SMART BUILDINGS AS BUILDING BLOCKS OF A SMART CITY Professor Saifur Rahman Virginia Tech Advanced Research Institute Electrical & Computer Engg Department National University of Singapore Singapore, 10 November, 2016 Virginia Tech Research Center Arlington, Virginia, USA PPT slides will be available at www.saifurrahman.org 2 2 1
Rooftop Solar Photovoltaics 3 Electric Vehicle Charging 4 4 2
Smart Buildings A smart building connects the building automation system with building operations, such as HVAC, lighting, water supply, sensor network and fire emergency. Implementing smart building solutions can provide: up to 30% savings of water usage up to 40% savings of energy usage reduce building maintenance costs by 10-30 percent Source: Smart Building Market To Grow 30% by 2020, http://www.iotsolutionprovider.com/smartbuilding/smart-building-market-to-grow-30-by-2020, December 2015. Smart Cities Smart cities address urban challenges such as pollution, energy efficiency, security, parking, traffic, transportation, and others by utilizing advanced technologies in data gathering and communications interconnectivity via the Internet. Source: Internet of Things Philippines Inc., http://www.iotphils.com/solutions/smart-cities/#prettyphoto, December 2015. 3
From Smart Buildings to Smart Cities 7 Smart City Energy Smart Grid Smart Buildings Supported by ICT and distributed networks of intelligent sensors, data centers/clouds Smart city: Complex system of interconnected infrastructures and services Energy: Smart electric power grids, smart gas networks, smart water systems Smart grid: Bi-directional flows of energy, remote control/automation of power, integrated distributed energy Smart buildings: Intelligent building automation systems, smart devices, productive users, grid integration Demand-sensitive LED Lighting Project #1 #2 #3 #4 #5 #6 #7 #8 8 4
HPS vs LED Existing HPS Lamps (Dec 2010) New LED Lamps (June 2012) People/cars are clearly visible under the white LED light. 9 June 11, 2012 @ 9:14PM Light Intensity = 80% 10 5
Comparison of kw Consumption of two Street lighting Systems (HPS vs LED) 4 Power Consumption (kw) 3 2 1 Motion Sensors waking up LED streetlights from 50% to full brightness during 11:00pm to 4:00am. 0 5:00 PM 6:30 PM 8:00 PM 9:30 PM 11:00 PM 12:30 AM 2:00 AM 3:30 AM 5:00 AM 6:30 AM 8:00 AM HPS vs LED Monthly Electricity Consumption 1,800 Average electricity savings of 75% was experienced after the installation. Avoided CO2 emission was 6,127 kg/year. 1,600 1,400 Monthly Electricity Consumption (kwh) 1,200 1,000 800 600 400 200 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec HPS (2011) 1,510 1,289 1,300 1,116 1,027 903 972 1,123 1,210 1,384 1,444 1,552 LED (2012) 444 371 319 264 253 222 240 275 306 364 397 43312 6
Smart Buildings & IoT Evolution 1980 1990 2000 2010 2020< Building Control HVAC control Lighting control Building Automation Building management Building control Building Performance Energy management Remote monitoring Remote control Smart Building Intelligent buildings Green buildings Grid integration Internet of Thing (IoT) Connected Devices 0.2 billions 6.92 billions 50 billions Virginia Tech (VT) Solution For Smart Buildings BEMOSS is a Building Energy Management Open Source Software (BEMOSS) solution that is engineered to improve sensing and control of equipment in small- and medium-sized commercial buildings. BEMOSS www.bemoss.org BEMOSS monitoring and control: Three major loads in buildings HVAC Lighting loads Plug loads BEMOSS value: Improves energy efficiency and facilitates demand response implementation in buildings. 14 7
Why BEMOSS? 15 15 BEMOSS Interoperability Communication Technologies q Ethernet (IEEE 802.3) q Serial Interface (RS-485) q ZigBee (IEEE 802.15.4) q WiFi (IEEE 802.11) RS-485 Data Exchange Protocols q BACnet (IP and MS/TP) q Modbus (RTU and TCP) q Web (e.g., XML, JSON, RSS/Atom) q ZigBee API WEB q Smart Energy (SE) q OpenADR (Open Automated Demand Response) Smart Energy Profile (SEP) 16 8
BEMOSS Software Architecture Layer 1 User interface layer Layer 2 Application & data management layer Layer 3 Operating system and agent layer Layer 4 Connectivity layer Scheduling Tampering detection Web UI Device Discovery agent User Interface Operating System and Agent Monitoring agents VOLTTRON TM - Information Exchange Bus (IEB) Network agent API Translator for RadioThem. User Management Alarm/Notific ations Application Control agents API Translator for Wattstopper Load management API Translator Mobile UI Demand response OpenADR agent Platform agent API Translator for WeMo Metadata Database (PostgreSQL) Time-Series Database (Cassandra) Cloud sources (e.g. OpenADR, Email server, weather services) 17 BEMOSS Plug & Play With BEMOSS discovery agent, we know: The device is present in the building. Device model number, e.g., 3M-50. What the device can do, e.g., monitor temperature and adjust set point. Sensors/ BEMOSS automatically discovers new load controllers deployed in a building PowerMeters CT30 Power meter (WiFi) (Modbus) Power CT50 meter (WiFi) (BACnet/M odbus) CT80 (ZigBee SE) ICM (WiFi) Nest (WiFi) HVAC Load Controllers 18 VAV controller (Modbus) RTU (Modbus) Philips Hue (WiFi) BEMOSS Core Lighting load controller Light switch (WiFi) (BACnet) Lighting Load Controllers Step-dimmed ballast (ZigBee) Smart plug (WiFi) Light sensor (BACnet) Occupancy sensor (BACnet) Plug load controller (BACnet) Smart plug (ZigBee) Plug Load Controllers 18 9
BEMOSS: Solutions for Small Buildings BEMOSS Core HVAC Controllers Plug Load Controllers ZigBee mesh ZigBee mesh 19 Lighting Load Controllers 19 BEMOSS Scalability: Solutions for Larger Buildings BEMOSS Core Floor 2 Floor 1 BEMOSS Zone 2 BEMOSS Zone 1 20 10
BEMOSS Potential Applications Integration of machine learning algorithms to get better understanding of power consumption in buildings Integration of algorithms to manage a large amount of data collected from load controllers/sensors Integration of algorithms to allow management of multiple buildings in a transaction-based energy network 21 21 BEMOSS has been deployed in three buildings Building 1 Virginia Tech Architecture Building Location: Alexandria, VA Demonstration: HVAC, plug load control Building 2 Equipment Bureau Location: Arlington, VA Demonstration: Lighting control Building 3 Virginia Tech building Location: Blacksburg, VA Demonstration: HVAC control 22 11
Building 1 VT Building in Alexandria, VA 1021 Prince St., Alexandria, VA 22314 Area: 25,000 SF Energy: 14-25 MWh/mo. Peak load: 61 kw 23 Internal BEMOSS Deployment Power meter Environmental sensor (CO2, noise, temperature, humidity) BEMOSS core Thermostat Motion sensor Plug load controller 24 12
Impact of cooling set-points on energy usage 0.8 0.7 Outdoor Temperature ( 0 F) Base cooling set-point ( 0 F) 81, 74.0 Saving energy (kwh/hr) 0.6 0.5 0.4 0.3 0.2 84.4, 75.0 83.9, 73.0 87, 73.25 0.1 82.5, 73.0 83.9, 73.0 0 0 0.5 1 1.5 2 2.5 3 3.5 Cooling set-points difference( F) RTU Power Rate : 4 kw 25 Building 2 Office Building, Arlington, VA 2701 S Taylor St, Arlington, VA 22206 Office building size: 5,000 sqft Electricity consumption: N/A 26 13
Internal BEMOSS Deployment 27 Energy Consumption for February 2016 Location of Lights Open Office Area Bureau Chief s Office Conference Rooms A & B Scheduled Dimming Level from 6:30 AM to 9:00 PM Open Office Area A: 50 Open Office Area B: 45 Desk Area: 60 Meeting Area: 50 Conference Room A: 50 Conference Room B: 45 Total Measured Energy Consumption for February 2016* (kwh) Total Calculated Energy Consumption without Dimming for February 2016* (kwh) Energy Saving by dimming for February 2016* (%) 150.81 244.33 38.28 17.39 27.17 35.97 14.96 26.99 44.55 Total 183.16 298.49 38.64 * Data reflects some missing dates in February 2016 14
Building 3 Commercial Office Space in Blacksburg, VA 460 Turner St, Blacksburg, VA 26041 Retailed building: ~50,000 sqft Peak demand: ~160kW 29 Electricity consumption: 46-65MWh/month BEMOSS Hardware Deployment 30 15
HVAC Energy Savings by Increasing Temperature Set Points Test Result: Given that the outdoor average temperature is fixed, the daily energy saving is expected to be 7.25 kwh/degree set point increase. Average Set Point Increase (degree Fahrenheit) 1 2 5 Daily Energy Saving (kwh) 7.25 14.5 36.26 Daily Energy Saving Percentage* 2.97% 5.94% 14.85% * The base value for this percentage calculation is the expected daily energy consumption when outdoor average temperature is equal to indoor average set point, in this case, 244 kwh. 31 Solar PV System in BEMOSS Platform 32 16
Remote PV Data Access with BEMOSS 33 PV Inverter Data Access from BEMOSS 34 17
Battery Storage Data Access from BEMOSS 35 Battery Storage Project at Virginia Tech in Alexandria, VA Battery Cells 5 kw 12 kwh 36 18
Peak kw reduced 37 Supply grid services No net metering. 38 19
Transitioning from a Research Project to a Commercial Enterprise 39 www.bemcontrols.com 40 20
www.bemcontrols.com 41 www.bemcontrols.com 42 21
Thank You Professor Saifur Rahman Virginia Tech Advanced Research Institute Virginia, USA (www.saifurrahman.org) 22