Smart Grid Deployment Experience and Utility Case Studies
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Transcript of Smart Grid Deployment Experience and Utility Case Studies
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 IT3. Smart Grid Case Studies
• Duke Energy Ohio Case Study• TPDDL Smart Grid Journey• Global AMI Deployment Overview
Presentation Structure
3
Developing the Utility Roadmap - Smart Grid Maturity Model
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 Model1 2
2Enabling
Investing based on clear strategy, implementing first projects to enable smart grid
1Initiating
Taking the first steps, exploring options, conducting experiments, developing smart grid vision
0Default
Default level (status quo)
SGMM Product Suite
Breaking new ground; industry-leading innovation5
Pioneering
Optimizing smart grid to benefit entire organization 4
Optimizing
Integrating smart grid deployments across the organization
3Integrating
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
Strategy, Mgmt & Regulatory
SM
R
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
WA
M
Asset monitoring, tracking & maintenance, mobile workforce
Technology
TE
CH
IT architecture, standards, infrastructure, integration, tools
Customer
CU
ST
Pricing, customer participation & experience, advanced services
Value Chain Integration
VC
I
Demand & supply management, leveraging market opportunities
Societal & Environmental S
E 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
• 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
Def
inin
g IT
-OT
for U
tiliti
es
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
Driv
ers
for I
T-O
T C
onve
rgen
ce 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 fasterto 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
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 Grid9
ITOT
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
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 AnalysisUnbalanced load flow
calculations
Optimum Switching plan determined
IT
OTKey
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
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
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
Key
Ele
men
ts
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
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%
Key
Ele
men
ts
Source: Duke Energy Ohio Smart Grid Audit and Assessment, 2011
System Voltage Reduction
Off-cycle / off-season meter
reads
Regular meter reads
Meter opera-tions – Avoided
capital costs
Vehicle Management
Power Theft Meter accuracy improvement
Remote Meter Diagnostic
Others Total
156
5450
17 10 8 8 7
73
383
383
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 De-ferment
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
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TPDDL Smart Grid Case Study
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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
1818
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
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
C O M MN.
N E T W O R K
TRANSCO GRID STN
TPDDL GRID STN
DIST SUB STN
CUSTOMERS
D I SASTER RECOVERY
WEB
DA
EMS
DMS
OMS
SCADA
CRM
Billing
SAP
GIS
Call Centre
AMR
Smart Grid Initiatives – ICT Architecture (2/7)
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RG-3 SUB Ring 1STM 4 Σ2
2
2 2
2
2
Σ
Σ
RG-5
PUSA ROAD
RANIBAGH GRID
Saraswati garden
NARAYANA PH-I
CORE RINGSTM 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
Σ2
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 BAGHSHAKTI NAGAR DO
GTK Grid
SHALIMAR BAGH
PITAM PURA DO
PP III
PP II
MGP-II
INDER PURIHUDSON 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 RingFiber Main Ring
Σ Grids2 Enterprise DATA Σ2 Enterprise and Grid
VSNL VSNL Gateway for internet
RAMA ROAD Σ
Σ2
Σ2
Σ2
Σ2
Σ2
2NARELA DO
DSIDC2 NARELA
SUB Ring 3STM 4
SUB Ring 2STM 4
SUB Ring 4 STM 4
SUB Ring 5STM 4
Smart Grid Initiatives – Communication Network (3/7)
Entire TPDDL network over Six Rings covering all grids to serve system operations and other applications
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
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
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
Smart Grid Initiatives – Business Process Digitisation (7/7)
Integrated GIS-SAP-SCADA-DMS-OMS
GIS
Survey
Digitization
Redlining
SAP-PMDesign Manager
Asset Management
SCADAOperations M
anagement
DMS
OMS
Vehicle Tracking
Field Automation
Consumer Indexing
Consum
er Managem
ent
SAP-ISU
All Customer interactions and processes automated for providing Best-in-Class services
26
Turnaround Snapshot
Parameter Unit Jul 02 Mar 15%
changeOperational 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.
Cum
ulati
ve b
enefi
ts
Technology Complexity
New Technologies > New Applications > Increased Benefits:
28
Current scope
Shaping Demand
Additional services
Cum
ulati
ve b
enefi
ts
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)
30
Way Ahead… (4/6)
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”…
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Way Ahead… (5/6)
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)
ATO
ADO
AMI
Expected cost benefit scenario…
Bene
fits
Cost
4Q 20132Q 2009 2Q 20111Q 2007
• Grid Substation Automation System;
• SCADA System;
• Communication 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 1Score # 1.69
SGMM - Level 2Score # 2.5
SGMM - Level 3Score # 3.6
SGMM - Level 4Score # 4.5
The journey so far and the future steps…
34
Conclusions
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
South Korea (Jeju)
Total investment: USD 91 MN AMI: 2190 households, 45 large consumersBenefit: USD 75 MN (Private)
Sweden
Total investment: Euro 1.5 bn / 6 yr Smart Meter: 5.2 millionBenefit: service quality improvement, customer satisfaction and improved safety on the network.
Ireland Pilot
AMI: 6000 MeterEnergy Reduction: 2.5%Peak Reduction: 8.8%
USA (California)
Total investment: USD 750 MNSmart Meter: 1.7 MNBenefit: Increased operational efficiency and reliability
Global AMI Deployment Results Summary
Canada(Ontario)
Smart Meter: 4.5 MillionProject Cost:$1 billion CDN for AMI installationProject 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 MillionProject Cost: Euro 2.1 Billion/5 yrBenefit: 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
Key
Ele
men
ts
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
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.
Thank You