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Transcript of Dr. Rahul Tongia Advisor, India Smart Grid Forum Fellow, Brooking Institution Adj. Professor,...
Dr. Rahul TongiaAdvisor, India Smart Grid Forum
Fellow, Brooking InstitutionAdj. Professor, Carnegie Mellon
Tech. Advisor, India Smart Grid Task Force
November 15, 2014Smart Grid Boot Camp, BMS
Smart Grids, Smart Future, and More…Why it won’t be Business as Usual
• DISCLAIMER: These slides are supplementary material or complementary to a live lecture – they may not be complete in a standalone manner
Smart Grids have Arrived in India!
But one has to be cautious where we are…
• Outlook Business Aug. 7, 2010
No single Definition of Smart Grids• “A smart grid delivers electricity from suppliers
to consumers using digital technology to save energy, reduce cost and increase reliability.”
-- Wikipedia
(More formal definitions are far more complex)E.g., US Dept. of Energy talks of 7 Functionalities
4© Dr. Rahul Tongia
Stylized Definition of Smart Grids
A Smart Grid is a Transformation of the power system based on harnessing digital communications and control
Utilities will be able to: Know what power is going where, and when Charge “appropriately” for it Control the use of (if not flow) of power
Although Advanced Metering Infrastructure (AMI) is considered to be the basic building block for a Smart Grid, the Smart Grid is not just AMI! The Smart Grid is a much broader set of technologies and solutions
5© Dr. Rahul Tongia
6
Centralized Generation
Transmission Network
Supplier Transactions
Inter-Connections
Distribution Network
Meters & Displays
Distributed Generation
Electric Vehicles
Loads and Appliances
Energy Efficiency
Micro-Generation
Consumer Behavior
CONSUMER DEMAND
Smart Grid
Smart Metering / AMI
© Dr. Rahul Tongia
Broad Aspects and Drivers for Smart Grids
• Generation– Distributed– Renewable
• Transmission– Improve transfer capacity– Reliability (avoid blackouts)
• Distribution– {Includes consumption}– Area of most effort– One aspect is “smart metering”– Others include Demand Response aka Load Control
• Dynamic instead of mere DSM7© Dr. Rahul Tongia
What Smart Grids really mean
8
• 1. More choices– Includes renewables
• 2. Better quality and service• 3. Greater resiliency / robustness• 4. Increased efficiency and asset
utilization
• Cost Implications*
↑ ?
↑ ↑
↑ ↑↓ ↓
Drivers for Smart GridsUS and OtherDeveloped Countries
– Meter reading– Grid modernization– Robustness– Saving $$
• Deregulation exposed a lot of costs
– Some consumers saw 20-40% increase in tariffs
• Needs Time of Use (ToU) if not Real Time Pricing (RTP)
9
Indian (Developing Country)
– Power system has challenges
• Loses Rs. 1+/kWh on average
• Supply << Demand– 20+% shortfall
– Growth is a big need– Theft is a major concern
• Large segment of load is unmetered (agriculture)
– Reforms ongoing• May allow new operating
models
Future (or even Subtle) Drivers
• US and Others– Carbon and green– Bi-directional power
• (Plug in) Hybrid vehicles
– New services• Home automation• Home monitoring• Green Power
10
• India– Remove the “human element”
in operations– The peak is NOT industrial– Smart peak management
• No more load shedding• Even in emergencies can allow
smart control
– LEAPFROG
Why we NEED Smart Grids
• Cost of supply is only increasing• Ancillary Services (non-kWh markets/contracts)
are lacking in India• Availability of supply is limited
– Load shedding is expensive to consumers• Peak demand is growing faster than baseload
11
Indian Examples of Functionalities
• Loss reduction– Requires precise and full metering– 15 minute or 30 minute or even hourly readings can
help give visibility for operations• Ending load shedding
– Only two options• Buy more (peak) power• Reduce Demand• (Third “Option” is to load shed!)
12
Peak is growing faster than average (Independent System Operator-New England [ISO-NE] Example)
13
[Source: Kathleen Spees, CMU/CSTEP]
© Dr. Rahul Tongia
Peak Load in ISO-NE Change Between 1980 and 2006
[Source: Kathleen Spees, CMU/CSTEP]
Load Duration Curve – Karnataka 2011
15
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
Total Demand
Restricted Demand
Hour
Dem
and
(MW
)
1~8760
(few missing points)Source: KPTCL Load Despatch (raw data)
16
Saving Energy
• Old Way: Demand Side Management (DSM)– Examples?– Issues?
• New Way: Demand Response– Reduce load (demand) in response to a signal– Dynamic, by definition– Essentially, become a Virtual Power Plant
AutomatedMeter
Reading
Outage Managem
ent
Better load forecasting
Theft
detectionRevenue
Protection
AMI Active Load Control
Advanced Metering Infrastructure (AMI) = Basic building block for a Smart Grid
Challenges in doing a Pilot
• Pilot may be limited to “off the shelf” components/design• Need vendors/partners with SG experience and expertise• Design goals
– Open standards– Scalability– Modularity
• Must rethink the entire ecosystem of providers– This is not like R-APDRP
• There is no SRS or template• The solutions are evolving and must be iterative
– “Lowest Cost” per se is a false choice• Lifecycle costs matter• Performance (functionality) matters• Pilots will always be more expensive!
18© Dr. Rahul Tongia
Pilot Projects:Possible Varying Functionality in stages
(not necessarily linear)
• Smart Metering• Reliability and Robustness (supply switching)• Renewables, storage, and distributed generation• Load control and Demand Response
– Smart Appliances– Signaling to consumers and devices [who controls is TBD]
• Sensor networks, etc.
19
ICT for Power Systems:Accounting → Auditing → Monitoring → Control
(R-APDRP) © Dr. Rahul Tongia
MESCOM/CSTEP Mini-Pilot• Designed, implemented and demonstrated some
functionalities (AMI, consumer Load control and street light control)
• Proof-of-concept (first of a kind) demo
• Adding a Smart Node with current technology static meters to achieve precision metering and load control– Mixed loads– Monitor usage (and losses) with high precision– Control/curtail loads
• Street lights• Aggregate consumer loads
– Ability to end load-shedding at feeder level• All consumers can get assured (minimum) supply 24/7
– Total size ~90 nodes (few DTs)
20© Dr. Rahul Tongia
MESCOM Mini-Pilot
21
Adding a Smart Node (memory, logic, communications, and connect/disconnect switch) with current technology static meters to achieve precision metering and load control
Proof-of-concept (first of its kind) demo
Data Concentrator
Street Light Controller
11kV Line
Smart Node+
Energy Meter
Central Server Schematic Diagram
1 2 - - - - - - n1 2 - - - - - - n
DT 1 DT 2
Data Concentrator
Street Lights
© Dr. Rahul Tongia
22
Industrial Area
Site of the Pilot Project at Mangalore:
Smart Node
23
Existing Energy meter Smart Node
Smart Node = Memory, Logic, Communications, and remote Connect/Disconnect
© Dr. Rahul Tongia
0
0.2
0.4
0.6
0.8
1
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
00:00:00 02:00:00 04:00:00 06:00:00 08:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00 00:00:00
Pow
er F
acto
r
Load
(KW
)
Time of Day
Load and Power Factor variation at randomly selected flat (No. 18 above) within MESCOM Smart Grid Pilot
15 Minute Readings
LOADMonday SundayMarch 7, 2011 March 6, 2011
Power FactorMonday SundayMarch 7, 2011 March 6, 2011
0
50
100
150
200
250
300
350
400
450
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18* 19 20 21 22 23 24
kWh
House (Randomized)
March 2011 consumption at each occupied flat at Kadri Heights Apts (MESCOM Smart Grid Pilot)
Average = 166 kWh
166 kWh would incur energy charges of Rs. 564(marginal rate of Rs. 4.2/kWh)
Observations from the mini-Pilot
24© Dr. Rahul Tongia
Impacts on Consumers
• Carrots– No load shedding – Increased accuracy in Billing– (possible) Personalized data (e.g. Home Display)– Better knowledge and control over energy usage – Opportunity to reduce energy bills (Using ToU tariffs)– Option for pre-paid connection– Less Power outage or less momentary interruptions– Quick fault detection– Faster restoration of faults– Better Power Quality– No Regional Blackouts
• Sticks– Penalties for violations or non-compliance – Possibility of disconnection (remotely)– Increased accuracy in billing (bill may go up, and no more chicanery)
25© Dr. Rahul Tongia
Lessons Learned and Moving Ahead• Scaling to thousands of consumers and then the city
– Back end integration– Cyber security– In-home signaling and/or display
• New pricing schemes (with regulatory approval)• Generating data for doing a TRUE cost-benefit
analysis• Business models that can be funded and viability
• Phased plan for moving forward…
26© Dr. Rahul Tongia
What do we need?• Improved solutions
– IT adage: “Faster, Cheaper, Better – Pick any two”– Every ingredient exists today
• Need these to become– Robust– Modular– Inter-operable– Standards-based
• Open, multi-stakeholder discussions– Harness SG Forum, perhaps– SGs succeed if the solution is right
• SGs FAIL because of the consumer’s unhappiness
27© Dr. Rahul Tongia
Let’s not wait forever (or even until perfection)
• SGs are and will remain a moving target– Think broadband (or even buying a computer)
• SGs will take years to roll-out– Southern California Example
• Planning, pilots, and permissions took many years• Held some 140 INTERNAL workshops on SGs
• Some have opined that we are late…• SG are a process, not a product
– Need to innovate more, and work with technology companies• Utilities must articulate their needs, wish list, priorities, etc.• Use case scenarios – Southern California pioneered these – need INDIAN use
cases
28© Dr. Rahul Tongia
One size doesn’t fit all
• A large fraction of benefits will come from a sub-set of consumers– Ala 80:20 rule
• Let’s target those first– E.g., Apartment complexes– Urban areas are likely more viable (in the short run)
• But can’t do some things in parts– E.g., monitoring, load control
• Smallest size is likely a feeder (11 kV)
• This adage applies at the supply side as well– A lot of the effort is not for hardware, but customization, integration,
management, etc.
29© Dr. Rahul Tongia
There are trade-offs in Smart Grid Design
30
BusinessCase
Policy / Regulations
Technology
DESIGN
Conclusion
• A Smart Meter is a lynchpin for a Smart Grid– It needs to be a commodity+ +
• Cheap• Standardized• Easy to figure out (“idiot-proof”)
– Utility more so than consumer– Dashboards, GUI, etc.
• Added features and value• Within a viable ecosystem
– Which relates to functionalities
“The best way to predict the future is to invent it”– Alan Kay
31
(c) Dr. Rahul Tongia 32
Easy Prediction: Change
• Utilities are in trouble• The pressures are increasing• Something has to “break”
• What is Business as Usual?– What is the per capita consumption of electricity?– What’s wrong with the Indian number?
(c) Dr. Rahul Tongia
India is (luckily) not the West
33
For India, it’s not renewables but theft and tiered slabs (tariffs) that can have similar impacts
Source: The Appalachian Voice
34
Problems at Utilities• Fundamentally: unable to deliver electricity service
– Supply << Demand– Shortfall numbers are too low (officially under 5% now)
• Bad methodology, lack of reserves, instantaneous, etc.– Lack of access as well (30% without connections; more without service)
• Losses are high– Financial
• Tariff based• Leakage based
– Technical• Indian system losses are high
– Long rural feeders– Lower voltage transmission lines
(c) Dr. Rahul Tongia 35
Political Economy does matter
• Utilities are (mostly) state-controlled• Lose ~ Rs. 1/kWh sold (!)• Accumulated losses are measured in % of GDP• Is there a “simple” fix?
– Not really– A pure market system would result in higher prices
• Difficult to manage the transition• Electricity has a social contract (worldwide)
• There are subsidies and cross-subsidies
(c) Dr. Rahul Tongia 36
Utilities and change
• First round of reforms (1990s)– Supply aka Generation oriented (and FDI)
• 2nd Round– Unbundling (but not quite privatization, for the most)
• Ongoing– Operational improvements– “Electrification”– R-APDRP– RGGVY– etc.
(c) Dr. Rahul Tongia 37
Renewables and India
• Global Challenges– Variability– Location-specific nature– Price
• What is “grid parity”?
• Indian Challenges– Weaker grid– No reserves– Peak is in the evening
(c) Dr. Rahul Tongia
Renewables are not a panacea – but they add Complexity
• What is the problem you’re trying to solve?– More “green power” vs. reduced carbon vs. energy security?– Cost-effective renewable power is medium or large-scale only
• Most of India’s challenges are of– Last mile electrification (still have that with village or larger systems)– Peak period shortfall (rarely green power coincident)
• Variability is an unsolved problem– IEEE suggests treating renewable power as negative demand– We have a very effective Demand Response program in India
• Unfortunately, it is mandatory = load-shedding (in most cities)
– The larger mindset of accepting load-shedding must end
38
(c) Dr. Rahul Tongia 39
0 87600 175200 262800 350400 438000 525600
Variation of Actual Wind Power Generation (MW) in Karnataka1 Minute Intervals (1st Oct 2010 – 28th Sep 2011)
Oct’10 Dec’10 Jan’11 March’11 May’11 July’11 Sept’11
Source: raw data from KPTCL
40
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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1 1 1 1
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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
19-Mar
29-Mar
8-Apr
18-Apr
28-Apr
8-May
6:00 PM 7:00 PM 8:00 PM 9:00 PM27-Feb
9-Mar
6 PM 7 PM 8 PM 9 PM 10 PM27 Feb9 Mar
19 Mar29 Mar 8 Apr18 Apr28 Apr 8 May
Time of day
Day
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%0%
20%
40%
60%
80%
100%
Probability= Fraction of days when there was an outage at a given time
Fraction ofthe evening
For one location in Raichur: Outages in red
Outages: Data Logger Results
Work by S. Harish with R. Tongia
41
Outages: Data Logger Results
41
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
19-Mar
29-Mar
8-Apr
18-Apr
28-Apr
8-May
6:00 PM 7:00 PM 8:00 PM 9:00 PM27-Feb
9-Mar
6 PM 7 PM 8 PM 9 PM 10 PM27 Feb 9 Mar
19 Mar
29 Mar 8 Apr
18 Apr
28 Apr 8 May
Time of day
Day
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%0%
20%40%60%80%
100%
Probability= Fraction of days when there was an outage at a given time
Fraction ofthe evening
For one location in Raichur: Outages in red
Work by S. Harish with R. Tongia
(c) Dr. Rahul Tongia 42
How to fix things?
Data → Information → Knowledge → Wisdom
Think of R-APDRP, and DT meters
Should have beenAccounting → Auditing → Monitoring → Control
(Monitoring would have just taken data-centric communications, not GSM, push logic, and a battery)
(c) Dr. Rahul Tongia 43
A few timeframes
• 2015 – Is that the short term?– One framing is using existing systems better
• 2017 – Is that medium term (12th Plan)?– Can implement new solutions but those are chosen today
• Long term: new ideas, solutions, and deployments
• Planning for Smart Meters = 20-25 years– 5 years of planning, pilots, roll-outs– 15-20 years of service– Can you predict what timeframes (granularity) is required then?
Daily uploads? Hourly? 15 Minutes?
(c) Dr. Rahul Tongia 44
The Future: Complexity
• “Bapu – if you only knew how much it costs us to keep you in poverty”
- Sarojini Naidu to Gandhiji
• Analogy: Keeping things simple is very hard (and complex)– Big Data / information overload
(c) Dr. Rahul Tongia 45
Why is the Future Bigger and More Complex?
SIZE• Overall Indian Power Sector 20 years from now
– GDP growth at least 6%• At elasticity of 1, implies 3.2x the power consumption• Inflation and price rises of 6% also give 3.2x
– THUS, about 10x today’s market size• Or approximately 600-700 Billion US$ market
(c) Dr. Rahul Tongia 46
Complexity of Electricity
• Commodity vs. public good (it’s both!)– We sell it like a basket of fruit (Rs./kWh), but the
basket has lychees, mangos, bananas, etc. (peak, off-peak, green, etc.)
• A broader systems approach to energy is missing– Housing, urbanization, etc. impact electricity use (even
ventilation, windows, etc.)
(c) Dr. Rahul Tongia 47
How many data points?
• 15 minute readings = over 30,000 readings per consumer– That is JUST the static information– A systems, networked (flow) analysis is far more
complex
• ICT has improved and shall continue to do so– Real time and predictive load-flow analysis
• In transmission, will need Locational Marginal Pricing (moving away from Postage Stamp Pricing)
• ToD is almost inevitable– Can start with procurement and bulk consumers
(c) Dr. Rahul Tongia 48
More data is coming…
• Who owns the data?
• Who has rights to it?
• Do you still trust the cloud?– Cloud is one
(extreme) form of outsourcing…
(c) Dr. Rahul Tongia 49
Security and Privacy issues will become very important
• Security and health risks are a reason cited for public opposition
• Smart Grid– At the very least people can know if you’re home or not– Electricity data sas been subpoenaed in divorce cases– Police have used it to bust Marijuana growers– But there is far more one can know, e.g., wealth
• Hypermetering (~50 msec) experiment at CMU– Can tell if you drink caffeinated vs. decaffeinated coffee
(c) Dr. Rahul Tongia 50
CyberSecurity is often just paid lip-service
• Security is a PROCESS, not a black box• Anyone who says “but we are encrypting”
doesn’t understand security– Encryption is point to point only– Keys CAN and WILL be compromised
• If it happens to American Express and Citibank, it can happen to anyone
– How do find out? Recover?
• Ultimately, from a utility point of view, security is a USABILITY challenge– How does one associate a Smart Fridge to a Smart
Grid?
(c) Dr. Rahul Tongia 51
Some Broader Trends
• Electrification of vehicles– Is that good or bad?– “That depends”
• Is the load in the peak or off-peak?
• Increasing demand for choice and quality– Urbanization– Under 25 (today) are majority of population
• Increased willingness to pay for a SERVICE– Even the poorest pay Rs. 20+/kWh for lighting
• Increasing consumer choice and empowerment– Web 2.0 is already about content from the edge
(c) Dr. Rahul Tongia 52
Some electricity trends and factors• Increase in PLF cannot go on• Losses avoidable via a Smart Grid are (perhaps) just 10-
15%– Growth in 20 years is 3-4 times!
• Pricing of electricity will make things much worse– All new generation is expensive– Older plants are amortized, but no more are there
• India doesn’t have mark to market pricing
• Discoms WILL have to become viable enterprises– Public vs. Private is a red herring
• Individualization and customization will become important– For competition and consumer choice (e.g., green power,
better quality power, etc.)
(c) Dr. Rahul Tongia 53
The Transformed Future
• Present– Make enough supply to best meet predicted demand
• Future– Manage demand to meet available supply (as well)
• Important with widespread, distributed renewables
• This can be market-based, command-and control, hybrid, etc.– ALL of these need lots of data
(c) Dr. Rahul Tongia 54
How do we move forward?
• Heterogeneity is a double-edged sword– Must seek out positive outliers
• Set performance standards– Fridges are a classic example – State of California set the
targets and left it to industry on how to innovate
• Understand types of pilots– Learning pilots– Deployment pilots
• Innovate– Mobiles took off not just due to technology
• Pre-paid was a big help for developing regions
(c) Dr. Rahul Tongia
Costs
“If you think education is expensive, try ignorance”
– Derek BokFormer President Harvard
University
If you think smart grids are expensive, try BAU– Increasing procurement costs– Customer Churn– Higher than necessary opex, losses– Missed opportunities for new revenues– Inefficient socialization of true costs
Will you pay for more reliability?
• Indians are very price sensitive???• Would you pay Rs. ___ for lowering or ending
load-shedding?• How do you end load-shedding?
56
There is value in solving the challenges:Can’t think of one thing in isolation
• Networking and Communications• Security• Privacy• Usability• Cost• Lock-in• Etc.
57
Privacy in the IoT (Internet of Things)• Smart TV (Michael Price’s experience, NYU)
– "The amount of data this thing collects is staggering. It logs where, when, how, and for how long you use the TV. It sets tracking cookies and beacons designed to detect ‘when you have viewed particular content or a particular email message.’ It records ‘the apps you use, the websites you visit, and how you interact with content.’ It ignores ‘do-not-track’ requests as a considered matter of policy.
– "It also has a built-in camera — with facial recognition. The purpose is to provide ‘gesture control’ for the TV and enable you to log in to a personalized account using your face. On the upside, the images are saved on the TV instead of uploaded to a corporate server. On the downside, the Internet connection makes the whole TV vulnerable to hackers who have demonstrated the ability to take complete control of the machine.
– "More troubling is the microphone. The TV boasts a ‘voice recognition’ feature that allows viewers to control the screen with voice commands. But the service comes with a rather ominous warning: ‘Please be aware that if your spoken words include personal or other sensitive information, that information will be among the data captured and transmitted to a third party.’ Got that? Don’t say personal or sensitive stuff in front of the TV."
• You can disable the data collection, but you lose all the Smart TV features• "Indeed, as the 'Internet of Things' matures, household appliances and physical objects will
become more networked. Your ceiling lights, thermostat, and washing machine — even your socks — may be wired to interact online. The FBI will not have to bug your living room; you will do it yourself.“
Smart Grid – What data sits where, why, for how long, and who has rights to it?
58
Smart Meter Privacy
• At the very least, a thief can know if you’re home– Is that a reason to NOT to (near) real-time updation?
• Has been used in divorce cases, drug busts, etc.• It’s more subtle – wealth can be inferred via a
Smart Meter
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A few myths
• I’m following the standard– Which standard? WHY?
• Open Standard– Compliance can take money
• This is already in widespread use…of course it’s safe
Before we get too fixated on “Standards”
• There is no “one” standard• Is a portfolio of standards
• Key is interoperability and layering (just like the Internet)
• Some things should be understood for what they are or aren’t:
• E.g., ISO (or CMM, SGMM)
61Photo: A. Thatte (used with permission)© Dr. Rahul Tongia
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Smart Grids are a Journey, not a Destination
• OR, a Process, not a Product• How good are your processes?• Who has heard of Capability Maturity Model
(CMM)?• There is an analogy for Smart Grids: SGMM
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Smart Grid Maturity ModelSeptember 2009
© 2009 Carnegie Mellon University
What is the Smart Grid Maturity Model?
A management tool to help utilities • Identify where they are on the smart grid landscape• Develop a smart grid vision and roadmap • Prioritize options and support decision making• Benchmark themselves against the industry• Measure their progress
SGMM is composed of two dimensions• Levels of maturity • Domains
The six levels of maturity (Levels 0 – 5) represent the progression of a utility in adopting and deploying smart grid technologies and practicesThe eight domains are logical groupings of related capabilities and characteristics at each maturity level
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Smart Grid Maturity ModelSeptember 2009
© 2009 Carnegie Mellon University
How can the SGMM be used?
Strategically
To establish a shared picture of the Smart Grid journey
To communicate the Smart Grid vision, internally and externally
To use as a strategic framework for business and investment objectives
To plan for technological, regulatory and organizational readiness
To benchmark and learn from others
TacticallyTo use as guide to develop a specific
roadmap or blueprint
To assess and prioritize current opportunities and projects
To use as a decision making framework for investment purposes
To assess resource needs to move from one level to another
To measure progress
A maturity model can move an entire industry forward.
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Smart Grid Maturity ModelSeptember 2009
© 2009 Carnegie Mellon University
Some 60 Diverse Utilities Have Used the SGMM
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Smart Grid Maturity ModelSeptember 2009
© 2009 Carnegie Mellon University
Most Are Just Getting Started on the Smart Grid Path
SGMM is online today
• Version 1.2 today• Used by many, many dozens of utilities• SGMM Model
The core of the SGMM product suite is a model of smart grid maturity in an electric power utility. The model's 175 characteristics are organized into eight domains:– Strategy, Management, and Regulatory– Organization and Structure– Grid Operations– Work and Asset Management– Technology– Customer– Value Chain Integration– Societal and Environmental
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Smart Grid Maturity ModelSeptember 2009
© 2009 Carnegie Mellon University
5: Innovating Next Wave
4: OptimizingEnterprise-Wide
3: Integrating Cross Functional
2: Functional Investing
1: Exploring & Initiating
0: StartingStrategy,
Management& Regulatory
Organization & Structure
GridOperations
Work & AssetManagement
CustomerManagement & Experience
Technology Value ChainIntegration
Societal & Environmental
Aver
age
and
Rang
e of
Mat
urity
Sco
res
SGMM Domains
Technology, Environmental Domains Lead
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Smart Grid Maturity ModelSeptember 2009
© 2009 Carnegie Mellon University
Benefits of Participating
Leading utilities have found the SGMM a valuable tool for charting their position and developing their smart grid roadmap
Participating companies enjoy complete confidentiality of their data and results and gain valuable insights
• Detailed report benchmarking results against industry aggregates• Access to custom reporting and coaching on interpreting results• Best practices research• Collaboration opportunities
Joining the SGMM user community enables you to help shape the evolution of the model and contribute to industry transformation
Smart Grids are a matter of time…
• What policy needs are there to make these a reality?
• Cost Benefit Analysis• Who pays?• Who benefits?• Think of ending load-shedding• Standardization (if not standards)
– Interoperability (“Is Windows a Standard?”)– Plug-and-play
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We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run…
- Roy Amara
www.indiasmartgrid.org
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