Ponencia Luis Santos EDP Posibles modelos de negociso por la adopción del ENRIMA

1. Oviedo, 27 February 2014
Paolo Michele Sonvilla
Minerva Consulting & Communication
Ahorros energéticos obtenidos con el
EnRima DSS

2. An Integrated Approach to Optimal Energy
Operations in Buildings
P. Rocha1
M. Groissböck2
A. Siddiqui1,3
M. Stadler2
1
University College London
2
Center for Energy and Innovative Technologies
3
Stockholm University
e-nova 2013 Conference,
15 November 2013

3. Background
EU policy objectives for year 2020 include:
• ↓ greenhouse gas emissions by ≥ 20% below 1990 levels
• ↑ contribution of renewable resources to EU energy consumption
to 20%
• ↓ primary energy use by 20% relative to projections
=⇒ energy efficiency of
existing buildings
must be improved

4. Background
Multiple objectives & combinations of resource-load pairs
=⇒ operational optimisation model (Hobbs, 1995)

5. Decision Support Schema

6. Lower-Level Operational Module1
• Determines operation of heating, ventilation & cooling systems given:
• thermodynamics of conventional heating & HVAC systems
• building’s physics
• external temperatures & solar gains
• internal loads
• Range for zone temperature =⇒ endogenous space heat & cooling
demand
1Groissböck et al. (2013), Liang et al. (2012)

7. Upper-Level Operational Module
• Determines sourcing of energy & operation of installed equipment
• Upper-level constraints:
• Energy balance equation:
EnergyPurchased − EnergySold + EnergyOutput − EnergyInput +
EnergyFromStorage − EnergyToStorage = Demand
• Technology capacity limits
• Energy trading limits
• Energy storage constraints
• King and Morgan (2007), Marnay et al. (2008), Stadler et al. (2012),
Pruitt et al. (2013)

8. Integrated Operational Optimisation
Model
minimise Energy trading costs + technology operation costs
subject to Upper-level constraints:
Energy balance
Technology capacity limits
Energy trading limits
Storage constraints
Lower-level constraints:
Zone temperature update & bounds
Energy flows & operational constraints for radiators
Energy flows & operational constraints for HVAC systems

9. Numerical Examples
• Two test sites:
• Centro de Adultos La Arboleya (Siero, Spain), from Fundación
Asturiana de Atención y Protección a Personas con
Discapacidades y/o Dependencias (FASAD)
• Fachhochschule Burgenland’s Pinkafeld campus (Pinkafeld,
Austria)
• Typical winter day, hourly decision intervals
• Cases:
• FMT: Fixed mean temperature
• OPT: Optimisation

10. Operating Scenarios for FASAD
• Scenario 1 (Baseline):
• Conventional heating and natural ventilation
• 1293.3 kW and 232.6 kW natural gas-fired boilers, 5.5 kWe CHP
unit
• Exogenous daily end-use electricity demand of 691 kWhe and
domestic hot water demand of 1592 kWh
• Flat energy tariff rates: 0.14 e/kWhe for electricity purchases, 0.05
e/kWh for natural gas purchases
• Electricity feed-in tariff (FiT) of 0.18 e/kWhe
• Scenario 2: Revocation of FiT
• Scenario 3: Regulation imposes that zone temperature ≤ 21◦
C
• Scenario 4: Installation of a 7.58 kW solar thermal system

11. FASAD’s Results
Scenarios 1, 2 and 4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
−4
−2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
FMT
Time (h)
Temperature(o
C)
Estimated Zone Temperature
= Required Zone Temperature
External Temperature
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
−4
−2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
OPT
Time (h)
Temperature(
o
C)
Lower Limit Temperature
Optimal Zone Temperature
Upper Limit Temperature
External Temperature

12. FASAD’s Results
FMT OPT
Space Heat Cost CO2 Space Heat Cost CO2
Demand Emissions Demand Emissions
(kWh) (e) (kg) (kWh) (e) (kg)
Scen. 1,2,4 700 42 154 494 30 108
-29% -29% -30%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
10
20
30
40
50
60
70
80
90
100
Space Heat Demand
Time (h)
SpaceHeatDemand(kWh)
FMT
OPT
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Natural Ventilation
Time (h)
NaturalVentilation(m
3
/s)
FMT
OPT

13. FASAD’s Results
FMT OPT
Space Heat Cost CO2 Space Heat Cost CO2
Demand Emissions Demand Emissions
(kWh) (e) (kg) (kWh) (e) (kg)
Scen. 3 558 34 123 474 29 104
-15% -15% -15%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
−4
−2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
FMT
Time (h)
Temperature(o
C)
Estimated Zone Temperature
= Required Zone Temperature
External Temperature
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
−4
−2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
OPT
Time (h)
Temperature(
o
C) Lower Limit Temperature
Optimal Zone Temperature
Upper Limit Temperature
External Temperature

14. FASAD’s Results
FMT OPT
Primary Cost CO2 Primary Cost CO2
Energy Emissions Energy Emissions
(kWh) (e) (kg) (kWh) (e) (kg)
Scen. 1 4071.0 213.7 809.9 3847.9 202.0 764.9
-5.5% -5.5% -5.5%
Scen. 2 3798.8 218.0 757.3 3576.1 206.4 712.3
-5.9% -5.3% -6%
Scen. 3 3917.3 205.6 778.9 3827.2 200.9 760.7
-2.3% -2.3% -2.3%
Scen. 4 4019.6 211.0 799.5 3796.6 199.3 754.5
-5.5% -5.5% -5.6%

15. Operating Scenarios for Pinkafeld
• Scenario 1 (Baseline):
• Heating and HVAC systems
• 1.28 kWp PV system
• Exogenous daily end-use electricity demand of 543 kWhe
• Flat energy tariff rates: 0.15 e/kWhe for electricity purchases, 0.08
e/kWhe for electricity sales, 0.08 e/kWh for district heat purchases
• Scenario 2: Installation of a 100 kWp PV system & availability of an
electricity FiT (0.18 e/kWhe)
• Scenario 3: Change to a time-of-use (TOU) electricity purchasing tariff
(0.16 e/kWhe at 7:00-14:00 and 17:00-20:00, 0.15 e/kWhe at
14:00-17:00, 0.14 e/kWhe otherwise)
• Scenario 4: Installation of a 75 kW solar thermal system

16. Pinkafeld’s Results
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
−4
−2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
FMT
Time (h)
Temperature(o
C)
Estimated Zone Temperature
= Required Zone Temperature
External Temperature
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
−4
−2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
OPT
Time (h)
Temperature(
o
C)
Lower Limit Temperature
Optimal Zone Temperature, Scenarios 1−3
Optimal Zone Temperature, Scenario 4
Upper Limit Temperature

17. Pinkafeld’s Results
FMT OPT
Space HVAC Cost CO2 Space HVAC Cost CO2
Heat Elec. Emis- Heat Elec. Emis-
Demand Demand sions Demand Demand sions
(kWh) (kWhe) (e) (kg) (kWh) (kWhe) (e) (kg)
Scen. 1–3 696 5.73 55.9 20.9 629 3.64 50.5 18.9
-10% -37% -10% -10%
Scen. 4 696 5.73 53.7 20.1 644 3.91 48.8 18.2
-7.5% -38% -9% -9%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
10
20
30
40
50
60
70
80
90
100
Space Heat Demand
Time (h)
SpaceHeatDemand(kWh)
FMT
OPT, Scenarios 1−3
OPT, Scenario 4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
0.5
1
1.5
2
2.5
3
HVAC Ventilation
Time (h)
HVACVentilation(m
3
/s)
FMT
OPT, Scenarios 1−3
OPT, Scenario 4

18. Pinkafeld’s Results
FMT OPT
Primary Cost CO2 Primary Cost CO2
Energy Emissions Energy Emissions
(kWh) (e) (kg) (kWh) (e) (kg)
Scen. 1 1987.5 137.9 29.5 1851.2 132.2 27.5
-6.9% -4.1% -6.8%
Scen. 2 1989.4 113.0 29.6 1853.1 107.3 27.5
-6.9% -5.1% -7.1%
Scen. 3 1987.5 139.4 29.5 1851.2 133.7 27.5
-6.9% -4.1% -6.8%
Scen. 4 1933.3 135.7 28.7 1808.8 130.5 26.9
-6.5% -3.9% -6.3%

19. Summary
• Short-term building energy management model consisting of
upper- and lower-level operational modules
• Evaluated using data from two EU test sites and plausible future
operating scenarios
• 10-30% ↓ space heat demand and associated CO2 emissions
• 5-7% ↓ overall primary energy consumption
• Reflects load-shifting behaviour
• Future work:
• Multi-criteria objective function
• Further policy insights