© 2009 ibm corporation smarter energy: the promise of cyber-physical energy systems shivkumar...

© 2009 IBM Corporation

Smarter Energy: The Promise of Cyber-Physical Energy Systems

Shivkumar Kalyanaraman, [email protected]

Senior Manager, Next Gen Systems & Smarter Planet Solutions

IBM Research – India.

© 2009 IBM Corporation2

Thanks & Acknowledgements

David MacKay’s book: Sustainable Energy without the Hot Air.– http://www.withouthotair.com/download.html

Thanks to several IBM’ers from whom this material has been drawn: – Deva Seetharam (smart energy research lead, IRL), Jeff Katz, Reji Kr. Pillai, Jayant

Kalagnanam, Ron Ambrosio, Colin Harrison …

Other sources: – McKinsey – MITEI (MIT Energy Institute)– National Renewable Energy Research (US/DoE)– European Smart Grids Technology Platform


The world is smaller.

The world is getting smarter.

The world is flatter.

Every human being, organization, city, nation, natural or man-made system is becoming instrumented, interconnected and intelligent.

These cyber-physical systems are leading to new possibilities for progress.

Smarter Planet => Cyber-physical Systems

+ + =


Adapted from: “Instrumenting the Planet”, IBM Journal of Research & Development, March 2009

Data modeling and analytics to create insights from data to feed decision support and actions

Comparison of historical data, with newly collected data

Data collection + networked back to IT

Data Integration

MeteringMeteringSensingSensing

Real Time Data Integration

Real Time Data Integration

Real Time +Historical Data

Real Time +Historical Data

Data Modeling + Analytics

Data Modeling + Analytics

Visualization + DecisionsVisualization + Decisions

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Under the Covers: Measuring, Monitoring, Modeling, and Managing


Smarter Planet: Different Domains and Verticals

Smart traffic systems

Smart water management

Smart energy grids

Smart healthcare

Smart food systems

Intelligentoil field

technologies

Smart regions

Smart weather

Smart people

Smart supply chains

Smart cities

Smart retail


Smarter Energy


Population


Per-Capita Energy Consumption


Analog Divide

550 Million (75%) Sub-Saharan Africans don’t have access to electricity

700 Million (50%) south Asians don’t have access

According to International Energy Agency, 1.4 Billion people will not have access to electricity in 2030

Many areas in the world including India and China have several hours of power cuts during the day


Climate Change: GHG Emissions linked to fossil-fuel energy

Note: Energy by itself is not the problem: only fossil-fuel generated energy is… (renewable/sustainable energy sources are part of the solution)


Smart Grid A Smart Grid supplies high-quality electrical energy to all its users all the time with zero damage to the environment.

Methods:• Energy Conservation & Management [DEMAND side]

• Loss (both technical and non-technical) minimization; Robust operation/ real-time view / responsiveness [Grid OPERATIONS]

• Use renewable energy sources (eg: solar, wind etc): & integrate w/ storage & transportation (eg: plug-in hybrid vehicles) [SUPPLY side]

• Smart Grids, more broadly, enable Smarter energy choices:• Linkages to Transportation, Utilities optimization


Smart Energy: Demand-Side Conservation & Management


Quick Refresher: Energy & Power Units

Energy: kWh (1 unit), joules– A 1kW microwave left on for an hour = 1kWh = a 40W bulb left on for a day

Power: kW, MW, GW, TW (or kWh/d or kWh/d/person) – Eg: 1 GW Nuclear power plant; $500-1000 CAPEX per kW of generation (or $1B/GW)– Also: kWh/d… Eg: 40 W = 1kWh/d; average car use: 40 kWh/d/p in UK.

Power Density: W/m2

– Solar PV/CSP: 5-20 W/m2, Wind: 2-5 W/m2, Biomass: <0.8 W/m2

– Determines how much area is needed to scale up production. Note: usually very large (country-sized) areas needed for several sustainable sources like solar, wind, wave etc

Energy density: kWh/litre or kWh/kg – For hydrocarbons (eg: petrol) energy density is roughly 10 kWh/litre.– Eg: With average car economy of 12 km/litre & 50 km/day in a single-person car => ~40

kWh / d / p


Demand-side: Buildings & Heating Reduce average temperature difference: turning thermostats down.

– Thermostat from 20 to 15-degrees => halve the heat loss !

Reduce building leakiness: triple glazed windows, draught proofing

Increase efficiency of heating system: heat pumps/refrigerators: CoP of 4 vs current CoP of 0.9.– Better than combined heat/power (CHP) since heat is not a free by-product!– Eg: 3.6 kW of heating when using just 0.845 kW of electricity.

Thermal Storage for buildings/residential blocks: convert electricity to heat when cheap, and store heat. Solar Thermal: Solar energy for heating water, cooking etc (especially in India etc).


Smart Grids: Demand Shaping and Shaving (Demand Response)

Base Load

System Load

Hour of the Day

Intermediate Load

Peak

Elastic demand response

Illustrative

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 2223 24

PR

ICE

($/

MW

H)

P_r

P_p

P_op

P_pr

RETAIL PRICE

PEAK DEMAND

OFF-PEAK DEMAND

USAGE (MWH)

SUPPLY

Q_pfQ_pdr

REDUCTION IN PEAK LOAD

INCREASE IN OFF-PEAK LOAD

Q_odrQ_of

Distribution for Cost savings/B28

Values in Millions

0.000

0.200

0.400

0.600

0.800

1.000

Mean=1.502934E+08

60 120 180 24060 120 180 240

5% 90% 5% 80.8773 212.3077

Mean=1.502934E+08


Smart Homes/Buildings: Context-Sensitive Demand / Response

Demand/Response for Commercial/Residential: being sensitive to people & context. Aggregatable to buildings, blocks, cities… Inclusive of PHEVs, homes, offices…

1. Receive Appliance Usage Signals2. Perform on -board learning and analysis and inform central system3. Receive “Situation” info . from the central system4. Activate smart control of appliances

Wattzup Home Gateway

Wattzup Server

Presence and Location Data

COTS /Custom -Built Networked Energy

Monitors /Controllers

Location Online Presence and Activity

High -End Data Stream Management Engine

Presence and Context Reasoning and

Detection

Presence Virtualisation


Demand-side: Smarter Transportation (beyond congestion-charging)…

Average Car: (one person): 12km/litre, 50 km/day, 10 kWh/litre => ~40 kWh/d/p

– Car (1 person) energy efficiency: 80 kWh per 100p-km [BASELINE].

– Car pool (4 persons): 20 kWh per 100p-km

Petrol car engine efficiency: 25% (mostly thermodynamics & waste in making air swirl)

– Biking: 1.6-2.4 kWh/100p-km (20 kmph): 40X energy efficient !

Public transport: 10X energy efficient !

– 8-carriage train (full): 1.6 kWh per 100p-km (w/o traveling slowly or low weight!)

– Full buses (7 kWh per 100p-km);

– Full subway trains: (3-9 kWh per 100 seat-km)

Challenges: Last-mile to reach public transportation, end-to-end dynamic route planning, uncertainties in switching delays…

– Cell-phones and electric vehicles can help (see next slide) !


Smart Energy + Transportation: Electric cars / E-bikes

Electric motors in cars use ~10 kW with efficiency of 90-95% Electric cars:

– Reva/G-Wiz: 21 kWh / 100 p-km; 75 km range

– Tesla Roadster: 15 kWh / 100 p-km ; 350 km range ! Electric scooters: ~4-5 kWh / 100 p-km (lower speeds/range/costs): 20X energy efficient!

– Electric vehicles: better today even if power generation is coal !

– (provided transmission/distribution losses are modest, and the grid is robust – not yet true in India. R-APDRP will help.)

Cost, batteries, recharging issues, range, safety, speed etc: techno-economic debate …

– EV + PV or EV + wind offer interesting smart grid opportunities

E-bikes in China: lower total cost of ownership. E-bikes, China: 30% sales! (65M total)

Re-engineering Transportation for Growth markets: Opportunity to help design a mobile-phone aided system for on-demand E-bikes/E-cars (for the last mile) + rapid public transit.

Deliver faster commute time, lower cost, flexibility & 10X energy efficiency. Simultaneous optimization for smart energy and smart transportation. Challenge: depends upon the electric grid for energy & IT for asset/financial management


Smart Energy: Grid Operations


Smarter Grids for a Smarter Planet | IBM Confidential 21

IBM’s Global Smart Metering and Smart Grid Activities

American Electric PowerBC HydroBELCOCenterPoint EnergyConsumers EnergyDominion EnergyEntergyEPCORHydro OneHydro OttawaHydro-QuébecIESOLondon HydroNB PowerNYC-based utilityOncorOntario Energy BoardPacific Gas & ElectricPacific Northwest National LaboratoryPECOPepco Holdings IncProgress EnergySempra EnergySouthern California EdisonToronto Hydro

Country EnergyEnergy Australia NDPLShanghai Power

AEM TorinoASM BresciaDONG EnergyEDFEDF EnergyEDPEnBWEnemaltaEnelEon

EnergiMidtESB NetworksGöteborg EnergiOxxioRWE npowerScottish & Southern

Energy30 Italian distributorsE-EnergieTerna


22

Phasor Measurement Units (PMUs) & Synchrophasors: Networked Sensors for Electric Transmission Networks

System Components:– Synchrophasors– Emulators– Application Overlay Network

IBM Assets: R3 messaging, System S

Research Questions– Real-time data acquisition and control in 10-100ms time-scales– Network coding– Network reliability– Network security

Fig: Phasor representation of sinusoidal signal: Sampled by a PMU (Phasor Measurement Unit)


PhasorNet: NASPInet Emulation – System Overview

API to access PG Overlay

Phasor Gateway Overlay - R3 Messaging

PDC Overlay

Application Domains- SystemS

Application

PGPG

PDC Simulator

Data ClassModel for Data Generation for each class

PUBLISH

SUBSCRIBE

(Application requirements – e.g., Data Class)


Phasornet: NASPInet Emulation – Planned Test Network

IIT - Kharagpur

IBM Research Bangalore

IIT - Chennai

PDC Simulator

Phasor Gateway

SystemS Application

Legends

IBM Research Delhi

R3 Overlay Connection


SynchroStreams: Stream Computing + Synchrophasor Analytics

PDCs SPDC Application

PD

C1

PD

C2

PD

C3

0.850.870.890.910.930.950.970.991.011.031.05

0 5 10 15 20

Time (sec)

Sta

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Bus 4

Bus 5

Bus 6

Bus 7

Bus 8

Bus 9

0.850.870.890.910.930.950.970.991.011.031.05

0 5 10 15 20

Time (sec)

Sta

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Bus 4

Bus 5

Bus 6

Bus 7

Bus 8

Bus 9

Normal condition For contingency 9-6

-20

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1 6 11 16

Time (sec)

Sta

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Bus 7

Bus 8

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For contingency 9-3 Unstable!!!

Fault point


Other problems in analytics

Smart Grid tomography … (similar to network tomography)–Unknown state estimation from lots of measurements–Dynamic state estimation

Early warning systems using distributed stream computing

Deep contingency analysis in near-real-time.

… etc


Smarter Energy:

Supply Side Transformation to Renewables, Distributed Generation & Storage


Supply-Side: Renewables’ Cost Trajectories: Cheaper now vs Cheaper Later


Supply-Side: Renewables: Solar

Raw solar potential: Peak: 1000 W/m2. Adjustment for angle, time-of-day, clouds => 110 W/m2 in UK.

– Anchorage (87), Los Angeles (225), Honolulu (248)… < 275 W/m2 Solar thermal: heating water w/ solar: 50% efficiency possible.

– 13 kWh/d/p potential in UK; assuming 110 W/m2 of raw solar Solar PV: Typical solar panels have efficiency of ~10%; expensive

ones ~20%.

– Potential (UK): Max 22 W/m2 => 5kWh/d/p with roof panels.

– Large-scale solar farms in Europe: 5 W/m2 in Germany.

– Real potential: large-scale deserts around the world Solar biomass: Poor power density => doesn’t scale well.

– Sugarcane (1.6 W/m2), Jatropha (.2 W/m2), Miscanthus (.3 W/m2)

Solar PV large-scale farm: Bavaria

Monthly Solar thermal potential (UK) Solar Biomass: low W/m2

Raw solar power incidence


Desert Solar: Concentrated Solar Power (CSP)


Concentrated Solar: Desert Lands, w/ water close by are assets!

Solar generation + HVDC lines for transmission.

HVDC is preferred: less physical hardware, less land area, and smaller power losses.

The power losses on a 3500 km-long HVDC line, incl conversion from AC to DC and back is ~15%

Coastal desert areas better: desalination + solar power !

IBM is working with Saudi Arabia on a solar + desalination project.

Red square can power all of UK!Yellow square: all of Europe& N.Africa!


EV + PV: Electric Vehicles + Distributed Solar Photovoltaics

Solar in the daytime (green bar) => sell-back to the grid: high price/unit Electric vehicle charging in the nighttime (orange bar): low price / unit

+


Wind / Wave Sun makes wind; wind makes waves: energy lost in each step of conversion. Wind blows faster at higher altitudes & offshore; and energy is a function of the cube of wind speed! Wind farm: 2 W/m2 (onshore) ; 3 W/m2 (offshore)

– Note: this inefficiency requires very large wind farms even at the windiest of places. Wind energy is highly variable (even when aggregated!) –

– Use electric cars to store wind-generated energy and sell back to the grid (EDISON consortium)

– Pumped storage (pump water up a hill + dam) Wave has other challenges: lower efficiency of conversion and a lot more steel required per kW

generated.


What’s smart?

EDISON* research consortium, a Denmark-based collaborative aimed at developing an intelligent infrastructure that will make possible the large scale adoption of electric vehicles powered by sustainable energy

Smarter Outcomes

• Upward of 10% of the country's vehicles to be all electric or hybrid electric during the coming years

• Minimize CO2-emissions linked to electrified transport

• Maximize the use of renewable energy

• Enable smart technologies to control electric vehicle charging and billing and to ensure the stability of the overall energy system

EV + Wind: EDISON project. Intelligent infrastructure for large scale adoption of electric vehicles using sustainable energy

*EDISON = Electric Vehicles in a Distributed and Integrated Market using Sustainable Energy and Open Networks


Energy Storage: Batteries, Flywheels, Pumped Storage

Fossil fuels: coal (8 kWh/kg) – petrol (13kWh/kg) Flywheel: peak of ~100 Wh/kg (i.e. 0.1 kWh/kg) Batteries: Li-ion: <160 Wh/kg; and ~300-500 recharge cycles

– Vanadium flow: 5X faster charge than Lead-acid; 10K cycles recharge Super-capacitors: small amounts of energy (1kWh); fast charge/recharge (braking) Pumped storage: low energy density but renewable

– Fjords atop hills are interesting locations;

– Hydro-electric plants can be reversed to offer storage & peaking power

– Artificial reservoirs (tall towers + storage + condensing passing clouds) “If battery energy density can go up by 5X (i.e. 1-2 kWh/kg), the world is your

Oyster” – Stephen Chu, US Energy Secretary, Nobel Laureate

(Lifetime/renewability)

Li-Iron Phosphate(~300 Wh/kg,1000+ cycles,Fast charging)


Smarter Energy linkages with other Utilities: Eg: Water

© 2009 IBM CorporationIBM ConfidentialApril 19, 2023

Water in Power Production: Mass Flows:1 kWh Electricity Prod’n

Note: huge amounts of cooling water and CO2 emissions…6Wh iPhone charge: ½ liter of water used in power production. 40% of US water (twice the water in the Nile river) used in power production!


Energy Generation: Water Use vs Carbon Emissions


Australian Drought & Water Grids


Brisbane Drought Experience & Water Grids

Brisbane Drought options:– Conserve water: residents cut daily water use from 300 L to 129 L– Channel water from where it is to where it isn’t:

• Water grid hooked up 14 existing dams and weirs as well as a new desalination plant and three advanced wastewater-treatment plants. 182ML/day capacity of pipelines

– New water sources: de-salination and waste-water treatment: 300ML/day (50% of needs)– Water management (organizationally) was streamlined and made truly regional.

Challenges: When rains returned, political backlash against wastewater plants returned! – Wastewater output now sent to coal plants !– Rainwater harvesting widely adopted: lawn, toilet use…: 10 times power-inefficient

Energy tradeoff: average household in South East Queensland now pays about $2 per kL for water, up from $1 a few years ago

– Eg: Running the desalination and treatment plants during off-peak hours, for example, would cut electricity costs

South East Queensland’s $9 billion water grid, which promises to drought-proof the region – also boosting the electricity consumed by the water system.

– Relies on energy-intensive water technologies like desalination and wastewater recycling

New South Wales: – Replacing gravity-fed canals with pressurized irrigation pipelines: lower water loss due to

evaporation, loss, imprecise irrigation, but at the cost of higher energy inputs ($2.5M/yr)


Energy & Water: Malta Malta has the highest density of private wells in the

world—30 per square kilometer—for a total of about 8600 (free of charge)!.

– Cisterns at all homes can harvest rain water Tapping the shrinking aquifers that supply the country

with 60 percent of its potable water. – Saltwater infusions into acquifers: freshwater lens

(floating over seawater) becoming thinner.– Acquifer replenishment after wastewater treatment

+ disinfections 30+% comes from three seawater reverse-osmosis

(RO) plants, located at Pembroke, Ghar Lapsi, and Cirkewwa.

Price of electricity determines 75% price of RO water. – Desal energy cost for 1ML dropped from 5KWh to

2.8kWh.

IBM building unified smart electricity / water grid: detect theft, fair distribution, efficient admin.

– Precise control of pressure / flow rates, salinity levels (lower levels – less pressure/energy to desalinate)

– Plugging physical leakage vs commercial leakage (theft!) – meters not sensitive enough to measure very low water flow

– IBM’s work: allows compare individual meters with zonal meters – to detect leaks


• Customers will pay only for the energy they actually use

• Customers will be able to switch to a pre-pay service, similar to mobile phone pre-payment

• Commercial losses will be reduced through monitoring of electricity and water grids

• Remote management of electricity supply

• Sophisticated analysis of consumption patterns, enabling a real-time view of energy use to identify opportunities for reduction

• Customers will have an Internet portal to track energy consumption

Unified Sensing: Malta will become the first country to implement an end-to-end electricity and water smart utility system

What’s smart?

Smarter Outcomes

Smart electricity and water meters, advanced IT applications enabling remote monitoring, management, meter readings and meter suspensions, customer energy management portal


Platforms Near-term Medium-Term Long-Term

Smart Infrastructure

Facilities: Building H&C - Demand Mgmt and Networked Appliances; Data Center - Temperature Control – Buildings

Grid: Optimize T&D Infrastructure; Automated metering And technology; Grid Security – Scenario Analysis and Cyber Security

Transportation: Congestion Mgmt and Route Optimization; Mass Transit Fleet Optimization (grows in medium-term); Telecommuting

Facilities: Green Building Operations and Systems Integration; Green Building Design

Facilities & Grid: Distributed Generation & Renewables

Grid: Networked Energy Storage Water Mgmt: Networking, Control Systems &

Scenario Analysis Transportation: Networked Mass Transit

Facilities: Green Building Maintenance

Water Mgmt: CRM & Automated Metering

Data Modeling & Analytics

Climate Modeling: Tracking Scientific Impacts and Weather Modeling

Source Fuels: Oil and Gas - Locating Oil and Gas Reserves and Well Optimization and Oil Sand Processing

Climate Modeling: Catastrophe Modeling; Policy Scenario Analysis, Tracking & Compliance Mgmt

Water Mgmt: Supply Forecasting & Scenario Analysis

Source Fuels - Nuclear Energy: Scenario Analysis & Plant Safety; Waste Disposal Scenario Planning

Carbon Mgmt: Carbon Sequestration Technology

Climate Modeling: Health Modeling Carbon Mgmt: Synthetic Biology

Business Process Optimization

Source Fuels: Clean Coal - Asset and Lifecycle Mgmt; Nuclear Energy - Asset Mgmt and Plant Life Extension and Plant Monitoring and Optimization; Oil & Gas - Equipment Maintenance and Remote Management; Plant Safety - Employee Safety Tracking

Manufacturing & IT Technology: Data Center Mgmt - Optimize Chip Manufacturing; Temperature Control and Power Management - IT Equipment

Source Fuels: Oil and Gas - Improve Plant Capacity

IT Technology: Transportation - Component Optimization – On-Board; Vehicle Diagnostics

Manufacturing & IT Technology: SCM - Green Supply Chain; Manufacturing Process H&C - Audits – Energy

Carbon Mgmt: Trading Optimization and Management; Footprint – CO2 Emissions ID & Verification; Sequestration Efficiency Management

Manufacturing & IT Technology: Manufacturing Process H&C - Mfg Process - Asset Mgmt and Cap. Financing

Source Fuels: Renewables - Asset & Lifecycle Management, Energy Trading, Tracking & Billing and SCM Renewable Optimization

Carbon Mgmt: Sequestration Inventory Management

Energy Efficient Technologies & Services

Power 6, consolidation onto z-series, dynamic consolidation/virtualization

Facilities engineering for data centers (c.f. Smart Infrastructure/Facilities)

Data Center as Networked Appliance Smart Buildings Power management in storage systems Energy efficient software

Zero Carbon Data Center

Summary: Smarter Energy & Utilities: Near vs Long Term

Important role for IT middleware in sensing, efficient interconnection, & intelligence (modeling, analytics & optimization)


Thanks!

Contact: [email protected]: www.shivkumar.org

© 2009 ibm corporation smarter energy: the promise of cyber-physical energy systems shivkumar...

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