ICT framework in Smart Grid Communication Infrastructures

Transcription

1 ICT framework in Smart Grid Communication Infrastructures Prof. Yi Qian Department of Electrical and Computer Engineering University of Nebraska, NE, USA January 30, 2017

2 Outline The ICT framework in smart grid Overview Communication infrastructure Data sources Control centers Big data and cloud computing in smart grid The need for big data analytics Techniques for big data analytics in smart grid Cloud computing in smart grid Conclusion 2

3 Introduction to Smart Grid RELIABILITY ENHANCED DEMAND DEVICES RESPONSE EFFICIENCY SMOOTH PEAK LOAD SMART GRID UPGADE ELECTRICITY GENERATOR RENEWABLE SOURCES EVOLUTION BIG DATA ADVANCED CLOUD COMPUTING CONTROL COMMUNICATIONS TECHNOLOGIES WIRELESS TRANSMISSION SYSTEM METER SERVICE TWO WAY INFORMATION NETWORK CONSUMER 3

4 Motivations & Objectives of Smart Grid Higher Penetration of Renewables Smart Charging of Electric Vehicles Consumers to Control Energy Bills Efficient Grid Operations & Reduced Losses Reduced Distribution Outages Improved System Reliability & Security Increased productivity Improved utilization Lower Greenhouse Gas Emission Facilitated renewable resource generation Enhanced customer experience Adherence to regulatory constraints 4

5 What is Smart Grid? An upgrade on upgrading generation, transmission, and distribution systems Incorporating advanced information and communications technologies (ICT) and control DoE defines Smart Grid in terms of key functions Enabling active participation by consumers to adjust consumption based on price and overall demand Better matching generation and demand Integrating renewable (e.g., solar, hydro, wind, etc.) and distributed power generation sources Providing more and better energy storage options Improving power quality, reliability, and enhancing resiliency: wide area situational awareness (WASA) Demand response 5

6 The ICT Framework in Smart Grid 6

7 Proposed ICT Framework (Big) Data Analytics Information and Communication Technologies (ICT) Two way communications RAW DATA USEFUL INFO 7

8 Networks in the ICT Framework Private networks: deployed by utility companies Networks in the advanced metering infrastructure (AMI) Metering data gathering Demand response control message distribution Networks in the wide area monitoring systems (WAMS) Monitoring data gathering Controlling message distribution Etc. Public networks: Internet based public network service Remote monitoring and control from smart phones E.g., smart appliances that have Wi Fi connection to the Internet Data transmission through cellular network service E.g., transactional data from EVs Applying public cloud computing service For big data analytics Etc. 8

9 Advanced Metering Infrastructure (AMI) Backbone (Fiber optics) MDMS (Metering Data Management System) Advanced metering infrastructure (AMI) enables two way communication between utilities and customers. 9

10 Source: #prettyPhoto 10

11 Smart Appliances Figure sources: and the economy of things/ grid the future of home appliances/ lennox icomfort wi fi thermostat sonos for air conditioners/ products/the lowdown on modern electric vehicles a month controlling my coffeemaker over wifi/ 11

12 Data Sources Internal data sources: data sources that are in the power grid Smart meter Deployed at the customer side by utility companies Monitors the power consumption as well as power quality of a household May participate in smart appliance control Grid monitoring sensors Deployed over transmission lines, substations, etc. Monitors power transmission quality Real time report of anomaly May take quick action to prevent further damage Renewable power sources Data gathered for better operational management E.g., charge/discharge of energy storage units Attach/detach to the power grid Etc. 12

13 Data Sources (cont d) External data sources: data sources that are not controlled by the utility company Weather forecast Existing weather forecast system is reliable The grid may have its own weather forecast system for certain applications Weather forecast is important to the management of some renewable power sources Weather forecast may also be applied to big data analytics in demand response Information of electrical vehicles Location and routes of EVs can be used to estimate the amount of electricity needed and the time when it is needed Remote control (of smart home) from users Control of micro grids Power generation/consumption of micro grids Attach/detach micro grids Etc. 13

14 Control Centers Control centers are deployed and operated by utility companies. Local control centers (LCC) Deployed by utility companies for data collection and local data analytics E.g., deployed in substations May not need data exchange among different LCCs A cloud control center (CCC) Hired from public service providers for big data analytics Processing data that has been pre processed by LCCs Authentication server (AS) with a private key generator (PKG) Controlled by the utility company for the security of the hybrid cloud service Figure source: auctions data centers google cloud attacks amazon weeks news/ 14

15 Power Generators Conventional energy sources (i.e., fossil fuel based power generators) Can be adjusted according to grid demand Sudden load change causes extra fuel waste Emission of greenhouse gas Renewable energy sources (e.g., solar farm, wind turbines, etc.) The electricity generation can hardly be adjusted according to grid demand More expensive operational & maintenance cost Environment friendly power sources Picture sources: change/ us epa seek 30 percent co2 cut power plant emissions within next 16 years cdn.com/files/2014/04/wind turbines17.jpg 15

16 Big Data and Cloud Computing in Smart Grid 16

17 Smart Grid Generates Big Data Data grows vastly in volume, velocity and variety in smart grid Smart meters will generate over 100 petabytes a year More data will be generated from various sources Smart electric vehicles (EVs, hybrids) Transmission line monitoring sensors (e.g., PMUs, etc.) Substations monitoring sensors Renewable energy sources Etc. Data to be processed in (near) real time for fast response Real time monitoring data from WAMS Real time location information of EVs Frequently generated data from renewable energy sources Figure source: launches smart grid initiative/#.vx0kgudu4wa 17

18 Big Data Analytics in Smart Grid With useful information from big data analytics Achieve efficient demand response Measure and predict real time loads Forecast energy usage to generators Forecast pricing to customers Being able to analyze data in order to recognize fraud or predict maintenance or identify threats. Identify anomalies in real time (e.g., for PMU data) Prevent privacy leakage (e.g., for metering data) Aggregate data across operations to garner a better, more intelligent understanding of renewable energy sources, such as wind an solar Etc. Figure source: data why should we care/ 18

19 Demand Response in Smart Grid Demand response (DR) is applied for balancing supply and demand Increase of power generation efficiency Lower the cost of electricity in wholesale markets Lead to lower retail rates. Consumers are involved in the operation of the power grid General methods Direct load control (DLC) For example, power companies cycle air conditioners and water heaters on and off during periods of peak demand Utility company oriented Smart Pricing Reduce or shift electricity usage during peak periods in response to time based rates, or other forms of financial incentives Customer oriented 19

20 Demand Response (cont d) Communication Communication link link Power line Control center Demand Response Fluctuating power load Wind turbine Conventional Hydroelectric PV Nuclear Residential Industrial Business Smoothed power load? 20

21 Big Data Analytics in DR The closer to real time, the better grid efficiency in DR More data exchange overhead More computational overhead More security concerns 21

22 Cloud Computing for Big Data Analytics Cloud computing is powerful and cost efficient for big data analytics There is no need to invest on the whole infrastructure, only the infrastructure for the private portion of the cloud is deployed by utility companies It is easier to implement applications in cloud Public cloud computing has virtually unlimited resources The utility companies need not worry about upgrading capacity for large scale system The infrastructure in cloud can also be re scaled according to adaptive requirements It is easier to access cloud service from a variety of smart devices 22

23 Big Data Analytics in DR Real time DR is hard to achieve due to the enormous transmission and computation overhead DR may depend on predictions in the near future Machine learning is a key technique related to big data analytics Find power consumption pattern for power generators Find smart pricing pattern for customers Data analytics for real time anomaly detection Example: power consumption prediction of one household based on temperature. 23

24 Proposed Distributed Learning Metering data learning at DAPs Achieves global optimal Fast convergence Less computational overhead Distributed Centralized 24

25 Conclusion 25

26 Conclusion Smart grid is a massive cyber physical system Advanced ICT are applied in smart grid communication infrastructures Smart grid generates big data Big data analytics is important to DR, monitoring system, and other applications in smart grid Many issues remain open in big data research of smart grid communications 26

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