Smart Grid Deployment Experience and Utility Case Studies
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Duke Energy implemented a smart grid project in Ohio with the objectives of improving reliability, reducing costs, and enabling greater customer access to energy use data. The project invested $100 million to install over 140,000 smart meters and distribution automation equipment, benefiting both customers and utilities. Customers gained near real-time energy use data and more accurate billing while utilities saw decreased outage times, reduced system losses and improved data for planning.
![References
[1] "Smart Grid Maturity Model Update - Volume 3," Software Engineering Institute, Carnegie Mellon, 2011.
[2] Jeff Meyers, P.E , "How the Convergence of IT and OT Enables Smart Grid Development," Schneider Electric, 2013.
[3] Sharelynn Moore, Itron;Stephen Butler, Teradata, "Active Smart Grid Analytics™ Maximizing Your Smart Grid
Investments," Itron White Paper, 2009.
[4] Jennifer Hiscock, Natural Resource Canada (Canada); Doon-Joo Kang, Korea Electrotechnology Research Institute,
"AMI Case Book 2.0," 2014.
[5] ABB, "IT/OT Convergence : How their coming together increases distribution system performance," 2012.
[6] metavu, "Duke Energy Ohio Smart Grid Audit and Assessment," 2011.
[7] ABB, "Convergence of Information and Operation Technologies (IT & OT) to Build a Successful Smart Grid".
[8] TCS, "A process approach to Smart Grid deployment," 2013.
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Smart Grid Deployment Experience and Utility Case Studies
- 1. Smart Grid Deployment Experience and Utility Case Studies Partnership to Advance Clean Energy-Deployment (PACE-D) Technical Assistance Program
- 2. 1. Developing Utility Smart Grid Roadmap – Smart Grid Maturity Model • SGMM Overview • SGMM Domains and Levels • SGMM Tata Power Case Study 2. Smart Grid Integration of OT and IT 3. Smart Grid Case Studies • Duke Energy Ohio Case Study • TPDDL Smart Grid Journey • Global AMI Deployment Overview Presentation Structure 2
- 3. Developing the Utility Roadmap - Smart Grid Maturity Model 3
- 4. SGMM is a management tool that provides a common framework for defining key elements of smart grid transformation and helps utilities develop a programmatic approach and track their progress. Smart Grid Maturity Model 1 2 2 Enabling Investing based on clear strategy, implementing first projects to enable smart grid 1 Initiating Taking the first steps, exploring options, conducting experiments, developing smart grid vision 0 Default Default level (status quo) SGMM Product Suite Breaking new ground; industry-leading innovation 5 Pioneering Optimizing smart grid to benefit entire organization 4 Optimizing Integrating smart grid deployments across the organization 3 Integrating SGMM Levels Global Intelligent Utility Network Coalition (GIUNC) developed SGMM and it is currently under the stewardship of the Software Engineering Institute at Carnegie Mellon University Source: SEI http://www.sei.cmu.edu/ 4 SGMM would allow utilities to assess their current smart grid position and reach consensus on the direction and pace of their smart grid journey. SGMM provides a guiding framework to utilities in smart grid planning and implementation efforts
- 5. Strategy, Mgmt & Regulatory SMR Vision, planning, governance, stakeholder collaboration Organization and Structure OS Culture, structure, training, communications, knowledge mgmt Grid Operations GO Reliability, efficiency, security, safety, observability, control Work & Asset Management WAM Asset monitoring, tracking & maintenance, mobile workforce Technology TECH IT architecture, standards, infrastructure, integration, tools Customer CUST Pricing, customer participation & experience, advanced services Value Chain Integration VCI Demand & supply management, leveraging market opportunities Societal & EnvironmentalSE Responsibility, sustainability, critical infrastructure, efficiency Smart Grid Maturity Model - Domains Source: SEI http://www.sei.cmu.edu/ 1 2 5 Domains are logical groupings of smart-grid-related capabilities and characteristics for which the SGMM defines a maturity progression. Each level of maturity within a domain is fully described by a set of expected characteristics and a set of informative characteristics.
- 6. Smart Grid Integration of IT and OT 6
- 7. • Enterprise Resource Planning • Enterprise Asset Management • Mobile Workforce Management • Customer Information Systems • EMS • SCADA • GIS • DMS • Asset Management • Substation Automation Execution, monitoring and control of the electric system Commercial decision making, planning, business processes management and resource allocation Historically, OT and IT for distribution operations have been developed, maintained, and used in silos in a utility organization IT OT DefiningIT-OT forUtilities The need to integrate new types of assets/agents to the electric network and make them “operationally ready” Siloed Smart Grid applications won’t support efficient operation of the distribution system, the full value of the smart grid lies in integration of IT and OT Convergence of IT and OT – Moving away from Process Silos DriversforIT-OT Convergence Different streams of information are stored in silos, resulting in lack of a synchronized view of asset information Large quantity of information with Smart Grid - The IT/OT system must quickly sort through and identify the operationally relevant data points 21 3 7
- 8. Information Technology Big data analytics to generate critical insights and automated actions Insights drive just-in- time work to optimize enterprise Large volumes of data for visibility into condition and status Operational Technology Real time monitoring and control of critical field assets Benefits of the IT/OT Converged Enterprise Respond faster to real time conditions - lower operating and capital costs Accurate data at all times- Improved alignment between operations and business goals Transparent, on-demand reporting enables better decision making and alignment to achieve energy savings goals Convergence of IT and OT – Moving away from Process Silos IT-OT integration helps to streamline the management of the overall system and offers improved workflow and simplified task execution thus enabling high-speed and high-quality decisions. Source: Ventix Presentation, IT/OT Convergence IT/OT Convergence 8
- 9. Convergence of IT and OT – Use Case Enterprise Asset Management Traditional Scenario Convergence of IT-OT Stored Asset Data Maintenance Activity Enterprise Asset Management Real time asset data - SCADA Asset Health Model – Predictive Analytics, trending & forecasting of equipment performance Equipment Alarms/ Notification/ Root Cause/ Potential FaultBased mostly on manufacturer specifications of standard maintenance and required work Work Management System (WMS) Work Order – Replace/Repair Asset Health Monitoring –Automatic monitoring of tasks on all assets in a substation in near real time using and enabling preventive maintenance Source: ABB: Convergence of Information and Operation Technologies (IT & OT) to Build a Successful Smart Grid 9 IT OT Key EAM (IT) store and manage asset data EAM manages maintenance task for asset. In traditional scenario - no consideration of actual working or loading conditions, connectivity, operational parameters, etc. EAM gets near real time data from SCADA (OT) Advanced applications implemented to perform predictive maintenance, trending and forecasting of equipment performance. Analysis used to determine impact of asset performance on overall system (technical & economic) and also remedial actions given via WMS (IT) to field staff improve the asset’s performance. 1 2
- 10. 10 Self Healing Networks – Automatic network monitoring enabling isolation of fault and minimizing its impact on end customers FLISR Application (fault location, isolation, and service restoration) Fault Current Indicator Status Breaker/ Switch Status SCADA Switching control action sent to Field Devices GIS – Network Model Data Analysis Unbalanced load flow calculations Optimum Switching plan determined IT OT Key Source: IT/OT convergence, ABB Review FLISR application gets real time inputs such as fault current, faulted circuit indicator status, breaker/ switch status and network model from GIS Using inputs application determines optimal switching plan to isolate the fault and restore service to as many customers as possible Unbalanced load flow calculations using network model performed to determine any voltage violations for the possible switching plans Once the optimal switching plan has been chosen, the appropriate control actions can be transmitted to the field devices through SCADA (OT) communications Convergence of IT and OT – Use Case Convergence of IT and OT in Smart Grid foster new applications like predictive asset maintenance, smart self- healing and many others which in turn increase efficiency and reduce costs in the industry 1 2
- 11. Smart Grid Case Studies 11
- 12. Duke Energy – Ohio Smart Grid A brief overview of the project background and scope Project Highlight Background Project objectives Project desired outcomes For Consumers • Improved accuracy of billing. • Energy use information available in near real time For Utilities • Decreased billing calls due to reduced bill estimates. • Reduced outage time. • Reduction of system losses due to improved modeling. • Improved data for investment planning 2 1 • To implement distribution automation to help prevent and shorten outages • To enable AMI and reduce the need for estimated bills • To enable remote service connections and disconnections for faster customer service • To capture and post daily energy usage data online so customers can make wiser energy decisions • To incorporate more renewable, distributed generation into the grid • Total investment of USD 100 mn allotted for Ohio grid modernization project in AMI and DA application • ~140,000 new smart grid meters have been installed since 2008 in Ohio impacting 700,000 consumers Sources • eia.gov/analysis/studies/electricity/pdf/sg_case_studies.pdf • naruc.org/international/Documents/Duke%20Smart%20Grid%20%20-%20Don%20Schneider%20Duke%20Energy.pdf
- 13. Duke Energy – Ohio Smart Grid Comparison of traditional grid operations and smart grid operations post deployment of Advanced Metering Infrastructure (AMI) Meter Readers walk from house to house to capture electric and gas meter data with handheld equipment No capability to understand if a customer issue was on the utility or customer-side of the meter Traditional meters did not offer capabilities to detect tampering (mis-wired or bypassed meters) Traditional meters need to be replaced over time resulting in regular capital cost Smart meters send interval data directly to the utility and hence eliminating most of annual meter reading labor costs Real-time remote diagnostic helped determine if meter is operating normally. If meter was receiving voltage, no field personnel are sent to investigate. Smart meters generated tampering alarms and monitored meter data to identify theft. This resulted in increased revenue by 0.5% of overall revenue Smart meters do not require the use of equipment related to manual meter reads such as handheld devices resulting in reduced costs Traditional Operations Smart Grid Operations KeyElements Traditional meters and associated handheld equipment decrease in accuracy over time, requiring routine testing Due to their digital nature, smart meters do not require regular testing to ensure accuracy hence resulting in reducing testing and refurbishment costs Meter Reads Meter Diagnostics Power Theft Capital Costs Operational Costs Source: Duke Energy Ohio Smart Grid Audit and Assessment, 2011
- 14. Duke Energy – Ohio Smart Grid Comparison of baseline grid operations and smart grid operations post deployment of Advanced Metering Infrastructure (AMI) Outage Detection Billing No capability to detect the outage locations and extent of customer outage Issuance of bills were delayed by as much as two days With capability to analyze and detect customer outage using real time meter data it avoided “already restored” tickets and reduced assessor labor costs Bills to be made available on the first day of the billing cycle leading to acceleration of cash collections and interest expense reduction Traditional Operations Smart Grid Operations Apart from financial benefits, implementation of smart grid technologies like AMI provided social benefits through reduction in fuel consumption, CO2 emissions, increasing energy efficiency, and enabling a cleaner environment Vehicle Management Traditionally meter readers used meter reading vehicles to manually read meters on door-to-door routes Metering data is communicated via wireless network to utility which reduces need for manual meter reads, resulting in the reduction of vehicles used for meter reading Accuracy Improvement Traditional meters on average, register a slightly lower energy use reading than actual consumption. The electric smart meters do not have moving parts and can correct temperature-related error, making them inherently more accurate and resulting in revenue gains of 0.3-0.35% KeyElements Source: Duke Energy Ohio Smart Grid Audit and Assessment, 2011
- 15. 383156 54 50 17 10 8 8 7 73 System Voltage Reduction Off-cycle / off- season meter reads Regular meter reads Meter operations – Avoided capital costs Vehicle Management Power Theft Meter accuracy improvement Remote Meter Diagnostic Others Total Estimated 20 Year Net Present Value of Operational Benefits (in USD million) Duke Energy – Ohio Smart Grid Estimation of NPV of operational benefits through deployment of Advanced Metering Infrastructure (AMI) and Distribution Automation (DA) system Break-up of benefits based on savings category 35% 34% 17% 14% O&M Cost Savings Fuel Cost Savings Capital Deferment Incresed Revenue Total 20 Year NPV Savings USD 383 million Source: Duke Energy Ohio Smart Grid Audit and Assessment, 2011 Source: Duke Energy Ohio Smart Grid Audit and Assessment, 2011 A number of operational benefits are unlocked as a result of AMI implementation which generate positive NPV for the project - Thus allaying fears of utilities, if any of high initial costs of smart grid implementation Break-up of benefits based on functionality $212 Million, 55% $171 Millon, 45% DA AMI Total 20 Year NPV Savings Source: Duke Energy Ohio Smart Grid Audit and Assessment, 2011
- 16. TPDDL Smart Grid Case Study 16
- 17. 17 Regular Power Cuts, Black Outs & Brown Outs 20,000 applications pending for New Connections - even Attribute change (Name, Load etc.) requests were pending for years 1,00,000 Billing Complaints - 15% of the customer base complaints pending in files Erroneous Customer Database – 50% of customers had some form of an error Absence of Customer Relationship approaches – virtually no emphasis on customer comfort No Digitization - Limited Computerization / Absence of CRM for tracking and monitoring of Customer Complaints Nothing moved unless long hours were spent standing in queues Initial Challenges – 2002
- 18. 18 18 Regulator (DERC) • Operational Excellence • Consumer Satisfaction • Affordable Tariffs • Sectoral Subsidy Elimination • Ethical, Safe and Environmental Friendly Practices Consumers • 24X7 Supply • Affordable Tariffs • Ethical, Safe and Environmental Friendly Practices • Error Free and Timely Services • Proactive Communication Community Business Associates • Support to local communities • Ethical, Safe and Environmental Friendly Practices • Ethical and Safe Practices • Timely Payment • Proactive Communication • Long Term Association Requirement of Enhanced Consumer Satisfaction while following Ethical, Safe and Environmental friendly operations Stakeholder Expectations
- 19. Communication Infrastructure (OF, Radio) SCADA/EMS/DMS (Siemens Sinaut Spectrum 4.5) Grid Station Automation Enterprise Resource Planning (SAP) Distribution Automation GIS (GE Small World 4.0) Network Planning Tool (CymeDist 3.5) Automatic Meter Reading (Homegrown) Outage Management System (GE PowerOn 2.1) Enterprise Application Integration 19 TPDDL Smart Grid Story – Milestones (1/7)
- 20. 20 C O M M N. N E T W O R K TRANSCO GRID STN TPDDL GRID STN DIST SUB STN CUSTOMERS D I S A S T E R R E C O V E R Y W E B DA EMS DMS OMS SCADA CRM Billing SAP GIS Call Centre AMR Smart Grid Initiatives – ICT Architecture (2/7)
- 21. 21 RG-3 SUB Ring 1 STM 4Σ2 2 2 2 2 2 Σ Σ RG-5 PUSA ROAD RANIBAGH GRID Saraswati garden NARAYANA PH-I CORE RING STM 16 FIBER RING - TPDDL RANIBAGH CCC NEW ROHTAK ROAD Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ Σ 2 Σ Σ 2 Σ Σ Σ Σ Σ WZP-II INDER VIHAR AZAD PUR WAZIRABAD CIVIL LINES SARASWATI GARDEN PANDU NAGAR VSNL S PARK KESHAV PURAM DO ROHTAK ROAD RAM PURA TRI NAGAR ASHOK VIHAR H BLOCK CCC GULABI BAGH SHEHJADA BAGH SHAKTI NAGAR DO GTK Grid SHALIMAR BAGH PITAM PURA DO PP III PP II MGP-II INDER PURI HUDSON LINES WZP-I ASHOK VIHAR GRID MGP-1 Σ2 RG-IVRG-22 RG-23 BAWANA GRID-6 POOTH KHURD GRID BAWANA WATER WORKS and Bawana DO DSIDC A7, NARELA DSIDC1 NARELA RG-1 PP-1 HDR’PUR SGTN JAHANGIR PURI AIR KHAMPUR BADLI RG-6 RG-II Fiber Sub Ring Fiber Main Ring Σ Grids 2 Enterprise DATA Σ2 Enterprise and Grid VSNL VSNL Gateway for internet RAMA ROAD Σ Σ2 Σ2 Σ2 Σ2 Σ2 2 NARELA DO DSIDC2 NARELA SUB Ring 3 STM 4 SUB Ring 2 STM 4 SUB Ring 4 STM 4 SUB Ring 5 STM 4 Smart Grid Initiatives – Communication Network (3/7) Entire TPDDL network over Six Rings covering all grids to serve system operations and other applications
- 22. Fully Scalable System Complete relay data monitoring Metering data for Energy Audit DC system monitoring ACDB system Monitoring OLTC remote operation & monitoring SCADA Compatible Stations 22 Sixty seven 66/11 KV & 33/11 KV Unmanned Automated Grid Substations catering to TPDDL Peak Demand of 1700 MW Smart Grid Initiatives – Automation (4/7)
- 23. 23 Smart Grid Initiatives – Unmanned Grid Stations (5/7) Sixty seven 66/11 KV & 33/11 KV Unmanned Automated Grid Substations catering to TPDDL Peak Demand of 1700 MW
- 24. 24 Distribution Automation through SCADA: Centralised Load Dispatch Centre. Remote Monitoring and Control of Sub- Transmission and Distribution Network. Real time monitoring of Generation and Transmission through SLDC and NRLDC interface. Automated Fault Identification & Isolation, Service restoration, Load forecasting & Load Management. Smart Grid Initiatives – SCADA (6/7)
- 25. 25 Smart Grid Initiatives – Business Process Digitisation (7/7) Integrated GIS-SAP-SCADA-DMS-OMS GIS Survey Digitization Redlining SAP-PMDesign Manager Asset Management SCADA OperationsManagement DMS OMS Vehicle Tracking Field Automation Consumer Indexing ConsumerManagement SAP-ISU All Customer interactions and processes automated for providing Best-in-Class services
- 26. 26 Turnaround Snapshot Parameter Unit Jul 02 Mar 15 % change Operational Performance AT&C Losses % 53.1 9.87 81% System Reliability – ASAI -Availability Index % 70 99.96 43% Transformer Failure Rate % 11 0.77 95% Peak Load MW 930 1704 83% Length of Network Ckt. Km 6750 13006 93% Street Light Functionality % 40 99.57 149% Consumer Related Performance New Connection Energization Time Days 51.8 4.6 91% Meter Replacement Time Days 25 3 88% Provisional Billing % 15 2 87% Defective Bills % 6 0.12 98% Bill Complaint Resolution Days 45 6 87% Mean Time to Repair Faults Hours 11 1.34 88% Call Center Performance - Service Level % - 91 Payment Collection Avenues Nos. 20 6725 33525% Consumer Satisfaction Index % - 84
- 27. Way Ahead… (1/6) Current scope Shaping Demand Additional services • Meter reading; • Basic outage management; • Theft detection; • Prepayment; • Billing; • Limited automation. • Real time pricing; • Micro-grids; • Fault prediction; • Smart grid switching; • Home energy automation; • Distributed generation from fuel cells, solar, and online backup generation; • OUTAGE MANAGEMENT. • Time of Use & Peak pricing; • In-home displays; • Integrated disconnect; • Home energy management; • Confirmed load control; • Net metering/ solar; • Home energy audit; • Advanced fault monitoring; • Use of Spatial technologies. Cumulativebenefits Technology Complexity New Technologies > New Applications > Increased Benefits: 27
- 28. 28 Current scope Shaping Demand Additional services Cumulativebenefits Integrate existing services to new platform; Regulatory approvals for Capex. Transform existing services using advanced communications; Regulatory approvals for Capex; Create new markets. Utility challenges in implementing new technologies… Technology Complexity Way Ahead… (2/6)
- 29. How do we get there….. Modern Grid Milestones: Advanced Metering Infrastructure (AMI) Advanced Distribution Operations (ADO) Advanced Transmission Operations (ATO) Advanced Asset Management (AAM) Way Ahead… (3/6) 29
- 30. Way Ahead… (4/6) 30 Characteristic AMI ADO ATO AAM Enables Active Consumer Participation √ √ Accommodates all Generation & Storage Options √ √ √ Enables new products, services and markets √ √ √ Provides PQ for digital economy √ √ √ √ Optimizes Assets & Operates efficiently √ √ √ √ Anticipates and responds to System Disturbance √ √ √ √ Resiliency to Attack & Natural Disaster √ √ √ Keeping the “End in Mind”…
- 31. Way Ahead… (5/6) 31 AMI establishes communications to the loads, assists revenue management and empowers the consumer. AMI and DR Distribution (ADO) Transmission (ATO) Asset Management (AAM) Expected sequence of milestones…. AAM optimises and improves asset management. ADO enables self healing, improves sales and optimises Opex. ATO optimises Capex, addresses congestion in transmission lines & reduces Opex.
- 32. Way Ahead… (6/6) 32 Expected cost benefit scenario… Benefits Cost
- 33. 4Q 20132Q 2009 2Q 20111Q 2007 • Grid Substation Automation System; • SCADA System; • Communica tion Infra- structure. • Broad band over Power Line (BPL); • DA; • DMS / OMS; • Enterprise Application Integration (EAI); • Billing Systems (SAP); • Distributed Gen (DG); • Network Asset Mgmt. • AMI; • DSM; • Mobile Workforce Management (MWM); • Smart Grid pilot roll out – Stage 1. • Generation Integration; • Transmission Integration; • Smart Grid roll out – Stage 2. Phase 1 Phase 2 Future PhasePhase 3 4Q 2016 33 TPDDL – Proposed Smart Grid Deployment SGMM - Level 1 Score # 1.69 SGMM - Level 2 Score # 2.5 SGMM - Level 3 Score # 3.6 SGMM - Level 4 Score # 4.5 The journey so far and the future steps…
- 34. Conclusions 34 1. SGMM provides a good starting point for utilities to integrate smart grid into its business processes 2. Convergence of IT and OT provides improved decision making abilities enabling efficiency in operation and enhanced customer experience 3. Deployment experience across countries indicates significant benefits at different levels in the distribution segment
- 35. South Korea (Jeju) Total investment: USD 91 MN AMI: 2190 households, 45 large consumers Benefit: USD 75 MN (Private) Sweden Total investment: Euro 1.5 bn / 6 yr Smart Meter: 5.2 million Benefit: service quality improvement, customer satisfaction and improved safety on the network. Ireland Pilot AMI: 6000 Meter Energy Reduction: 2.5% Peak Reduction: 8.8% USA (California) Total investment: USD 750 MN Smart Meter: 1.7 MN Benefit: Increased operational efficiency and reliability Global AMI Deployment Results Summary Canada(Ontario) Smart Meter: 4.5 Million Project Cost:$1 billion CDN for AMI installation Project Benefit: $1.6 billion CDN Global large scale AMI deployment is underway – Countries are realizing ROI through improved service quality, increased operational efficiency and reliability while improving customer satisfaction Source: AMI Case Book Version 2.0, ISGAN 35 Italy (Telegestore Project) Smart Meter: 32 Million Project Cost: Euro 2.1 Billion/5 yr Benefit: Euro 500 Million (yearly saving) 1.5TWh Energy recovered
- 36. Duke Energy – Ohio Smart Grid Comparison of traditional grid operations and smart grid operations post deployment of Distribution Automation (DA) system Load Tap Changers and capacitors in traditional grids not automated Difficult to detect faulty capacitors, capacitors might be offline for a year before being detected No real time data or automation to fine tune system for conditions like peak load Algorithms in the DMS continually make control decisions based on real-time voltage readings (eg. Reduce the voltage drops along the line) providing energy savings and thus reduction in fuel cost Equipment monitoring, faulty capacitors can be identified and repaired or replaced immediately. This improved capacitor effectiveness and enabled the avoidance/deferral of capital expenditures. DMS is engaged to activate fine tuning. Fine tuning enables more efficient distribution of power and resulted in less capital investment for handling peak load and improved overall operating expenses Traditional Operations Smart Grid Operations KeyElements No capability to analyze real time load data or perform automatic on-demand load switching Improved grid data access and analysis capabilities is used for optimized load switching. Resulting in delayed capacity upgrades by one-two years thus deferring capital expenditures. System Voltage Reduction VAR Management System Fine- tuning Asset Management Source: Duke Energy Ohio Smart Grid Audit and Assessment, 2011 36
- 37. References [1] "Smart Grid Maturity Model Update - Volume 3," Software Engineering Institute, Carnegie Mellon, 2011. [2] Jeff Meyers, P.E , "How the Convergence of IT and OT Enables Smart Grid Development," Schneider Electric, 2013. [3] Sharelynn Moore, Itron;Stephen Butler, Teradata, "Active Smart Grid Analytics™ Maximizing Your Smart Grid Investments," Itron White Paper, 2009. [4] Jennifer Hiscock, Natural Resource Canada (Canada); Doon-Joo Kang, Korea Electrotechnology Research Institute, "AMI Case Book 2.0," 2014. [5] ABB, "IT/OT Convergence : How their coming together increases distribution system performance," 2012. [6] metavu, "Duke Energy Ohio Smart Grid Audit and Assessment," 2011. [7] ABB, "Convergence of Information and Operation Technologies (IT & OT) to Build a Successful Smart Grid". [8] TCS, "A process approach to Smart Grid deployment," 2013. 37
- 38. Thank You