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2010 Smart Grid Enabled Value-Added Services for Residential and Small Commercial Buildings-ppt_图文

Smart Grid Enabled Value-Added Services for Residential and Small Commercial Buildings Chellury Ram Sastry Rob Pratt Pacific Northwest National Laboratory (PNNL) Chellury.Sastry@pnl.gov Rob.Pratt@pnl.gov
ConnectivityWeek 2010

Smart Grid Described as a Matrix of Primary & Enabling Assets and Functions Providing Benefits
manage peak load

technology areas ? ? ? ? ? ?
DR DG DS DA/FA EVs & PHEVs
Smart Meters PMUs

Functions

Value Streams

wholesale operations

?

The Business Case: Σ Values > Σ Investments renewables integration + Σ Incentives reliability
ancillary services energy efficiency

? ?

Incentives to Engage Assets



The key to a successful business case: Having invested in an asset, plan to use it continually to provide as many value streams as possible!

Primary Assets

Enabling Assets

Capital Investments

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Enable deployment & delivery of energy saving services (value streams) with minimal investment over & above what was required for installation of smart grid assets. ? ? ? Identification of energy efficiency measures (in the process of making buildings ready for demand response programs). Feedback on energy use, costs, and carbon footprint including “Peer Group” comparisons. Equipment/appliance fault detection & diagnostics:
? Detection & quantification of degradation in efficiency ? Causes and potential corrective actions

Energy Management Services for Residential & Small Commercial Buildings*

Focus of this presentation

Leverage data (& communication) from smart grid assets: interval data from smart meters & DR signals (for e.g., on/off signals from thermostat, run-times and duty cycles, etc).
*Small Commercial Buildings: Less than 50,000 ft2 in floor area Note: Large commercial buildings and industrial customers generally have sophisticated automation systems installed and dedicated staff to provide energy services, with or without smart grid.

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Residential Energy Management Services: Customer Potential

Source: “Parks Associates: All rights reserved.”

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Residential Energy Management Services: Customer Potential
80% to 85% of Households Willing to Pay $80 to $100 for Cost-saving Equipment if Guaranteed 10% to 30% Savings

Source: “Parks Associates: All rights reserved.”

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Key Enabling Technologies for Value-Add Services
Establish fine-grained base-line energy use models Current meter data: monthly resolution PRISM* method to estimate base load heating, and cooling

Very difficult to discern end-use consumption. Lowest in the summer

AC is load still invisible *Fels M and C Reynolds. 1993, Princeton University.

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Key Enabling Technologies for Value-Add Services
Smart meter data: Large amounts of hourly resolution energy consumption data. Heating, cooling, and other base load can be discerned with reasonable precision.
Hour 19 Hour 20 Hour 21 Hour 22 Hour 23 Hour 14

Hours 1 through 10, no cooling load. Hours 11 though 22, air-conditioning load shows with increased clarity. Hourly heating & cooling curves (models) can be estimated through advanced ‘learning’ techniques.

Hour 13

Hour 14

Hour 15

Hour 16

Hour 17

Hour 18

Hour 7

Hour 8

Hour 9

Hour 10

Hour 11

Hour 12

Hour 1

Hour 2

Hour 3

Hour 4

Hour 5

Hour 6

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Key Enabling Technologies for Value-Add Services
Non-intrusive disaggregation of energy consumption by end use and appliance type

Source: Google Power Meter

? Disaggregation accomplished through appliance signatures and looking for on/off events in meter data. ? First proposed by George Hart, MIT, 1992. Subject of extensive research since then.

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Key Enabling Technologies for Value-Add Services
Typical home appliances’ operation not characterized by non-overlapping On/Off signals making it challenging to disaggregate precisely.

Source: Whirlpool (IEEE P&E Magazine, May/June 2010)

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Key Enabling Technologies for Value-Add Services
Consider 3 loads (appliances) with different power draws turned on sequentially over a 30 second interval
Source: Lou et. al, ASHRAE, 2002.

Sampling starts at t = 0 Data for only two of the appliances is0.125-sec. obtained with All three appliances disaggregated andsamples 10-sec. at 10-sec intervals, inbut thefor appearance ofsampling 2 appliances onethe increasing samplingresulting intervals, the 60-sec. interval, three the power use from 21.5 kW to large 32 kW and the other (which really two appliances appear as one appliance in which the is power appliances) increasing power47 use from 32 kW to 47 kW. changes from 21.5 kWthe to about kW.

Sampling starts at t = 5

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Key Enabling Technologies for Value-Add Services
Key challenges* involved in the non-intrusive disaggregation of energy consumption by end use and appliance type from meter data ? The disaggregation is determined from steady-state power consumption and requires waiting until transients settle down. ? Metering sampling rate & timing of appliance turning on/off relative to sample time affects disaggregation. ? Several devices in a home might have power consumptions of similar magnitude, and frequent cycling can occur, and their operational times may overlap. ? Building an appliance-signature library that includes all possible appliances (type, make, model, year, etc.) is impractical if not impossible. Smart meter data augmented with smart appliance/equipment DR signals, for e.g., on/off signals from thermostat, run-times and duty cycles etc, can result in unequivocal disaggregation by end use and appliance type.

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Matthews et. al, CMU, ICE08

Smart Grid Enabled Equipment/Appliance Diagnostics: Heat Pumps and Air Conditioners
Diagnostic Issue: Declining efficiency could be detected long before complete failure. In the interim, user comfort achieved, albeit with energy wastage. Track coefficient of performance (COP) of heating/cooling system to look for anomalies and deviations

Heating

Cooling

HVAC
UA/COPcooling UA/COPheating 1 1

IAT - OAT

UA

? IG

( IAT

i

? T bal )

Tbal: Indoor/Outdoor air temperature difference when in balance (HVAC = 0) Source disaggregated meter data disaggregated meter data smart thermostat

Information HVAC: heating/cooling power consumption IG: power consumption for miscellaneous loads Ambient indoor air temperature on balance IATi

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Smart Grid Enabled Equipment/Appliance Diagnostics: Airside Economizer
Airside Economizer: A duct and-damper arrangement and an automatic control system to supply outdoor air to reduce or eliminate the need for mechanical cooling during mild or cold weather. Key Diagnostic Issue: Enormous energy wasted when dampers get stuck and remain in the open (100%) position all the time. This may not affect comfort and hence not obvious.
Heating Cooling

HVAC

700

IAT - OAT

No AC compressor between Tbal and 700. Track this gap, absence of this gap indicates economizer fault.

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Smart Grid Enabled Equipment/Appliance Diagnostics: Schedules & Operation
Key Diagnostic Issue: Continuous monitoring of schedules and operation presents opportunities for energy savings. For e.g. verifying the operation of night setback of thermostats or identifying abnormal lighting and plug load schedules.
D
Lighting

N
24

Plug Loads 24 Thermostat Setpoint 24 Fans 24 HVAC Time of day 24

Rule-based schedule diagnostics to look for deviations/ anomalies: ? Correlations between schedules of different lighting, HVAC & plug loads ? Thermostats set up too early/set down to late (based on lights & plugs) ? Fans left on at night ? Lights and plugs too high at night

Lighting, Plug Loads, Fans, HVAC: Disaggregated loads from smart meter data Tsetback: Thermostat set points

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Smart Grid Enabled Equipment/Appliance Diagnostics: Direct Impacts
Estimated Direct Impacts for Enabling Mass Deployment of Diagnostics in Residential and Small Commercial Buildings*

*Pratt et .al, The Smart Grid: An Estimation of the Energy and CO2 Benefits

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Smart Grid Enabled Service Delivery
Third Party Energy Service Providers (ESP) Internet Energy Services Interface (ESI) Utility AMI
Smart Appliances

Customer Premise HAN
PC
Energy Services Delivery Platform

In Home Display (IHD)

iPhone

Requirement: Architecture & interoperability standards to facilitate analysis of smart grid data to provide diagnostic services at any of the following locations: ? Utility ? ESP location ? Within customer premise

Smart meter

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Smart Grid Enabled Service Delivery
Key technical issues that need to be resolved: ? ? ? ? ? Minimum smart meter data rate needed for accurate load disaggregation Interoperability & standards for data exchange AMI network bandwidth for data transfer (centralized processing) Data retention & storage requirements, Energy required by processing unit should not be significant compared with energy savings from services.

Key regulatory & policy issues that need to be resolved: ? Who owns the energy-use data? Customer? Utility? ? Who gets the carbon credits for the energy saved & associated reduction in carbon footprint?

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