flex-offers: unified handling of flexibility in...

Aalborg University, Denmark

Nov 4, 2016

Flex-Offers: Unified handling ofFlexibility in Electricity Consumption

and Production

Laurynas Šikšnyssiksnys@cs.aau.dk

THE FLEX-OFFER CONCEPT

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Flexible Offer (flex-offer) allows unified modelling of a flexibleconsumer/producer in ALL these cases

Flexible Electricity Consumers and Producers

•No flexibility •Time-shift flexibility •Time-shift flexibility•Amount flexibility• [Total Amount requirement]

•Amount flexibility withintra-time unitdependencies

Earliest starttime

Latest endtime

totalMinEnergy ≤ ≤ totalMaxEnergy

Time

kW

Hour 1 2 3 4

No or simple flexibility patterns Complex flexibility patterns

Previously usedenergy is high

Previously usedenergy is low

Slide 3

TBP1 all the examples are from households, perhaps you could add a few for the industrial/commercial cases we have eg in ArrowheadTorben Bach Pedersen; 1.11.2016

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Flex-Offer Aggregation and Disaggregation

DisaggregationAggregation Scheduling

Aggregation

Prosumer flex-offers

Disaggregation

Scheduled prosumer flex-offers

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(Simple or Aggregated) Flex-Offer Lifecycle

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(Simple or Aggregated) Flex-Offer Lifecycle

Time

kW

09:00 09:15 09:30 09:45 10:00 10:15 10:30 10:45 11:00

10

20

30

Start time

v1

v2

v3

v4

(One feasible)Flex-Offer Schedule

Generate ON/OFF signals tofulfil the schedule

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(Simple or Aggregated) Flex-Offer Lifecycle

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Summary

ØFLEX-OFFERS: A POWERFUL CONCEPT APPLICABLE IN MANYSMART-GRID APPLICATIONS:

• Demand supply balancing• Electricity trading• Congestion management• …

USING FLEX-OFFERS BOTH FORELECTRICITY AND DISTRICT HEATING

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Flex-Offers for electricity and heating

ØOBSERVATION 1: kW and kWh can be used for both electrical andthermal energy, and so can Flex-OffersØOBSERVATION 2: Flex-Offers for different types of energy should be

treated separatelyØOBSERVATION 3: For interplay, energy conversion needs to be

modelled as a flex-offer

E.g., A house heated with a heat-pump and comfort constraints

Temperature x(t),

Heater power u(t),

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Example of modelling energy conversion

Constraints on input and state

R – thermal resistance (oC/kW);C – thermal capacitance (kWh/ oC);h – coefficient of performanceqa – ambient temperature (oC)qr – required temperature (oC)d – user temperature band (oC)

1. Linear Time Invariant State Space Model

Earliest start Latest end

kW

1 2 3

6.5

0

3. FO with EnergyFlexibility

2. Exact polyhedrons 4. FO with Energy Flexibilityand Dependencies

kW

1 2 3

8

0Earliest start Latest end

= 1. .

FLEX-OFFER RELATED PROJECTS ANDMAJOR ACHIEVEMENTS

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Related Projects/Initiatives

www.mirabel-project.euwww.totalflex.dk

www.arrowhead.eu

2010-2013

• Scalable ICT system for:• Higher RES integration• Demand-supply balancing• Flex-offer management

• Develops and demonstratesa market-based system for

flexibility trading andcongestion

management

2012-2016 2013-2017

• Largest European auto-mation project of all time

• Collaborative automationand interoperability ofnetworked devices in (1)production, (2) end-userservices, (3) smart buildingsand infrastructure, (4)

electro-mobility, and (5)markets of energy.

2016-

• Will integrate,and demonstrate a group of

smart-grid technologies for thecost effective use of demandresponse in distribution grids

2015-2021

• Research on how to utilizesoftware and data for smartersolutions in heath, traffic,energy, and communityservice domains

Flex-Offers

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Major results: Hierarchical ICT infrastructure

ØFlexible demand and supply can lead to 7-13% cost reduction for BRPs.ØFlexible demand and supply improves RES integration significantly: 70% of the

negative impact of fluctuating renewables can be neutralized if 15% of the energyconsumption is flexible and intelligently controlled by the BRP.ØHouseholds can reduce energy bills by 10-20%

(BRPs)

measurementsflex-offers

prices

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Major results: Flex-Offer Aggregation

f1

f2

f3

f4

f5

Omitted as it does not satisfies the

G1

G2Grouping

f1

f3

f2

f4

f5

Groupingparameter

s

Bin-packing

G1

G21

f1

f3

f2

f4

f5

G22

bin-packing constraints

Bin-packingparameters

N-to-1aggregation

F1

F2

Aggregationparameters

For all forms of flex-offers, a number of flex-offer (dis-)aggregation techniques were developed: simple,incremental, with bin-packing, with balancing.

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Major results: Flex-Offer Aggregation

ØMillions of flex-offers can be aggregated anddisaggregated in seconds

ØAggregation+Scheduling+Disaggregation is BETTERAND FASTER than just Scheduling

ØAggregated flex-offers of the desired form can begenerated:

• “More flexibility NOW”• “More flexibility LATER”• “Keep balance conditions”

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Major results: Flex-Offer Market

ØPERFORMED AN ANALYTICAL AND EXPERIMENTAL VALIDATION OFFLEXIBILITY IN THE DANISH REGULATION AND SPOT MARKETS:• On average, 50% of the energy demand from a

household is flexible.• BRPs/Aggregators can achieve 49% reduction in the

regulation cost with just 3.5% of energy demandbeing flexible

ØCONSIDERED FLEXIBILITY PRICING AND DEVELOPED AMARKET FOR FLEXIBILITY (FLEX-OFFERS)

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Major results: Flex-Offer Market

ØDEFAULT SCHEDULE AND PRICES FOR DKWH:

Time interval 11:00-11:15

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Major results: Flex-Offer Market

ØFLEXIBILITY TRADING: 11:00-11:15

DSO

Existing electricity markets

Market Place forflexibility

flex-offer

TVPP

Electricitycompany Aggregator

CVPP

BRP

Buying flex-offer

Selling flex-offer

Flexible Resources

Buying flex-offer

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Summary

ØFLEX-OFFERS• Powerful concept for unified modelling of flexibility• Suitable for negotiation, planning/trading, control, and billing• Validated in a number of Danish and EU projects

ØKEY REFERENCES• Matthias Böhm, Lars Dannecker, Andreas Doms, Erik Dovgan, Bogdan Filipic, Ulrike Fischer,

Wolfgang Lehner, Torben Bach Pedersen, Yoann Pitarch, Laurynas Siksnys, Tea Tusar: Datamanagement in the MIRABEL smart grid system. EDBT/ICDT Workshops 2012: 95-102

• Luis Lino Ferreira, Laurynas Siksnys, Per Pedersen, Petr Stluka, Christos Chrysoulas, Thibaut Le Guilly,Michele Albano, Arne Skou, César Teixeira, Torben Bach Pedersen: Arrowhead compliant virtualmarket of energy. ETFA 2014: 1-8

• Laurynas Siksnys, Emmanouil Valsomatzis, Katja Hose, Torben Bach Pedersen: Aggregating andDisaggregating Flexibility Objects. IEEE Trans. Knowl. Data Eng. 27(11): 2893-2906 (2015)

• Laurynas Siksnys, Torben Bach Pedersen: Dependency-based FlexOffers: scalable management offlexible loads with dependencies. e-Energy 2016: 11:1-11:13

THANK YOU!

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Background

ØRENEWABLE ENERGY IS CHALLENGING:

ØSOLUTION: MAKE DEMAND AND SUPPLY FLEXIBLE

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Use-case Example1. A consumer arrives home at 10pm and wants to rechange

the electric vehicle‘s battery at the lowest possible price bythe next morning

2. The consumer‘s LEDBMS generates a FO:

3. A negotiation with the BRP/aggregator is started:

4. The consumer is rewarded for its offered flexibility

kW

t

10 pmEarliest Start Time

6 amLatest Start

Time

8 amLatest End

Time

2h

Profile

Time Flexibility Interval

3 amStart Time

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Major results: Flex-Offer Aggregation

Time

Amount/Dt

f1

Time

Amount/Dt

f2

Time

Amount/Dt

Sf1

Sf2

Sf3

f3

Time

Amount/Dt

fA

tes =min (sf1,sf1,sf1)

tf(fA)=1

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Prosumers(millions)

Aggregators(thousands)

TSOs(few)

FlexOffer Management Platform

FlexOffers

Schedules

Marketbids

Markets(few)

DSOs(hundreds)

Techniques for flexibility extraction from the smart-metermeasurements

Techniques for flexibility detection and extraction at theappliance levels

• Scalable techniques for aggregating FlexOffers anddisaggregating FlexOffer schedules

• Techniques for balancing energy and avoiding gridcongestions during FlexOffer aggregation

AggregatedFlexOffers

AggregatedFlexOffers

DisaggregatedSchedules

DisaggregatedSchedules

• Cost-effective FlexOffer scheduling techniques for energybalancing

• Integrated system for FlexOffer data prescriptive analyticsand management

• Algorithms for predicting grid congestions

• Flexibility market approach• Evaluation of flexibility value in energy regulation

markets

• Service-oriented pilotdemonstration

www.arrowhead.eu