Ponencias de la jornada técnica “Proyectos europeos en eficiencia energética en edificación”

Oviedo, 27 February 2014

Paolo Michele Sonvilla

Minerva Consulting & Communication

Ahorros energéticos obtenidos con el EnRima DSS

An Integrated Approach to Optimal Energy

Operations in Buildings

P. Rocha1 M. Groissböck2 A. Siddiqui1,3 M. Stadler2

1University College London

2Center for Energy and Innovative Technologies

3Stockholm University

e-nova 2013 Conference,

15 November 2013

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 ofexisting buildingsmust be improved

Background

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

Decision Support Schema

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 & coolingdemand

1Groissböck et al. (2013), Liang et al. (2012)

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)

Integrated Operational OptimisationModel

minimise Energy trading costs + technology operation costs

subject to Upper-level constraints:Energy balanceTechnology capacity limitsEnergy trading limitsStorage constraints

Lower-level constraints:Zone temperature update & boundsEnergy flows & operational constraints for radiatorsEnergy flows & operational constraints for HVAC systems

Numerical Examples• Two test sites:

• Centro de Adultos La Arboleya (Siero, Spain), from FundaciónAsturiana de Atención y Protección a Personas conDiscapacidades y/o Dependencias (FASAD)

• Fachhochschule Burgenland’s Pinkafeld campus (Pinkafeld,

Austria)

• Typical winter day, hourly decision intervals

• Cases:• FMT: Fixed mean temperature• OPT: Optimisation

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, 0.05e/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

FASAD’s ResultsScenarios 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

02468

101214161820222426283032

FMT

Time (h)

Tem

pera

ture

(o C)

Estimated Zone Temperature = Required Zone TemperatureExternal 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

02468

1012141618202224262830

OPT

Time (h)

Tem

pera

ture

(o C)

Lower Limit TemperatureOptimal Zone TemperatureUpper Limit TemperatureExternal Temperature

FASAD’s Results

FMT OPTSpace 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 23 240

10

20

30

40

50

60

70

80

90

100Space Heat Demand

Time (h)

Spac

e He

at D

eman

d (k

Wh)

FMTOPT

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8Natural Ventilation

Time (h)

Natu

ral V

entila

tion

(m3 /s

)

FMTOPT

FASAD’s Results

FMT OPTSpace 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

02468

101214161820222426283032

FMT

Time (h)

Tem

pera

ture

(o C)

Estimated Zone Temperature = Required Zone TemperatureExternal 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

02468

1012141618202224262830

OPT

Time (h)

Tem

pera

ture

(o C)

Lower Limit TemperatureOptimal Zone TemperatureUpper Limit TemperatureExternal Temperature

FASAD’s Results

FMT OPTPrimary Cost CO2 Primary Cost CO2Energy 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%

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, 0.08e/kWhe for electricity sales, 0.08 e/kWh for district heat purchases

• Scenario 2: Installation of a 100 kWp PV system & availability of anelectricity 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 at14:00-17:00, 0.14 e/kWhe otherwise)

• Scenario 4: Installation of a 75 kW solar thermal system

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

02468

101214161820222426283032

FMT

Time (h)

Tem

pera

ture

(o C)

Estimated Zone Temperature = Required Zone TemperatureExternal 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

02468

101214161820222426283032

OPT

Time (h)

Tem

pera

ture

(o C)

Lower Limit TemperatureOptimal Zone Temperature, Scenarios 1−3Optimal Zone Temperature, Scenario 4Upper Limit Temperature

Pinkafeld’s ResultsFMT OPT

Space HVAC Cost CO2 Space HVAC Cost CO2Heat 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 23 240

10

20

30

40

50

60

70

80

90

100Space Heat Demand

Time (h)

Spac

e He

at D

eman

d (k

Wh)

FMTOPT, Scenarios 1−3OPT, Scenario 4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240

0.5

1

1.5

2

2.5

3HVAC Ventilation

Time (h)

HVAC

Ven

tilatio

n (m

3 /s)

FMTOPT, Scenarios 1−3OPT, Scenario 4

Pinkafeld’s Results

FMT OPTPrimary Cost CO2 Primary Cost CO2Energy 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%

Summary

• Short-term building energy management model consisting ofupper- and lower-level operational modules

• Evaluated using data from two EU test sites and plausible futureoperating 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