assisting energy management in smart buildings and microgrids

Assisting Energy Managementin Smart Buildings and Microgrids

Andrea Monacchi

Smart Grids Group,Institute of Networked and Embedded Systems,

AAU Klagenfurt

[email protected]

September 28th, 2016

Introduction Energy Awareness Interoperability Automating Energy Management Conclusions

Monitoring and Coordination of Distributed Resources

• Increasing use of renewables and electric loads

• Coping with complexity with bottom-up approach: MicrogridsI Autonomous sub-systems optimizing own resourcesI Shift from centralised to mesh of microgrids

• Demand responseI Direct control vs indirect demand-side control (price)I User in the loop vs autonomous device operation

Andrea Monacchi Assisting Energy Management in Smart Buildings and Microgrids

2/24


How to boost energy awareness and efficiency?


3/24


MONERGY and the GREEND Dataset

MONERGY: Interreg project on energy efficiency1 Identified Usage Scenarios in CAR and FVG

I Analysis of web survey (∼ 400 people)

2 Measurement campaignI 1+ year active Power (P) at 1 HzI Commercial HW + Own SoftwareI Openly released and referenced

• http://monergy-project.eu

• Monacchi et al. Strategies for Domestic Energy Conservation in Carinthia and Friuli-Venezia Giulia. IEEE Int. Conf. of the

Ind.El.Soc. (IECON 2013).

• Monacchi et al. GREEND: An Energy Consumption Dataset of Households in Italy and Austria. IEEE Int.Conf. Smart Grid

Communications. 2014.


4/24


Feedback effectiveness

Effective strategies:

• Increase feedback resolution and timeliness

• Tailor feedback to actual energy usage


5/24


Intervening in CAR and FVG

Research methodology:

1 Analysis of consumption factors2 Formulation of strategies

I replacement of lighting devicesI device diagnosticI stand-by sheddingI device shifting (off peak)

3 Potential of up to 34% savings • Advices only for used devices

• Advices ranked based on preferences

• Implemented as widget in Mjolnir

Long-term involvement:

• “what would you do?” think aloud with 7 users

• Satisfaction questionnaire showing only initial curiosityMonacchi et al. An Open Solution to provide personalized feedback for building energy management. IOS Jour. of Amb. Int. andSmart Env. (accepted minor corr.) 2016.


6/24


How to achieve device and data interoperability towards amicrogrid energy market?


7/24


Architecture for Device and Data Interoperability

Application Application API

Device Stub

Context Device Profiles Environmental

Model

Driver1: Smart Appliance

Driver2: Smart Meter

Driver3: NILM

Service Interface

Network API

Power Line

Legacy Device

Supervisor Information flow

Logical connection

Power flow

Applica'on Layer

Data Layer

Service Layer

Network Layer

Electric Layer

Driver4: Smart Outlet

Network API

Data Manager

• Device stub + KB

• Data refers shared ontologies

• RDF triples (S, P, O)

• SPARQL to query

• Monacchi et al. Integrating households into the Smart Grid. IEEE Work. Model and Sim. of Cyber-Physical Systems. 2013.

• Egarter, Monacchi, Khatib, Elmenreich. Integration of Legacy devices into Home Energy Management Systems. Springer Journ.

Ambient Intelligence and Humanized Computing. 2015.


8/24


Integrating legacy and Smart Devices: Taxonomy

Appliance Type

Cleaning Device

Entertainment Device

Kitchen Device

Office Appliance

Cool Appliance

Transporta:on Device

Laundry Appliance

Energy Ra:ng

Energy Ra:ng US Energy Ra:ng Oceania

Energy Ra:ng Europe

Energy Ra:ng Canada

State

Signature

Permanent Device Signature

Model Based Device Signature

Service Physical Service

Virtual Service Smart Service

Status

Appliance Smart Appliance Transi:on

Load Iden:fica:on Model

Observa:on

Feature

Steady State Feature

Voltage

Current

Power

Ac:ve Power

Reac:ve Power

Apparent Power

Appliance Model

Appliance Profile


9/24


Integrating legacy and Smart Devices: Water Kettle

Appliance WaterKe.le

Physical Service

Status


Device 0WaterKe.leType

hasIndividual

hasIndividual

ServiceHeatWater

Ke.leStatusOn

Ke.leStatusOff

Ke.leStatusPaused

hasService

Ke.leState0

hasCurrentState

hasStatus hasIndividual

hasIndividual Ke.leSignaturehasSignature

hasIndividual

hasIndividual hasIndividual

State hasIndividual

Lakeside Labs

hasManufacturer

False

True

isControllable

isUserDriven

60

0

5

1800

0 0

hasDuration

hasOrder

hasWorkingPowerTolerance

hasPeakPower

hasInterruptionSensitivity hasDelaySensitivity

0.03

Water heating Service

This is the water heating functionality of the kettle

hasConsumption

hasSeviceName

hasDescr

iption

class

individuals

hasIndividual

Object property

Datatype property


10/24


How to automate the design of controllers for energy prosumers?


11/24


HEMS Simulator

• FREVO Java Evolutionary Framework

• Prosumer as an ANN, trained via evolution

• Prosumers trade power in a double-sided auction


12/24


Modeling controllers for energy prosumers

• Trading tendency τ ∈ [−1.0, 1.0] (-1 sell, 0 skip, +1 buy)

• F = R + (δg · Igrid)− C

Monacchi et al. HEMS: A Home Energy Market Simulator. Springer Journal of Computer Science - Research and Development 2014.


13/24


HEMS Simulator: Service Interruption

1 second power provisioning traded in a UCDA auction

• Service interruption and delayed start of big loadsAndrea Monacchi Assisting Energy Management in Smart Buildings and Microgrids

14/24


Dynamic allocation sizing using forward contracts

• Aggregate storage and production of uGrid

• Multiple allocation durations with different prices (SLAs)

• Price based on expected supply/demand (i.e. congestion)

510

025

0

500

1000

3000 Power

fit

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

get

Pri

ce

P_re [W]

51002505001000

Goal: Π = (ΠuGrid + Πfeedin) − (Csupply + Creimbursement )

• Monacchi et al. Assisting Energy Management in Smart Buildings and Microgrids. Springer Journ. Ambient Intelligence and

Hum. Computing. 2016.


15/24


Simulation Scenario

Generation model: 3.3 kWp

• W0: clear-sky model

• W1: real weather

Sunlight intensity Klagenfurt

22Jun

2015

23 24 25 26 27 28

timestamp

0.0

0.2

0.4

0.6

0.8

1.0

clear-skymeasured

June 2015

> Pgrid plans:

- P0: 0 kW

- P1: 1.5 kW

- P2: 3 kW

- P3: 3 kW (6 am to 6 pm), else 1 kW

- P4: 3 kW

- P5: 6 kW

> get: 0.29 Eur/kWh (6 am to 9 pm), else 0.15 Eur/kWh

> fit: 0.04 Eur/kWh (6 am to 9 pm), else 0.02 Eur/kWh

> SLAs: 10, 30, 60, 120, 600, 1800 secs

Device starting: Austrian GREEND site #2

02:00:00

05:00:00

08:00:00

11:00:00

14:00:00

17:00:00

20:00:00

23:00:00

Time

0

500

1000

1500

2000

2500

Pow

er

02:00:00

05:00:00

08:00:00

11:00:00

14:00:00

17:00:00

20:00:00

23:00:00

Time

0

200

400

600

800

1000

1200

1400

1600

1800

Pow

er

TVNAS/Computerwashing machinedryerdishwasherlaptopfood processorcoffee machinebread machine

Operation model (P[kW ], d[sec])TV (0.18, 3600)Dishwasher (2.1, 300), (0.1, 120), (0.3, 60), (0.1, 120), (2.1, 300)

Dryer (2.5, 120)10

W.machine (2.1, 120), (0.3, 300), (0.2, 120), (0.6, 300), (0.2, 60)Fridge (0.2, 30), (0.16, 600)Coffee m. (2, 60)

• Loads as truth-telling reactive agents

• Price sensibility disabled (0.9 Eur/kWh)

• First-SLA-long-enough selection strategy


16/24


Rule-based strategies

Pessimistic broker• SLAs priced proportionally to duration

• Matches mostly single-unit SLAs

• Minimizes losses

• Can lead to service interruptions

Optimistic broker• SLAs priced regardless of duration

• Matches SLAs depending on customers

• Maximizes availability

• Can reduce reactivity, competition and lead to

losses

Availability

Plan1 Plan2 Plan3 Plan4 Plan50.75

0.80

0.85

0.90

0.95

1.00

Pes. W0Pes. W1Opt. W0Opt. W1

A = MTBFMTBF+MTTR

• Pgrid backs volatility in Pre

• more Pgrid → higher market volume


17/24


Learning contract brokers

ANNA

Load1 Loadn

Energy pricePowerline

BrokerLoadmax_supply, [price

1, … , price

n]

[amount1, …, amount

n]

LocalGeneration

SmartMeter

Storage

Power

Price

get

fit

Pre

Pgrid

+ Pre

Pshift

1 t

5 t

10 t

Available power

Available contracts

fed into grid

allocated

t+1 t+3

75%

50%

25%

t

P

50W

100W

used

used

used

pricen

price2

price1

Day of year

Hour of day

Pre

Pgrid

ft

get

pricen

price2

price1

Pre

Pgrid

ft

get

Sunlight int.

pricen

price2

price1

Pre

Pgrid

ft

[Pgrid

]

pricen

price2

price1

Sunlight int.

Day of year

Hour of day

[get]

get

[ft]

[Pre

]

Pre

Pgrid

ft

[get]

get

[ft]

[Pre

]

[Pgrid

]

ANNB

Load1 Loadn

Energy pricePowerline

BrokerLoadmax_supply, [price

1, … , price

n]

[amount1, …, amount

n]

LocalGeneration

SmartMeter

Storage

Power

Price

get

fit

Pre

Pgrid

+ Pre

Pshift

1 t

5 t

10 t

Available power

Available contracts

fed into grid

allocated

t+1 t+3

75%

50%

25%

t

P

50W

100W

used

used

used

pricen

price2

price1

Day of year

Hour of day

Pre

Pgrid

ft

get

pricen

price2

price1

Pre

Pgrid

ft

get

Sunlight int.

pricen

price2

price1

Pre

Pgrid

ft

[Pgrid

]

pricen

price2

price1

Sunlight int.

Day of year

Hour of day

[get]

get

[ft]

[Pre

]

Pre

Pgrid

ft

[get]

get

[ft]

[Pre

]

[Pgrid

]

• time = sin(π · ttmax

)

• Training:I Trained on 1 day real dataI Winter vs summer in KlagenfurtI 3LN vs FMN

• Evaluation:I Ideal vs real weatherI 1 day (learned) and 1 week

• Service Availability: slight differences between ANNA and ANNB

• All Brokers learn to minimize reimbursement costs• FMN seem worse than the 3LN version


18/24


Traded SLAs realised with ANNA

Plan Weather SLA duration

0 10 30 60 120 600 1800

1Ideal 0/0 0/0 8/51 1/24 0/98 8/89 0/3

Real 0/0 0/1 5/41 0/3 0/5 11/55 2/5

2Ideal 0/0 0/0 14/156 2/34 0/104 38/290 0/0

Real 0/0 0/0 21/152 1/23 0/64 45/276 0/0

3Ideal 0/0 0/0 21/144 3/43 2/106 47/280 0/0

Real 0/0 0/0 21/137 3/43 2/106 47/273 0/0

4Ideal 0/0 0/0 21/159 3/43 2/108 47/297 0/0

Real 0/0 0/0 21/159 3/43 2/108 47/297 0/0

5Ideal 0/0 0/0 21/157 3/43 2/108 47/295 0/0

Real 0/0 0/0 21/157 3/43 2/108 47/295 0/0

Summer

1Ideal 0/0 0/0 15/99 6/42 16/66 27/246 0/0

Real 0/0 0/1 15/72 2/22 2/53 23/202 0/0

2Ideal 0/0 0/0 22/158 6/42 16/66 28/184 0/0

Real 0/0 0/1 21/145 6/36 18/70 27/170 0/0

3Ideal 0/0 0/0 19/139 6/42 16/66 25/171 2/18

Real 0/0 0/0 21/156 6/42 16/66 27/200 0/6

4Ideal 0/0 0/0 20/150 6/42 16/66 38/296 0/0

Real 0/0 0/0 21/152 6/42 16/66 39/298 0/0

5Ideal 0/0 0/0 20/150 6/42 16/66 38/296 0/0

Real 0/0 0/0 20/150 6/42 16/66 38/296 0/0

• real weather→ short SLAs

• more Pgrid

→ longer SLAs


19/24


Availability for the ANNA

Rule-based broker


0.80

0.85

0.90

0.95

1.00


ANNA broker


0.965

0.970

0.975

0.980

0.985

0.990

0.995

1.000

Left: Winter, Right: SummerIDEAL (circle: 1 day, star: 1 week)REAL (plus: 1 day, triangle: 1 week)


20/24


Conclusions


21/24


Contributions

• Campaign and GREEND Dataset

• Ontology and process for Integrating Smart/Legacy Devices

• Energy advicing and Mjolnir

• Prosumer modeling and HEMS Simulator to automate design

• Smart-uGrid broker to mitigate service interruption


22/24


Future Work

• Integration with middlewares and NILM frameworks

• Energy awareness in non-residential environments (e.g. AAU)

• Possibility to export and employ learned HEMS controller

• Assessment of simulation-physical environment gap

• Further comparison of power brokerage models


23/24


The End

Thanks for your attention!

Andrea Monacchi

Smart Grids group

Institute of Networked and Embedded Systems

AAU Klagenfurt


24/24

Ops, you went too ahead!


1/22

Integrating legacy and Smart Devices: Ontology

Appliance Type

Energy Ra0ng

Appliance

Load Iden0fica0on Model

Service

Virtual Service

Physical Service Signature

Model based Signature

Status

State

Observa0on

Feature

Transi0on

hasType

hasEnergyRating hasLoadIdentificationModel

hasS

ervic

e string

Boolean

hasManufacturer

Boolean

isUserDriven

isControllable

string

hasDescription

string

hasName

string

hasM2MInterfaceLocation

double

hasConsumption

hasStatus

hasStatus

hasStateModel

int

hasDelaySensitivity

int

hasOrder int

hasInterruptionSensitivity

double

hasPeakPower

int

hasS

tate

Dur

atio

n int


int

Integer hasUnixStartTime

hasElapsedDuration

hasCurrentState

hasInitialObservation

hasFeature

string

double

hasObservedValue

hasMeasurementUnit

hasTransition hasNextObservation

double

hasTransitionProbability

int

hasObservationDuration

class

property

subclass

Object property

Datatype property


2/22


Appliance WaterKe.le

Physical Service

Status


Device 0WaterKe.leType

hasIndividual

hasIndividual

ServiceHeatWater

Ke.leStatusOn

Ke.leStatusOff

Ke.leStatusPaused

hasService

Ke.leState0

hasCurrentState

hasStatus hasIndividual

hasIndividual Ke.leSignaturehasSignature

hasIndividual

hasIndividual hasIndividual

State hasIndividual

Lakeside Labs

hasManufacturer

False

True

isControllable

isUserDriven

60

0

5

1800

0 0

hasDuration

hasOrder


hasPeakPower

hasInterruptionSensitivity hasDelaySensitivity

0.03

Water heating Service

This is the water heating functionality of the kettle

hasConsumption

hasSeviceName

hasDescr

iption

class

individuals

hasIndividual

Object property

Datatype property


3/22


Observa(on Transi(on

hasObservation

hasTransition

Transi(onKe/le OnOff

Transi(onKe/le OffOff

Transi(onKe/le OnOn

Transi(onKe/le OffOonIni(alObserva(on

Ke/le

SecondObserva(on Ke/le

hasNextObservation

hasNextObservation

hasIndividual

hasIn

dividu

al

hasIndividual

hasIndividual

hasI

ndivi

dual

hasIndividual

Ac(ve Power

ObservedFeature Ke/le1

ObservedFeature Ke/le0

hasFeature

hasF

eatu

re

hasIndividual

hasIndividual

1800

hasObservedValue

0

hasObservedValue

0.4

hasFeature

hasFeature

0.9 0.6 0.1

hasTransitionProbability hasTransitionProbability hasTransitionProbability hasTransitionProbability

class

individuals

hasIndividual

Object property

Datatype property


4/22

Integrating legacy and Smart Devices: Query


5/22

GREEND Deployments

Residents Place Deployment

Retired couple (2 P) Spittal / Drau (AT) coffee machine, washing machine, radio, water kettle, fridge w/ freezer,

dishwasher, kitchenlamp, TV, vacuum cleaner

Young couple (2 P) Klagenfurt (AT) fridge, dishwasher, microwave, water kettle, washing machine, radio w/

amplifier, drier, kitchenware (mixer and fruit juicer), bedside light

Mature couple w/ Adult Son (3 P) Spittal / Drau (AT) TV, NAS, washing machine, drier, dishwasher, notebook, kitchenware,

coffee machine, bread machine

Mature couple w/ 2 young kids (4 P) Klagenfurt (AT) entrance outlet, dishwasher, water kettle, fridge w/o freezer, washing

machine, hair drier, computer, coffee machine, TV

Young couple (2 P) Udine (IT) total outlets, total lights, kitchen TV, living room TV, fridge w/ freezer,

electric oven, computer w/ scanner and printer, washing machine, hood

Mature couple w/ Adult Son (3 P) Colloredo di Prato plasma TV, lamp, toaster, stove, iron, computer w/ scanner and printer,

LCD TV, washing machine, fridge w/ freezer

Mature couple w/ 2 young kids (4 P) Udine (IT) total ground and first floor (lights + outlets + white goods + air condi-

tioner + TV), total garden and shelter, total third floor

Retired couple (2 P) Basiliano (IT) TV w/ decoder, electric oven, dishwasher, hood, fridge w/ freezer, kit-

chen TV, ADSL modem, freezer, laptop w/ scanner and printer


6/22

Energy Consumption Datasets: categories

1 Few households, very short campaign, very high resolutionI Load disaggregation communitity

2 Few households, long-term campaign, very low resolutionI Energy modeling forecasting

3 Many households, medium length, low resolutionI Statistical significance

4 Many devices, no household association, low durationresolution

I Device modeling


7/22

Energy Consumption Datasets: existing ones

Dataset Country Duration Houses Features Resolution

BLUED US 8 D 1 I, V, switch events 12 KHz

REDD US 3-19 D 6 V+Q (agg), P (sub) 15 KHz

UK-DALE UK 499 D 4 P (agg), P (sub) 16 KHz

AMPds CA 1 Y 1 I, V, pf, f, P, Q, S 1 min

iHECPDS FR 4 Y 1 I, V, P, Q 1 min

HES UK 1 M 251 P 2 min

OCTES FI, IS, UK 4-13 M 33 P, price 7 sec

ACS-F1 CH 1 H NA I, V, Q, f, phi 10 sec

Tracebase DE NA NA P 1-10 sec

iAWE India 73 D 1 I, V, f, P, S, E, phi 1 Hz

Sample US 7 D 10 S 1 min

Smart* US 3 M 1 sub, 2 sub/agg P+S (circuits), P (sub) 1 Hz

GREEND AT,IT 1 Y 8 P 1 Hz


8/22

Satisfaction questionnaire: I

1 “it takes short time to learn the meaning of the buttons”

2 “the position of the buttons is logical”

3 “I understand what happens when I click the buttons”

4 “the advices are unusual, inventive, original”

5 “the advices are useful to improve energy efficiency”

6 “The advices are doable”

7 “I can learn something from the advices”

8 “I would use this widget every day”

9 “I would use this widget again”


9/22

Satisfaction questionnaire: II

q1 q2 q3 q4 q5 q6 q7 q8 q93

2

1

0

1

2

3


10/22

Microgrid modelling

1 Smart MeterI Grid-energy tariff (get)I Feed-in tariff (fit)I Capacity model

2 Local generators and storage

I Generation modelI Reservation price ψsi

3 Competing electrical loadsI Operation modelI Usage ModelI Price sensitivity ψbi


11/22

Modeling controllers for energy prosumers

• Willingness to run (ω) as P(t) Probability to start

• Operation as sequence of σn statesI σi = (Pi , di , χ

si )

I Device start delay sensitivity χb

I State start delay sensitivity χs

I State interruption sensitivity χi


12/22

Learning prosumer controllers

F = R + (δg · Igrid)− C , (1)

• R delivered utility upon device operation completion• Igrid income from fed energy

• δ penalties

C =δg · Cgrid + δb1

Bof

∑bj∈Bo

f

dbj +

δs1

Bsf

∑bj∈Bs

f

dsj + δi1

Bof

∑bj∈Bo

f

dcj +

δi (B∑j=1

vij +S∑

i=1

vii ) + δm(B∑j=1

vmj +S∑

i=1

vmi )+

δl(B∑j=1

vpj +S∑

i=1

vpi ) + δn(B∑j=1

vnj +S∑

i=1

vni ).

(2)


13/22

Broker performance measures

• Peak-to-average ratio (PAR) PAR = PmaxPavg

• Service availability A = MTBFMTBF+MTTR

• System reactivity R = CBPCBP+CNBP

• Economic profit (Π) Π = I − C


14/22

Rule brokers: Metrics for 100 evaluations

PAR Availability

Plan1 Plan2 Plan3 Plan4 Plan520

40

60

80

100

120



0.80

0.85

0.90

0.95

1.00


System reactivity Broker’s profit


0.2

0.4

0.6

0.8

1.0Pes. W0Pes. W1Opt. W0Opt. W1


2

3

4

5

6

7

8



15/22

Rule brokers: Metrics for 100 evaluations

Income FIT Income


1

2

3

4

5

6



1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5


Supply costs Reimbursement costs


0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0



0.01

0.02

0.03

0.04

0.05

0.06

0.07



16/22

ANNA Fitness landscape

Winter:

0 100 200 300 400 500Generations

0.20.40.60.81.01.21.41.61.82.0

Fitn

ess

3LN_h2_g0

3LN_h2_g1

3LN_h2_g2

3LN_h2_g3

3LN_h2_g4

Summer:

0 100 200 300 400 500Generations

1.0

1.5

2.0

2.5

3.0

3.5

Fitn

ess

3LN_h2_g0

3LN_h2_g1

3LN_h2_g2

3LN_h2_g3

3LN_h2_g4


17/22

ANNB Fitness landscape

Winter:

0 100 200 300 400 500Generations

0.20.40.60.81.01.21.41.61.82.0

Fitn

ess

3LN_h2_g0

3LN_h2_g1

3LN_h2_g2

3LN_h2_g3

3LN_h2_g4

Summer:

0 100 200 300 400 500Generations

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Fitn

ess

3LN_h2_g0

3LN_h2_g1

3LN_h2_g2

3LN_h2_g3

3LN_h2_g4


18/22

ANNA Availability

ANNA Availability


0.965

0.970

0.975

0.980

0.985

0.990

0.995

1.000

IDEAL (circle: 1 day, star: 1 week)REAL (plus: 1 day, triangle: 1 week)


19/22

ANNB Availability

ANNB Availability


0.965

0.970

0.975

0.980

0.985

0.990

0.995

1.000



20/22

ANNA Broker minimizing reimbursement costs

ANNA reimbursement costs


0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08



21/22

ANNB Broker minimizing reimbursement costs

ANNB reimbursement costs


0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08



22/22

assisting energy management in smart buildings and microgrids

Data & Analytics