Information Technology and Infrastructure: Benefits, Costs, and Dependencies MIRIAM HELLER, Ph.D. NATO SCIENCE PROGRAMME in conjunction with the Carnegie Bosch Institute ADVANCED RESEARCH WORKSHOP Life Cycle Analysis for Assessing Energy and Environmental Implications of Information Technology Budapest, Hungary September 2, 2003

Sept. 2, M. Heller ©Messages  ICT Confers Benefits To Infrastructure Systems; (Avoided) Costs May Be Easier to Quantify  Infrastructure Systems Differ from Other Manufacturing and Service Systems  Infrastructure Dependencies May Give Way to Indirect Environmental and Energy Consequences, Which Could Figure Into Life Cycle Cost/Benefit Analysis of ICT and Infrastructure System Planning and Management

Sept. 2, M. Heller ©TOPICS  Infrastructure Systems  Infrastructure Interdependencies  Benefits and Costs of IT and Infrastructure Systems  Related IT and Infrastructure Research – Cyber* Futures at NSF  Challenges for Research

Sept. 2, M. Heller © A Definition of Infrastructure Systems  Networks of facilities and institutions  Essential to life, economic well- being, and national security.  Support the flow of people, energy, other resources, goods, information, and basic services

Sept. 2, M. Heller © Critical Infrastructures (PDD 63) Potable & Waste Water Banking & Insurance GovernmentGovernment Emergency Response TransportationTransportation Oil & Gas ElectricityElectricity Telecom- munications

Sept. 2, M. Heller © Integrated Information Systems

Sept. 2, M. Heller © ICT Benefits for Infrastructure Systems Tim e Performance and Efficiency Baseline from Core Utility Processes Automated Monitoring, Sensing, Data AcquisitionProcess Control / Supervision (Adapted from Heller et al.,1999) Shared Objectives Enterprise ArchitectureEnterprise Integration/ Optimization Shared Data Communications ArchitectureProduct Integration/ Interoperability Industrial EcologyCommunity Eco-efficiency/ Sustainability Shared Resources / Environment

Sept. 2, M. Heller © Infrastructure Systems: Some Reflections  Differ from Manufacturing Systems – Provide critical services / lifelines – Geographically distributed – One-offs with many degrees of freedom – Highly interconnected – Subject to uncertain and uncontrollable ambient conditions  Life-Cycle Modeling Differences – Uncertainty High consequence / low probability events vs. slow consequence / high probability events Life-span definition (whole-life) – Complexity

Sept. 2, M. Heller © Transportation Oil & Natural Gas ELECTRICITY Potable & Waste Water Emergency Response Government IT &TELECOM Banking & Finance Infrastructure Interdependencies Switches, control systems Storage, pumps, control systems, compressors e-commerce, IT Pumps, lifts, control systems Signalization, switches, control systems e-government, IT Medical equipment Water for cooling, emissions control Water for production, cooling, emissions control Fire suppression Cooling Fuel transport, shipping Chemicals transport Transport of emergency personnel, injured, evacuation Communications SCADA Trading, transfers SCADA Communications Location, EM contact Generator fuels, lubricants Heat Fuels, lubricants Fuels, Heat Currency (US Treasury; Federal Reserve ) DOE; DOT Regulations & enforcement FERC; DOE Personnel/Equipment (Military) Financing, regulations, & enforcement SEC; IRS FEMA; DOT DOT EPA Detection, 1 st responders, repair Financing & policies

Sept. 2, M. Heller © Köningsberg on the Pregel River with 7 bridges. Cross each bridge exactly once and return to starting position. In 1736, Leonhard Euler the Swiss Mathematician idealized this as a system of nodes and arcs. Euler proved that it cannot be done unless every node is connected to every other with even degree. Science of Engineered Networks

Sept. 2, M. Heller ©  Random networks, generated by randomly connecting a new node with an existing node, have on average, the same number of connections per node, e.g., National Highway System (Barabási, 2002). Distribution of nodes connections is normal.  Scale-free networks (WWW, air traffic routes, social networks) arise when new nodes connect preferentially to already well-connected nodes. Most nodes have few connections: a few nodes are heavily connected hubs. Distribution of nodes connections follows a power law. Science of Engineered Networks: Dependencies

Sept. 2, M. Heller © Power Grid Outages Follow Power Law Frequency (per year) of outages > N Data from NERC (Amin, 9/10/01)

Sept. 2, M. Heller © ICT Impacts Infrastructure Systems Example: 2001 California Power Crisis  Disrupted fuel production, refining, and distribution, sometimes cut off fuel supplies to the very plants that should have been generating their electricity  Interrupted water distribution affected the state's agribusiness  Soaring wholesale power prices impacts rippled through the region, leading to relaxation of salmon- protection and air-quality regulations and shutdown of aluminum mills in Washington state. Idaho farmers curtailed potato production to exploit Idaho Power Company's electricity buy-back program

Sept. 2, M. Heller © Coupled Systems Frameworks : Rinaldi et al., 2001 Type of Failure Infrastructure Characteristics State of Operation Types of Interdependencies Environment Coupling/ResponseBehavior Loose/Tight Linear/Complex Escalating Cascading Common Cause Spatial Temporal Operational Organizational Economic Legal/ Regulatory Technical Social/ Political Physical Cyber Logical Geographic Adaptive Inflexible Stressed/ Disrupted Repair/ Restoration Normal Business Public Policy Security Health/ Safety Natural Environment ?

Sept. 2, M. Heller © State of the Water/Wastewater System  Size – 15,000 Publicly-Owned Wastewater Treatment Plants – 100,000 Pumping Stations – 160,000 Public Potable Water Systems  Operations – Accounts for 3-7% Total US Electricity Consumption – ASCE Estimates $12 Billion Needed for Maintenance 2012

Sept. 2, M. Heller © ICT Benefits for Water/Wastewater Systems Tim e Performance and Efficiency Baseline from Core Utility Processes (Adapted from Heller et al.,1999) Shared Objectives Utility Business ArchitectureUtility Integration/ Optimization Shared Data Utility Communications ArchitecturePlant Integration/ Interoperability Automated Monitoring, Sensing, Data AcquisitionProcess Control / Supervision Process Level IT (SCADA, GIS, EMS, CIS, MMS, LIMS, hydraulic, water quality, and distribution network models  Reduced Chemical and Energy Consumption, Lower Operating Costs, Improved Regulatory Compliance, Higher Reliability, and Improved Customer Service, Inventory Control, and Maintenance Management

Sept. 2, M. Heller © Harnassing Complexity through Shared Resources Energy and Water Quality Management Systems (Jentgen, 2001) Energy Cost Scheduler (Electric Utility) OperationsOperations Water Quality Analyzer Water Source Analyzer Raw Water Supply/ Water Treatment Plant Pump Stations Wastewater Treatment Plant DistributionDistribution CustomerCustomer CollectionCollection Consumption Forecast Program Automated Maintenance Management System Water Consumption Forecast Management Scheduler Clearance Approvals System Operating Plan Schedule & Control Operating Plan Clearance Work Orders Water Law Water Rights Water Priorities Performance Criteria Hydro Schedule Energy Cost Schedule Interruption Scheduler Signal Operations Planner & Scheduler System Scheduler: Surface Water Treatment Plant Pump Stations DistributionCustomerCollection Wastewater Treatment Operations Planner & Scheduler System Scheduler: Surface Water Treatment Plant Pump Stations DistributionCustomerCollection Wastewater Treatment Water Resource Schedule/Constraints Water Quality Alarms SCADA Data Water Quality Operating Constraints Water Quality Data Utility’s Historical Operating Data Performance Criteria Lab & Field Samples Operating Plan Regulations Power Supply Contract Terms/Conditions Power Suppliers’ Price Schedule

Sept. 2, M. Heller © Potential ICT Benefits for Water/Wastewater Shared Resources / Environment Shared Data Tim e Performance and Efficiency Shared Objectives Baseline from Core Utility Processes Automated Monitoring, Sensing, Data Acquisition Utility Communications Architecture ProcessPlantUtility/Facility Control /Integration/Integration/ SupervisionInteroperabilityOptimization Utility Business Architecture (Adapted from Heller et al.,1999) Industrial EcologyRegional Eco-efficiency/ Sustainability

Sept. 2, M. Heller © Industrial Symbiosis Example: Baytown’s Water Infrastructure (Nobel & Allen, 1998)  21 process, 5 utility streams  75 feasible reuse pathways identified

Sept. 2, M. Heller © Linear Program Formulation I2 GC WTP WWTP I1 I3 Fresh Reclaimed Reused Disposed Exchange Feasibility  Based on water quality parameters (e.g., TOC, TSS, TDS)  Creates input for cost optimization – feasible exchange pathways, i.e., “arcs” – “type” of water – transportation costs

Sept. 2, M. Heller © Industrial Symbiosis: Optimal Water Use (Nobel & Allen, 1998) Metrics Scenario mgd %  $/day %  Base Case ,554- Minimum Cost %57,165-47% Minimum Fresh Water %85,098-22% Fresh Water Usage Cost

Sept. 2, M. Heller © ICT Benefits for Oil and Gas Infrastructure Example: BP’s Texas City Plant  “Project Future” (Bylinsky, Fortune, “Elite Factories,” 9/1/2003) – Combined Refinery / Petrochemical Plant – $30 bbl Oil  $60 of Gasoline, Diesel, Jet Fuel, p-Xylene – 2,740 Employees – 2-year, $75 Million Investment in Computerization and Automation of 650 Key Valves  Returns On Investment – Start-up Time Reduced from 2 Weeks to 3.5 Days – Real-Time Equipment Setpoints Based on Ambient Temperature, Weather, and Product Prices – 3% Less Electricity Used – 10% Less Natural Gas Used – 55% Increase in Productivity } $ Millions and Tons GHG Saved

Sept. 2, M. Heller © State of Oil and Gas Infrastructure Systems  Size – Ports, Refineries, Transportation – 2,000 Petroleum Terminals – Almost 1 Million Wells – 2,000,000 Miles of Oil Pipelines – 1,300,000 Miles of Gas Pipelines and Increasing  Operations – Pipeline and Distribution System Leak Detection Monitoring and Control Systems More Efficient Use of Existing Pipe Aging  Coupled Economic Models on Natural Gas and Electric Power

Sept. 2, M. Heller © State of the Transportation System  Size – 125,000 Miles of National Highway System – 25,000 Miles of Public Roads – 3.76 Million Miles of Other Roads  Operations – FHWA : > $78 Billion / Year Idled Away in Congestion – 50% Total US Petroleum Consumed by Highway Vehicles – > 1/3 GHG Due to Surface Transportation – Major Source of Photochemical Smog and Other Air Pollution – > 40,000 Fatalities / Year Over Past Decade

Sept. 2, M. Heller © Potential ICT Benefits for Transportation  Inform on-line buyers of environmental impacts of shipping options (NAE, 1994; Hawken et al., 1999; Sui & Rejeski, 2002) – Ship or rail: BTU/ton-mile – Truck : >2000 BTU/ton-mile – Air freight : > 14,000 BTU/ton-mile  Reduce Travel: Telework, Telecommute, Teleconference, Virtual Tradeshows  Improve Urban Planning and Policy regarding – Land use – Environmental quality – Social equity – Infrastructure operations and maintenance  Increase On-Board Traveler Productivity

Sept. 2, M. Heller © Potential ICT Benefits for Transportation  Advanced Traveler Information Systems  (Real-time) Influence on Traveler Behavior and Improved Traffic Models  Intelligent Computer Vision Enhanced Traffic Modeling  Improved Traffic Models & Collision Avoidance  Real-time Emissions Monitoring  Coupled Traffic and Air Quality Models  Wireless Communications Networks  Improved Data Acquisition, Data Management, and Traffic Control  Congestion Pricing  Control Demand  En-route Commerce  Optimize Supply  Optimal and/or Dynamic Routing  Intermodal Models  Improved Transportation Models

Sept. 2, M. Heller © State of the Electric Power Grid  Size – ~200,000 Miles of Transmission Lines – 5000 Power Plants, 800,000 Megawatts  Transmission level (meshed network of extra high voltage, > 300 kV, & high voltage, kV, connected to large generation units and very large customers; tie-lines to transmission networks, and to sub-transmission level)  Sub-transmission level (radial or weakly coupled network with some high voltage, kV, but typically only 5-15 kV, connected to large customers and medium sized generators)  Distribution level (tree network of low voltage, or volts, and medium voltage, kV, connected to small generators, medium- sized customers, and to local low-voltage networks for small customers)

Sept. 2, M. Heller ©  Urbanization  load growth – 2.1+ % annual national growth over last 25-years  result in a 50% increase by State of the Electric Power Grid  Nearly no new HV transmission lines in last 25 years  , 30% growth in total U.S. electricity demand is met with transmission network growth of 15% –Re-regulation with privatization –Uncertainty ROIs –NIMBY –Right-of-way restrictions for T&D expansion –Tightening fuel supplies to meet increased demand

Sept. 2, M. Heller © State of the Electric Power Grid  Operations – 8/15/03 blackout affected > 20 millions of people, water supply, wastewater conveyance, transportation, communications, hospitals, banking, and retail sales ICT safety equipment tripped to protect power plants and contain the outage causing cascading failures 9 nuclear power plants automatically powered down safely – EPRI : $1.5 billion for July-Aug 1996 power blackouts – CEIDS : $119 billion / year in power quality disruptions

Sept. 2, M. Heller © Potential ICT Benefits for Electric Power  EPRI/DoD Complex Interactive Networks Initiative  Goal: Develop tools that enable secure, robust and reliable operation of interdependent infrastructures with distributed intelligence and self-healing abilities  Systems’ approach to complex networks: advancing mathematical and system-theoretic foundations – Target theoretical and applied results for increased dynamic network reliability and efficiency – Identify, characterize, and quantify failure mechanisms – Understand interdependencies, coupling and cascading – Develop predictive models – Develop prescriptive procedures and control strategies for mitigation or/and elimination of failures – Design self-healing and adaptive architectures – Trade-off between robustness and efficiency

Sept. 2, M. Heller © “The best minds in electricity R&D have a plan: Every node in the power network of the future will be awake, responsive, adaptive, price- smart, eco-sensitive, real-time, flexible, humming - and interconnected with everything else.” “The best minds in electricity R&D have a plan: Every node in the power network of the future will be awake, responsive, adaptive, price- smart, eco-sensitive, real-time, flexible, humming - and interconnected with everything else.” —Wired Magazine, July The Energy Web: The Energy Web: “…a network of technologies and services that provide illumination…” From M. Amin, 2001

Sept. 2, M. Heller © Enabling ICT for Electric Infrastructure  Materials: Superconductors and wide bandgap semiconductors  Monitoring: WAMS, OASIS, SCADA, EMS  Analysis: DSA/VSA, PSA, ATC, CIM, TRACE, OTS, ROPES, TRELSS, market/risk assessment  Control: FACTS; Fault Current Limiters (FCL)  Distributed resources: Fuel cells, photovoltaics, Superconducting Magnetic Energy Storage (SMES)  Next generation: integrated sensor; 2-way communication; "intelligent agent" functions: assessment, decision, learning; actuation, enabled by advances in semiconductor manufacturing From M. Amin, 2001

Sept. 2, M. Heller © Intelligent Adaptive Islanding From M. Amin, 2001

Sept. 2, M. Heller © System Risk is a Function of System State P(H t,s ) = probability of a hazard at time t (and system state s) P(D s |H t,s ) = probability of a particular level of vulnerability of a system in state s given a hazard at time t (and system state s) E(L|D s ) = expected losses conditioned on the vulnerability of system in state s E(L) =  E(L|d s ) * P(d s |h t,s ) * P(h t,s ) h t,s dsds

Sept. 2, M. Heller © Life-Cycle Infrastructure Asset Management Life-Cycle Design Emergency Response, Diagnosis Multi-Objective Multi-stakeholder Decision-Making Multi-Objective Multi-stakeholder Decision-Making Multi-Objective Multi-stakeholder Decision-Making Multi-Objective Multi-stakeholder Decision-Making Life-Cycle Analysis –Internal, Direct ImpactsInternal, Direct Impacts –External, Indirect ImpactsExternal, Indirect Impacts –Systems EvaluationSystems Evaluation Predictive Maintenance, Sensing, Monitoring, Data (Storage, Transmission, Retrieval) Modeling, Simulation, Recovery, Corrective Maintenance, Deconstruction, Reuse Detection, Preventive Maintenance, Lifetime Extension, Early Warning Social/ Cultural Values Policy/ Law Financial/ Insurance Instruments Organizational Theory Communication / Education Prediction Planning, Training and Preparedness

Sept. 2, M. Heller © Multi-Objective Multi-stakeholder Decision-Making  Allocation problem over various investment options, over various stages of development (R&D, development, implementation) over time with risk/uncertainty  Multiple objectives : efficiency, reliability, security, resiliency, sustainability B/C ( S&M) B/C (ER) 1 ~ 2 ~ 3 : indifferent wrt ER 1 is infeasible wrt obj. S&M 2 >> 3 : 2 dominates 3  Multiple stakeholders : different institutional boundaries, missions, resources, timetables, and agendas

Sept. 2, M. Heller © Challenges for Research in Life-Cycle Analysis of IT and Infrastructure  Critical Infrastructure Inventory Data – Scalable Environmental Knowledge Architecture  Models of Individual Infrastructure Systems  Models of Coupled Infrastructure Systems  System Response and Resiliency – System state /vulnerability analysis – Consequence models (boundaries, data, methods) – Extreme value statistics – Substitute services / alternate pathways  Measures of Network Performance  Life-Cycle Infrastructure Asset Management Modeling

Sept. 2, M. Heller © “CyberInfrastructure” Vision  “Atkins report” – Blue-ribbon panel, chaired by Daniel E. Atkins  Calls for a national-level, integrated system of hardware, software, & data resources and services  New infrastructure to enable new paradigms of scientific/ engineering research and education

Sept. 2, M. Heller © What CyberInfrastructure Means  Infrastructure that enables distributed, reliable, real-time collaboration and analysis requiring large-scale, dynamic information storage and access  Examples of components to be integrated: – Major computational processing capabilities – Unique experimental facilities – High-speed networks – Tele-participation and tele-operation tools – Networks of data collection devices – Data/metadata storage and curation – Data analysis and information extraction tools – Universal access

Sept. 2, M. Heller © What Makes CyberInfrastructure Unique  Cyberinfrastructure : more than the sum of its component parts – the key is integration CyberInfrastructure isn’t just…Unless it also involves… Individual infrastructure components (e.g., devices that collect data, data mining as a science, or big computing resources) Playing an integrative role in a larger system Sharing distributed data across research groups or disciplines Transforming data into meaningful information Data and resources that are collected, processed, and used by a community Distributing collection, storage and access across multiple locations and communities

Sept. 2, M. Heller © Examples of Early CyberInfrastructure  George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES)  Extends national capacity for earthquake engineering through unique, shared infrastructure  What makes NEES CyberInfrastructure? – Real-time video & data enable participation from remote sites – Real-time communications allow experiments to span facilities, link physical experiments with numerical simulation – 15 experimental facilities linked by common network, data repository, tools, metadata

Sept. 2, M. Heller © Examples: NEES’s Distributed Users and Distributed Resources Unique Laboratory Facilities Equipment Site 1 Equipment Site 2 Equipment Site 3 Equipment Site Other Site A Other Site B Practitioners Emergency Communities K-14 Education User Communities Earth.Eng. Researchers Data Repositories & Computational Resources NEES Consortium NEESgrid

Sept. 2, M. Heller © Other NSF ICT-Relevant Programs  CLEANER Small Planning Grants – Nick Clesceri, BES,  Sensors and Senor Networks – Shih-Chi Liu, CMS,  Information Technology Research  Cybertrust and Cybersecurity

Sept. 2, M. Heller © Thank You For Your Attention ! MIRIAM HELLER, Ph.D. Infrastructure & Information Systems Program Director National Science Foundation Tel: