champéry fepset - hevstechno economical data building simulation to determine demand synthesizing...

Supervised and transfer learning methodsto assist energy system optimization

Dasun Perera

1Solar Energy and Building Physics Laboratory,

EPFL, Switzerland

2nd FEPSET Champéry 2019

Overview of the presentation

• Changes take place in the superstructure of energy systems

• Machine learning for energy system designing: why?

• What are the limitations of supervised learning?

• From supervised learning to transfer learning

2


Superstructure of the energy systems

+ +

Dispatchable generator

A combination of Dispatchable generator

Dispatchable generators and non dispatchable generators

+

+Dispatchable generators, non-dispatchable generators,

dispatchable & non dispatchable storage

+

+Dispatchable generators, non-dispatchable

generators and cascade storage

Dispatchable generators, non-dispatchable generators and storage

+

Load MismatchExcess Generation

Time

Dispatchable Energy Sources

Energy Storage

Grid

Conversion of other modes of energy

Energy Storage

Grid

Demand

Non-dispatchable Generation

Demand/Generation

3


Designing urban energy systems

4

A.T.D. Perera et-al; Applied Energy, 2017 (190),

pp. 232–248


Machine learning is already there..

5

Karni Siraganyan et-al; Energies (Revision submitted)

Dan Assouline et-al; Applied Energy; 2018 (217); PP. 189-21Yearly mean global horizontal &diffuse radiation (Gh), in kWh/m2


6

Intention of using machine learning for energy system designing

Dense urban areas:

3D model by Nahid

Mohajeri

Classification of

climatic zones in

Switzerland

Classification of demand based on size of the

village/city

Classification based on climatic

condition

Collecting dataClustering

Classification based

on size


on climatic conditions

How to come up

with this map for

distributed

energy systems ?

• Reduce computational time for the

designing process

• Move into regional and national scale

Physical models and data driven approaches


Energy system designing

7

The challenge!!2nd FEPSET Champéry 2019

Considering energy systems as cyber physical systems: Reinforcement learning

8

Considering each energy system separately in the optimization

Considering the entire energy internet as a single component in the optimization

Optimizing energy systems considering energy interactions through information

exchange and learning

Energy hub

Energy Network

Information flow <Heat Demand>

<Electricity Demand>

<Renewable gen.>

<price of electricity >

<Price of heat>

time = 0 1 2 3 4 5

Feat

ure

s –

Dim

1Time – Dim 2

Computational time

Fully connected ~118000 Seconds

CNN ~220000 Seconds


Considering uncertainties and extreme events due to climate change …

9

Simulation 1) Basic energy conversion models used for energy system simulation· Wind turbine model, SPV model, ICG

model, etc.2) Dispatch strategy

Formulation of objective functions· Grid Integration level

· Net present cost

Constraints for Pareto optimization

Decision space variables

Objective space· Objective function value· Constraint violation

Techno economical data

Building simulation to determine

demand

Synthesizing weather data using regional

climate model

Scenarios for robust and stochastic blocks

Mapping decision space variables into objective space

Regional climate model and building simulation

Collecting techno-economical data

Simulation block

Stochastic block

Robust block

Building simulation to develop scenarios for energy demand

Regional climate model future climate data

Scenarios developed for renewable energy potential

Converting climate related data into energy system

related information

L M N O

0

20000

40000

60000

80000

100000

120000

Cases

Co

mp

uta

tio

nal ti

me (

Seco

nd

s)

32

33

34

35

36

37

38

R C

PU

/GP

U

A.T.D. Perera et-al; Applied Energy (Revisions submitted)


10

2D/3D overview of the building stock

Archetypes

Dense building stock

Less dense building stock

Energy Hub

SPV Panels

Wind Turbines

Internal combustion Engine

Electricity demand of the appliances

City center

PeripheryHeating and cooling demand

Computational time ~47000 Seconds

Considering energy systems along with urban energy infrastructure

A.T.D. Perera et-al, Applied Energy 2018 (222), PP. 847–860

A.T.D. Perera, et-al, Scientific Reports (Manuscript in revision)


11

"There are three kinds of lies: lies, damned lies, and statistics." Benjamin Disraeli

Dense urban areas: 3D

model by Nahid

Mohajeri

Classification of

climatic zones in

Switzerland


village/city


condition


Classification based on

size

Classification based on

climatic conditions

How to come up

with this map for

distributed energy

systems ?2nd FEPSET Champéry 2019

Simulation based optimization of energy systems

• Surrogate models have been used for– PEM Fuel cell design, wave energy converters, building energy simulation– Solar thermal energy systems (Bornatico et-al,2013, Energy (57) pp.653–62 )

– Integrated energy system design (Sánchez & Martí,2018, J Clean Prod (178),PP. 325–42)

None of these studies consider the operation of the energy system in detail

12

Decision Space· Decision variables for system

design· Decision variables for

dispatch strategy

Hourly wind speed, solar irradiation, demand, price of

electricity in the grid

Hourly simulation considering 8760 time steps

Energy system model

Dispatch Strategy

Initial capital cost

Objective space· Grid interactions· System reliability· Net Present Value

AEM


Introducing supervised learning

13

Decision Space· Decision variables for system

design· Decision variables for dispatch

strategy

Surrogate model based on ANNObjective space

· Grid interactions· System reliability· Net Present Value

Surrogate model

Training the surrogate model using AEM

Neural NetworkNumber of

hidden layersNumber of neurons in each layer Mean AEP

AR5 9 20,18,18,18,18,16,16,16,16 2.93

AR6 1320,18,18,18,18,18,18,16,16,16,16,

16,162.85

AR7 2 25,25 3.09

AR8 2 50,50 2.33

AR9 4 50,50,50,50 1.48


Two different paths to optimize the energy system

14

Reproduction of Population (Crossover and Mutation)

System Simulation

Evaluate1) Objective Functions

· LEC· Grid Interactions

2) Constraints Violation · Unmet load fraction

Update Population

Update Archive

Generation of initial population

Stop

Check no of Generations Achieved?

Hourly Renewable

Energy Potential

Hourly Electricity

Load Demand

Initialize the Archive

Selecting Members from Archave and Population

Conditions in the grid

Start

Decision vector

Surrogate model




Decision vector

Pat

h 1

: Su

rro

gate

Mo

de

lP

ath

1:

Surr

oga

te M

od

el

Ele

me

nts

of

the

Op

tim

izat

ion

A

lgo

rith

m

Ele

me

nts

of

the

Op

tim

izat

ion

A

lgo

rith

mEl

em

en

ts o

f th

e O

pti

miz

atio

n

Alg

ori

thm

Ele

me

nts

of

the

Op

tim

izat

ion

A

lgo

rith

m

Time series demand data for the simulationTime series demand data for the simulation

Time series wind speed data for the simulationTime series wind speed data for the simulation

Pat

h 2

:Act

ual

En

gin

ee

rin

g M

od

el

(AEM

)

Pat

h 2

:Act

ual

En

gin

ee

rin

g M

od

el

(AEM

)

Path 2

Path 1

Path 2Path 1


Results Surrogate model Vs AEM

15

0 5 10 15 20 25 30 35 40

0.4

0.6

0.8

1.0

1.2

1.4

Region Q

Surrogate Model

AEM

Net

Pre

se

nt

Valu

e (

x1

06 U

SD

)

Grid Integration Level (%)

Region P


Analyzing Region Q

NPV

(x106USD)

GI

(%)

SPV

capacity

Wind

capacity

wind :

SPV

Battery

banks

ICG

capacity

ICG:non-

dispatchable

A A-AEM 0.6461 13.10 67.6 75 1.11 9 10 7.01

A-S 0.6261 13.43 59.8 85 1.42 11 10 6.91

BB-AEM 0.6132 17.44 67.6 70 1.04 8 10 7.27

B-S 0.5966 17.73 63.7 90 1.41 10 7.5 4.88

CC-AEM 0.5935 20.32 62.4 75 1.20 8 10 7.28

C-S 0.5798 20.29 55.9 90 1.61 9 7.5 5.14

DD-AEM 0.5605 25.89 58.5 65 1.11 8 10 8.10

D-S 0.5582 24.64 58.5 85 1.45 10 10 6.97

160 5 10 15 20 25 30 35 40

0.4

0.6

0.8

1.0

1.2

1.4

Region Q

Surrogate Model

AEM

Net

Pre

se

nt

Valu

e (

x1

06 U

SD

)


Region P • Ratio between wind:SPV and wind+SPV: ICG

are close to each other

• System designs looks close to each other


Analyzing Region P

17

NPV

(x106USD)

GI

(%)

SPV

capacity

Wind

capacity

wind :

SPV

Battery

banks

ICG

capacity

ICG :non-

dispatchable

EE-AEM 0.6680 10.81 67.6 80 1.18 9 10 6.78

E-S 0.7000 10.09 70.2 65 0.93 14 10 7.40

FF-AEM 0.7204 5.77 68.9 85 1.23 10 10 6.50

F-S 0.8167 5.95 71.5 45 0.63 20 10 8.58

GG-AEM 0.7420 3.35 75.4 80 1.06 11 10 6.44

G-S 0.9772 3.32 58.5 40 0.68 20 12.5 12.69

HH-AEM 0.7573 2.60 75.4 85 1.13 11 10 6.23

H-S 1.1578 1.93 55.9 25 0.45 20 15 18.54

II-AEM 0.7623 1.42 68.9 85 1.23 10 10 6.50

I-S 1.1986 1.49 81.9 0 0.00 20 25 30.53

0 5 10 15 20 25 30 35 40

0.4

0.6

0.8

1.0

1.2

1.4

Region Q

Surrogate Model

AEM

Net

Pre

se

nt

Valu

e (

x1

06 U

SD

)


Region P

Reduce grid interactions

• Notable increase in

• energy storage

• capacity of ICG

• ICG to non-dispatchable generation

for the surrogate model


Surrogate model Vs AEM

• Surrogate models can learn the general pattern• Its not powerful representing complex operation• Active learning: train the surrogate model handle

complex operation 18

0 5 10 15 20 25 30 35 40

0.4

0.6

0.8

1.0

1.2

1.4

Region Q

Surrogate Model

AEM

Net

Pre

se

nt

Valu

e (

x1

06 U

SD

)


Region P

10 15 20 25 30 35 40 45 50

0

25

50

75

100

125

150

175

200

CNN

4 FC

3 FC

2 FC

Fuzzy

Ge

ne

ratio

n f

rom

th

e I

CG

(%

)

Grid integration level (%)

Region required active learning


Merging the strengths: surrogate + AEM

Surrogate model is fast & AEM is accurate

19


Optimization Algorithm (HOA)

20

Reproduction of Population (Crossover and Mutation)

Update Population

Update Archive

Generation of initial population

Stop


Initialize the Archive


Start

Surrogate model




Decision vector

Surr

oga

te M

od

el

Surr

oga

te M

od

el

Ele

me

nts

of

the

Op

tim

izat

ion

A

lgo

rith

m

Ele

me

nts

of

the

Op

tim

izat

ion

A

lgo

rith

mEl

em

en

ts o

f th

e O

pti

miz

atio

n

Alg

ori

thm

Ele

me

nts

of

the

Op

tim

izat

ion

A

lgo

rith

mReproduction of Population (Crossover and Mutation)

Update Population

Update Archive

Stop



Start

Ele

me

nts

of

the

Op

tim

izat

ion

A

lgo

rith

m

Ele

me

nts

of

the

Op

tim

izat

ion

A

lgo

rith

mEl

em

en

ts o

f th

e O

pti

miz

atio

n

Alg

ori

thm

Ele

me

nts

of

the

Op

tim

izat

ion

A

lgo

rith

m

System Simulation




Renewable Energy

Potential

Hourly Electricity

Load Demand


Decision vector

Act

ual

En

gin

ee

rin

g M

od

el (

AEM

) A

ctu

al E

ngi

ne

eri

ng

Mo

de

l (A

EM)

System Simulation

1) Objective Functions· LEC· Grid Interactions


Renewable Energy

Potential

Hourly Electricity

Load Demand


Decision vector Sim

ula

tio

n t

he

De

sign

So

luti

on

s O

bta

ine

d f

orm

th

e S

urr

oga

te M

od

el

Sim

ula

tio

n t

he

De

sign

So

luti

on

s O

bta

ine

d f

orm

th

e S

urr

oga

te M

od

el

Population and achieve(Pareto solutions) obtained from surrogate

model

Evaluate Objective function Values, Constraints and check

the dominancy of Pareto solutions

Updated Archive and Population obtained

STEP 1 STEP 2 STEP 3


Supervised learning can reduce the computational time

21

0 10 20 30 40 50

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Surrogate

HOA 1

HOA 2

HOA 3

AEM

0 20 40 60 80 100

Computational time as a percentage of AEM

Surrogate model

HOA 1 (REG 1:8)

HOA 2 (REG 2:7)

HOA 3 (REG 3:6)

AEM

Net

Pre

se

nt

Valu

e (

x1

06 U

SD

)



22

Intention of using machine learning for energy system designing

Dense urban areas:

3D model by Nahid

Mohajeri

Classification of

climatic zones in

Switzerland


village/city


condition



on size


on climatic conditions

How to come up

with this map for

distributed

energy systems ?

• Reduce computational time for the

designing process

• Move into regional and national scale


Transfer learning: adapting the models

23

Surrogate model based on ANN

Surrogate Model trained using

Transfer Learning (SMTL)

Optimization algorithm

Optimization algorithm

De

cisi

on

sp

ace

va

ria

ble

s o

f th

e P

are

to s

olu

tio

ns

of

Sta

ge

1

Stage 1 Stage 2

Surrogate model based on ANN

Surrogate Model trained using

Transfer Learning (SMTL)

Creating data set for transfer learning using

AEM and GPU computing

• transfer learning arise when the data can be easily outdated

• when the distribution changes, most statistical models need to be rebuilt from scratch

• it is expensive to recollect the needed training data and rebuild the models

• in such cases, knowledge transfer or transfer learning between task domains is desirable

Sinno Jialin Pan & Qiang Yang, IEEE Trans Knowl Data Eng, 2010 (22) PP. 1345-59


Six scenarios considered

24


Transfer learning Vs AEM

25

0 10 20 30 40 50

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Initial Scenario

Wind 1

Wind 2

Ne

t P

rese

nt

Va

lue

(x

10

6 U

SD

)


0 10 20 30 40 50

0.4

0.5

0.6

0.7

0.8

0.9

1.0 Initial Scenario

Demand 1

Demand 2

Net

Pre

se

nt

Valu

e (

x1

06 U

SD

)


0 10 20 30 40 50

0.4

0.5

0.6

0.7

0.8

0.9

1.0 Initial Scenario

SPV 1

SPV 2

Net

Pre

se

nt

Valu

e (

x1

06 U

SD

)


0 5 10 15 20 25 30 35

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Demand 2 (AEM)

Demand 2 (Surrogate)

Ne

t P

res

en

t V

alu

e (

x1

06 U

SD

)


0 5 10 15 20 25

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Demand 1 (AEM)

Demand 1 (Surrogate)

Ne

t P

res

en

t V

alu

e (

x1

06 U

SD

)


0 10 20 30 40 50

0.4

0.5

0.6

0.7

0.8

0.9

1.0 Wind 1 (AEM)

Wind 1 (Surrogate)

Net

Pre

se

nt

Valu

e (

x1

06 U

SD

)


0 5 10 15 20 25

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Wind 2(AEM)

Wind 2 (Surrogate)

Net

Pre

se

nt

Valu

e (

x1

06 U

SD

)


0 5 10 15 20 25 30 35 40

0.4

0.5

0.6

0.7

0.8

0.9

1.0

SPV 1 (AEM)

SPV 1 (Surrogate)

Ne

t P

res

en

t V

alu

e (x

10

6 U

SD

)


0 10 20 30 40

0.4

0.5

0.6

0.7

0.8

0.9

1.0

SPV 2 (AEM)

SPV 2 (Surrogate)

Net

Pre

se

nt

Valu

e (

x1

06 U

SD

)


Sce

nar

ios

for

De

man

dSc

en

ario

s fo

r W

ind

Sce

nar

ios

for

SPV2nd FEPSET Champéry 2019

26

SMTL

AEM

0 20 40 60 80 100

Time taken as a percentage of AEM model

Optimization

Training neural network

Generating the datapoints

Comparison of AEM and transfer learning

A notable reduction in computational time thanks to transfer learning and

GPU computing



27

Physical models and data driven approaches

Wrong: Top down statistical models

Correct: Transfer learning

Still we have one main challenge!!


Conclusions

• Urban physics, climate uncertainty, cyber physical interactions make energy system designing process to be extensively time consuming

• Supervised learning along with AEM can speed up the computation

• Active learning might be helpful to improve the accuracy of the surrogate models

• Transfer learning is effective when adapting the model for different demand, generation scenarios

• This enables to reach regional, national and continental scales

• Lets hope geometric deep learning (GDL) will help to address the issues related to the Grid!!

28


29

“…. things take longer to happen than you think

they will, and then they happen faster than you

thought they could.”

Rudiger Dornbusch


Thank you

30


Moving into system design

Dispatch strategySystem configuration

optimization


optimization

Dispatch

strategy

Decoupling the problem

Decoupling the time-dependent problem into small-

size single-period sub-problems that can be solved

(Value)

Treating the multi-period large-size problem as unique as a policy related problem

and solving it(Policy)

Piacentino & Cardona, Energy Convers. Manag. (2008) 31


32

Optimizing transfer function

DemandRenewable energy

generationState of charge of the

energy storage etc

Input

Operating stateDispatch strategy

Operating condition of the devices

State transfer function

Dufo Lopez et al, 2005, Solar Energy

Hochmath, 1997, Solar Energy

+ Augustin, 2008, Int. J. Hydrogen Energy

Augustin, 2015, Applied Energy


optimization


optimization

Dispatch

strategy

Decoupling the problem


Optimizing operating state

CHP

Weber et al, 2006, Applied Thermal Engineering

Halgamuge et al, 2014, Applied Energy

+

CCHP

R. Evins, 2015, Energy

+

CCHP

+

33

DemandRenewable energy

generationState of charge of the

energy storage etc

Operating state of the system for each time

step

Optimizing thestate

“Overall the optimization process took 70 h (nearly 3 days), whereas without parallel solving it would have needed 210 h (nearly 9 days)”


Operation Design

• Simple dispatch strategies (load following, frugal discharge, cycle charging etc)

• Combined dispatch strategies (rule based)

• Finite state machines/Finite automata theory (Augustin et-al, 2015, Applied Energy) (Perera et-al, 2013, Energy)

• Fuzzy-automata (Perera et-al, 2017, Applied Energy)

• Black-box models assisted with supervised and semi supervised learning+ moving into reinforcement learning? (Perera et-al, 2017, CISBAT)

White Models

Grey Models

Black box models

34


Overview of the dispatch strategy

35

RE(t)

0

De

pth

of

Dis

char

ge (

Do

D)

[x2

]

0

1

1

1,2W

2,2W

3,2W

1,1W 2,1W3,1W

0

Max

DoD

DO

D/

1,1

1,

,22,11

,22,11,22,11

ji

jiji

wxwxfor

wxwxforwxwxy

0MaxELDELD /

Decision Space variables

Dynamic dispatch used in hybrid vehicles

(Li et al, J. Power Sources, 2009)

Perera et-al, 2017, Applied Energy2nd FEPSET Champéry 2019

Assessing grid integrated electrical hubs

0 5 10 15 20 25 30 35 40

0

20

40

60

80

100

120

140

160

(%)demand Anual

ror transfe generation Anual

Grid interaction level GIFG

(%)

0 5 10 15 20 25

0

20

40

60

80

100

120

140

160

(%)demand Anual

ror transfe generation Anual

ICG

SPV and Wind

Total

Grid interaction level GIFG

(%)

LEC- GITFG Pareto Front LEC- GIFG Pareto Front

GIFG = σ𝑡=1𝑡=8760 𝑃𝐹𝐺(𝑡) /σ𝑡=1

𝑡=8760 𝑃𝐸𝐿𝐷(𝑡)

GITG = σ𝑡=1𝑡=8760 𝑃𝑇𝐺(𝑡) /σ𝑡=1

𝑡=8760 𝑃𝐸𝐿𝐷(𝑡)

GITFG = σ𝑡=1𝑡=8760 |𝑃𝑇𝐺 𝑡 | +|𝑃𝐹𝐺 𝑡 |

σ𝑡=1𝑡=8760 𝑃𝐸𝐿𝐷(𝑡)

36Perera et-al, 2017, Applied Energy



37


Extending the concept into cities



Extending the concept into cities

39

SPV Panels

Wind Turbines



Urban micro-climate

Climate change

Energy demand

Urban form

Renewable energy

potential


Work flow to extend the boundaries



Influence of urban climate on energy demand and wind speed



Influence of urban climate on energy systems



Influence of the urban form

43

SPV Panels

Wind Turbines



Urban micro-climate

Climate change

Energy demand

Urban form

Renewable energy

potential


Influences of the urban form

44Perera et-al, 2018, Scientific Reports (Manuscript under review)


Influences of the urban form

45

0 10 20 30 40 50

1.0

1.5

2.0

2.5

3.0

3.5 A10

B3

C6

D2

E3

NP

V (

x 1

06 C

HF

)


Perera et-al, 2018, Scientific Reports (Manuscript under review)


Influences of the climate change

46

SPV Panels

Wind Turbines



Urban micro-climate

Climate change

Energy demand

Urban form

Renewable energy

potential


Designing climate resilient energy systems

Influence of climate uncertainty and extreme climate events on energy infrastructure

47(Vahid Nik, Applied Energy –2016)

• Quantifying the risks introduced by weather poses a challenge due to its high stochasticity and multi-dimensional impact (Panteli and Mancarella, ElectrPower Syst Res 2015 )

• Quantifying the impacts of climate change by translating climate data into energy system relevant data is challenging (P Dowling, Energy Policy, 2016


Converting climate relevant information into energy system relevant data

48


Pareto fronts obtained

49

0 5 10 15 20 25 30 35 40

2

3

4

5

6

7 Moderate scenario (TD)

Extreme demand (ED)

Exteme renewable energy potential (ER)

Exteme renewable energy potential and demand (EDR)

Net P

rese

nt V

alu

e (

x10

6E

uro

)

Grid integration level (%)

• Neglecting extreme events can lead to a significant drop in power supply reliability up to 12%

• A significant shift in objective function values

• Deterministic models will result in very low renewable energy fraction and higher cost

• Important to move into stochastic optimization where presenting climate scenarios is challenging


Future perspectives

50

SPV Panels

Wind Turbines



Urban micro-climate

Climate change

Energy demand

Urban form

Renewable energy

potential


From Designing to Assessment

51Manfren et-al, Appl. Energ. (2011)


Taking climate flexibility into energy system designing process

Identify the requirements and criterions

to be considered for

the assessment

Critical Indicators

Basic Indicators

Preference indicators

Model each criterion for the computation

model

Refine Objective functions for

Pareto optimization

Pareto optimization

Analyze Pareto fronts

Developing boundary matrix

for decision making

Analyzing the Level

diagrams

Limitations in initial investment and

operation

Environmental impact

Energy security

Geographical limitations

Life cycle simulation

Fine-tuning weight matrix

for Fuzzy TOPSIS

Dimension reduction

Pareto optimized following dimension

reduction

Final Energy System Design

Understanding the design requirements Energy system designing tool box

Pareto Analysis

Classification of design criterions

Multi-criterion Decision Making

Set of Pareto solutions

0.16 0.18 0.20 0.22 0.24 0.26 0.28

0

10

20

30

40

50

60

Region C LEC-ICC

LEC-LCO2

LEC-GI

LEC-WRE

GI

(%)

LEC ($)

52

A.T.D. Perera, V. M. Nik, D. Mauree, and J.-L. Scartezzini, “An integrated approach to design site specific distributed electrical hubs combining optimization, multi-criterion assessment and decision making”, Energy 2017 (134), PP. 103-120


A holistic platform to assess the energy transition…

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0%

20%

40%

60%

80%

100%

0 10 20 30 40 50 60 70 80 90

Gen

erati

on

of

PV

an

d w

ind

Restrictions for anual net purchased energy [%]

PV Wind Turbines

Morgane Le Guen, Lucas Mosca, A.T.D. Perera, Silvia Coccolo, Nahid Mohajeri, Jean-Louis Scartezzini, “Improving the energy sustainability of a Swiss village through building renovation and renewable energy integration” Energy and Buildings, 2018


A holistic platform to assess the energy transition…

54

Future Scenarios for 2030 and 2050 Energy demand for future scenarios

Changes in the energy system for scenariosNahid Mohajeri, A.T.D. Perera, Silvia Coccolo, Morgane Le Guen, Lucas Mosca, Jean-Louis Scartezzini “Assessments of sustainable development scenarios for a Swiss village to 2050 through renewable energy integration.” (Manuscript in review: Renewable Energy)


Research Outlook

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Moving into regional scale

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• Quantify renewable energy integration into the residential sector in EU countries

net gross heating demand [kWh/m2]

RE generation as a % of the demand

• Archetypes to represent a building stock• Capital city represent the whole country


Overview of scenarios considered for energy supply simulations:

• Scenario 1: Net heating and coolingdemand for the architype with presentclimatic conditions and present buildingcharacteristics.

• Scenario 2: Net heating and coolingdemand for the architype with futureclimatic conditions and present buildingcharacteristics.

• Scenario 3: Net heating and coolingdemand for the architype with futureclimatic conditions and buildingrefurbishment.


Inter-comparison of scenarios

Country CityConstruction

Year ClassInfiltration

rateNet Annual Heating

Demand (kWh/m2)

Net Annual Cooling

Demand (kWh/m2)

Net Annual Heating

Demand (kWh/m2)

Net Annual Cooling

Demand (kWh/m2)

Net Annual Heating

Demand (kWh/m2)

Net Annual Cooling

Demand (kWh/m2)

Cyprus Nicosia … 1980 0.7 47.34 34.73 40.71 40.72 28.08 27.86

Italy Rome … 1900 0.4 86.55 4.46 74.75 10.44 63.38 9.31

Spain Madrid … 1900 0.2 87.48 32.12 81.41 39.53 43.49 22.97

Greece Athens … 1980 0.2 91.59 33.44 80.22 57.97 43.97 31.78

Bulgaria Sofia … 1918 0.2 96.73 5.67 84.53 10.74 47.78 5.41

Bosnia-Herzegovina Sarajevo … 1945 0.4 107.98 3.54 97.21 5.91 50.02 2.79

Serbia Belgrade … 1918 0.6 108.18 7.95 93.22 15.14 55.77 9.92

Netherlands Amsterdam … 1964 0.2 108.31 0.56 104.41 0.66 60.00 0.33

France Paris … 1914 0.7 108.50 4.92 103.46 6.30 65.46 3.89

Slovenia Ljubljana … 1945 0.3 113.19 5.11 105.78 7.16 57.98 3.31

United Kingdom London … 1918 0.7 117.77 1.61 120.69 2.59 64.34 1.28

Norway Oslo … 1955 0.5 117.82 0.10 111.88 0.24 83.42 0.35

Belgium Brussels … 1945 0.2 119.61 1.83 114.75 2.32 40.51 0.53

Ireland Dublin … 1950 0.2 131.91 0.00 117.64 0.05 89.24 0.01

Poland Warsaw … 1945 0.5 146.00 2.98 132.89 3.90 71.86 3.43

Switzerland Berne … 1945 0.7 152.30 0.90 145.33 1.89 97.68 1.71

Germany Berlin … 1859 0.4 152.71 12.02 139.81 13.37 94.79 7.89

Czech Republic Prague … 1920 0.9 153.48 1.11 139.59 1.50 98.35 0.92

Sweden Stockholm … 1960 0.9 156.84 0.40 139.52 0.65 83.51 0.71

Denmark Copenhagen … 1850 0.2 179.11 1.05 167.39 1.33 85.58 1.01

Scenario 1 Scenario 2 Scenario 3


Results: Energy Demand : Scenario 1

Net Annual Heating Demand

Net Annual Cooling Demand

Source: Spinoni et al. 2017


Comparison of Scenario 1, 2 and 3

Climate change : -8%Building Refurbishment : -36%

Climate change : 54%Climate change and Building Refurbishment : -12%


Overview of scenarios considered for energy supply simulations:

• Scenario 1: Present scenario(2018)

• Scenario 2: 2050 scenariowith different national gridprices

• Scenario 3: 2050 scenariowith a uniform Europian grid


Scenario 1:Energy Supply (Presentconditions)

Avg Ren Frac = 3.6%

COE = 0.18 $/kWh

Avg Ren Frac = 4.5%

COE = 0.18 $/kWh

70% Grid Restriction


Case 1: PV System

Source: Wikimedia.org


Scenario 2: 2050 scenario with different national grid prices

Avg Ren Frac = 18%

COE = 0.20 $/kWh

Avg Ren Frac = 22%

COE = 0.162 $/kWh



Case 2: PV + Wind System

Source: awstruepower.com


From Scenario 2 to 3

Case 3 disadvantageous due to:

• Higher cost

Case 3 advantageous due to:• Higher average Renewable Energy

Fraction• Lower average Carbon Emissions 2nd FEPSET Champéry 2019

Identify the promising pathways for energy transition

1, 1, 3, 3,1

1, 1,

( ) ( )Index = *100

( )

h i c i h i c i

h i c i

d d d d

d d

+ - +

+

, 1 , 22

, 1 , 2

PV PV

WIND WIND

COE COEIndex

COE COE

-=

-

13

, 1 , 2

, 1

( )*100PV PV

PV

IndexIndex

COE COE

COE

=-

4

1

, 1 , 2

, 1

( )*100WIND WIND

WIND

IndexIndex

COE COE

COE

=-

Percentage change in demand

Change in the COE of PV panels and wind turbines

Change in demand with respect to change in COE of PV panel

Change in demand with respect to change in COE of wind turbines

Index Definition


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Conclusions

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1. Energy transition demand for a rapid change in the energy system superstructure

2. Urban climate plays a vital role in the design process of urban energy systems3. The influences of climate change, urban form on urban energy systems are

vital to consider4. Simple deterministic models makes it difficult to consider the

aforementioned aspects5. Extending the models from urban scale to regional and national scale is

important6. There are basic bottleneck which well beyond complex engineering that can

be handled by educating the communities.

“…. things take longer to happen than you think they will, and then they

happen faster than you thought they could.”

Rudiger Dornbusch2nd FEPSET Champéry 2019

Thank you

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champéry fepset - hevstechno economical data building simulation to determine demand synthesizing...

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