Virginia Tech Research Center Arlington, Virginia, USA

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1 Energy Efficiency, Renewable Energy, & Smart Grid Research at Virginia Tech Presentation at Tsinghua University Workshop 23 May, 2017 Beijing, China Prof. Saifur Rahman Virginia Tech Advanced Research Institute Virginia Tech Research Center Arlington, Virginia, USA PPT slides will be available at 2 2

2 Global Growth of Renewables Challenges & Mitigation 4

3 Hydroelectricity In over 120 years Global Hydro Share

4 Global Wind Installed Capacity ( ) Wind energy reached the 500 GW milestone in 20 years For Hydro it took over 70 years 7 Global Solar PV Installed Capacity

5 Global Solar PV Addition in 2015 US Off-shore Wind Resource Mid-Atlantic Area

6 Virginia Offshore Wind Technology Advancement Project (VOWTAP)

7 Commercial Project Footprints Two 6-MW demonstration turbines Commercial project area may contain anywhere from 230 to MW turbines depending on spacing for maximum costeffectiveness and minimum environmental impact (1,400 to 2,000 MW) Offshore Wind Demonstration Project Innovative twisted jacket foundation substructure

8 PJM Load (MW) 160, , , , , , ,000 90,000 80,000 Wind Output & Load Mismatch (PJM) (A peak day in June, 06/08/2011) 70,000 PJM Load a Peak Day in June 2011 Wind Power Output 60, Time (Hour) Data source: 2,500 2,300 2,100 1,900 1,700 1,500 1,300 1, PJM Wind Power Output (MW) ERCOT (Texas) Load vs Actual Wind Output (10/11/ /18/2011) Data source: 1 6

9 Problem Definition: Challenge High Wind Energy Penetration Challenges Non-correlation between wind and load profiles Wind power forecast error 17 BPA load,wind power, and net load profiles of a week in May BPA actual wind power, hour-ahead and day-ahead wind power forecast profiles Source: Hamideh Bitaraf Problem Definition: Solutions Improve forecasting methods Increase flexible generation Solutions for high wind penetration challenges Wind spillage Energy storage Demand response Source: 008/01/pondering_wind_energy_possibil.html 18 Source: Hamideh Bitaraf

10 Energy Storage Technologies Pumped Hydro Energy Storage Sodium Sulfur (NaS) Battery Compressed Air Energy Storage 19 Source: Hamideh Bitaraf Demand Response Rebound may have to be adjusted 20 Source: Hamideh Bitaraf

11 Impact of scheduling both DR and Energy Storage 21 Source: Hamideh Bitaraf Impact of generation mix 22 Different generation mix and their resultant spilled wind energy Source: Hamideh Bitaraf

12 Role of the Smart Grid in Alleviating Electrical Power System Stress Conditions Through Demand Response PI: Dr. Saifur Rahman Objective: To design and develop a smart grid sensing and control hardware and software platform that enables efficient and flexible demand response programs with customer choice. Partnership for Innovation Program Sponsored by US National Science Foundation 23 Load Duration Curve Dominion Virginia Power Probability that peak loads exceed 16,000 MW is only 5% of the time Peak load of 19,140 MW 3,140 MW or 16.5% of peak load

13 Peak load and its duration In the US 20% of the load happens 5% of the time In Australia 15% of the load happens 2.5 days in a year or less than 1% of the time In Egypt 15% of the load happens 1% of the time Demand Response Load Control at the Customer Level can provide significant short term load reduction A cheaper option to get load relief How to incentivize the customer? 26

14 Flexible Demand Response Scheme GRID OPERATOR Substations Smart meter Home Management System (HMS) Customers Customers 27 Home Energy Management Unit Clothes dryer (4-6kW) AC unit (2-3kW) Demand limit (kw) External control signal from a utility Water heater (4-6kW) HEM Unit Gateway Distribution board & meter Electric vehicle ( kW) Load Controller Data communication & control signal

15 Example- Power Consumption (kw) (17:00 20:30) Before Demand Response: 4kW Heating Coil +0.4kW Motor Shedding Priority Dryer After Demand Response: Demand (kw) 90 F 1.2 kw AC 80 F Room temperature 70 F 120 F 4 kw WH* 100 F Water temperature 80 F HVAC WH* Demand (kw) 90 F 80 F Room temperature 70 F 120 F 100 F Water temperature 80 F 3.6 kw EV EV 8kW Source: Shengen Shao * WH = Water heater 29 Intelligent Interconnected Grid Intelligent Load Demand or price-driven control of appliances Sensors Detect outages, fluctuations, and disturbances Distribution Network Interconnected micro grids Microgrid Distributed Arch. Local monitoring and control Bulk Power Plant Wind Power Park Smart Inverters and Storage Minimize voltage and power fluctuations Saifur Rahman Control Room Functions Balance electricity supply/demand across the grid

16 Control at a Smart Building Level 31 Building Energy Management Open Source Software (BEMOSS) Platform PI: Dr. Saifur Rahman The objective is to develop a web-based Building Energy Management Open Source Software (BEMOSS) platform for optimizing electricity usage and building operations in small and medium-sized buildings. Sponsored by US Department of Energy 32

17 CT80 (ZigBee SE) ICM (WiFi) Nest (WiFi) VAV controller (Modbus) RTU (Modbus) BEMOSS Plug & Play Sensors/ BEMOSS automatically discovers new load controllers deployed in a building PowerMeters CT30 Power meter (WiFi) (Modbus) Power CT50 meter (WiFi) (BACnet/M odbus) HVAC Load Controllers 33 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. Philips Hue (WiFi) BEMOSS Core Light switch (WiFi) Lighting load controller (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 BEMOSS: Solutions for Small Buildings BEMOSS Core HVAC Controllers Plug Load Controllers ZigBee mesh ZigBee mesh 34 Lighting Load Controllers

18 BEMOSS Scalability: Solutions for Larger Buildings BEMOSS Core Floor 2 Floor 1 BEMOSS Zone 2 BEMOSS Zone 1 Solar PV and Smart Inverter Integration

19 Solar PV and Smart Inverter Integration(Cont d) Solar PV and Smart Inverter Integration (Cont d)

20 Thank You Dr. Saifur Rahman Joseph Loring Professor and Director Advanced Research Institute 39