Dr. Roger Dougal Professor University of South Carolina.

Dr. Roger DougalProfessorUniversity of South Carolina

Integrated Energy Management at Consumer

INTEGRATED ENERGY MANAGEMENT AT THE CONSUMER SITE

Roger A Dougal, Gregory Professor and Chair of EE

University of South CarolinaCo-director, GRAPES

Site Director, Electric Ship R&D Consortium

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Energy Management System

Integrate renewable sources, energy storage, and load management in homesEfficient all-in-one power electronics package to manage DC and AC power flowsIntegrate smart grid, home, and web communicationsPermit islanded operation during outages

React to time-of-use or real-time pricingUse PV and battery to reduce consumption at peak timesManage loads, safely and transparentlyCoordinate plug-in electric vehicles (PEV)Participate in load curtailment programs with minimal inconvenience to homeowner Reduce monthly cost for customers

Reduce grid-side demand during peak times

Residential example


The Opportunity

Widespread adoption of photovoltaics appears inevitable – competitive with user pricesOther on-site power alternatives (e.g. fuel cells) are likely to become viablePower pricing is volatile – minute-by-minuteReal time or time-of-use pricing encourages responsible power behavior – become more “Danish”Scheduling of power flows can produce financial rewardsEnergy storage is coming (even if only in the form of EVs)Local management of power offers price and security advantagesEmerging markets in power aggregation offer business opportunitiesUbiquitous internet permits coordination

Source: Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections, US DOE Nov 2012

Source: PJM.com 2013-02-09


Our Research Environment

Bring the EE perspectiveHybrid power sources, ship power systems – all tough power balancing problems because self-contained – like a microgrid.Multidisciplinary – power electronics, power systems, electrochemical power sources, pv power sources, energy storage, controls, thermal systems.


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Local dc Generation

Distant ac Generation Local dc Generation


Time sensitive loads

Electric Power Problem is Partly a Thermal Problem

Shrinking:desktops becoming

iPads

Shrinking:Incandescents

becomingCFLs, LEDs

Better thermal efficiency of home envelope increases thermal time constant, and enables greater flexibility in power scheduling

Majority of residential electric

load is thermal

Time insensitive loads


Opportunities to Reduce Cost

Reduce demandReduce loads , especially during peak pricing intervals – e.g. raise thermostatRespond to utility requests for curtailment

Time-shift consumptionMove loads temporally from peak price time to other time, e.g. pre-cool house, pre-heat water, before on-peak pricing intervalStore energy in battery, use or sell back to grid later

Manage renewable powerMove renewable power use to high price timesSell excess power to utility at high price times


Why does Time affect Cost?

0 2 4 6 8 10 12 14 16 18 20 22 240

10

20

30

Hour

¢/k

Wh

On-peak

Shoulder

Off-peak

Winter pricing strategy

Morning peak Evening peak

Afternoon dip(insolation, not home, etc)

Characteristic winter day demand

~25%


Southeastern Pricing Schedules

1 4 7 10 13 16 19 220

5

10

15

20

25

30

35

Hour

¢/kW

h

Time of Use Pricing is (currently) a consumer optionTOU schedules vary between providers, some offering two-tier and some three-tier Typically 12 hours off-peak and 6 hours on-peak; off-peak during weekendsSchedule varies by seasonSome utilities offer load curtailment programs: reduce baseline load for a period of time and receive a $/kW reward

TOU weekday schedule, SCE&G (www.sceg.com)

SummerWinter

Time Of Use pricing encourages “Danish” (responsible) behavior


PV Power Feed-in Pricing

Time of use (TOU) is the most widely adopted time-varying pricing structure todayFeed-in tariffs vary widely throughout the country. More progressive states have high FITs for PV while others only accept solar energy at “avoided cost” (SC: Duke, Santee Cooper, SCE&G)Dynamic pricing mostly used for commercial and industrial sites as well as load curtailment programs


Local Energy Management

DC - AC

Inverter

Current usual PV implementation:Grid-interactive inverter and “Net metering”. During grid failure, PV cannot supply house.

Smart power router with Grid Disconnect

Whole-house power management system:During grid failure, system operates independently from grid, “islanded mode” maintaining power supply within the house

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Every Instance Different

Electric AC and Water Heaterbuilt 1995 - 99

Electric AC and Water HeaterBuilt 1995 - 99

Electric Heating & Cooling Built after 2000

Electric AC and Water Heaterbuilt before 1986

Electric Heating & CoolingBuilt 1995 – 99

Built before 1985

Jan 1, 2008 Jan 2, 2008 Jan 1, 2008 Jan 2, 200812 AM 12 AM 12 AM 12 AM

0 0

0

0

0

0

30

30

30

30

30

30

kW

kW

kWkW

kW

kW

From AEP data

Six different homes, six different power characteristics


Storage is Useful

Avg. Demand

TOU Price

TOU Price

PV production does not always align with price peaks

Storage is required to decrease demand during peaks

Urban house<3000 sq. ft,.Electric water heating, Built prior to 1986

Avg. PV power

Avg. Demand

Avg. PV power

Mis-alignment during winter

Better-alignment during summer


Power Conversion Circuits


Control Hierarchy

System control is the highest decision-making level – the one that interfaces to external data sources and controlsApplication control layer bridges between the system and three lower control layersHardware control manages specific low-level switching actions at high frequencies (10’s of kHz and up) to manage voltage and current behavior for

Battery charge and dischargePower extraction from photovoltaicAC power flow to or from the grid


Some Control Inputs

Battery charge or discharge rate

Thermostat setpoints

Power pricing

Battery state of charge

Predicted heating/cooling demands

Temperature

Wind

Rain/skycover

Water tempAnticipated PV production

Homeowner comfort preferences

Homeowner activities and behavior

Room temp

Anticipated next day

Actual next day

High LevelControl Decisions


System Control

System control algorithmsBattery state-of-charge and state-of-health estimation Weather forecast and corresponding prediction of power consumptionPower price and estimated price behavior (in short-term markets)Load and homeowner behavior predictionPV power prediction


Reduce On-Peak Demand

Solar power is small during (winter) morning and evening peaks.

Battery storage is required to shift energy into on-peak periods.


Even Just a Battery Saves Money

Battery depth of discharge limited to 40%Charger and inverter costs/efficiencies consideredPayback period (including battery change-out) is 14 years(very dependent on power pricing, of course!)

No PV installed

Results from simulation

$0.20 /kWh on-peak

$0.06 /kWh off-peak

Price

Shift consumption from on-peak to off-peak


System with Battery and PV

Complete system with 50 kWh batteries and CA=0.50 (kWp rating = 50% of daily energy demand in kWh)Customer #2 has a payback period of 15.44 years and displaces 9.90 MWh of on-peak energy consumptionAdding more PV capacity – e.g. say twice as much PV array

Does little to further decrease on-peak energy consumptionDramatically increases the payback period


Short and Long Term Pricing22

Highly variable pricing

Many different power markets on different time scales.

In some markets, values can sometimes be negative

Encourages creative power management strategies

edata.pjm.com


Extend to Commercial Customers23

Building Agent #1 Building Agent #2

Load & Source Agents Load & Source Agents

Status & Sensor Data Status & Sensor Data

Negotiation Agent

PVDiesel Gen

HVAC Battery Wind Lighting

Weather Data

(Internet)

Negotiation or biddingstrategy use

Requests for load curtailment

Weather Data

(Internet)

Market Price Data

(Internet)

Power Provider


Representative Commercial

Nursing home in upper Midwest50 kW max demand

1 17 33 49 65 81 97 1131291451611770

10

20

30

40

Jan 6, 2012 Jan 7, 2013

kW

12 AM 12 AM

Measured power demand


Forecasting Methods

Need to predict load and DERs available for bidding and energy management

Long-term - Seasonal or monthly baselineDay-aheadVery Short-term (15-min)

Several forecasting methods were examined

AutoregressiveExponential smoothingKalman based correction (for very short term)

Forecasting algorithms used by negotiating agent

Forecasting performance of day-ahead load and weather forecasting

4/6/2012 0:00 4/6/2012 5:00 4/6/2012 10:00 4/6/2012 15:00 4/6/2012 20:000

10

20

30

40

50

60

70

80

90

100

0

10

20

30

40

50

60

Nursing Home Day-Ahead Load & Temperature Forecast, Friday, April 6,

2012

Actual Load (kW) Forecasted Load (kW)Actual Temp (°F) Forecasted Temp(°F)

Load

(kW

)

Tem

pera

ture

(°F)

Forecasted Temp.

Forecasted Load

Error=5.90 %


Load Leveling Strategy26

Battery storage only, no PVLimit peak grid demand to 35 kW (target)Uncertainty included for

Day ahead forecast15 min. battery scheduling

Grid demand limit is sometimes exceeded because of limited battery capacity


Static On-Peak Shaving Strategy, with PV

On-peak

Off-peak

PV offsets grid demand

Battery is recharged during off-

peak

Battery peak shaving (2.7 kW) &

Curtailment (16.5% baseline)

Battery peak shaving (2.7 kW) &

Curtailment (16.5% baseline)

Battery is recharged during off-

peak

Accurate forecasted

peak matching

Lowest MAPE day forecast (5.9%)

Cloudy Day

Total Savings = $74 (24%

reduction)


Presentation Take Aways

Energy storage and photovoltaic sources coordinated through power electronics and intelligent controls can provide cost benefits to residential and commercial power customers

Smart scheduling and coordination of power resources can have significant financial benefits and increase uptake of renewable energy resources

Many renewable sources of electric power are highly-variable, and non-dispatchable. Loads may be semi-predictable but only partially manageable. Load buffers are essential (batteries, or fast-ramping sources)

Sources fueled by renewable fuel would do well to consider ramp rate requirements and not assume constant output conditions. We can’t all try to be like nuclear plants!

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Dr. Roger DougalProfessorUniversity of South Carolina

Dr. Roger Dougal Professor University of South Carolina.

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Transcript of Dr. Roger Dougal Professor University of South Carolina.