Post on 22-Dec-2015
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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
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
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
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
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
8
10
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