Smart Grid: Some Issues and Challenges

Transcription

1 Smart Grid: Some Issues and Challenges Olivier Sentieys IRISA/INRIA Université de Rennes 1 sen5eys@irisa.fr

2 Grid? 2

3 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 3

4 Towards a Smarter Grid Distribu5on is not efficient (>20% loss) Maximal demand drives genera5on Power consump5on peaks? Deal with renewable sources? 4

5 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 5

6 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:// energy- system 6

7 Architectural view of the SG Applica5on Informa5on Management Communica5on Infrastructure Monitoring and Control Power system components INSTITUT DE RECHERCHE EN INFORMATIQUE ET SYSTEMES ALEATOIRES 7

8 Architectural view of the SG Applica5on Risk limi5ng, load levelling Informa5on Management Communica5on Infrastructure Monitoring and Control Power system components Distributed data service Op5cal fibre, wireless Communica5on network protocols Smart sensor and actuators Wireless sensor networks Power electronics Control engineering 8

9 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 9

10 Buildings Over 70% of total electricity use in the US Good target for analyzing and reducing energy use e.g. UCSD microgrid [DOE05] [Aga10] 10

11 Buildings Energy use survey Office buildings in CA Day electricity usage Summer day in CA [CCEUS] 11

12 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 12

13 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:// Research efforts [Jiang09][Kim09] Challenges Granularity of metering and control Cost of installa5on and deployment 13

14 Energy Metering and Control ViridiScope system provides real- 5me appliance- level power es5ma5on Indirect Power Monitoring Concept Autonomous Sensor Calibra5on Framework [Kim09] Magne5c Acous5c Light 14

15 Energy Metering and Control hwp://energy.ucsd.edu, CSE Building 15

16 Detailed Energy Breakdown Plug loads, Servers, Lightning, Mechanical 16

17 Desktop Computer With sleep management 17

18 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 18

19 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] 19

20 Occupancy Sensing [Aga10] 20

21 Occupancy Sensing: Accuracy [Aga10] 21

22 Occupancy Sensing: Energy Savings [Aga10] 22

23 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 23

24 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 24

25 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] 25

26 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 26

27 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 , Zigbee, Bluetooth LE Sensor and/or relay 27

28 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 mw on ac5ve period 28

29 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 Energy Source Characteristics Efficiency Harvested Power Light Outdoor Indoor 10~24% 100 mw/cm µw/cm 2 Thermal Human Industrial ~0.1% ~3% 60 µw/cm 2 ~1-10 mw/cm 2 Vibration ~Hz human ~khz machines 25~50% ~4 µw/cm 3 ~800 µw/cm 3 RF GSM 900 MHz WiFi ~50% 0.1 µw/cm µw/cm

30 Typical energy budget (WSN node) What are the main sources of energy consump5on? Radio: 30-70mW Processor: 5-10mW Digital Rx Sensor Subsystem Tx Sensor Computation Subsystem Power Supply Low- Power MCU Power Subsystem Radio Tx Radio Rx Processor Communication Subsystem Tx Rx 30 30

31 WSN Pla orm PowWow: power op5mized hardware/sozware framework for wireless motes 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 for the same applica5on scenario hwp://powwow.gforge.inria.fr [Berder10] 31

32 Fine- Grain Power Ga5ng Power on/off of specialized tasks x energy gains w.r.t. sozware on microprocessor (TI MSP430) [Pasha10] 32

33 Radio Transceiver Op5miza5on Le5Bee chip (CEA LETI) Power consump5on Function RX (ma) TX RF LO 4 7 PLL Analog Digital Biasing Tx è dbm Rx è dbm [Bernier08] Trends: Wake- up radio, Ultra- Wide Band INSTITUT DE RECHERCHE EN INFORMATIQUE ET SYSTEMES ALEATOIRES 33

34 Energy Harves5ng STMicroelectronics Thermogenerator, solid- state thin- film bawery, 2.4 GHz wireless link IMEC Vibra5on harves5ng by MEMS piezoelectric power genera5on INSTITUT DE RECHERCHE EN INFORMATIQUE ET SYSTEMES ALEATOIRES 34

35 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 35

36 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 36

37 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 37

38 Smart grid 38

39 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 [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 [Agarwal2010] Y. Agarwal et al., Occupancy- Driven Energy Management for Smart Building Automa5on, ACM BuidSys, November [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, [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, [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 [DOE] US Department of Energy, The Smart Grid: An Introduc(on, [Chassin2008] D. Chassin, K. Schneider and C. Gerkensmeyer, GridLAB- D: An Open- Source Power Systems Modeling andsimula5on Environment, Transmission and Distribu5on Conference and Exposi5on, [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 , Taipei, Taiwan, May [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 [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, [Bernier2008] C. Bernier et al., An Ultra Low Power SoC for 2.4 GHz IEEE Wireless Communica5ons, IEEE ESSCIRC, 2008 hwp:// energy- system hwp:// of- energy