Smart control of multiple energy commodities on district scale
Frans Koene
Sustainable places, Nice, 1-3 Oct 2014
1
Partners
2
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
10
[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]
6
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
7
Business Concepts based on flexibility
Case Buyer of flexibility
Objective
1
Prosumers (aggregated)
reduce energy bill
(buy at low prices)
2
Energy retailer / BRP
maximise the margin between
purchases and sales of energy
3
Balancing Responsible Party
reduce imbalance in portfolio
(BRP)
4
Distribution System Operator peak shaving (avoiding capacity
(DSO)
problems)
5
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)
9
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
10
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
20
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
14
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
16
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