Flattening Peak Electricity Demand in Smart Homes. Sean Barker, Aditya Mishra, David Irwin, Prashant Shenoy, and Jeannie Albrecht

Transcription

1 SMARTCAP: Flattening Peak Electricity Demand in Smart Homes Sean Barker, Aditya Mishra, David Irwin, Prashant Shenoy, and Jeannie Albrecht University of Massachusetts Amherst Williams College Department of Computer Science

2 Pervasive Computing in Smart Homes! Smart homes: efficiency, automation, convenience! Enabled by pervasive computing Smart meters, energy sensors, load controllers Appliance integration! Greening smart homes Why? 73% of U.S. electricity! Economic benefits: infrastructure, energy costs! Environmental benefits: carbon footprint, renewables 2

3 Demand-Side Energy Management! Control consumer-side energy demand Respond to energy availability Reduce peak usage, fluctuations Heater Dryer! Components of DSEM Monitoring (data collection) Control (e.g., load shifting) Peak Usage Light Time Off-Peak Power Shiftable Load! We focus on performing peak load reduction 3

4 Benefits of Peak Load Reduction! For utilities: Lowered peak grid demand Infrastructure savings Transmission & distribution 47%! (loss current 2 )! For consumers: Variable pricing cost savings Battery efficiency Assist with capping 4

5 Challenges of Peak Load Reduction! Change user behavior Users don t want to! Maintain household routines! Inflexible loads Lights off until 9 pm! Unacceptable: lights, TV Inconvenient: dishwasher! Goal: transparent peak reduction No user cooperation No negative impact 5

6 Outline! Motivation! Home measurement study What can we schedule transparently?! SmartCap scheduler How can we perform transparent peak reduction?! Evaluation on home data How effectively does SmartCap flatten demand?! Conclusions 6

7 Types of Loads! Interactive loads Not OK to interfere! Controlled by users TV, lights, microwave Little scheduling freedom!! Background loads Not controlled by users A/C, refrigerator, heater Don t care how objective is met Significant scheduling freedom! OK to change on/off times! 7

8 SmartCap Measurement Study! Home monitoring deployment 3 occupants, one year (so far)! Instrumented all outlets and switches Lights, TV, dishwasher, freezer, A/C, etc. renewables grid power SmartCap Gateway power readings Panel Meter power readings commands Outlet/Switch Meters Programmable Switches Appliances 8

9 1. Background Loads are Significant! Few major background loads heat recovery ventilator refrigerator freezer dehumidifier air conditioner (x3)! 8% of loads but 59% of energy use Load Peak Average Quantity Refrigerator 456W 74W 1 Freezer 437W 82W 1 HRV 1129W 24W 1 Dehumidifier 55W 371W 1 Main A/C 146W 35W 1 Bedroom A/C 1 571W 28W 1 Bedroom A/C 2 571W 141W 1 Background 4715W 1277W 7 Interactive 9963W 887W 85! Background loads are few but major energy users 9

10 2. Background Loads are Periodic! Periodicity: regular on/off intervals! Mostly (but not fully) independent of user behavior am 9 am 1 pm 5 pm 9 pm 1 am 5 am freezer HRV regular intervals unusual event irregular intervals 5 am 9 am 1 pm 5 pm 9 pm 1 am 5 am am 9 am 1 pm 5 pm 9 pm 1 am 5 am refrigerator dehumidifier am 9 am 1 pm 5 pm 9 pm 1 am 5 am! Background loads vary but have useful periodicity 1

11 3. Interactive Loads are Unpredictable! Peaky total load Brief, high-power devices! Many unpredictable individual loads Human usage patterns May change over time Power (watts) television mealtime peaks 5 am 9 am 1 pm 5 pm 9 pm 1 am 5 am Time interactive coffeepot 1 6! Peak reduction must compensate for interactive loads 5 5 am 9 am 1 pm 5 pm 9 pm 1 am 5 am entertainment center 5 am 9 am 1 pm 5 pm 9 pm 1 am 5 am am 9 am 1 pm 5 pm 9 pm 1 am 5 am lamp 5 am 9 am 1 pm 5 pm 9 pm 1 am 5 am 11

12 Flattening in SmartCap! Background loads cycle on and off! Exact on/off times (mostly) don t matter! Schedule cycling of background loads (a) no scheduling (b) with scheduling peak = 3W power A/C 3 A/C 2 power peak = 1W A/C 1 A/C 1 A/C 2 A/C 3 one hour period one hour period! Interleave background loads to flatten peaks 12

13 Scheduling Loads: Slack! Periodic background loads exhibit slack Measure of how long background load can remain off Based on guardband (e.g., fridge temperature range) Active cooling: accumulating slack Power (watts) Power Temperature Temperature (F) Time (6 hours) 37 Passive warming: consuming slack! Control slack by modifying device duty cycle 13

14 SmartCap Scheduler! Schedule based on remaining slack! Least Slack First (LSF) Operate loads in order of ascending slack! Online scheduler Respond to foreground (interactive) loads (c) offline scheduling (d) online scheduling interactive loads power peak = 2W power interactive loads peak = 1W A/C 1 A/C 2 A/C 3 one hour period one hour period! Preempt background loads by interactive loads 14

15 SmartCap Evaluation! Evaluate LSF on home data Computed per-period slack Linear slack model! Flattening metric Average deviation from mean Power (watts) Power Temperature Time (6 hours) Temperature (F)! Flattening period (a) One day (b) Four hours Daytime Nighttime! High deviation period (mealtimes) or low (nights) 15

16 Evaluation: Smart Home! Day-long periods Flattening on 91% of days 16% average flattening! Four-hour periods High variance (31%) >2% flattening Low variance (69%) <3% flattening Untimely interactive loads % Deviation Decrease % Deviation Decrease % LSF Improvement No Improvement >1kW Variance Days (a) Each Day % LSF Improvement No Improvement hour Periods (c) High-range 4-hour Periods Up to 5% improvement! LSF especially good at flattening high peak periods 16

17 Evaluation: Electric Vehicle! Electric Vehicle (EV) Peaky typical usage Grid unreadiness at scale EV load added to home data Power (watts) No Scheduling LSF (3.kW) Peak power periods reduced Percentage of Time! EV is good candidate for LSF: 22% flattening 17

18 Background Evaluation: Lab Testbed! Live testbed for active control, repeatability Smart appliances via programmable Insteon switches! Active background scheduling in LSF Aggregate Power (watts) No Scheduling LSF Background loads pushed forward Time (4 hours)! 23% flattening (background + interactive) on testbed 18

19 Related Work! Demand-side energy management Load shifting [Keshav, GreenNet 1] Prediction [Schülke, SmartGridComm 1] Batteries [Zhu, BuildSys 11], [Bar-Noy, WEA 8]! Background schedulers Optimizing for renewables [Taneja, SmartGridComm 1] Offline scheduling [Bakker, SmartGridComm 1] 19

20 Conclusions! Demand-side energy management for peak reduction! SmartCap flattens transparently Modifies only background loads Interactive loads unaffected! 2-3% flattening using Least Slack First! Additional savings possible with modest user changes Subject of ongoing work 2

21 Questions? Sean Barker Department of Computer Science

22 Least Slack First Threshold! Threshold power to preempt loads Start scheduling when threshold is reached Power (watts) am No Scheduling LSF (2.2kW) A/C's 1, 2, 3 activate 7 am! Adaptive threshold (moving average of past use) 8 am Time (Hours) Stacking triggers scheduling 9 am 22