attività di ricerca del gruppo gecos sui sistemi ... · 1 kwelchp unitbasedon stirling cycle 10-20...
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1
Dr. Ing. Emanuele MartelliPolitecnico di Milano, Dipartimento di Energia
Attività di ricerca del gruppo GECOS sui sistemi
trigenerativi avanzati, i multi-energy systems e
l'efficienza energetica dei processi industriali
February 7th 2018 , Ferrara
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22
1. Chemical Technologies and Processes and NanoTechnologies Division2. Electrical Division3. Nuclear Engineering Division4. Thermal Engineering & Environmental Technologies Division5. Fluid Dynamic Machines, Propulsion & Energy Systems Division
• Fluid-dynamics of turbomachines• Internal combustion engines• Propulsion and combustion• Group of Energy COnversion Systems (GECOS):
www.gecos.polimi.it
The Department of Energy at Politecnico joins researchers originally belonging to 5 divisions.It has 130 permanent researchers and professors
THE DEPARTMENT OF ENERGY @ POLITECNICO,
DIVISIONS & OUR GROUP
1 Emeritus professor (prof. Macchi)3 Full professors (Lozza, Consonni, Chiesa)9 Associate/Assistant professors6 RTDA4 Post docs5 Temporary researchers10 PhD students
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33MAIN RESEARCH AREAS
1. CARBON CAPTURE TECHNOLOGIES
2. RENEWABLE ENERGY SOURCES AND WASTE-TO-
ENERGY
3. ENERGY STORAGE, HYDROGEN AND SYNTHETIC
FUELS
4. MICRO-GRIDS AND MULTI-ENERGY SYSTEMS
5. ENERGY EFFICIENCY AND SYSTEMS OPTIMISATION
6. ORC, S-CO2 AND ADVANCED POWER CYCLES
7. FUEL CELLS
>10 ongoing EU projects (FP7-H2020)
Tens of ongoing research contracts with industries
Cequiv balances to atmosphere for F-T liquids OUT: photosynthesis (MPGs, soil&root C), electricity credit (2,852 tC/day)
IN: upstream emissions, vented at plant, fuels burned in vehicle,s (2,852 tC/day)
accumulation in
soil and root
1,022 tC/day
polygeneration
plant
carbon storage
4,337 tC/day
fuel fo
r
tran
sp
ort
ati
on
1,8
10 t
C/d
ay
electricity
production
452 MWee
1,6
07 t
C/d
ay
pra
irie
gra
sses u
pstr
eam
em
issio
ns
83 t
C/d
ay
co
al u
pstr
eam
em
issio
ns
225 t
C/d
ay
char
53 tC/day
coal
5,328 tC/day
2,449 MWLHV
prairie grasses
1,607 tC/day
668 MWLHV
carb
on
ven
ted
735 t
C/d
ay
arrows’ width proportional to C fluxes
COAL + MPGs TO F-T LIQUIDS + ELECTRICITY, WITH CCS
1,0
32 M
WL
HV
ph
oto
syn
thesis
cre
dit
fo
r e.e
.
223 t
C/d
ay
Cequiv balances to atmosphere for F-T liquids OUT: photosynthesis (MPGs, soil&root C), electricity credit (2,852 tC/day)
IN: upstream emissions, vented at plant, fuels burned in vehicle,s (2,852 tC/day)
accumulation in
soil and root
1,022 tC/day
polygeneration
plant
carbon storage
4,337 tC/day
fuel fo
r
tran
sp
ort
ati
on
1,8
10 t
C/d
ay
electricity
production
452 MWee
1,6
07 t
C/d
ay
pra
irie
gra
sses u
pstr
eam
em
issio
ns
83 t
C/d
ay
co
al u
pstr
eam
em
issio
ns
225 t
C/d
ay
char
53 tC/day
coal
5,328 tC/day
2,449 MWLHV
prairie grasses
1,607 tC/day
668 MWLHV
carb
on
ven
ted
735 t
C/d
ay
arrows’ width proportional to C fluxes
COAL + MPGs TO F-T LIQUIDS + ELECTRICITY, WITH CCS
1,0
32 M
WL
HV
ph
oto
syn
thesis
cre
dit
fo
r e.e
.
223 t
C/d
ay
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4
4OPTIMIZATION OF MULTI ENERGY SYSTEMS (MES) AND CHP
Types of optimization problems associated to MES (for microgrids and DHC
networks):
1. Design/retrofit of the system («investment planning»)
2. Long-term operation planning accounting for yearly constraints
(incentives/seasonal storage)
3. Short-term scheduling (unit commitment)
4. Optimal control (dynamic models of units and networks)
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5
5OPTIMIZATION OF MES AND CHP: ONGOING COLLABORATIONS
ETH, Zurich
GECOS
group
EPFL, Lausanne
Skoltech, Moscow
Univ. of Malaga
MIT, Boston
Univ. of Parma
Univ. of Bologna
DEIB, Polimi
LEAP
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66
Objective: minimize the Daily/Weekly Operating Cost
𝑡=1
24·7
CFuel,tot,t +
𝑡=1
24·7
𝐶𝑂&𝑀,𝑡𝑜𝑡,𝑡 +
𝑡=1
24·7
C𝑠𝑡𝑎𝑟𝑡−𝑢𝑝,𝑡𝑜𝑡,𝑡 ∓
𝑡=1
24·7
𝐸𝑙𝑡𝑜𝑡,𝑡
Given:
➜ Forecast of Electricity demand profile➜ Forecast of heating and cooling demand profile➜ Forecast of production from renewables➜ Forecast of time-dependent price of electricity (sold and purchased)➜ Performance maps of the installed units➜ Operational limitations (start-up rate, ramp-up, etc) of units➜ Efficiency and Maximum capacity of storage systems
Indep. variables: on/off of units, load of units, storage level in each time period t
SHORT-TERM SCHEDULING PROBLEM
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77SHORT-TERM SCHEDULING PROBLEM
Constraints:
➜ Electric energy balance constraint Ɐ t (linear)➜ Heating energy balance constraint Ɐ t (linear)➜ Cooling energy balance constraint Ɐ t (linear)➜ Start-up constraints Ɐ t, Ɐ unit (linear)➜ Ramp-up constraints Ɐ t, Ɐ unit (linear)➜ Performance maps of units Ɐ t, Ɐ unit i (nonconvex)
Nonconvex MINLP
Available MINLP optimizers cannotfind the optimal solution
Amaldi et al., 2017. Short-term planning of cogeneration energy systems via MINLP, in SIAM book: “Advances and Trends in Optimization with Engineering Applications”.
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88MILP WITH PWL APPROXIMATION OF MAPS
Basic idea: conversion into MILP via linearization of the performance maps
Advantages of MILP formulations:- Guarantee on the global optimality of the solution- Super-efficient commercial MILP solvers (e.g., CPLEX, Gurobi)
Use
full
effe
ct
Bischi et al. 2014. Energy, Vol. 74
1-D PWL approximation
2-D PWL approximation with the «triangular method»
D’Ambrosio et al. 2010. Op. Res. Letters
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99
Computational time:
1 day operation: < 1 sec
1 week operation: < 2 min
Up to 18% primary energy saving comparedto usual operation strategies!
MILP PWL FOR DAILY AND WEEKLY PROBLEMS
-1000
0
1000
2000
3000
4000
5000
6000
7000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hig
h T
em
pe
ratu
re H
eat
[kW
h]
Hour
Cogenerated ICE Cogenerated GT GT Post Firing
Auxiliary Boiler Downgraded Demand
-4000
-3000
-2000
-1000
0
1000
2000
3000
4000
5000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Ele
ctri
c E
ne
rgy
[kW
h]
Hour
Generated ICE Generated GT Consumed HP
Purchased Sold Demand
qhigh,GT,PF,Not,cog
qlow,los
fICEqhigh,ICE
fAB, LT
fAB,HT
qlow,AB
llow,stor
fGT
qlow,ICE
qdeg
qhigh,AB
Heat Pump,
compression
qlow,HP,comp
E
l
e
c
t
r
i
c
G
r
i
d
elHP,comp
elpur
qhigh,cust
qlow,cust
elsold
elGT
fGT,PF,Not,cog
qlow,GT
qhigh,GT
U
S
e
r
ICE
H
e
a
t
S
t
o
r.
GT+PF
Aux. Boiler, LT
elcust
elICE
Aux. Boiler, HT
High temperature heat
ElectricityBischi et al. 2014. Energy, Vol. 74
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1010OPTIMAL OPERATION WITH COGENERATION INCENTIVES
𝐹𝑢𝑒𝑙_𝑟𝑒𝑓 − 𝐹𝑢𝑒𝑙_𝐶𝐻𝑃
𝐹𝑢𝑒𝑙_𝑟𝑒𝑓≥ 10%
𝑄𝑡ℎ+𝑃𝑒𝑙
𝐹𝑢𝑒𝑙≥ 75%
First Principle Efficiency Primary Energy Saving (PES)
Easily rearranged as linear constraints but yearly-basis!
1. COGENERATION INCENTIVES: if CHP units allow to save fuel comparedto “business as usual systems”, subsides are granted (additional revenue)
Conditions to be met for ICE CHP:
2. SEASONAL STORAGE SYSTEMS: Underground systems (water tanks,aquifers, etc), sorption systems (e.g., sodium sulfide), thermochemicalstorage systems and H2 storage.
Challenge: operation must be optimized considering the whole year!
IDEA: Rolling-horizon algorithm (Bischi et al. 2018. Energy, in press)
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1111ROLLING HORIZON ALGORITHM FOR OPTIMAL OPERATION WITH INCENTIVES
Test case: CHP system of a large hospitalComputational time: 21 hours (worst case) applicable for day-ahead
scheduling
About 7% higher revenue (yearly basis) compared to the weekly optimal operation.
fAB, LT 1
fICE 1
fAB, HT 2
elICE 2
Aux. Boiler LT 1
qlow,los
fAB, LT 2
fAB, HT 1
qlow,AB 1
llow,stor
fICE 2
qdeg
qhigh,AB 1
E
l
e
c
t
r
i
c
G
r
i
d
elpur
qhigh,cust
qlow,cust
elsold
elICE 1
qlow,ICE 2,
qhigh,ICE 2
U
S
e
r
H
e
a
t
S
t
o
r.
ICE 2
Aux. Boiler LT 2
elcust
Aux. Boiler HT 1
Aux. Boiler HT 2
ICE 1
qlow,ICE 1
qhigh,AB 2
qhigh,diss,AB 1
qhigh,diss,AB 2
qhigh,diss,ICE 1
qhigh,diss,ICE 2
qdiss,low,ICE 2
qhigh,ICE 1
qdiss,low,ICE 1
qdiss,low, AB 2qlow,AB 2
qdiss,low, AB 1
Bischi et al. 2018. Energy (in press)
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1212
Given:
➜ Forecast of power production from renewables and its uncertainty➜ Forecast of energy demand profiles and its uncertainty➜ Forecast of electricity prices and its uncertainty➜ Performance maps of the installed units➜ Operational limitations (start-up rate, ramp-up, etc) of units➜ Efficiency and Maximum capacity of storage systems
Determine:
Nominal set-points: on/off of units, nominal load of units, storage man.Correction rules: how to adjust units loads during operation
OPTIMAL OPERATION OF MES AND CHP SYSTEMS UNDER
FORECAST UNCERTAINTY
Constraints: meet energy demands, technical limitations of units, performance maps, etc
EFFICITY PROJECT (LEAP-Polimi, CIDEA, CIRI-EA, CERR) co-funded by Emilia Romagna Region (POR-FESR 2014-2020)
Minimizing the daily operating cost in the most probable scenarios
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1313
OPTIMAL OPERATION OF MES UNDER FORECAST UNCERTAINTY:
ROBUST MILP
Time [h]
Pel [kW]
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1414
Given: A catalogue of possible units (e.g., CHP ICEs, HP GTs, boilers, heat pumps,
etc) and the list of available sizes (discrete or continous) A catalogue of heat storage systems Forecast of future energy demand profiles (whole year) of each building/site Forecast of future energy prices and their time profiles of each building/site
Determine: Which units and heat storage system to install in each district/building The sizes of the units and storage systems The required energy connections between sites/buildings
Considering: Nonlinear size effects on investment costs and efficiency On/off and part-load operation of the units
Objectives: Maximize the NPV/Minimize the energy consumption/CO2 emissions
OPTIMAL DESIGN OF MES AND CHP SYSTEMS
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1515DESIGN OPTIMIZATION APPROACHES
Linearization without decomposition(Gabrielli et al. 2018, Applied Energy, in press)(Zatti et al, 2017, Comp. Aided Chem. Eng., 40)
Units’ selection, sizes and operation optimized in a single large scale MILP
Advantages:• Linear problem (computationally efficient)• Global optimality guarantee• Uncertainty can be rigorously handled
Disadvantages: Scale effects on investment costs must be
linearized Size effects on efficiency must be
approximated
Design-scheduling decompositiom(Elsido et al., 2017. Energy Vol. 121)
Upper level (evolutionary alg.): optimizes design variables (selection/sizes)Lower level: MILP scheduling problem
Avantages:
• Size effects accounted for on both performance and costs
• Possibility of considering many operating periods solved in sequence
Disadvantages:
Slow convergence rate of evolutionaryalgorithms
No optimality guarantee
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1616OPTIMAL DESIGN OF MES: DESIGN OF DH NETWORKS FOR A CITY
Deterministic MILP solutionCAPEX+OPEX= 21,92 M€
site 2
Q12,max = 0,9 MW Q23,max = 0,9 kW
Q13,max = 4,1 MW
HS
1,4 MW
11MW
32,6MWh
site 1
ES
1,4 MW
78,5MWhHS
site 3
HS
1,4 MW
13,7MWh
1,4 MW
site 2
Q12,max = 1,8 MW Q23,max = 1,3 kW
Q13,max = 2,5 MW
HS
1,4 MW
6,7MW
41,5MWh
site 1
ES
1,4 MW
78,5MWhHS
site 3
HS
4,5MW
6,2MWh
4,5MW5MW
Stochastic MILP solutionCAPEX + OPEX= 18,22 M€
HS
heatpump
gasturbine
heatstorage
thermalconnection
boiler
Accounting for uncertainty of forecasts leads to a cost saving of about 20%!
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1717OPTIMAL DESIGN OF MES: RETROFIT OF DH NETWORKS
GR3 Lamarmora
Caldaie Lamarmora
Recupero Calore:
Ori Martin
PdC Fumi
Recupero Calore: Altre
industrie
Caldaie Extra
P.d.C(Verz.+Caff+Concesio)
Solare Termico
Sto
rage
Ti
po
2
Sto
rage
Ti
po
3
Sto
rage
Ti
po
3
Sto
rage
Ti
po
1
Sto
rage
Ti
po
1
TU Linea3
TU Linea2
TU Linea1
PdC Fumi
PdC Fumi
Caldaie Centrale Nord
TmandataUtenze RTRTritorno
DH network of Brescia University of Parma Campus
Collab. with Univ. Of Brescia
EFFICITY project:
Collab. with LEAP, CIDEA & SIRAM
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1818LABORATORIES
LAB for MICRO-COGENERATION (LMC)
Test bench for performance and emissions of small scale co- and tri-generation systems (ICE, Stirling engines, PEM, SOFC, etc with < 100 kWel, 300 kWth), electrolizers and H2 production systems (at steady-state and transientconditions).
SOLAR TECH
Facility for developing and testing photovoltaic, photovoltaic-thermal and concentration systems.
MICRO-GRIDS LAB
Facility for testing control strategies of hybrid micro-grids (solar panels, CHP engines, batteries, thermal storages, etc).
LABORATORIO ENERGIA AMBIENTE PIACENZA (LEAP)
Research and consultancy activities in the areas of energy efficiency, waste-to-energy plants, biomass-fired plants, and pollutant emissions.
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19
19
19LAB FOR MICRO-COGENERATION (LMC)
1 kWel CHP unit based on Stirling cycle
10-20 kWel CHP units based on internalcombustion engines
20-30 kWel CHP units based on PEM fuel cell with steam reformer
1 kWth membrane reactor test stand (metallicmembranes for hydrogen separation and application to fuel processing )
0.5 kWth Thermo-photo-voltaic (TPV) generator
17
0
230 4 x 2.5 kWel SOFC CHP unit
At steady-state and transient / cyclic conditions Controlled and measured fuel / electricity exchange,
temperature, pressure and mass flow of all I/O hot/coldcircuits.
30 kWel - 18 kWLHV H2 electrolyzer unit
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20
20LABORATORIO DI SISTEMI SOLARI - SOLAR TECH LAB
PV system optimization: - improvements of commercial devices/technologies (e.g., PV, PVT, inverters, etc)- development of innovative concepts/prototypes (e.g., solar-driven heat pump)- development of accurate forecasting toolsFacilities:- PV and PVT panels with adjustable tilt angle and distance- Thermal oil loop to test PVT panels- Meteorological station
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21
21
21LEAP
• Ricerca nel settore MES e sistemi CHP in collaborazione con il gruppo GECOS
• Consulenza scientifica per il recupero di calore/energia da processi industriali (BP, GE, etc)
• Consulenza tecnica e procedurale per l’accesso a sistemi incentivanti sulla produzione efficiente di energia termica: CAR (Cogenerazione ad Alto Rendimento) e TEE (Titoli di Efficienza Energetica)
Audit energetico degli impianti;
Valutazioni tecnico-economiche sui meccanismi di remunerazione dell’energia;
Verifica della rispondenza alle normative di riferimento;
Validazione e supporto tecnico dei modelli per il calcolo del PES (Primary Energy Saving);
Assistenza per la presentazione dei documenti verso il GSE.• Consulenza tecnica e procedurale per il riconoscimento degli incentivi per la produzione di
energia elettrica da impianti a fonte rinnovabile: In particolare: per impianti a biomassa e impianti ibridi alimentati da rifiuti parzialmente
biodegradabili.
Attività LEAP nel settore cogenerazione ed efficienza industriale
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2222MAIN PUBLICATIONS ON MES/CHP/MICRO-GRIDS OPTIMIZATION
1. Gabrielli et al., 2018. Optimal design of multi-energy systems with seasonal storage. “AppliedEnergy” (in press).
2. Zatti et al., 2017. A three-stage stochastic optimization model for the design of smart energydistricts under uncertainty. “Computer Aided Chemical Engineering” Vol. 40, pp. 2389-2394.
3. Elsido et al., 2017. Two-stage MINLP algorithm for the optimal synthesis and design of networksof CHP units. Energy, Vol. 121, pp. 403-426..
4. Taccari et al. 2015. Short-term planning of cogeneration power plants: a comparison betweenMINLP and piecewise-linear MILP formulations. Computer Aided Chem. Eng., 37, pp. 2429-2434.
5. Bischi et al., 2014. Tri-Generation Systems Optimization: Comparison of Heuristic and MixedInteger Linear Programming Approaches. Proceedings of ASME Turbo-Expo 2014.
6. Bischi et al., 2014. A detailed optimization model for combined cooling, heat and power systemoperation planning. Energy, Vol. 74, pp. 12-26.
7. Bischi et al. 2016. Distributed cogeneration systems optimization: multi-step and mixed integerlinear programming approaches. International Journal of Green Energy, Volume 13.
8. Mazzola et al., 2015. A detailed model for the optimal management of a multigood microgrid.Applied Energy, Vol. 154.
9. Mazzola et al., 2017. Assessing the value of forecast-based dispatch in the operation of off-gridrural microgrids. Renewable Energy, Vol. 108.
10. Mazzola et al., 2016. The potential role of solid biomass for rural electrification: A technoeconomic analysis for a hybrid microgrids in India. Applied Energy, Vol. 169.
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Gabrielli et al. 2018, Applied Energy, in press
OPTIMAL DESIGN OF A SWISS ENERGY DISTRICT (WITH ETH)
Selected unitsPossible units