SmartG rid: Some&Issues& and&Challenges&people.rennes.inria.fr/Olivier.Sentieys/presentations/...3...

Smart Grid: Some Issues and Challenges

Olivier Sentieys IRISA/INRIA

Université de Rennes 1

[email protected]

2 ECOFAC’2012

INSTITUT DE RECHERCHE EN INFORMATIQUE ET SYSTEMES ALEATOIRES

Grid ?

3 ECOFAC’2012


Stupid (but dependable) Grid

•  Grid is (s5ll) a one-‐way broadcast process – Genera5on, transmission, distribu5on

•  The system was designed 120 years ago!

hWp://oncor.com/images/content/grid.jpg

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Towards a Smarter Grid •  Distribu5on is not efficient (>20% loss) •  Maximal demand drives genera5on

–  Power consump5on peaks ? •  Deal with renewable sources?

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Towards a Smarter Grid

•  Example in California •  RDS signals to send real-‐5me electricity prices •  Encourage household to use cheap electricity •  Smooth the energy consump5on over a day

•  A smarter grid is needed •  Application of information and communication

technologies to optimize electrical power generation, delivery and use

INSTITUT DE RECHERCHE EN INFORMATIQUE ET SYSTEMES ALEATOIRES 6

ECOFAC’2012

Smart Grid

•  Genera5on drives demand

•  Energy reduc5on and op5miza5on technologies

•  Integra5on of renewable genera5on becomes easier and more cost-‐effec5ve –  Cheap energy prices when the wind blows

hWp://www.greenbusiness5mes.com/tag/intelligent-‐energy-‐system

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Architectural view of the SG

Power system components

Monitoring and Control

Communica5on Infrastructure

Informa5on Management

Applica5on

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Architectural view of the SG

Power system components

Monitoring and Control

Communica5on Infrastructure

Informa5on Management

Applica5on

•  Power electronics •  Control engineering

•  Smart sensor and actuators •  Wireless sensor networks

•  Op5cal fibre, wireless •  Communica5on network protocols

•  Distributed data service

•  Risk limi5ng, load levelling

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Smart Microgrid

•  Local-‐area electrical grid – electricity genera5on, transmission and storage

•  with ability to respond to dynamic changes in energy supply – co-‐genera5on and demand adjustments

•  Prototypical testbeds for research on smartgrids

10 ECOFAC’2012


Buildings •  Over 70% of total electricity use in the US •  Good target for analyzing and reducing energy use

•  e.g. UCSD microgrid

[DOE05]

[Aga10]


ECOFAC’2012

Buildings

•  Energy use survey –  Office buildings in CA

•  Day electricity usage –  Summer day in CA

[CCEUS]

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Open Challenges in Smart Grid

•  Energy Metering and Control – Real-‐5me, at mul5ple scales

•  Occupancy Sensing (in Buildings) – Sensors and algorithms

•  Data Collec5on and Management – From wired to wireless

•  Energy Op5miza5on and Control – Data analysis and fusion

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I. Energy Metering and Control •  Accurate energy metering at mul5ple scales

–  Inden5fy dominant energy – Analyzing long term trends

•  Several commercial energy meters – See e.g. hWp://www.google.com/powermeter

•  Research efforts [Jiang09][Kim09]

•  Challenges – Granularity of metering and control – Cost of installa5on and deployment

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Energy Metering and Control

•  ViridiScope system provides real-‐5me appliance-‐level power es5ma5on –  Indirect Power Monitoring Concept – Autonomous Sensor Calibra5on Framework

Magne5c

Acous5c

Light

[Kim09]

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Energy Metering and Control

•  hWp://energy.ucsd.edu, CSE Building

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Detailed Energy Breakdown

•  Plug loads, Servers, Lightning, Mechanical

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Desktop Computer

•  With sleep management

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II. Occupancy Sensing •  Accurate occupancy detec5on and tracking

–  sensors + detec5on algorithm –  energy reduc5on

•  light, HVAC (Hea5ng, Ven5la5ng, and Air Condi5oning) •  Sensors

–  Passive infrared (PIR) sensors •  Mainly movement detec5on •  Drawbacks: line-‐of-‐sight, false posi5ves, false nega5ves

–  Ultrasonic sensors, magne5c reed switch, CO2 sensors –  Camera systems with detec5on algorithms –  Computer ac5vity –  Hybrid systems

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Occupancy Sensing

•  Occupancy detec5on algorithm – e.g. PIR + reed switch

•  reed switch = door open or closed –  open door = room occupied

•  PIR sensor = movement –  closed door = ? –  door close event + movement = occupied – What if a visitor closes the door while the main occupant is at his desk ?

»  con5nue PIR detec5on + 5meout

[Aga10]

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Occupancy Sensing [Aga10]

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Occupancy Sensing: Accuracy [Aga10]

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Occupancy Sensing: Energy Savings [Aga10]

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III. Op5miza5on and Control •  Analyzing sensor data from all sources

– plug loads, energy use – occupancy informa5on, network traffic

•  Controlling dominant loads – HVAC (10% -‐ 35%) –  IT equipments (25% -‐ 40%) – Lightning (9% -‐ 15%) – Compu5ng servers inc. cooling (30% -‐ 40%)

•  Microgrid-‐scale energy management

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IT Equipments

•  “Turn-‐off light and equipment when they are not in use” – Desktop PCs s5ll consume 60-‐75W when idle…

•  Low-‐power sleep modes during period of low u5liza5on – Sleep mode consumes only ~1W

But waking up can be long and asleep machines cannot answer to external requests

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IT Equipments

•  Wake-‐on-‐Lan – « magic packet » broadcast frame containing

•  FF FF FF FF FF FF followed by 16 repe55ons of the target 48-‐bit MAC address

•  Somniloquy [Aga09]

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Microgrid energy management

•  Managing energy consump5on of subsystems – Recharge of electric vehicles, heat water – Shizing computa5ons in servers

•  Managing mul5ple energy sources – Varia5on in renewable energy produc5on – Price signals from imported energy

e.g. Periods of cheaper electricity or abundant PV genera5on can be used for energy storage or by the HVAC system to pre-‐cool buildings

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IV. Data Collec5on and Management •  Wired communica5ons have a very high installa5on cost

•  Wireless sensor networks become increasingly used –  Dense network of small nodes sensing the physical world and communica5ng through wireless links

–  Very ac5ve research area –  Standards: IEEE 802.15.4, Zigbee, Bluetooth LE

Sensor and/or relay

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WSN System Requirements •  Simplified deployment, fault tolerance

– No maintenance and baWery replacement •  Network characteris5cs

–  Low mean distance –  Limited amount of data – Mul5-‐hop rou5ng

•  Low cost, small size •  Long autonomy, low energy consump/on

–  Towards autonomous self-‐powered sensor nodes –  0.1-‐1 mW on ac5ve period

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Autonomous Self-‐Powered Nodes ? •  A WSN node is limited by the total energy it can store or scavenge from the environment – Need a dras5c reduc5on in the total consumed energy

29

Energy Source Characteristics Efficiency Harvested Power

Light Outdoor Indoor

10~24% 100 mW/cm2

100 µW/cm2

Thermal Human Industrial

~0.1% ~3%

60 µW/cm2

~1-10 mW/cm2

Vibration ~Hz–human ~kHz–machines

25~50% ~4 µW/cm3

~800 µW/cm3

RF GSM 900 MHz WiFi

~50% 0.1 µW/cm2

0.001 µW/cm2


ECOFAC’2012

Typical energy budget (WSN node) •  What are the main sources of energy consump5on ? – Radio: 30-‐70mW – Processor: 5-‐10mW

Sensor Low-

PowerMCU

Power Supply

Tx

Rx

Sensor Subsystem

Computation Subsystem

Power Subsystem

Communication Subsystem

Radio Tx

Radio Rx

Processor

Tx

Rx

Digital

30

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WSN Pla|orm •  Open source hardware developed at IRISA/INRIA

– MSP430+CC2420 –  Power management (sleep, wake-‐up)

•  FPGA for hardware accelera5on –  100x energy gains

•  Voltage and frequency scaling –  30%-‐50% energy reduc5on

•  Asynchronous rendez-‐vous MAC protocols –  12x-‐15x less power than 802.15.4 for the same applica5on scenario

PowWow: power op5mized hardware/sozware framework for wireless motes

hWp://powwow.gforge.inria.fr

[Berder10]

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Fine-‐Grain Power Ga5ng •  Power on/off of specialized tasks •  75-‐350x energy gains w.r.t. sozware on microprocessor (TI MSP430)

[Pasha10]

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Radio Transceiver Op5miza5on •  Le5Bee chip (CEA LETI)

– Power consump5on

•  Tx è 13.5 mW @ -‐2 dBm •  Rx è 8.5 mW @ -‐85 dBm

•  Trends: Wake-‐up radio, Ultra-‐Wide Band

Function RX (mA) TX RF 0.5 2.73 LO 4 7 PLL 0.35 0.35 Analog 0.2 0.4 Digital 0.5 0.25 Biasing 1.5 0.5

[Bernier08]


ECOFAC’2012

Energy Harves5ng

•  STMicroelectronics –  Thermogenerator, solid-‐state thin-‐film baWery, 2.4 GHz wireless link

•  IMEC –  Vibra5on harves5ng by MEMS piezoelectric power genera5on


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Energy Harves5ng

•  TI/Cymbet –  Solar and in-‐door light harves5ng with photo-‐voltaic (PV) cells, thin-‐film rechargeable baWery

•  Infineon –  Vibra5on harves5ng for 5re pressure monitoring

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Summary and Challenges

•  SG will revolu5onize the way electricity is produced, transmiWed and delivered

•  Energy reduc5on and management at different scales

•  A common standard for smart grid is certainly a key driver (but…)

•  Massive deployment of smart metering

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Summary and Challenges •  Accurate simula5ons of grid models

–  huge amount of compu5ng power –  e.g. GridLAB-‐D

•  Use and behaviour profiling algorithms •  System on Chip technology

–  low power, small form factor, 3D stacking –  smart meters will become cheaper

•  BaWery technology –  large energy storage

•  Security and reliability


ECOFAC’2012

Smart grid

References [Agarwal2011] Y. Agarwal et al., Understanding the Role of Buildings in a Smart Microgrid, IEEE/ACM Conference on

Design Automa5on and Test in Europe (DATE '11), March 2011. [CCEUS] Itron Inc. California Commerical End-‐Use Survey. hWp://capabili5es.itron.com/ceusweb [Varaiya2011] P. Varaiya et al., Smart Opera5on of Smart Grid: Risk-‐Limi5ng Dispatch, Proc. of the IEEE, Vo. 99, No. 1,

January 2011. [Agarwal2010] Y. Agarwal et al., Occupancy-‐Driven Energy Management for Smart Building Automa5on, ACM BuidSys,

November 2010. [Jiang2009] X. Jiang, S. Dawson-‐Haggerty, P. DuWa and D. Culler, “Design and Implementa5on of a High-‐Fidelity AC

MeteringNetwork, Informa(on Processing in Sensor Networks, 2009. [Kim2009] Y. Kim, T. Schmid, Z. M. Charbiwala and M. B. Srivastava, “ViridiScope: Design and Implementa5on of a

FineGrained Power Monitoring System for Homes”, Proc. 11th Intl. Conf. on Ubiquitous Compu(ng, 2009. [Agarwal2009] Y. Agarwal et al., Somniloquy: Augmen5ng Network Interfaces to Reduce PC Energy Usage, USENIX

Symposium on Networked Systems Design and Implementa5on (NSDI ’09), April 2009. [DOE] US Department of Energy, The Smart Grid: An Introduc(on, 2009. [Chassin2008] D. Chassin, K. Schneider and C. Gerkensmeyer, “GridLAB-‐D: An Open-‐Source Power Systems Modeling

andSimula5on Environment, Transmission and Distribu5on Conference and Exposi5on, 2008. [Pasha2009] M. A. Pasha, S. Derrien, and O. Sen5eys. Ultra low-‐power fsm for control oriented applica5ons. IEEE

Interna5onal Symposium on Circuits and Systems, ISCAS 2009, pages 1577 – 1580, Taipei, Taiwan, May 2009. [Pasha2010] M. A. Pasha, S. Derrien and O. Sen5eys, A Complete Design-‐Flow for the Genera5on of Ultra Low-‐Power

WSN Node Architectures Based on Micro-‐Tasking, Proc. of the IEEE/ACM Design Automa5on Conference (DAC) Anaheim, CA, USA, June 2010.

[Alam2011] M. Alam, O. Berder, D. Menard, T. Anger, O. Sen5eys, A Hybrid Model for Accurate Energy Analysis of WSN Nodes, Journal of Embedded Systems, 2011.

[Bernier2008] C. Bernier et al., An Ultra Low Power SoC for 2.4 GHz IEEE802.15.4 Wireless Communica5ons, IEEE ESSCIRC, 2008

hWp://www.greenbusiness5mes.com/tag/intelligent-‐energy-‐system hWp://www.blog.telecomfuturecentre.it/blogs/futurecentre/category/future-‐of-‐energy

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