Smart control of multiple energy commodities on district scale
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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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Challenge Facilitate the implementation of large shares of renewables in energy supply systems Daily mismatch
Annual mismatch
How can we match energy supply and demand? -
Energy storage
-
Smart control of appliances→ time shift of demand 3
Simulation environment
Models of components
Control algorithm to match supply & demand of heat and electricity
Dynamic aggregated model of buildings in the district
boiler PV CHP storage Simulation Engine
GUI
Electricity and DHW profiles
Business models based on flexibility of demand
H E A T R E C O V E R Y S Y S T E M F O R S H O WE R T ype o f B uilding
D is t ric t Us a ge F a c t o r
H R E f f ic ie nc y
Inf ue nc e in C o ns um p.
Single Family Ho uses (SH)
10 0 .0 0 %
5 0 .0 0 %
8 5 .0 0 %
A partment B lo cks (A B )
10 0 .0 0 %
5 0 .0 0 %
8 5 .0 0 %
S pe c if ic A v e ra ge C o ns um pt io n
P e rc e nt a ge P ro f ile
D a y o f t he we e k SH
AB
SH
AB
1
0 .0 2 1 k Wh/ da y·m ²
0 .0 2 2 k Wh/ da y·m ²
P ro f ile N º1
P ro f ile N º1
2
0 .0 2 1 k Wh/ da y·m ²
0 .0 2 2 k Wh/ da y·m ²
P ro f ile N º1
P ro f ile N º1
3
0 .0 2 1 k Wh/ da y·m ²
0 .0 2 2 k Wh/ da y·m ²
P ro f ile N º1
P ro f ile N º1
4
0 .0 2 1 k Wh/ da y·m ²
0 .0 2 2 k Wh/ da y·m ²
P ro f ile N º1
P ro f ile N º1
5
0 .0 2 1 k Wh/ da y·m ²
0 .0 2 2 k Wh/ da y·m ²
P ro f ile N º1
P ro f ile N º1
6
0 .0 2 1 k Wh/ da y·m ²
0 .0 2 2 k Wh/ da y·m ²
P ro f ile N º1
P ro f ile N º1
7
0 .0 2 1 k Wh/ da y·m ²
0 .0 2 2 k Wh/ da y·m ²
P ro f ile N º1
P ro f ile N º1
4
Aggregated building model
=
F.G.H. Koene et al.: Simplified building model of districts, proceedings IBPSA BauSIM 22 -24 Sept 2014, Aachen, Germany
Inputs building model – Size, volume, windows, orientation – Thermal insulation – Thermal set points for heating & cooling – Internal heat generation – Parameters automatic solar shading
Agent based technology
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[kW] power [kW] consumed consumed power
8 6 4 2 0
-2 0
5
10
15
20
-4 -6 -8 -10
electricity price [€ct/kWh]
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Multi Commodity Matcher HP thermal power bid
electr price
electr price
HP electrical power bid
heat price
heat price aggr. thermal power bid
electr price
electr price
aggr. electrical power bid
heat price
heat price
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
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Prosumers (aggregated)
reduce energy bill (buy at low prices)
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Energy retailer / BRP
maximise the margin between purchases and sales of energy
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Balancing Responsible Party reduce imbalance in portfolio (BRP)
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Distribution System Operator peak shaving (avoiding capacity (DSO) problems)
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Transmission System Operator (TSO)
reduce imbalance on national level 8
Case studies
Tweewaters (BE)
Houthaven (NL)
Bergamo (IT)
Freiburg (GE)
Dalian (CN)
• Supply: CHP (heat + electricity) + peak boilers (heat) + market (electricity) + DH • Demand: residential consumers (heat + electricity) + market (electricity) • Flexibility: CHP + smart appliances
• 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)
• 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
• Supply: CHPs + boilers, centralized heat storage + DH • Demand: residential buildings + commercial/ public buildings
• 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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Scenarios 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
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Example: district of Houthaven, Amsterdam
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)
Virtual heat price
Space heating– RES scenario
Indoor temperature of building I4B1 in scenario 2 22 20 18 16 14 1 Jan
2 Jan
3 Jan
4 Jan Time
5 Jan
6 Jan
7 Jan
2
Power [ W/m ]
Consumed thermal power for heating for scenario 2 100 50 0 1 Jan
2 Jan
3 Jan
4 Jan Time
5 Jan
6 Jan
7 Jan
12
Virtual heat price
Space heating– smart scenario
Indoor temperature and flexibility boundaries of building I4B1 in scenario 3 25 20 15 10 1 Jan
2 Jan
3 Jan
4 Jan Time
5 Jan
6 Jan
7 Jan
2
Power [ W/m ]
Consumed thermal power for heating for scenario 3 40 20 0 1 Jan
2 Jan
3 Jan
4 Jan Time
5 Jan
6 Jan
7 Jan
13
Virtual electricity price
(Virtual) Electricity price as determined by the Multi Commodity Matcher in scenario 2 20
Power [ W/m ]
Space cooling – RES scenario
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10
0 18 Jun
19 Jun
20 Jun
21 Jun Time
22 Jun
23 Jun
24 Jun
2
Consumed electrical power for cooling for scenario 2
10
0 18 Jun
19 Jun
20 Jun
21 Jun Time
22 Jun
23 Jun
24 Jun
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Virtual electricity price
Space cooling – smart scenario
(Virtual) Electricity price as determined by the Multi Commodity Matcher in scenario 3 20 10
0 18 Jun
19 Jun
20 Jun
21 Jun Time
22 Jun
23 Jun
24 Jun
2
Power [ W/m ]
Consumed electrical power for cooling for scenario 3 20 10
0 18 Jun
19 Jun
20 Jun
21 Jun Time
22 Jun
23 Jun
24 Jun
Energy bill for cooling reduced by 36% 15
Results (preliminary) Tweewaters kWh/m2, %, kg CO2/m2, €/m2
50 45 40 35 30 25 20 15 10 5 0
BAU
100 90 80 70 60 50 40 30 20 10 0
BAU Green Smart
Electricity % electr demand by RES
Green kWh/m2, %, kg CO2/m2, €/m2
Electricity % electr demand by RES
Heat % heat by CO2 Electricity Heating Demand RES emissions bill bill
Dalian
Smart
Heat % heat by CO2 Electricity Heating Demand RES emissions bill bill
200 180 160 140 120 100 80 60 40 20 0
BAU Green Smart
Electricity % electr demand by RES
Heat % heat by CO2 Electricity Heating Demand RES emissions bill bill
Bergamo 250 kWh/m2, %, kg CO2/m2, €/m2
kWh/m2, %, kg CO2/m2, €/m2
Houthaven
200 150
BAU Green
100
Smart 50 0 Electricity % electr demand by RES
Heat % heat by CO2 Electricity Heating Demand RES emissions bill bill
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Conclusions 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 17
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