in development) (in development) (in design) (in design) • Automated KPI data validation • Dynamic KPI dashboards • Outage Event Recorder • Planned outage optimization • Predictive Outage Analytics • Accurate ETR • Predictive vegetation management • Asset health analysis • System health analysis • Lifecycle analysis and portfolio optimization • Revenue Protection • Power Quality and Reliability • Load Forecasting and Research • PV load (dis)aggregatio n/ hotspot analysis • Wind load (dis)aggregatio n and hotspot analysis • EV penetration/ impact analysis • DER load orchestration • Social media integration • Customer Segmentation • Customer Engagement • Sentiment Analysis Research Opportunities Impact of High Penetration of Rooftop Solar PV on the Distribution System New Applications of Power Electronics (my Power Electronics magazine article – August 22, 2013 issue) • Substation Transformer On-line Tap Changer • Low Voltage Network Dynamic Grid Edge Controllers • Increased capability from Inverters The Death Spiral (Intelligent Utility magazine article – November /December 2013 issue) • Impact of High Penetration of Rooftop Solar PV in the State of Queensland, Australia Thank You! The agile fractal grid RED Substation BLUE Substation RED Substation BLUE Substation RED Substation BLUE Substation RED Substation BLUE Substation But what if simple back feeding is not enough? Resiliency from Advanced Sensor Technology + Advanced Forecasting + Advanced Analytics + Advanced Control Apply all “smart grid technologies” in a coordinated way – Smart feeder switching – Advanced sectionalization – Rolling disconnects (down to meter level) – Dispatchable backup generators – Distributed energy – Advanced Volt/VAR control – Storage ……. That’s how the agile grid can work on a feeder. How would it look at a system level On a nice day, the utility is running smoothly. Everything is “Hot” A bad storm comes through and some areas lose power But backup power, distributed generation, and stored energy allow some areas to island and continue operation… Smart control systems allow the islands to network This may allow some power to be restored in additional areas as the utility works to bring dark areas on line Soon, everyone has power The smaller grids reconnect with the utility OK, it is an interesting concept, but how does it work? All grid applications have the same basic structure Implement Action Implement Action Derive Decision Derive Decision Perform Analysis Perform Analysis Transform and Organize Data Transform and Organize Data Collect Data Collect Data Abstraction Model Action Decision Analysis Information Data
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