maria briola aspasia georgakopoulou axelle delangle ......axelle delangle maria-aliki efstratiadi...

energy futures lab

HEAT

Group 7Poster No: 09-14

Maria BriolaAxelle DelangleMaria-Aliki Efstratiadi

Aspasia GeorgakopoulouPanagiotis LadasAdrian Regueira-Lopez

Sustainable Energy Futures, Annual Conference 2016Sustainable Energy Futures, Annual Conference 2016

energy futures lab 1/46

Introduction

Residential applicationsNon-residential applications

Industrial applications

energy futures lab

Simulating Heat and Electricity Demand in Urban Areas

Poster 14

Panagiotis LADAS

Supervisors:Dr. Koen H. van DamGonzalo Bustos-Turu, PhDDr. Salvador Acha

2/46


Aim & Research Motivation

Background• 54% of the world’s population

currently lives in urban areas• Cities account for around 75% of

global energy demand

Challenge: Understanding of the dynamics behind the energy demand in urban areas

Aim: Simulate the spatial and temporal energy loads in • Residential buildings• Non-residential buildings

City of London


Methodology

Agent-Based Model (ABM) following a bottom-up approach

Activity Schedule

Non-Residential Buildings

Residential Buildings

Heat Demand

Electricity Demand

Occupancy Levels

Full/part time worker

Student

Pensioner

Unemployed

Distribute Simulate


Case Study

Isle of Dogs area (East London)

Simulation of the greater area of London from SmartCity Model

• London Borough of Tower Hamlets• 42,000 residents• 93,000 people work in the area


Results: Daily ProfilesResidential Buildings

010002000300040005000600070008000

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00Elec

trici

ty D

eman

d (k

W)

Time (hh:mm)

Electricity Demand

Peak Demand• 7-8am: wake-up• 5-6pm: back from work/school• 10pm: all agents at residences

0

2000

4000

6000

8000

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Hea

t Dem

and

(kW

)

Time (hh:mm)

Heat Demand

Winter - Weekday Tower Hamlets


Results: Daily ProfilesNon-Residential Buildings

0

500

1000

1500

2000

2500

3000

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Elec

trici

ty D

eman

d (k

W)

Time (hh:mm)

Electricity Demand (without Tower Hamlets 033)

Peak Demand8am-6pm: working hours• Schools• Offices• Commercial Stores

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 2:00

Elec

trici

ty D

eman

d (k

W)

Time (hh:mm)

Electricity Demand

Canary Wharf

8am – 6pm

Tower HamletsWinter - WeekdayWinter - Weekday


Results: Annual Demand

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

Annu

al D

eman

d (M

Wh)

ValidationAnnual Demand VS DECC data

ResidentialGas

Consumption

Non-ResidentialGas

Consumption

Non-ResidentialElectricity

Consumption

ResidentialElectricity

Consumption

+5.6% +9.1%

+4.9%

Highly overestimated

92,60187,707

101,46593,039

187,433178,705

114,856

56,114

Validation


Conclusions

Process• Reproduces trends of real demand• Test different scenarios• Re-usable

Non-Residential Demand• Lack of adequate data• Calibration of the results

Computational Tool• Decision support tool for key stakeholders• Incorporates end-user behaviour

energy futures lab

Agent-Based Modelling of Residential Heat Demand in a District Heating Network

Maria BRIOLA

Supervisors:Dr. Koen H. van DamDr. Christoph Mazur

10/46

Poster 10


Challenges of DHN• Demand side barriers• Residents’ mistakes when using thermostats• Faults in secondary consumer systems

AimInvestigate different strategies that could be applied to achieve optimal operation and maximum efficiency of DHNs.

District Heating Networks (DHN)• Heat for an area is produced centrally• Heat is distributed through pipes

Scope of the thesis


Agent-Based Model

Simulates residential heat demand

Studies secondary and tertiary network

Tests different operational strategies

Agent-Based Model

Simulates residential heat demand

Studies secondary and tertiary network

Tests different operational strategies

Methodology


Layout of the DH block examined in this model

Substation

Flats

CHP

Primary side Secondary side

Tertiary side

Layout of the DHN

Tertiary or demand side of DHN

Water tank

• Domestic Hot Water system

CHP

• Under-floor space heating


Problem with the DHN• High return temperatures• High heat losses

Solution• Apply the ABM• Test three scenarios

- Scenario 1: Tank resizing scenario

- Scenario 2: DSM scenario (improved and ideal case)

- Scenario 3: Alteration of boosting periods scenario

Queen Elizabeth Olympic Park

Case study

East Village (residential area)

Case study

East Village (residential area)


0102030405060708090

100

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

Prim

ary t

empe

ratu

re (o C

)

Time (h)

Primary supply and return temperature

primary returntemperature

primary supplytemperature

Baseline scenario results

Average difference between primary supply and return temperature:

25oC

Morning peaks:residents prepare for work

Evening peaks:residents return from work0

0.5

1

1.5

2

2.5

3

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

Heat

dem

and

(kW

)

Time (h)

Residential heat demand

SpaceHeating

DomesticHotWater

Total heatdemand


• Scenario 1: lowest improvement

Comparison of scenarios

Change in heat losses

Change in difference between supply and return temperature

Change in fuel consumption

Change in primary return temperature

Scenario 1 -0.7% +0.96% -0.07% -0.32%Scenario 2 (improved

case)-3.16% +3.3% -0.76% -1.3%

Scenario 2 (ideal case) -1.8% +13% -1.38% -4.6%

Scenario 3 -12.3% - -2.7% -

+13% -4.6%

-12.3%

• Scenario 2: lowest return temperatures and higher difference between supply and return temperatures

• Scenario 3 lowest fuel consumption and heat losses

-2.7%


Case study• Combination of scenario 2 and 3 could be the

best solution

Stakeholders• Effective collaboration must be achieved

Model• Could be used as a decision support tool for DHN operators

ConclusionsConclusions

energy futures lab

Modelling and optimisation of a district energy centre

Poster 13

Adrian REGUEIRA-LOPEZ

Supervisors:Prof. Nilay ShahDr. Romain Lambert

18/46


Overview

System more complex than a domestic boiler

District energy schemes:

• Centralized energy production• Economies of scale

Energy centers:

“Big” equipmentSeveral technologies

Efficiency + CO2 savings


Need for an optimisation?

Too many factors to operate “manually” = Model

Queen Elizabeth Olympic Park


Results: annual

0%10%20%30%40%50%60%70%80%90%

100%

Biomass CHP NG Boiler

Heat

Out

put (

%)

Actual Scheme Profit-Optimization

+100 %

-350 %

Heat carbon int. (gCO2/kWh)

CO2 emissions (tons)

Profit(£)

Profit opt. 90.5 12.7k 5.73m

Carbon opt. 83.3 11.7k 5.36m

Actual scheme 103 - -

Profit optimization Carbon optimization

0%10%20%30%40%50%60%70%80%90%

100%

Biomass CHP NG Boiler

Heat

Out

put (

%)

Profit opt. Carbon opt.


Results: daily profiles

Actual operation

0

2

4

6

8

10

12

14

16

18

0:00

1:30

3:00

4:30

6:00

7:30

9:00

10:3

012

:00

13:3

015

:00

16:3

018

:00

19:3

021

:00

22:3

0

Heat

Out

put (

MW

)

Time (h)

0

2

4

6

8

10

12

14

16

18

00:0

001

:30

03:0

004

:30

06:0

007

:30

09:0

010

:30

12:0

013

:30

15:0

016

:30

18:0

019

:30

21:0

022

:30

Heat

Pow

er (M

W)

Time (h)

Modeled operation

Biomass boiler CHP NG Boiler Storage


Conclusions

Energy Center Operation• In order to achieve maximum efficiency in DE schemes,

energy centers operation should be based on an optimization model.

Model• Carbon emissions savings and higher profits can be realized through

the optimised operation

Operation-support tool• The proposed model could be used and further developed as an effective

decision support tool for the energy centres operation

energy futures lab

Modelling and optimisation of a district heating network’s marginal extension

Poster 11

Axelle DELANGLE

Supervisors:Prof. Nilay ShahDr. Romain LambertDr. Salvador Acha

24/46



District heating networks (DHN):

• Well known & mature technologies• Expansion already considered in several

DHNs

Limited research on DHN expansion

Main objectives:

• Modelling approach• Strategy to design & operate the energy

centre• Connection scenario to select


Case study: Barkantine

Existing network: • 22 buildings connected• 2.4 km of pipes• One existing energy centre

Extension considered:• 31 buildings to connect• 3.5 km of pipes• 12 years horizon time


Methodology

Spatial network extension costs

Model key outputsProductsConstraintsProblem

statementModel key

inputs

Heat demand analysis

Design of the energy centre

Maximise net present value or minimise GHG emissions under given constraints.

• Scenario 1• Scenario 2

-£�3,50�m-£�3,00�m-£�2,50�m-£�2,00�m-£�1,50�m-£�1,00�m-£�0,50�m£�0,00�m£�0,50�m£�1,00�m£�1,50�m£�2,00�m

Investment�a

nd�re

venues�(£

/year)

Year

Scenario�2�- Profit�maximisation

-£�3,50�m-£�3,00�m-£�2,50�m-£�2,00�m-£�1,50�m-£�1,00�m-£�0,50�m£�0,00�m£�0,50�m£�1,00�m£�1,50�m£�2,00�m

Investments�and�re

venues�(£

/year)

Year

Scenario�1�- GHG�emissions�minimisation


Main results

Optimisation performed

Objective function Scenario 1 (slow connections first)

Scenario 2 (quick connections first)

Profit maximisation Net Present Value (£) £10,101,800 £13,173,700

GHG emissions minimisation (DUKES)

GHG emissions (tCO2eq) 1,670 1,920

Pumping�costs

Connection�costs

Heat�revenue

Boiler�costs

Electricity�residue

O&M

Investments

Financially viableNot financially viable

Table 1: Design of the energy centre


Conclusions

DHN expansion:

• Financial & environmental advantages• Planning strategies are essential• Using an optimisation approach is crucial

Barkantine case study:

• Costs optimisation performed: Scenario 2.Financially viable

• GHG emissions minimisation: Scenario 1.Additional subsidies required

The model developed can be applied to other DHNs.

energy futures lab

Modelling and Optimisation of Distributed Energy Resources in Food Retail Buildings: An Investment

and Management Approach

Poster 09

Aspasia GEORGAKOPOULOU

Supervisors:Dr. Salvador AchaDr. Christos N. MarkidesProf. Nilay Shah

30/46


Research motivation and aim of the project

Investments on low-carbon

technologies

6000 buildings

1% of total UK emissions

High energy demand

Cost and emissions’reduction;Potential to maximisesavings with waste heat-conversion technologies.

Combined Heat and Power (CHP)

Aim of the project:“Optimal implementation and operation of gas-fired internal combustion CHP units,when integrated with heat recovery and conversion technologies.”


Method: Optimisation Model

Optimal technology Operational strategyTotal costTotal emissions

Gas & Electricity pricesTechnology optionsEnergy demandFuel employed


Method: Case studies

First case study: Supermarkets

Single-CHP

CHP+ORC

Second case study: Distribution

Centres

Single-CHP

CHP+ORC

CHP+Absorptionchillers (AC)

Electric demand

Heat demand

Cooling demand

Business-as-usual scenario


Results: first case study

Average savings per year (£) Savings (%)

Average carbon emissions reduction per year (tn CO2eq)

Reduction (%)

Minimum cost NG vs Baseline £193,500 32.5% 430 18.8%Minimum cost BM vs Baseline

£227,400 38.2% 2260 99.3%

CHP 530kW ORC 100kW

0 5000 10000 15000

Average annual powerproduced

Average annual heatproduced

Average annual gasconsumption

Average annual excessheat

MWh

CHP 530kW & ORC 100kW CHP 500kW

-5.5%

-10%

Payback period: 4 yearsROI: 321%


Results: second case study

Technology CHP 1280kWe & ORC 150kWe

CHP 1520kWe & AC 600kWth

Fuel Biomethane Biomethane

Average annual cost savings

37.8% 37.1%

Average annual carbon savings

99.8% 99.8%

IRR 37.8% 29.3%

ROI 277% 186%

Payback period 4 years 5 years

Total cost for CHP+AC 1.25% higher than for

CHP+ORC


Results: second case study

+70% export revenues

+25% gas cost +30% capital cost

CHP+AC Operational strategy:

• No grid-imported electricity;• 40% of electricity is exported;• AC covers 40% of cooling demand;• Boiler provides 15% of heat required.

CHP+ORC Operational strategy:

• No grid-imported electricity;• 10% of electricity is exported;• Electric chiller covering cooling demand• Boiler not contributing to heat generation;

-£6m -£4m -£2m £m £2m £4m

Total cost

Capital cost

Maintenance cost

Fuel cost

Carbon cost

Export Revenue

GBP

CHP 1520kW & AC 600kW CHP 1280kW & ORC 150kW


Conclusions

Optimisation model:Generic tool for selection of technology portfolios in commercial

buildings

Waste heat conversion technologies: Reduced gas consumptionIncreased CAPEX

Fuel employed: Biomethane for full decarbonisationNatural gas CHP+ORC: 19% emissions reduction

CHP coupled with ORC or AC: Comparable benefitsApplications in buildings with high cooling demand

energy futures lab

Water-Cooled Refrigeration Systemsin the Food Retail Industry

Poster 12

Maria-Aliki EFSTRATIADI

Supervisors:Dr. Christos N. MarkidesDr. Salvador AchaProf. Nilay Shah

38/46



Supermarket refrigeration: 30%-60% of total energy consumed in the stores

High amounts of low-grade heat rejected bythe air-cooled condensers to the ambient air

Current status: Air-cooled condenserssituated on the rooftop of the stores

Typical commercial air-cooled condenser

Could a water-cooled condenser rejecting heat to the soil via an intermediate closed water-circuit address this problem?


Methodology

Model Development

Model Validation

Model Modification

Performance Comparison

Financial Evaluation

Case StudyDirect–expansion transcritical CO2 system

Area: Leicester North, UK


Modelling Results(1/2)Re

ject

ed H

eat (

kW)

External temperature (degC)

Air-cooled systemWater-cooled system

High external temperature: Up to 5 times less heat rejected by the water-cooled condenserLow external temperature: Marginally higher performance of the air-cooled system

Air-cooled condenser

Water-cooled

condenser

Hybrid system

Optimum performance


Modelling Results (2/2)

65% of the year the air-cooled system consumes less energy than the water-cooled system

Main reason: Low average temperature throughout the year

Air-cooled system Water-cooled system Hybrid system

Yearly energy consumption (MWh/year)

696 673 656

Yearly electricity cost

£ 59,200 £ 57,500 £ 55,900

Yearly emissions (tnCO2/year)

321 311 303

-3.5%-5.5%

Marginal energy savings

Air-cooled systemWater-cooled system

External temperatureCold aisle temperature


Financial Evaluation

Retrofit with a Hybrid System

Water-cooled System in a new

store

Hybrid system in a new store

CAPEX £ 83,000 -£ 40,000 relative to BAU

+£ 7,000 relative to BAU

OPEX relative to BAU

+200% +150% +200%

Energy costs per year

relative to BAU

-£ 3,500 -£ 1,800 -£ 3,500

Total annualsavings relative

to BAU

£ 1,800 £ 1,100 £ 1,800

Payback Period >10 years Immediate payback

4.8 years

Systems downsized upon design


Conclusions

Air-cooled systems• Highly dependent on external conditions• Show higher performance levels in cold ambient conditions

Water-cooled systems• Less sensitive to external temperature variations• Systems downsized• Attractive economics for new stores applications

Model: Reliable but Case specificNeeds to be applied in other systems for a general understanding


Acknowledgments

Dr. Salvador Acha

Gonzalo Bustus-Turu, PhD

Dr. Romain Lambert

Dr. Christos N. Markides

Dr. Christoph Mazur

Dr. Koen H. van Dam

Prof. Nilay Shah


Thank you for your attention!

energy futures lab

APPENDICES

energy futures lab A1/A12

Substation

CHP

Flats

HIUHIUHIU

Primary side Secondary side

Tertiary side

Layout of the DH block examined in this model

SUnder-floor

heating system

HE

Manifold

Hot water tank

2-port valve

3-port valve

HIU

Simplified representation of the tertiary side studied in this model

Layout of the DHN


0

20

40

60

80

100

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

Tank

tem

pera

ture

(o C)

Time (h)

Water tank return temperature of a three bedroom flat

0

20

40

60

80

100

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

Seco

ndar

y tem

pera

ture

(o C)

Time (h)

Secondary supply and return temperature

Supplytemperature

Returntemperature

Baseline scenario results

0

2

4

6

8

10

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00Hot w

ater

cons

umpt

ion

(kW

)

Time (h)

Hot water consumption of of a three bedroom flat


Comparison of scenarios

energy futures lab

Results: heat

Daily half-hourly profiles

0

5

10

15

20

25

30

35

40

1009590858075706560555045403530252015105

Heat

Out

put (

MW

)

KY_chp1 SC_Chp1 SC_chp2 KY_bioKY_b SC_b KY_st Demand

0

5

10

15

20

25

30

00:0

001

:30

03:0

004

:30

06:0

007

:30

09:0

010

:30

12:0

013

:30

15:0

016

:30

18:0

019

:30

21:0

022

:30

Heat

Out

put(

MW

)

Time (h)

KY_stSC_bKY_b SC_chp2SC_chp1KY_chp1

Annual load duration curve

0

5

10

15

20

25

30

00:0

001

:30

03:0

004

:30

06:0

007

:30

09:0

010

:30

12:0

013

:30

15:0

016

:30

18:0

019

:30

21:0

022

:30He

at O

utpu

t (M

W)

Time (h)

KY_stSC_bKY_b SC_chp2SC_chp1KY_chp1KY_bioD(t)

A4/A12

energy futures lab

Results: other outputs

Cooling operation

0

2

4

6

8

10

12

14

0:00

1:30

3:00

4:30

6:00

7:30

9:00

10:3

012

:00

13:3

015

:00

16:3

018

:00

19:3

021

:00

22:3

0

Cool

ing

Out

put (

MW

)

Time (h)

SCstAbs. ChilE. ChilD_CHW

-15

-10

-5

0

5

10

15

20

00:0

001

:30

03:0

004

:30

06:0

007

:30

09:0

010

:30

12:0

013

:30

15:0

016

:30

18:0

019

:30

21:0

022

:30

Elec

tric

pow

er (M

W)

Time (h)

Grid (Export)Grid (Import)Abs ChillersD_intChillers

Electricity balance

0

5

10

15

20

25

00:0

002

:00

04:0

006

:00

08:0

010

:00

12:0

014

:00

16:0

018

:00

20:0

022

:00

Stor

age

Leve

l (M

Wh)

Time (h)

0

1

2

3

4

5

6

00:0

002

:00

04:0

006

:00

08:0

010

:00

12:0

014

:00

16:0

018

:00

20:0

022

:00

Stor

age

Leve

l (M

Wh)

Time (h)

Thermal store (heat) Thermal store (CHW)

Heat carbon int. (gCO2/kWh)

CO2 emissions (tons)

Profit(£)

Profit opt. 90.5 12.7k 5.73mCarbon opt. 83.3 11.7k 5.36m

Actual scheme 103 - -

Annual Results

A5/A12


Gathering of loads into clusters


Costs optimisation approaches

GHG emissions minimisation approaches

Comparison of the NPV obtained in each model


Comparison of the emissions obtained in each model

156 8270

5 00010 00015 00020 00025 00030 00035 00040 000

SC1 SC2 SC1 SC2 SC1 SC2

Boiler�displacement�method

Power�station�displacement�

method

DUKES�method Baseline�Scenario

GHG�emissions�(t CO2

eq)

V2 V2�- leading�approach V2�- matching�approach V3

156 8270

2 000

4 000

6 000

8 000

10 000

12 000

14 000

16 000

18 000

SC1 SC2 SC1 SC2 SC1 SC2

Boiler�displacement�method

Power�station�displacement�method

DUKES�method Baseline�Scenario

GHG�emissions�(t CO2

eq)

V2B V2P V2T V3B V3P V3T

Costs optimisation approaches

GHG emissions minimisation approaches


Year 2 Year 5

Year 10 Year 12

Heat production profiles, costs optimisation model (V3), day 1


Heat production profiles, emissions Heat productiominimisation

on profiles, emissionctionn model (V3T), day 1

Year 2 Year 5

Year 10 Year 12


Case Study: Leicester North

Air-Cooled Condenser

Air-cooling evaporatorLow temperature cabinets

LT Compressor

8

2

Air-cooling evaporatorIntermediate temperature cabinets

IT Compressor

6

1

37 bar

T_LT_cabinets=257 K

T_IT_cabinets=275 K

9

12

Expansion valve 1

Flash tank

Expansion valve 2

Expansion valve 4

3

10

11

4

7

Expansion valve 34 5

m_total

m_vapor

m_IT

m_LT

Thermal duty of the cabinets variable according to the cold aisle temperature in the store

Temperature difference at the outlet side of the condenser: 3 K

Minimum condensation pressure: 45 bar

ηis=80%

Isenthalpic expansion in

the valves


CAPEX of air-cooled andwater-cooled systemsAir-cooled systemTotal cost: £ 193,000

Costs of the heat rejection system increase the CAPEX

Water-cooled systemTotal cost: £ 200,000

maria briola aspasia georgakopoulou axelle delangle ......axelle delangle maria-aliki efstratiadi...

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