14 epfl-valais

©Francois Marechal -IPESE-IGM-STI-EPFL 2014

IPESEIndustrial Process and

Energy Systems Engineering

E-transition : the role of the E-technology for the Energy

transition

Prof François Marechal Industrial Process and Energy Systems Engineering

EPFL VALAIS-WALLIS




• E-technology for engineering – E-technology for modelling – E-technology for data structuring – E-technology for decision support – E-technology for understanding

• E-technology for operating • E-technology for teaching

E-techonology for the energy transition




Socio - ecologic - economic environment

The energy system

Conversion systems

Waste Emissions

Raw materials

Resources

Price

Availability

t

t

Products &Energy services products

products

Demand

Price

t

t




Socio - ecologic - economic environment

The energy transition

Conversion systems

Raw materials

Resources

Price

Availability

t

t

Waste Emissions

Products &Energy services products

products

Demand

Price

t

t




• For a given city

Energy services for the energy transition

Energy services

‣Technologies ?

‣Min. Costs and emissions

‣ Heat using existing district heating network (seasonal variation in T and load): 3357 kWhth/yr/cap

Waste to be treated (existing facilities for MSW and WWTP):

Resources

‣ Electricity (seasonal variation): 8689 kWhe/yr/cap

‣ Mobility: 11392 pkm/yr/cap

‣ MSW: 1375 kg/yr/cap ‣ Wastewater: 300 m3/yr/cap

‣ Biowaste: 87.5 kg/yr/cap

‣ Woody biomass: 18’900 MWhth/yr

‣ Sun (seasonal variation in T and load): 10‘328 MWhth/yr ‣ Hydro (existing dams): 187‘850 MWhe/yr

‣ Geothermal: 9496 MWhth/yr

?

??

??

??

40’000 inhabitants city in Switzerland (1000m alt.)




• Characterise and localise the technologies to be used to supply the energy services and the products needed over the lifetime of the system’s element at the time they are needed with the resources that are available

– Decisions to be taken • Technology : choice, size and location • The operating strategy • The services that are provided • The resources that are used

– Criteria • Minimum cost = OPEX + APEX • Maximum profit = NPV (i,lifetime,cost) • Minimum environmental impact = LCIA • Max renewable energy integration

– Constraints • Mass & Energy Balances • Context specs • Bounds

Engineering the energy transition

Yu,l, Su,l

fu,l,t(p) 8p, u, lfu!s,l,t(p) 8p, u, l, sfr!u,l,t(p) 8p, u, l, r




E-technology for engineering



Energy Systems EngineeringThe energy systems engineering methodology

Solutions

Energy services Resources Context & Constraints

Superstructure

System Boundaries

System performances indicators•Economic•Thermodynamic• Life cycle environmental impact

Results analysis•Exergy analysis•Composite curves•Sensitivity analysis•Multi-criteria

Technology options

Models

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0 5000 10000 15000 20000 25000 30000

T(K

)

Q(kW)

Cold composite curve

Hot composite curveHeat & Mass integration

Decision variables

Solving method

Thermo-economic Pareto

Multi-objectiveOptimization

Out=F(In,P)



Energy Systems EngineeringMulti-scale problem 9

9

EnA / EnMS

Common element is the energy planning

• Energy consumption profile determination

• Energy performance evaluation

• Energy baseline generation

• Identification and evaluation of improvement options

• Implementation and monitoring

Step 1

Step 2

• Systematic approach

• Methodologies

• Tools

Context Literature review Problematic 1st year research Research plan Conclusion

Step 3

Details

Scale

Technologies

Processes

IndustrialClusters

Districts

RegionsE-technologyfordecisionsupport

*Modellingtheinteractions* Identifyingthesynergies* Implementingthebestinteractions=>NetworkorGrid

MaterialsEngineering:

frommaterialsdevelopmenttosystemintegration




• Model of the interconnectivity (mass, heat, energy) • Model of the emissions (Equipment, Emissions) • Model of the cost (size => cost, maintenance)

E-technology for modelling

-Equipmentsizingmodel-Costestimation

Heattransferrequirement

Heattransfer

Thermo-chemicalconversion Model

MaterialstreamsProductstreams

ElectricityHeattransferrequirement

WaterstreamsWaterstreams

WastestreamsWastestreams

ElectricityUnitparametersDecisionVariables

Lifecycleemissions

Lifecycleofequipment-Production-Dismantling

MaintenanceInvestmentcost

LCAmodels

LCAmodels



Energy Systems EngineeringE-technology for integration

Unit documentation DescriptionPurposeLimitationAuthors

Unit model report Summary of resultsImportant dataErrorsValidity

Unit execution handler Input dataCalculation engines

Existing flowsheeting softwareMimetic models

Output dataIntegration/interconnectivityCostingLCA => Ecoinvent LCI

Model ontologyModel encapsulation

Common interface of a module for system integration

Flowsheeting tools •BELSIM-VALI•gPROMS•ASPEN plus•HYSYS•Matlab•Simulink•(CITYSIM)•MODELICA•Others possible

•CAPE-OPEN ?•PROSIM

Unit connectivity Layers connections

Layer ontologyData base

ModelData base

KnowledgeData base




A variable is the smallest data structure handled by OSMOSE. A variable is defined by1 Name or tagname that litteraly identifies the variable2 ID that is tagging the variable3 Parent indicates the module to which the variable belongs4 Value that defines one instance of the variable => Matrix

A priori value of the variable will exist for any time in the time sequence, for any period and for any scenario in which the system is calculated.

5 MinimumValue and a MaximumValue that defines the bounds for the value of the variables 6 DistributionType and DistributionParameters that defines the uncertainty of the value

7 DefaultValue that defines the value of the variable when it is created. this is also the recommended order of magnitude of the value recommended for the calculations

8 PhysicalUnit that defines the physical unit of the value of the variable9 DefaultPhysicalUnits that defines the physical unit with which the calculation have to be made, It means that a

preprocessing phase will have to convert the value from the PhysicalUnits to the DefaultPhysicalUnits as well as a post-processing phase that is going to convert from the DefaultPhysicalUnits to the PhysicalUnits .

10 Status that identifies if the variable has a fixed value or a value that will be calculated. The status should identify as well if the value is calculated for each time or if it is constant or if it requires recalculation for each time. Status is equivalent to ISVIT in OSMOSE.i x : value is unique for the scenario (i.e. 1 value/scenario)ii x0 : value is period dependent (i.e. 1 value/period and / period)The value may calculated during

preprocessing phase using the model (i.e. constant that is period dependent).iii x00 : the value is time dependent (i.e. 1 value/time and period and scenario).

where x = 1 : for constant to be fixed by the user or the optimizer

2 : for constant fixed by the user or the optimizer otherwise use the default value -1 : for values calculated by the model

11 Dependence Matrix a dependence matrix that identifies the dependence of the value with respect to the other values of the problem is stored. Each of the dependence includes the mention if the dependence is linear, in which case the value of the dependence is stored in the field as a couple (ID,numerical value), or it can be stored as a reference to a non linear dependence that could be a surrogate model or the access to the model that calculates the dependence.

Ontology of a value : E-technology for knowledge structuring

mu,l(t), Yi,u,l(t), Xi,u,l(t),⇧i,u,l(t),KPIk, m⇤u,l, Y

⇤i,u,l, X

⇤i,u,l,⇧

⇤i,u,l



Energy Systems EngineeringE-technology : Models & knowledge data base




LENI Systems

Flowsheet generation (2)Energy-integration model

Integrating heat recovery technologies in the superstructure

43 / 87

Process design

WOOD Natural Gas (SNG) CO2 (pure)




• Explicit – In flowsheeting software

• Automatic – Based on the connectivity description – Restricted matches – Routing

• Implicit – Heat cascades

• Combined – Mass and Energy

Modeling the systems interactions

Luc Girardin page 25/77

Travail de diplôme : Méthodologie de conception optimale d'un réseau hydrogène d'un site industriel.

7.2 DÉFINITION DE LA SUPERSTRUCTURE

Le réseau d'hydrogène est conçu comme une superstructure qui contient toutes les sources et puits desunités productrices/consommatrices d'hydrogène, et qui incorpore toutes les connections possibles entre cessources et ces puits (Figure 7.2). Toutes les connections de la superstructure ne sont pas admissibles, cequi revient à introduire dans le modèle des contraintes sur l'établissement des nouvelles connexions.

Le point de fonctionnement de la structure est défini par l'ensemble des données requises pour chaqueconsommateur, chaque producteur, et chaque unité.

Chaque source de la structure est caractérisée par :

! la pureté de l'hydrogène produit Xp

! la pureté en impuretés produites x p , j

! un débit d'hydrogène disponible délivré "mp

! le niveau de pression total du flux délivré P p .

En contrepartie, chaque puits est défini par :

! la pureté requise du flux d'entrée Xc

! les limites de composition en impuretés du mélange admis xc , jmax

! un besoin de débit d'hydrogène "mc

! la pression totale requise du mélange à l'entrée Pc .

Le nombre des puits et sources, regroupé au sein d'une même unité, est égal au nombre de niveaux distinctsde pureté des flux d'hydrogène admis, respectivement rejetés. Dans un premier temps, on peut admettre quechaque unité possède au maximum 1 consommateur et 2 producteurs. Un nombre plus élevé de ceséléments par unité correspond à un affinement du modèle, et entraîne une augmentation de la densité deconnexions au sein de la superstructure

On a vu que les unités regroupent, dans une même structure, des puits et des sources qu'on appelleconsommateurs et producteurs.

Elles sont définies par :

! l'identité des puits et sources qui la compose,

! leurs coordonnées (X,Y) géo-référencées,

! un identifiant, qui permet de les regrouper en sous-systèmes pour l'analyse dans les diagrammes dePincement (cf. Chapitre "Représentation graphiques").

25/77 18/02/05

Figure 7.1 : Schéma de la superstructure complexe

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T(K

)

Q(kW)


Hot composite curve



Energy Systems EngineeringE-technology for decision support

Flowsheeting tools •BELSIM-VALI•gPROMS•ASPEN plus•HYSYS•Matlab•Simulink•(CITYSIM)•MODELICA•Others possible

•CAPE-OPEN ?•PROSIM•UNISIM ?

Energy technology data base •Data/models interfaces•Simulation•Process integration interface•Costing/LCIA performances•Reporting/documentation•Certified dev procedure

Modeling tools integration

MILP/MINLP models Heat/mass integrationSub systems analysisSuperstructureHEN synthesis models

Optimal control models MILP/ AMPL or GLPKMulti-period problems

Sizing/costing data base LCIA database (ECOINVENT)

Process integration

Grid computing

Multi-objective optimisation Evolutionary - HybridProblem decompositionUncertainty

Decision support

GIS data base Industrial ecologyUrban systems

GUI : Spreadsheets, Matlab

Data Structuring

Technology models data base Energy conversionSharing knowledge




LENI Systems

Thermo-economic optimisationTrade-o�s: e⇥ciency and scale vs. investment

E⇥ciency vs. investment and optimal scale-up:

62 63 64 65 66 67 68900

1000

1100

1200

1300

1400

1500

1600

energy efficiency [%]

spec

ific

inve

stm

ent

cost

[€/k

W]

trade-off: efficiency vs.investment (& complexity)

TECHNOLOGY: drying: air, T & humidity optimisedgasification: indirectly heated dual fluid. bed (1 bar, 850°C)methanation: once through fluid. bed, T, p optimised (p = [1 15] bar)SNG-upgrade: TSA drying (act. alumina) 3-stage membrane: p, cuts optimised quality: 96% CH4, 50 barheat recovery: steam Rankine cycle T, p & utilisation levels optimised

input: 20 MW wood at 50% humidity (~4t/h dry)

0 20 40 60 80 100 120 140 160 180800

1000

1200

1400

1600

1800

2000

2200

2400

input capacity [MW]

spec

ific

inve

stm

ent

cost

[€/k

W]

scale-up objective: minimisation of production costs(incl. investment by depreciation)ε ~ 62%

ε ~ 66%

ε ~ 64%

ε ~ 68%

optimal configurations:increasing efficiency

discontinuities due tocapacity limitations of

equipment (diameter < 4 m)

1nb. ofgasifiers: 2 3 4 5 ...

61 / 87Gassner, Martin, and François Maréchal. Energy & Environmental Science 5, no. 2 (2012): 5768 – 5789.

• Thermo-economic Pareto Front

E-technology for identifying solutions




LENI Systems

Some resultsCmparing technologies and processes

Thermo-economic Pareto front(cost vs e�ciency):

LENI Systems

Quelques resultatsComparaison des technologies

Optimisation de toutes les combinaisions technologiques(cout et e�cacite):

� gaz. pressurise a chau�age direct est la meilleure option� The best solution is the pressurised directly heated gasifier

69 / 87

• Thermo-economic Pareto optimal processes

E-technology : Observing the decision space

Gassner, Martin, and François Maréchal. Energy & Environmental Science 5, no. 2 (2012): 5768 – 5789.

Note : 1.5 years of calculation time ! => parallel computing

Efficiency vs InvestmentSynthetic Natural Gas Process




• Graphical representations – Composite curves – Sankeydiagrams

• PDF reports • Sensitivity analysis • Pareto curves => data base of solutions

– Decision variables values

– Uncertainty analysis => most probable optimal solutions

E-technology for understanding the results

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0 5000 10000 15000 20000 25000 30000

T(K

)

Q(kW)


Hot composite curve

64 66 68 70 72 74 76 78 80 82 84 86 88600

700

800

900

1000

1100

1200

1300

SNG efficiency equivalent εchem [%]

Spe

cifi

c in

vest

me

nt

cost

cG

R [U

SD/k

W]

pressurisation

pressurisation, gas turbine integration,steam drying

& hot gas cleaning

hot gas cleaning & steam dryingwith heat cogeneration

453 K

513 K

5 %

35 %

1073 K

1173 K

573 K

873 K

1 bar

50 bar

573 K

673 K

573 K

673 K

0

1

40 bar

100 bar

623 K

823 K

323 K

523 K

SNG Outputminimal

maximal

min

max

min

max

min

max

Td,in Φw,out Tg Tg,p

pm Tm,in Tm,out yRankine

ps,p Ts,s Ts,u2

0 20 40 60 80 100 1200

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

SNG proccess configuration number

Pro

babi

lity

to b

e in

top

10 [−

]

Prod. costRes. profitabilityBM Break even

4.3. COMPARISON WITH CONVENTIONAL LCA 45

-500 %

-400 %

-300 %

-200 %

-100 %

0 %

100 %

200 %

scale-independent,conventional LCIA

(average lab/pilot tech.)

without cogenerationIntegrated industrial base case scenario (8 MWth)

with cogeneration

(a) Overall impacts

0

5

10

15

20

25

Remaining processes

Rape methyl ester

NOx emissions

Solid waste

Charcoal

Olivine

Infrastructure

Wood chips production

Electricity cons./prod.

Avoided NG extraction

Avoided CO2 emissions

UBP/

MJ w

ood

scale-independent,conventional LCIA

(average lab/pilot tech.)

harm

ful

bene

ficia

l

harm

ful

bene

ficia

l

harm

ful

bene

ficia

l

without cogenerationIntegrated industrial base case scenario (8 MWth)

with cogeneration

(b) Detailed contribution

Figure 4.3: Comparison of LCIA results obtained by conventional LCA and the proposedmethodology for Ecoscarcity06, single score, with detailed process contributions (Gerberet al. (2011a))

The differences between the conventional LCA and the base case scenario without and witha Rankine cycle are mainly due to the improved conversion efficiency obtained by applyingprocess design method, which has a direct influence on the quantity of SNG produced perunit of biomass. In the base case scenarios, the efficiency is considerably higher becausethe energy integration is optimized in the process design. As a direct consequence, the



Energy Systems EngineeringE-technology : Large renewable energy integration

Use B M

Use G M

Photo synthesis

S

CO2 CH4

DH

TS

EL

SOEC M

SOFC

RNE

Biomethane

Gasification

Storage

Dist

rict h

eatin

g

Co-electrolysis

Electrolysis

Renewable Electricity

Sun

Biomass

CO2

H2O2

CH4

Heat

Renewable Electricity

SunWind

Grid Electricity

Fuel appl.

O2

H2O

H2O

P MSun

Captureatm. CO2

CaptureFlue gases

B : BiomethanisationC : CO2 CaptureG : GasificationM : methanationS : CO2 separationP : Photocatalysis for CO2 reductionSOFC : Solid Oxide Fuel CellSOEC : Solid Oxid electrolysis CellET : Electro thermal storageCO2 : CO2 StorageCH4 : Methane StorageDH : District Heating

CO2

H2

O2

CH4

H2O

Elec

Ren Elec

atm. CO2

CO2

H2O

EPFL Valais-Wallis DemonstratorIntegration of processes in the conversion chain : CO2 valorisation options




•Coordinates attributed to locations – GIS : Data bases

• Buildings • Resources

– GIS :Infrastructure • Roads, district heating network,

electricity grid – GIS Tools : Routing, Layers, Representations

•Link between GIS data and Models –Link with unit models parameters –Access to GIS tools in the workflow

E-technology : Data bases and Geographical Information Systems

Locationi,(xi,yi,zi)

Location1,(x1,y1,z1)

Location2,(x2,y2,z2)

Geneva

T TTT -20

0

20

40

60

80

0 50 100 150 200 250 300 350 400

T(C

)Q(kW)

AirWaste Water

Heating Hot water

recoveryRecoverable

Heat requirement

Atagivenlocation:combinationofenergytechnologies



Energy Systems EngineeringE-technology : defining the interactions

1 Symbols

Roman lettersAn Annuities of a given investment [-]Ai,j,p Surface of the pipe of network p between nodes i and j [m2]B Arbitrarily big valueCaw Investment costs for air/water heat pump(s) [CHF]Cboiler Annual investment costs of the boiler(s) [CHF/year]Cfix Fix part of investment costs for a given device [CHF]Cgas Total gas costs [CHF]cgas Gas costs [CHF/kWh]Cgrid Total grid costs [CHF]cgrid Grid costs [CHF/kWh]C inv Investment costs of a given device [CHF]C inv

an Annual investment costs of a given device [CHF]Cpipes Total costs for the piping [CHF]Cprop Fix part of investment costs for a given device [CHF/kW]Cref Investment costs of a device chosen as reference [CHF]Cww Investment costs for water/water heat pump(s) [CHF]CO2

gas Total CO2 emissions for one year due to the combustion of gas [kg]co2

gas CO2 emissions due to the combustion of gas per kWh [kg/kWh]CO2

grid Total CO2 emissions for one year due to the consumption of electricity from the grid [kg]co2

grid CO2 emissions due to the consumption of electricity from the grid [kg/kWh]COP aw

t,k Coe⇤cient of performance for the air/water heat pump during period t at node k [-]COPww

t,k Coe⇤cient of performance for a water/water heat pump during period t at node k [-]cp Isobaric specific heat [kJ/(K· kg)]Disti,j Distance between nodes i and j [m]Ht Number of hours in period t [hour]Econs

t,k Electricity consumption during period t at node k [kW]Eexp

t,k Eletricity exported to the grid during period t by device e [kW]Egrid

t,k Electricity bought from the grid during period t [kW]Eaw

t,k Electricity consumed by the air/water heat pump during period t at node k [kW]Eww

t,k Electricity consumed by the water/water heat pump during period t at node k [kW]Eloss

t,k Electricity losses during period t [kW]Epump

t Pumping power for the network during period t [kW]Etech

t,k,e Electricity produced or consumed during period t at node k by device e [kW]Fm Maintenance factor [-]Gast Overall gas consumption during period t [kg]Lmin

e Minimum allowable part-load of device e [-]

Mbuildt,k

Mass flow of water from the network, circulating during period t through thebuilding at node k to heat up the building [kg/s]

Mpipet,i,j,p

Mass flow of water flowing during period t, in network p, from node i to nodej [kg/s]

Mmaxt,p,i,j

Maximum mass flow of water flowing in network p, from node i to node j, overall periods of time [kg/s]

M techt,k

Mass flow from the network being heated up by the device(s) at node k duringperiod t [kW]

MT buildt,k

Mass flow from the network flowing through a device at node k during periodt, to be re-heated, times its temperature [(kg/s)K]

2

MT pipet,i,j,p

Mass flow flowing during period t from node i to node j in the network p, timesits temperature [(kg/s)K]

N Expected lifetime for a given investment [year]Ploss Pressure losses in the pipes [Pa/m]Qaw

t,k Heat delivered by the air/water heat pump to the consumer at node k during period t [kW]Qboiler

t,k Heat delivered by the boiler at node k during period t [kW]Qcons

t,k Heat consumption at node k during period t [kW]Qnet

t,k Heat delivered by the network to the consumer at node k during period t [kW]Qnet ww

t,k Heat delivered by the network to the water/water heat pump at node k during period t [kW]Qtech

t,k,e Heat produced by device e located at node k during period k [kW]Qww

t,k Heat delivered by an water/water heat pump to the consumer at node k during period t [kW]r Interest rate [-]S Size of a given device [kW]Saw

k Nominal size of the air/water heat pump located at node k [kW]Sboiler

k Nominal size of the boiler at node k [kW]Snom

e Nominal power of a device [kW]Sref Size of a device chosen as reference [kW]Sww

k Nominal power of the water/water heat pump located at node k [kW]T atm Atmospheric temperature [K]T cold Temperature of the heat source for heat pumps [K]T cons

t,k Temperature at which the heat is required by the consumer at node k during period t [K]T hot Temperature of the heat sink for heat pumps [K]T net Design temperature of the network [K]v Velocity of the water through the pipes [m/s]X = 1 if a device exists, 0 otherwiseXaw

k = 1 if there is an air/water heat pump at node k, 0 otherwiseXboiler

k = 1 if there is a boiler at node k, 0 otherwiseXgas

e = 1 if device e needs gas to operate, 0 otherwiseXnode

k = 1 if a device e is implemented at node k, 0 otherwiseXtech

k,e = 1 if a device can be implemented at node k, 0 otherwiseXww

k = 1 if there is an water/water heat pump at node k, 0 otherwiseY i,j,p = 1 if a connection exists between i and j for network p , 0 otherwise

Greek letters�Theat Pinch at the heat-exchangers [K]�Tnet ww Temperature di⇥erence of the water in the network when it serves as heat source for water/water heat pump(s) [K]�boiler Thermal e⌅ciency of the boiler(s) [-]�ele Electric e⌅ciency of device e [-]�grid E⌅ciency of the grid [-]�the Thermal e⌅ciency of device e [-]⇥ Exergetic e⌅ciency [-]⇤ Density [kg/m3]

Indicesk nodesi, j connection from node i to node jt timee technologies

3

Networks superstructure

1 Symbols

Roman lettersAn Annuities for a given investment [-]Ai,j,p Area of the pipe between nodes i and j of network p [m2]B Arbitrarily big valueCaw Investment costs for air/water heat pump(s) [CHF]Cboiler Annual investment costs for the boiler(s) [CHF/year]Cfix Fix part of the investment costs for a given device [CHF]Cgas Total annual natural gas costs [CHF/year]cgas Natural gas costs [CHF/kWh]Cgrid Total annual grid costs [CHF/year]cgrid Grid costs [CHF/kWh]C inv Investment costs of a given device [CHF]C inv

an Annual investment costs of a given device [CHF/year]Cpipes Total annual costs for the piping [CHF/year]Cprop Fix part of the investment costs for a given device [CHF/kW]Cref Investment costs of a device chosen as reference [CHF]Cww Investment costs for water/water heat pump(s) [CHF]CO2

gas Total annual CO2 emissions due to the combustion of natural gas [kg/year]co2

gas CO2 emissions due to the combustion of natural gas [kg/kWh]CO2

grid Total annual CO2 emissions due to the consumption of electricity from the grid [kg/year]co2

grid CO2 emissions due to the consumption of electricity from the grid [kg/kWh]COPhp Coe⇤cient of performance of the central heat pumpCOP aw




t,e Eletricity exported to the grid during period t by device e [kW]Egrid

t,k Electricity bought from the grid during period t by node k [kW]Eaw



t Electricity losses during period t [kW]Epump


t,k,e Electricity produced or consumed during period t at node k by device e [kW]Fs Scaling factor [-]Fm Maintenance factor [-]Gast Natural gas consumption during period t [kg]Lmin

e Minimum allowable part-load of device e [-]Mbuild

t,k Water circulating during period t through the building at node k, to heat it up [kg/s]Mpipe

t,i,j,p Water flowing during period t, from node i to node j, in network p [kg/s]Mmax

i,j,p Maximum flow of water between nodes i and j, in network p, over all periods [kg/s]M tech

t,k Water being heated up by the device(s) during period t, at node k [kg/s]MT tech

t,k Water flowing through a device during period t at node k to be re-heated, times its temperature [(kg/s)K]

2

Q1..T

E1..T

Q1..T

E1..T

Q1..T

E1..T

Q1..T

E1..T

Q1..T

E1..TQ1..T

E1..T

Q1..T

E1..T

Q1..T

E1..T

Cinv =ne�

e=1

nk�

k=1

ae + ae � Se,k

Investment

Cgas =ne�

e=1

nk�

k=1

np�

t=1

HtQe,k,t

�the

1 Symbols























2

1 Symbols























2

Maintenance

Gas Grid

Electricity

Electricity

Emissions

1 Symbols























2

1 Symbols























2

Industry

Powerplant

Wastetreat.

Building plant.

. [5] Francois Marechal, Celine Weber, and Daniel Favrat. Multi-Objective Design and Optimisation of Urban Energy Systems, pages 39–81. Number ISBN: 978-3-527-31694-6. Wiley, 2008.

Water

Fuel

Water

FuelIndustrial plant.




• Mid-season typical day demand

E-technology : Integrating renewable sources

Time

Exe

rgy

Pow

er re

quire

men

t [kW

]

Heat

Electricity

PV production




Fuel-Cell GT

Domestic Hot water tanks

Heat pump/HVACElectrical gridNatural gas grid

Buildings Small industries

E-technology : Smart households

Smart Optimal Predictive Control Management BoxPrice

T ambient

Comfort T/Air

People Light

T storage

Batteries

PV

Solar panels

Heating/Cooling tanks

Reserve

Cons. profiles

Big Data grid

Water grid

Meteo

Smart info (WIFI-GPS-GSM)

T storageMass

Waste Water Grid

Cogeneration

CO2 grid



Energy Systems EngineeringMILP process integration model @location k

Water: mii, Tin, hin

return : moo, Tout, hout

Water: mio, Tin, hin

return : moi, Tin, hin

Technologies w, @location k period s and time t(s)Demand @location k, period s and time t(s)

Storage tank

2

Tst,2

HEX h1(t)

heat demand

Storage tank

1

Tst,1

...

Tst,...

Storage tank

l

Tst,l

...

Tst,...

Storage tank

nl

Tst,nl

HEX h2(t) HEX h

..(t) HEX h

l(t) HEX h

nl-1(t)

heat demand heat demand heat demand heat demand

heat excess

HEX c1(t)

heat excess

HEX c2(t)

heat excess

HEX c..(t)

heat excess

HEX cl(t)

heat excess

HEX cnl-1

(t)

Units for heat integration

HL HL HL HL HL HL

HEX: Heat exchangers

HL: Heat losses

Storage system

S. Fazlollahi, G. Becker and F. Maréchal. Multi-Objectives, Mul.-Period Optimization of district energy systems: II-Daily thermal storage, in Computers & Chemical Engineering, 2013a

Buildings inertia

Heat exchange by heat cascade model

Electricity balance

Existence : w in location k




• Energy system = for each VPP (n) – Investments (expected life time : 20 Years)

• Energy Conversion Units • Storage

– Operating costs (expected operation time = 25x8760h) • Management strategy

– Constraints • Energy services • Grid capacities • Grid constraints

Energy system design problem

8u 2 Units; 8n 2 Nodes ) Sizeu,n

8s 2 Storage; 8n 2 Nodes ) Vs,n

8s 2 Storage; 8n 2 Nodes; 8t 2 time ) mu,n(t), Vs,n(t), E(t)

Z Time

t=1(unitsX

u=1

(c+r (t)mr(u),n(t)) + c+e (t)E+(t))dt

8t 2 T ime :NodesX

n=1

E(t) Emax

(t)

8n 2 Nodes; 8t 2 T ime; 8c 2 Cons. ) Qn,c(t), En,c(t)

NodesX

n=1

UnitsX

u=1

1

⌧y,i

(I(Sizeu,n

) + I(Vs,n

))

8q 2 quarter(T ime) :NodesX

n=1

Zq+15

t=q

��En

(t)� En

(t� 1)�� dt �E

max

(t)



Energy Systems EngineeringE-technology : decision support tool

Predefined data

Project data base

OSMOSE LUA

Saved user dataEnergy technologies

Problems

ModulesDefault values

ThermodynamicSizing

Costing Life Cycle Inventory

EcoInvent

Work flow

ModelsSolving methods

in locations

GIS data base

GUI

Results data base

Layer Ontology

Superstructure

Yi,u,l(p(t)) = Fu(Xi,u,l(p(t)),⇧i,u,l(p(t)))




Design and Control of thermo-electric energy systems

Single family house : 4 pers 160 m2 : with heat pump

E-technology : Optimal design of solar systems

19.11.2015

Fig 4. Pareto front for HP system configuration

III

III

Long term storage : 85%


SF =kWPV

kWbuilding

Self sufficiency (SF)PV production/needs

Self consumption (SC)PV production used on site

SC

SF

SF and SC as a function of the PV areaOff-site storage

Extra Electricity from PVUsed by the building later

SC =kWused

kWPV





Single family house : 4 pers 160 m2 : with heat pump

Self sufficiency ?

19.11.2015

Fig 4. Pareto front for HP system configuration

III

III

Cas

e I

Energy system Off-site storage

PV array 88 m2 Battery 4.95 kWh HW tank 2.43 m3

Heat Pump 3.59 kW

Redox Battery 8.14 MWh 406.9 m3 (20 Wh/l) 4’070’000 € (500 €/kWh)

Cas

e III


PV array 156.9 m2 Battery 8.63 kWh HW tank 2.39 m3

Heat Pump 3.7 kW

Redox Battery 17.1 MWh 854.6 m3

8’550’000 €

Cas

e II


PV array 109.7 m2 Battery 7 kWh HW tank 2.46 m3

Heat Pump 3.5 kW

Redox Battery 10.8 MWh 540.2 m3

5’400’000 €

Annual energy balance







Comparison – typical operating periods (winter, summer, mid-season)

E-technology : model predictive control

19.11.2015

IIIII

IV

Shaded self-consumption Blue Import [kW] Green Export [kW] Red Generation [kW]

Winter Summer Mid-season

Case III

Case II

Case IV





• Self Sufficiency : + 40 % • Self Consumption : + 68 %

E-technology : Model predictive control

SF + 41 % SC + 68 %

NetZero without MPC




E-technology : technology coordination & market interaction

High electrcity cost during the afternoon Storage tank = 200 m3

Low cost cost during the afternoon Storage tank = 200 m3

Heating : 72315 kWh Electricity : 77897 kWhe

Electricity in : 99596 kWhe Electricity out : 8710 kWhe

Low price period Electricity in : 19345 kWhe

Electricity out : 5650 kWhe Electricity bought : 62894 kWhe

Low price period Electricity out : 4407 kWhe Electricity in : 1269 kWhe Balance : -3138 kWhe

Storage : 22480 kWhe

Engine : 2000 kWe Heat pump : 2000 kWe Storage 200 m3

Demand mean heating power = 3000 kW

Storage filling at night

Empty storage tanks before cheap elec price

Fill storage tanks during cheap elec price




E-technology for operation




• From design to operation

E-technology : Optimal management box

Optimization

ProblemResolution

MANAGEMENT

UNIT

MILP PROBLEM DESCRIPTION (AMPL )

Optimization Problem

data set -up

Predicteddata

Previous states

.mods .dats

u sh*u vlv*ub*ucg*

System Systemparameter

values

System Model

Prediction

Actual

State data

OPTIMIZATION (CPLEX )

States database(Current state & previous states )

(House structure - Matlab )

Data acquisitionand database

Tariff info

Tdhw

TshText

T in

Equations & constraints

Collazos et al., Computers and Chemical Eng. 2009

Grid connexions

Sensors

Operation set-points




• Predictive Control Algorithm : Moving horizon – hour 1 : set-point control + 24 h Cyclic : strategy

Predictive Controller




• Electricity cost is changing

Response to the market price variation

Same heat EEX market price 40% heating cost reduction




• Predictions – Ambient conditions – Presence + comfort – Prices – Energy services

• Multi-nodes – Interactions via sub grids

• Electrical (different type) • Heat/Cold • Information

• Integration with main grids – Electrical grid – Gas grid

Challenges for smart grids systems

Learning algorithm

Images, Courtesy:EngineerLive and Consumer Energy Report

Producers

Consumers




Fuel-Cell GT

Domestic Hot water tanks

Heat pump/HVACElectrical gridNatural gas grid

Buildings Small industries

E-technology : smart households operation

Smart Optimal Predictive Control Management BoxPrice

T ambient

Comfort T/Air

People Light

T storage

Batteries

PV

Solar panels

Heating/Cooling tanks

Reserve

Cons. profiles

Big Data grid

Water grid

Meteo

Smart info (WIFI-GPS-GSM)

T storageMass

Waste Water Grid

Cogeneration

CO2 grid



Energy Systems EngineeringE-technology : Optimal process control

2nd European Conference on Polygeneration - 30th March-1st April, 2011 - Tarragona, Spain

4 Perspectives of the integration of the trigeneration system

The approach presented above is based on the time averaging approach that allows to considerthat all the streams are simultaneous. Considering the batch operation dimension requires theadaptation of the approach to integrate in the analysis the calculation of the storage tanksthat are required to make the heat recovery feasible. When studying the trigeneration systemintegration, it will be necessary to size the tanks not only to allow the heat recovery but alsoto take opportunities from the electricity market. The trigeneration system is indeed a wayof storing electricity from the grid in the form of heat or cold. The heat or cold storage alsoallows the cogeneration unit to play the role of the peak shaving.

The final configuration is presented on figure 8. The optimization method based on amulti-objective optimization strategy presented by Weber et al. ([17]) allows to design thetrigeneration system and the storage tanks considering the use of a predictive optimal manage-ment strategy. In addition, methods like the one proposed by Collazos et al. ([11]) can be usedto implement the predictive optimal management strategy in a control system.

3

6

Process hot water

CIP system

CoolingGlycol

110 °C

50 °C

2 °C

EngineGas

Industrial process

Refrigeration/Heat Pump

Electricity

Figure 8: Storage tank system configuration

5 Conclusion

The optimal integration of trigeneration systems is realized in several steps. The first stepis the definition of the requirement followed by the definition of the heat recovery potentialbetween the hot and the cold streams of the process. This step is mandatory since it allows todefine the heating and cooling requirement to be satisfied by the trigeneration system. Otherapproaches based on the use of the present utility system would lead to bigger systems andunnecessary investment that would in addition prevent the future energy savings options. Thetrigeneration system is sized by first identifying the possible trigeneration options based on theanalysis of the Grand composite curve of the system. The configuration of the system is thendefined by applying an optimization model that calculates the best flows in the system. It hasbeen demonstrated that the proper analysis of the trigeneration system requires to accountfor the possible integration, not only at the level of the process, but also at the level of thepossible integration inside the trigeneration system. The example presented shows that thecombination of a refrigeration cycle where the condensation heat is used as a heat pump topreheat the process streams with a mechanical vapor recompression system that allows for

9

Smart Optimal Predictive Control Management Box



Energy Systems EngineeringE-technology : car power train management

6 THERMAL ELECTRIC HYBRID POWERTRAIN

Table 6.1 Continued: Peugeot 508 SW model characteristics.

FuelType DieselDensity [kg/l] 0.84Lower heating value [MJ/kg] 42.5

6.2 Driving cycle

The New European Driving Cycle is the current test cycle in Europe. All European lightweightvehicles, both conventional and hybrid, are assessed on this cycle in terms of fuel consumptionand emissions. The cycle, illustrated in Figure 6.2, is composed of four repetitions of an urbancyle (UDC) and one extra-urban cycle (EUDC). Basic characteristics of the cycle are given inTable 6.2.

0 200 400 600 800 1000 12000

20

40

60

80

100

120

140

Time [s]

Vehi

cle

spee

d [k

m/h

]

Figure 6.2 New European Driving Cycle.

Table 6.2 NEDC characteristics.

Distance [m] Duration [s] Average speed [km/h] Repeats [-]

UDC 1’017 195 18.54 4EUDC 6’956 400 62.22 1

Total cycle 11’023 1’180 32.26 -

6.3 Simulation results

The engine power and fuel consumption profiles are given in Figure 6.3. During decelerationphases fuel injection is cut o↵, as is the case for modern engines. Figure 6.4a gives the fuelconsumption points relative to the engine consumption point. The NEDC does clearly not posemuch di�culty for the 2.2 liter engine in terms of load as the consumption points are scatteredin the lower left quadrant. However this zone is not near the region of higher e�ciency.

Table 6.3 compares the simulated emissions result with that of the real vehicle. With anerror of less than 2%, the thermal powertrain model is deemed su�ciently precise.

52Collaboration with PSA

Driving cycle

5 PNEUMATIC HYBRID

Driving cycle

t

V

h

CVT control

Strategy estimator

mode = f(P, T, T 0s,!s)

Vehicle estimator

mV =P

F

Optimal ratio� 2 (�min, �max)

min(mfuel)||min(mair)||max(mair)

V V

mode

!w

Tw

Vehicle

mV =P

F

Vdelay Vdelay

CVT!s

!w

= �

Ps = f(Pw, ⌘)

!w Tw

�

Energy converter

Alternator

T 0s = Ts + TAT !s > 0

Strategy

mode = f(P, T, T 0s,!s)

Combustion

mfuel = f(!s, T 0s)

Pneumatic motormair = f(P, T, T 0

s,!s)

hair = f(P, T, T 0s,!s)

Pneumatic pumpmair = f(P, T 0

s,!s)

hair = f(P, T 0s,!s)

Mode filter

!s Ts

!s T 0s

!s T 0s

!s T 0s

!s T 0s

hair,pm mair,pm

hair,pump

mair,pump

mfuel

mode

Fuel tank

fEmissions = f(mfuel) [g CO2

/km]

mfuel

Air tankEnergy balance

Heat transfer model

mair hair

Ptank

Ttank

Ptank

Ttank

Figure 5.9 Pneumatic hybrid powertrain model flowchart.

42

4 THERMAL ELECTRIC HYBRID

DT

C1

G

PSD

C2ICE

FT

M⇠PE

BT

SC

Figure 4.3 Parallel thermal electric hybrid powertrain: BT : Battery pack; C1: Primary clutch; C2:Secondary clutch; D: Di↵erential; FT : Fuel tank; G: Alternator; ICE: Internal combustion engine;M : Electric motor; PE: Power electronics; PSD: Power split device; SC: Supercapacitor pack; T :Transmission.

DT

C1

G

PSD

C2ICE

FT

M⇠PE

BT

SC

(a) Thermal traction

DT

C1

G

PSD

C2ICE

FT

M⇠PE

BT

SC

(b) Electric traction

DT

C1

G

PSD

C2ICE

FT

M⇠PE

BT

SC

(c) Hybrid traction

DT

C1

G

PSD

C2ICE

FT

M⇠PE

BT

SC

(d) Regenerative braking

Figure 4.4 Parallel hybrid powertrain operating modes.

27

• ICE -> Drive • Fuel cell • Batteries • El Motor • Pneumatic storage • Transmission line • HVAC • Energy conversion => Size & Weight & Cost

Operation strategy

13 UTILITY INTEGRATION

−25 −20 −15 −10 −5 0 5250

300

350

400

450

500

550

600

Heat Load [kW]

Tem

pera

ture

[K]

Othersutilities

(a) Operating point: 2000 RPM and 2.2 bars.

−25 −20 −15 −10 −5 0 5−0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Heat Load [kW]

Car

not F

acto

r 1−T

a/T[−]

Othersutilities

(b) Operating point: 2000 RPM and 2.2 bars.

Figure 13.2 Organic Rankine cycle integration. Text = 0 �C

where the E+

E are the exergetic services of the engine hot streams. They can be computed fora given hot source i using the fundamental equation:

E+

i =

Z ✓1� Ta

Ti

◆�Q+

i

where Ta is the ambient temperature and Ti the temperature of the hot source. Since the specificheat of all hot streams is assumed to be constant, the integral can be removed by approximatingTi by the logarithmic temperature di↵erence:

TLTD =Tin � Tout

lnTin

Tout

The energetic and exergetic e�ciency values of the maps, with values between 9 and 12 % and30 and 40 % respectively are similar to results found in [10].

13.2 Refrigeration cycle

For the case where Text = 30 �C, a single refrigeration cycle is added as a cold utility to coolthe cabin to below ambient temperature. The cycle is composed of an evaporator, compressor,condenser and an expansion valve (Figure 13.5a) and uses the following processes:

• 1 ! 2: Evaporation

• 2 ! 3: Compression

• 3 ! 3”: Isobaric cooling

• 3” ! 4: Condensation

• 4 ! 1: Expansion

The following assumptions are made:

• The working fluid is ammonia, same as the ORC.

• Compressor isentropic e�ciency is 80 %.

101

Waste Heat Recovery * ORC integration

Component set

8.4 - Multi objective optimization results for hybrid electric vehicles with different usages

179

The life cycle inventory is done for each computation iteration of the decision variables and the environmental impacts are calculated according to the definition of the impacts in section 3.9.

8.4 Multi objective optimization results for hybrid electric vehicles with different usages

8.4.1 New European Driving Cycle (NEDC)

The solutions of a two objectives optimization converged on a Pareto Frontier optimal curve (Figure 8-6), representing the trade-off between the energy consumption and the cost of the vehicles on normalized driving cycle.

Figure 8-6: Pareto curve energy consumption to cost (color bar in thousands of Euros) –NEDC

The vehicle body mass of around 1500 kg is characterizing the D–class vehicles. These vehicles are usually used for family transportation or business trips on long distances. The D-class vehicles are well equipped and the technology is considered as additional value for the customer. The powertrain technology for low CO2 emissions is an important requirement for the customers of these vehicles. The official technical characteristics are referenced on the NEDC by the car makers. The Pareto curve for the NEDC (Figure 8-6) defines a wide range of points for improved powertrain efficiencies (25- 45.2%) and fuel emissions (30 to 140 g CO2/km). In contrast, a thermal powertrain D-class vehicle of 1660 kg of mass, with 2.2 diesel liters engine yields 17 % of powertrain efficiency and 151 g CO2 / km [147]. To archive such high powertrain efficiency in the Pareto curves for all usages, the electric half of the powertrain is increased and more electric energy stored in the battery is needed. Indead the battery capacity increases from 5 to 50 kWh (Figure 8-7 d). This considerably increases the vehicle mass (Figure 8-7 a). To follow the dynamic requirement on the cycle, the electric motor power also increases from 20 to 150 kW. The emitted tank-to-wheel CO2 emissions, which are proportional to the diesel consumption, are related to the hybridization ratio expressed through the electric motor and the thermal motor size.

HEV P-HEV REX

7 PNEUMATIC HYBRID POWERTRAIN

7 Pneumatic hybrid powertrain

This chapter presents the validation of the thermal powertrain underlying the pneumatic hybrid.The test is conducted for a Peugeot 308 (Figure 7.1) with a 1.2 liter gasoline engine on the NewEuropean Driving Cycle.

Figure 7.1 Peugeot 308.

7.1 Powertrain characteristics

The main powertrain model parameters are given in Table 7.1.

Table 7.1 Peugeot 308 model characteristics.

VehicleNominal mass [kg] 1075Wheel diameter [m] 0.406Rolling friction coe�cient cr [-] 0.015Bearing friction coe�cient µ [-] 0.002Aerodynamic coe�cient Scx = Ca ·A [m2] 0.65

Gearbox

Number of gears [-] 5E�ciency [-] 0.94Final drive1 [-] 4.05Ratio 1 [-] 3.02Ratio 2 [-] 1.6Ratio 3 [-] 1.13Ratio 4 [-] 0.86Ratio 5 [-] 0.68

Engine

Displacement [l] 1.2Number of cylinders [-] 3Rated power [kW] 60Maximum speed [RPM] 6000Maximum torque [Nm] 120Idle speed [RPM] 950Idle fuel consumption [l/h] 0.33Deceleration Fuel Cut-O↵ yes

FuelType GasolineDensity [kg/l] 0.745Lower heating value [MJ/kg] 42.7

55

Costs/emissions/efficiency

REX

Elv

Hyb.




E-technology for education



Energy Systems EngineeringE-technology for education : www.energyscope.ch

http://energyscope.ch



Energy Systems EngineeringE-technologies : Enabling the energy transition

Smart metering • Electricity consumptions • Heating/cooling • Renewable energy

Smart command • WIFI set points • Tracking • Distance management

Smart Control • Adaptative/predictive • Optimal • Fitted • Renewable energy integration • Demand side Management

Price signals

Predictions

Power/Energy reserveSmar

t opt

imal

con

trol a

nd m

anag

emen

t box

Equipment • Heat pump • Cogeneration • PV • Batteries • Storage tanks • Thermal solar • HVAC • Refrigeration • Biofuels • Distribution • …

End-Users • Power plants • Industry • Buildings • District systems • Storage

Building stocks/micro grids

Retrofit

Refurbishment

Sizing

Energy conversion

Services

Buildings Building complexes Small and medium industries

Districts

Renewable energy

Smart engineers Smart Systems

Efficiency

System design




Thank you for your attention

Prof François Marechal http://ipese.epfl.ch EPFL Valais-Wallis

http://ipese.epfl.ch