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Smart control of multiple energy commodities on district scale

Frans Koene

Sustainable places, Nice, 1-3 Oct 2014

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Partners

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How can we match energy supply and demand?

- Energy storage

- Smart control of appliances→ time shift of demand

Challenge

Facilitate the implementation of large shares of renewables in energy supply systems

Daily mismatch Annual mismatch

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Business models based on flexibility of demand

Control algorithm to match supply & demand of heat and electricity

Simulation environment

Simulation Engine

Models of components

boiler

CHP

GUI

storage

PV

Dynamic aggregated model of buildings in the district

Electricity and DHW profiles

District Usage Factor HR EfficiencyInfuence in Consump.

100.00% 50.00% 85.00%

100.00% 50.00% 85.00%

SH AB SH AB

1 0.021 kWh/day·m² 0.022 kWh/day·m² Profile Nº1 Profile Nº1

2 0.021 kWh/day·m² 0.022 kWh/day·m² Profile Nº1 Profile Nº1

3 0.021 kWh/day·m² 0.022 kWh/day·m² Profile Nº1 Profile Nº1

4 0.021 kWh/day·m² 0.022 kWh/day·m² Profile Nº1 Profile Nº1

5 0.021 kWh/day·m² 0.022 kWh/day·m² Profile Nº1 Profile Nº1

6 0.021 kWh/day·m² 0.022 kWh/day·m² Profile Nº1 Profile Nº1

7 0.021 kWh/day·m² 0.022 kWh/day·m² Profile Nº1 Profile Nº1

HEAT RECOVERY SYSTEM FOR SHOWER

Type of Building

Single Family Houses (SH)

Apartment Blocks (AB)

Day of the weekSpecific Average Consumption Percentage Profile

Aggregated building model

Inputs building model– Size, volume, windows, orientation– Thermal insulation– Thermal set points for heating & cooling– Internal heat generation– Parameters automatic solar shading

=

F.G.H. Koene et al.: Simplified building model of districts, proceedings IBPSA BauSIM 22 -24 Sept 2014, Aachen, Germany

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Agent based technology

-10

-8

-6

-4

-2

0

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0 5 10 15 20

cons

umed

pow

er [k

W]

electricity price [€ct/kWh]-10

-8

-6

-4

-2

0

2

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8

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0 5 10 15 20

cons

umed

pow

er [k

W]

electricity price [€ct/kWh]

-10

-8

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0 5 10 15 20

cons

umed

pow

er [k

W]

electricity price [€ct/kWh]

-10

-8

-6

-4

-2

0

2

4

6

8

10

0 5 10 15 20

cons

umed

pow

er [k

W]

electricity price [€ct/kWh]

-10

-8

-6

-4

-2

0

2

4

6

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0 5 10 15 20

cons

umed

pow

er [k

W]

electricity price [€ct/kWh]

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Multi Commodity Matcher

HP electrical power bid HP thermal power bid

heat priceheat price

elec

tr p

rice

elec

tr p

rice

aggr. electrical power bid aggr. thermal power bid

heat priceheat price

elec

tr p

rice

elec

tr p

rice

P. Booij et al.: Multi-agent control for integrated heat and electricity management in residential districts , proceedings of AAMAS - ATES conference, 6-10 May 2013, USA

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Business Concepts based on flexibility

Case Buyer of flexibility Objective

1 Prosumers (aggregated)reduce energy bill (buy at low prices)

5Transmission System Operator (TSO)

reduce imbalance on national level

2 Energy retailer / BRPmaximise the margin between purchases and sales of energy

3Balancing Responsible Party (BRP)

reduce imbalance in portfolio

4Distribution System Operator (DSO)

peak shaving (avoiding capacity problems)

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Case studies

Tweewaters (BE)• Supply: CHP (heat +

electricity) + peak boilers (heat) + market (electricity) + DH

• Demand: residential consumers (heat + electricity) + market (electricity)

• Flexibility: CHP + smart appliances

Houthaven (NL)• Supply: HPs, PV,

waste heat (incineration plant), ground source cold storage,…+ DHC

• Demand: low energy buildings - residential + commercial/public buildings

• Potentially demand response (smart appliances, pumps)

Bergamo (IT)• Existing energy

concept: DH + heat storage – shutdown of CHP

• Energy vision: different alternatives for heat production (centralized boiler, biomass..), PV (46 kWp)

• Demand: Residential buildings + commercial/public buildings

Freiburg (GE)• Supply: CHPs +

boilers, centralized heat storage + DH

• Demand: residential buildings + commercial/public buildings

Dalian (CN)• Supply: CHP + peak

boiler (heat) + sewage source / seawater source HP (heat/cold) + solar collectors + DH

• Demand: residential consumers + industrial use (heat + electricity + cold)

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1. Reference or BAU scenario- conventional sources for energy supply- electricity from the public grid- heat produced by de-central gas fired boilers. 

2. RES (Renewable Energy Sources) or green scenario with fixed energy demand- heat and electricity are (partly) produced with renewables (PV, biomas CHP)- no demand-side flexibility (i.e. no smart appliances)

3. Smart scenario or RES scenario with flexible energy demand and supply- renewable energy sources (as in 2nd scenario)- demand-side flexibility - business objective: local balancing and national balancing

Scenarios

201.300 m2 residential 13.900 m2 commercial

14 aggregated buildings

16.8 km heat network Copper plate grid No cold network

(electrical cooling)

Rooftop & District PV (4.5 kWp)

Example: district of Houthaven, Amsterdam

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1 Jan 2 Jan 3 Jan 4 Jan 5 Jan 6 Jan 7 Jan14

16

18

20

22

Time

Virt

ual h

eat

pric

e

Indoor temperature of building I4B1 in scenario 2

1 Jan 2 Jan 3 Jan 4 Jan 5 Jan 6 Jan 7 Jan0

50

100

Time

Pow

er

[ W

/m2 ]

Consumed thermal power for heating for scenario 2

Space heating– RES scenario

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1 Jan 2 Jan 3 Jan 4 Jan 5 Jan 6 Jan 7 Jan10

15

20

25

Time

Virt

ual h

eat

pric

e

Indoor temperature and flexibility boundaries of building I4B1 in scenario 3

1 Jan 2 Jan 3 Jan 4 Jan 5 Jan 6 Jan 7 Jan0

20

40

Time

Pow

er

[ W

/m2 ]

Consumed thermal power for heating for scenario 3

Space heating– smart scenario

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18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24 Jun0

10

20

Time

Virtu

al ele

ctrici

ty p

rice (Virtual) Electricity price as determined by the Multi Commodity Matcher in scenario 2

18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24 Jun0

10

20

Time

Pow

er

[ W

/m2 ]

Consumed electrical power for cooling for scenario 2

Space cooling – RES scenario

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18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24 Jun0

10

20

TimeVirt

ual e

lect

ricity

pric

e (Virtual) Electricity price as determined by the Multi Commodity Matcher in scenario 3

18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24 Jun0

10

20

Time

Pow

er

[ W

/m2 ]

Consumed electrical power for cooling for scenario 3

Energy bill for cooling reduced by 36%

Space cooling – smart scenario

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Results (preliminary)

0102030405060708090

100

Electricitydemand

% electrby RES

HeatDemand

% heat byRES

CO2emissions

Electricitybill

Heatingbill

kWh/

m2,

%, k

g CO

2/m

2 , €

/m2

Tweewaters

BAU

Green

Smart

05

101520253035404550

Electricitydemand

% electrby RES

HeatDemand

% heat byRES

CO2emissions

Electricitybill

Heatingbill

kWh/

m2,

%, k

g CO

2/m

2 , €

/m2

Houthaven

BAU

Green

Smart

0

50

100

150

200

250

Electricitydemand

% electrby RES

HeatDemand

% heat byRES

CO2emissions

Electricitybill

Heatingbill

kWh/

m2,

%, k

g CO

2/m

2 , €

/m2

Bergamo

BAU

Green

Smart

020406080

100120140160180200

Electricitydemand

% electrby RES

HeatDemand

% heat byRES

CO2emissions

Electricitybill

Heatingbill

kWh/

m2,

%, k

g CO

2/m

2 , €

/m2

Dalian

BAU

Green

Smart

Results are incomplete and preliminary

Net energy demand does not vary much between 3 scenarios

Increase of %RES in smart scenario depends on amount of flexibility

Depending on business case, benefits from smart scenario may be lower energy bill, peak shaving etc.

Future work using the simulation platform:

Effect of smart (predictive) agents

Use of electrical storage, i.e. electric vehicles

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Conclusions