D6.3 Final Workshop - · PDF fileD6.3 Workshop Case Study Results ... •MAIP / AIP include...
Transcript of D6.3 Final Workshop - · PDF fileD6.3 Workshop Case Study Results ... •MAIP / AIP include...
D6.3 Workshop Case Study Results p. 1
FC-EuroGrid PUBLIC
FC-EuroGrid
D6.3 Workshop Case Study Results
Due date of deliverable: 31.07.2011 Actual submission date: 26.02.2013
Start date of project: 01.10.2010 Duration: 24 months Organisation name of lead contractor for this deliverable: University of Birmingham Revision [final version]
Dissemination Level PU Public X
PP Restricted to other programme participants (including the Commission Services)
RE Restricted to a group specified by the consortium (including the Commission Services)
CO Confidential, only for members of the consortium (including the Commission Services)
D6.3 Workshop Case Study Results p. 2
FC-EuroGrid PUBLIC
Update history: none Acknowledgment: The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) for the Fuel Cells and Hydrogen Joint Technology Initiative under grant agreement no. 256810.
1
Subject AGENDA – Final Public Project Workshop Date 26 Feb 2013
Time / Location ENEA Offices, Rue de Namur 72-74, B-1000 Brussels
+32 2 512 0448 Author R. Steinberger-Wilckens, ChemEng, UoB Phone +44 – 121 415 8169 Fax E-Mail [email protected] Participants
Ellart de Wit (HyGear), Oliver Posdziech (sunfire), Mirela Atanasiu (FCH JU) [not present], R. Steinberger-Wilckens, G. Koreneff, B. Groh, L. Weber, Y. Mermond, M. Blesznowski, U. Birnbaum, Jo. Mertens, B. Ridell, N. Griessbaum, T. Koljonen, V. Cigolotti, M. Gaeta, A. Moreno
Tuesday, 26 Feb. 2013
Time Subject
Final Public Workshop
11:00 – 11:30
11:30 – 12:00
Introduction and Project overview Analysis methodology and data review
12:00 – 12:30 Simulation model and example results
12:30 – 13:00 Sensitivity analysis results
sandwich lunch break
13:45 – 15:30 Discussion on project results, implications for residential CHP installations across Europe and for FCH JU MAIP goals
15:30 – 16:00 Wrapping up
16:00 End of meeting
Programme Review Day Brussels, 28/29 November 2012
Evaluating the Performance of Fuel ll i lCells in European Energy Supply
Grids
FC‐EuroGridContract number FCH JU 256810
Robert Steinberger WilckensRobert Steinberger‐WilckensUniversity of Birmingham
Project descriptionProject description
• duration 01.10.2010 – 28.02.2013
• total budget 805.931 €, funding 588.982 € (73%)
• addresses the development of benchmarks and indicators to assess the impact of emplo ing f el cells for stationar applications in ario simpact of employing fuel cells for stationary applications in various European electricity grid environments
The consortium consists of 9 partners:
University of Birmingham (coordinator)
h l hForschungszentrum Jülich Grontmij AB
European Institute for Energy Research EIfER E.ON Ruhrgas
Teknologian tutkimuskeskus VTT ENEATeknologian tutkimuskeskus VTT ENEA
Institute of Power Engineering IEn EBZ GmbH
Problem addressedProblem addressed
• MAIP / AIP include no clear indications as to the desired minimum performance stationary fuel cells have to deliver in order to contribute to the EU goals of increasing energy efficiency and reducing GHG emissions and thus achieve funding
• stationary fuel cell performance has to match and surpass CHP requirementsy p p q
• useful indicators are‐ amount of CO2 avoidedamount of fossil energy avoided or substituted‐ amount of fossil energy avoided or substituted
‐ total and electrical efficiency
• the environmental benefits will vary according to the electricity grid the installation is situated in
• methodology of assessment has been established
Project goalsProject goals
• map European electricity grids with respect to performance indicators
• identify the most meaningful indicators
• develop a methodology for assessing the resources and environmental advantage delivered by fuel cells in the electricity market(s)
• derive actual performance figures in given scenarios (SFH and MFH) via• derive actual performance figures in given scenarios (SFH and MFH) via simulation models using static performance figures (efficiency, turn‐down ratio, etc.)
• run assessment exercises for various FC technologies in different European grids
• evaluate results and methodology• evaluate results and methodology
• discuss results with stakeholders in Europe in order to find a common agreement on methodology
• discuss results with US and Japanese institutions
Input Data: Power GenerationInput Data: Power Generation
D t b f t d t• Database of system data
Input Data: Indicatorsp
• Database of
INDICATORS
indicators: Structure
I1– Fuel Input
Electricity supply system Environmental footprint Economy
I1 Fuel Input
I2 – Electricity & heatoutput
I7 – National GHG emissions
I8 CO emission factor
I10 – Energy prices
I3 – Losses, net import, etc.
I4 – Final consumption
I8 – CO2 emission factor
I9 – Emission fromelectricity production
I11 – Externalcosts
I5 – Installed capacity
I6 – Fuelconsumption
Simulation Calculations
winter workday (cloudy)• calculation of FC performance punder ‘real’ operating conditions
summer workday
conditions
Benchmarking ApproachBenchmarking Approach
reference systems:1 Single Family Home (SFH)1. Single Family Home (SFH)2. Multi Family Home (MFH)
- Calculation of system performance with 1-hour time steps- Comparison of key figures of merit
* system CO2 emissiony 2* primary energy use* electrical and total efficiency
- different operating strategies: (1) heat & (2) electricity following, p g g ( ) ( ) y g,(3) economic optimisation
- different CHP technologies: Stirling, ICE, PEFC, SOFC
Technology ChoiceTechnology Choice
Efficiency Peak TotalCompany Name Capacity
Efficiency el
Peak boiler
Total efficiency
[kWel] [kWth] [%] [kWth]mCHP for residential applicationsapplicationsGas engine SenerTec Dachs 5.5 12.5 27 93
ecoPOWER ecoPower 4.7 1.3 - 4.7 4 - 12.5 24 96Vaillant/Honda ecoPower 1.0 1 2,8 22,5 12 - 30 90
Stirling engine EHE Wispergen 1 5.5 - 7.5 11 14 5 90Stirling engine EHE Wispergen 1 5.5 7.5 11 14.5 90BDR eVita 0.9 7 13 18 105
Fuel Cell Baxi Innotec Gamma 1.0 0.3 - 1 0.5 - 1.7 32 15 85Hexis Galileo N 1 2 30 20 90
CFCL Bl G 0 2 0 3 160 (at
20 85CFCL Blue Gen 0 - 2 0.3 - 1(
1.5kW)20 85
Alignment with MAIP
• “Notably specific technical and economic targets are to be developed and a technology benchmarking, based on a pan‐developed and a technology benchmarking, based on a panEuropean assessment, is to be performed for residential, commercial and industrial applications..”
t ib ti t t t tti f AIP d MAIP d th• contribution to target‐setting for AIP and MAIP and the technology assessment activities in evaluating programmeprogress
F t re perspecti esFuture perspectives
• report on energy efficiency and GHG impact assessment of stationary fuel cell technologies under various operating and gridstationary fuel cell technologies under various operating and grid constraints will be prepared for FCH JU and general public
• input to development of MAIP 2.0
• influence on current MAIP was not possible due to complexity of MAIP development process
Open Q estionsOpen Questions
• changes in future electricity grid carbon footprint (20‐20‐20 goals etc.) will reduce impact of NG driven FC‐DG(20 20 20 goals etc.) will reduce impact of NG driven FC DG
• development of ‘smart grids’, new storage options and new load patterns will have an impact on operational strategies of i CHP ( i t l l t k h i l l tmicroCHP (virtual power plants, peak shaving, local storage,
electromobility etc.)
• reduction of heating demand will reduce employment potential g p y pfor fossil fuel based microCHP
• biomass‐derived hydrocarbon fuels and green hydrogen may enter the fuel marketenter the fuel market
• with market entry, the impact at a systems level has to be analysed, not at a single point of use
Thank You for your Attention !Thank You for your Attention !
FC- Eurogrid DatabaseWorkshop Brussels 26 February 2013Workshop Brussels 26 February 2013
Bengt Ridell 26 February 2013
Status of the Atlas in WP1 base year 2008
Input template revison of Eurostat dataInput template revison of Eurostat data
SwedenSweden
DenmarkDenmark
WP 1 DatabaseWP 1 Database
The conditions are very different in the different European countriescountries
Eurostat data are in general Ok but we had to revise some of them
It is very difficult to get accurate data for heat production
One major discussion was how to handle fuel for CHP d tiproduction
AnalysisAnalysis methodologymethodology and data and data reviewreview
TheThe FCFC--EurogridEurogrid projectprojectTheThe FCFC--EurogridEurogrid projectproject
Marcin Błesznowski (Marcin Błesznowski (IEIEn)
INSTITUTE OF POWER ENGINEERING (INSTITUTE OF POWER ENGINEERING (IEnIEn))Warsaw, PolandWarsaw, Poland
Brussels, 26 February 2013
The final electricity consumption in MWh/capitaThe final electricity consumption in MWh/capita
25
20
15
10
5
-
The sectors’s share of electricity consumptionThe sectors’s share of electricity consumption
70%
50%
60%
40%
50%
20%
30%
0%
10%
0%
Industry sector's share of electricity consumptionTransport sector's share of electricity consumptionHouseholds sector's share of electricity consumption
CO2 emissions in tonnes CO2/GWh(2008‐2009 yr.)
600
700
400
500
300
100
200
0
Emission [tonnes/GWh] Germany France Poland Denmark UK Sweden Italy Finland NorwayNOX - average 0,43 0,09 1,45 0,36 0,59 0,03 0,22 0,46 0,00SO2 0 32 0 13 2 87 0 08 0 53 0 02 0 26 0 34 0 00SO2 - average 0,32 0,13 2,87 0,08 0,53 0,02 0,26 0,34 0,00Particals - average 0,23 0,01 0,15 0,02 0,02 0,01 0,01 0,03 0,00
Total GHG emissions from energy sector inTotal GHG emissions from energy sector in tonnes CO2 (2008‐2009 yr.)
700 000 000
800 000 000
500 000 000
600 000 000
700 000 000
400 000 000
500 000 000
200 000 000
300 000 000
0
100 000 000
Assessment of indicators
Indicator name: 2 87%2.49% 0.00% 0 00% 1 03%Poland DenmarkIndicator name:
The total fuel input share
0.57%
2.87% 0.00%
0.29%48.68%
29.99%
16.91%
0.00% 1.03%Poland Denmark
5 12%
0.00%0.00%12.55%0.00%
93.77% 3.39%
28.79% 0.00%5.16% 1.03%
4.18%0 67%
Germany France
Finland
Italy
26.55%
13.26%
55.06%
5.12%1.40%
13.79%
39.76%
32.50%
49.87%
13.69%
4.18%
2.96%
85.99%
0.67% Finland
37.66%
4.40%
16.88% 0.20%
17.09%
0.00% 1.03% Coal
Oil
0.64%7.00%
2.21% 0.75%1.52%0.00%
UK Sweden Norway
40.11%
0.82%
26.79%54.27%
Oil
Gas
Biomass, waste, peat, etc
Uranium
17.72%
77.80%
Brussels, 26 February 2013
0.74%Uranium
Other fuels
FC Eurogrid database and indicators
All the data base and the methodology to defineand calculate the indicators are published in
The Atlas descibing all countries with The Atlas descibing all countries with database summary and the different conditions in all countries
The Methodology handbook describing the calculation of the indicators presentation of the results
FC H JU is co‐financingthe FC‐EuroGrid Project.
AnalysisAnalysis methodologymethodology and data and data reviewreview
TheThe FCFC‐‐EurogridEurogrid projectprojectgg p jp j
Marcin Błesznowski (Marcin Błesznowski (IEIEn)
INSTITUTE OF POWER ENGINEERING (INSTITUTE OF POWER ENGINEERING (IEnIEn))INSTITUTE OF POWER ENGINEERING (INSTITUTE OF POWER ENGINEERING (IEnIEn))Warsaw, PolandWarsaw, Poland
Brussels, 26 February 2013
ContentFC H JU is co‐financingthe FC‐EuroGrid Project.
Content
• Scope of the WP2.
• Database structure methodology• Database structure, methodology.
• Assessment of idicators.
• Conclusions• Conclusions.
Brussels, 26 February 2013
Scope of the WP2FC H JU is co‐financingthe FC‐EuroGrid Project.
Scope of the WP2
Objectives of WP2:
• to define indicators (data, figures, metrics, etc.) characterising the EU powersupply system, its emissions and other selected criteria.
• to develop a common assessment framework for analysing data fromWP1• to develop a common assessment framework for analysing data fromWP1.
• to organise the data collected in WP1 in an operational database for furtheruse in the project.
Brussels, 26 February 2013
Database structureFC H JU is co‐financingthe FC‐EuroGrid Project.
Database structure
The indicator based methodology:The indicator based methodology:
DRIVERS RESPONSESDRIVERS
Energy intensity and consumptions, gross, domestic production,
RESPONSES
Energy savings, efficiency, renewable
energy, social efficiency awareness
STATE
Waste, emissions GHG, etc.
Brussels, 26 February 2013
Database structureFC H JU is co‐financingthe FC‐EuroGrid Project.
Database structure
INDICATORS
Electricity supply system Environmental footprint Economy
I1 – Fuel Input
I2 Electricity & heat output
I7 – National GHG emissionsI10 – Energy prices
I2 – Electricity & heat output
I3 – Losses, net import, etc.I8 – CO2 emission factor
I4 – Final consumption
I5 I t ll d it
I9 – Emission from electricity production
I11 – External costs
I5 – Installed capacity
I6 – Fuel consumption
Brussels, 26 February 2013
Database structureFC H JU is co‐financingthe FC‐EuroGrid Project.
Database structure
Matrix structure:
Indicatorno.
Indicatorname
Country Unit Formula Data Source
I1.1Total fuelinput
Country A GWh F_t 440 648 1input
I1.2 … Country B … … … …
I1 3 Country CI1.3 … Country C … … … …
… … … … … … …
Brussels, 26 February 2013
Assessment of indicatorsFC H JU is co‐financingthe FC‐EuroGrid Project.
Assessment of indicators
Indicator name:Indicator name:The total combustible fuel used for the production of power and heat in MWhfuel / capita
30
40
20
0
10
Combustible fuel input per capita Nuclear fuel input per capita
Brussels, 26 February 2013
Assessment of indicators
FC H JU is co‐financingthe FC‐EuroGrid Project.
Indicator name: 2 87%2.49% 0.00% 0 00% 1 03%Poland DenmarkIndicator name:
The total fuel input share
0.57%
2.87% 0.00%
0.29%48.68%
29.99%
16.91%
0.00% 1.03%Poland Denmark
5 12%
0.00%0.00%12.55%0.00%
93.77% 3.39%
28.79% 0.00%5.16% 1.03%
4.18%0 67%
Germany France
Finland
Italy
26.55%
13.26%
55.06%
5.12%1.40%
13.79%
39.76%
32.50%
49.87%
13.69%
%
2.96%
85.99%
0.67%
37.66%
4.40%
16.88% 0.20%
17.09%
0.00% 1.03%Coal
Oil
0.64%7.00%
2.21% 0.75%1.52%0.00%
UK Sweden Norway
40.11%
0.82%
26.79%54.27%
Gas
Biomass, waste, peat, etc.
Uranium
Other fuels
17.72%
77.80%
Brussels, 26 February 2013
0.74%Other fuels
Assessment of indicatorsFC H JU is co‐financingthe FC‐EuroGrid Project.
Assessment of indicators
Indicator name:Indicator name:
Total electricity output per capita in MWhel./capita Shares of fuel in gross electricity generation
25
30
80%
100%
15
20
40%
60%
0
5
10
0%
20%
0
Renewables Nuclear Fossils Waste & Others
Brussels, 26 February 2013
Assessment of indicatorsFC H JU is co‐financingthe FC‐EuroGrid Project.
Assessment of indicators
Indicator name:Indicator name:Electricity losses share in total electricity output
30%
20%
30%
10%
0%
Brussels, 26 February 2013
Assessment of indicatorsFC H JU is co‐financingthe FC‐EuroGrid Project.
Assessment of indicators
Indicator name:Indicator name:
The final electricity consumption in MWh/capita The sectors’s share of electricity consumption
20
25
50%
60%
70%
10
15
30%
40%
50%
-
5
0%
10%
20%
-
Industry sector's share of electricity consumptionTransport sector's share of electricity consumptionHouseholds sector's share of electricity consumption
Brussels, 26 February 2013
y p
Assessment of indicatorsFC H JU is co‐financingthe FC‐EuroGrid Project.
Assessment of indicators
Indicator name:Indicator name:Installed capacity in MWe
90 000
60 000
70 000
80 000
30 000
40 000
50 000
0
10 000
20 000
Fossil fuel fired power plants Nuclear power stations Hydro power stationsWind turbines Other
Brussels, 26 February 2013
Assessment of indicatorsFC H JU is co‐financingthe FC‐EuroGrid Project.
Assessment of indicators
Indicator name:Indicator name:
Transmission efficiency of produced electricity in power sector
Fossil and combustible fuel efficiency in electricity production
100%
80%
100%
40%
60%
80%
40%
60%
80%
0%
20%
40%
0%
20%
40%
Fossil fuel efficiency in electricity production
Combustible fuel efficiency in electricity production
Brussels, 26 February 2013
Assessment of indicatorsFC H JU is co‐financingthe FC‐EuroGrid Project.
Assessment of indicators
CO2 emissions in tonnes CO2/GWh(2008‐2009 yr ) Total GHG emissions from energy sector in
700 000 000
800 000 000
2 2/ (2008‐2009 yr.) gytonnes CO2 (2008‐2009 yr.)
600
700
400 000 000
500 000 000
600 000 000
300
400
500
0
100 000 000
200 000 000
300 000 000
0
100
200
300
00
Emissions from electricity production in tonnes/GWh
Emissions
[tonnes/GWh]Germany France Poland Denmark UK Sweden Italy Finland
NOx - avarage 0.43 0.09 1.45 0.36 0.59 0.03 0.22 0.46
SO2 - avatage 0.32 0.13 2.87 0.08 0.53 0.02 0.26 0.34
Brussels, 26 February 2013
Particles - avarage 0.23 0.01 0.15 0.02 0.02 0.01 0.01 0.03
Assessment of indicatorsFC H JU is co‐financingthe FC‐EuroGrid Project.
Assessment of indicators
Indicator name:
Electricity, gas, coal and oil prices (per kWh)
Electricit price
Country
Electricity price
(per kWh)Gas price (per kWh) Coal price (per kWh) Oil price (per kWh)
Domestic
(1)
Industry
(2)
Domestic
(3)
Industry
(4)
Domestic
(5)
Industry
(6)
Domestic
(7)
Industry
(9)
Germany 0.22 0.09 0.071 0.034 - - 0.076 0.039
France 0.12 0.06 0.059 0.292 - 0.008 0.1 0.026
Poland 0.14 0.10 0.053 0.032 - 0.023 - -
Denmark 0.28 0.08 - 0.035 - - - -
UK 0.15 0.08 0.039 0.026 - 0.012 - 0.030
Sweden 0.18 0.07 0.123 0.046 - - - 0.046
Italy 0.17 0.15 0.480 0.430 - - - -
Finland 0.15 - - 0.030 - 0.014 0.076 0.049
Norway 0.12 0.08 - 0.045 - 0.014 - -
(1) consumption < 2 500 kWh/year; (2) consumption > 2 500 kWh/year; (3) consumption < 15 000 kWh/year; (4) large industrial customers; average value for electricity and heat production;( ) p y ; ( ) p y ; ( ) p y ; ( ) g ; g y p ;(7) average value for electricity and heat production (8) average value for electricity and heat production
data for 2008 – 2009 yr.
Brussels, 26 February 2013
Assessment of indicatorsFC H JU is co‐financingthe FC‐EuroGrid Project.
Assessment of indicators
Indicator name:Indicator name:External cost for electricity production in € cent per kWh
Coal &Country
Coal &
LignitePeat Oil Gas Nuclear Biomass Hydro PV Wind
Germany3÷6 5÷8 1÷2 0.2 3 0.6 0.05
France 7÷10 8÷11 2÷4 0.3 1 1
Poland
Denmark4÷7 2÷3 1 0.1
UK 4÷7 3÷5 1÷2 0.3 1 0.15
SwedenSweden
Italy 3÷6 2÷3 0.3
Finland 2÷4 2÷5 1
Norway
Brussels, 26 February 2013
ConclusionsFC H JU is co‐financingthe FC‐EuroGrid Project.
Conclusions
• Development of the power supply infrastructure database.
• Indicators classified into 3 main groups (Electricity Supply, Environmental footprint, Economy).
• Assessment framework for analysing data.
Brussels, 26 February 2013
FC H JU is co‐financingthe FC‐EuroGrid Project.
Thank You
for attention!
Brussels, 26 February 2013
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
FC EurogridWP 4 - Sensitivity analysis of mCHP operations
Niklas Griessbaum, Yannick Mermond
EIFER
25.02.2012
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Table of Contents
1 Introduction
2 Input data and Case studies
3 Results: HD operation
4 Results: EF operation
5 Conclusions
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Table of Contents
1 Introduction
2 Input data and Case studies
3 Results: HD operation
4 Results: EF operation
5 Conclusions
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Context of WP 4Preceding workpackages/deliverables have provided:
A mCHP heating system model
Benchmarks of mCHP systems
Information and data of European energy systems
Data on typical domestic energy demand
Deliverable 4.2 combines these inputs to compile data onexpectable performance of mCHP systems in various applications
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
ObjectivesCompiling of system specifications and application contexts thatprovide prosperous ground for the integration of mCHP systems.The specifications include:
System
System efficiencies
System dimensions
Cycling behaviour
Application
Energy price thresholds
Load characteristics
Emission factors
A Identification of critical input parameters for the operation ofCHP systems by
B Qualification and Quantification of optimal values for theseparameters
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Methodology and output parameters
Analytical analysis of mCHP operation
Simulation based sensitivity analysis
KPIsEnvironmentally: CO2 savingsEconomically: Operation costTechnically: Working regime
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Table of Contents
1 Introduction
2 Input data and Case studies
3 Results: HD operation
4 Results: EF operation
5 Conclusions
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Categorization of Input parameters
Application
Demand: Usage of two loadcurves1.
Energy price varying ragesaccording to variousEuropean countries
Emission factors varying inranges according to gridtopologies in variousEuropean countries
System
Operation modesHD and HF
Machine characteristicsAbility to shut down (y/n)
Power Pel , Pth
Efficiencyηel , ηth and ηtot
1Load A and Load B (Finland 1970 SFH, France SFH) Differ from eachother by El demand, th demand load shape
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Problem description
Constant output
Boiler losses
Thermal (tank losses)
Variable output
CHP losses
El export/import
Blow off
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Problem description
Constant output
Boiler losses
Thermal (tank losses)
Variable output
CHP losses
El export/import
Blow off
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Problem description
Constant output
Boiler losses
Thermal (tank losses)
Variable output
CHP losses
El export/import
Blow off
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Problem description
Constant output
Boiler losses
Thermal (tank losses)
Variable output
CHP losses
El export/import
Blow off
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Table of Contents
1 Introduction
2 Input data and Case studies
3 Results: HD operation
4 Results: EF operation
5 Conclusions
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Output: Operative hours
Runtime increasing withlower Pth
Runtime higher for higherdemand (Load B)
Non stop operationachievable
R: ratio of electrical to thermal power
R = PelPth
= ηelηth
thus Pth = R ∗ Pel
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Output: CO2 savings
fgrid is the deciding factor
CO2 reductions increasewith higher fgrid
break even at about200 g/kWh (= fgas)
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Output: CO2 savings
fgrid is the deciding factor
CO2 reductions increasewith higher fgrid
break even at about200 g/kWh (= fgas)
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Output: CO2 savings
fgrid is the deciding factor
CO2 reductions increasewith higher fgrid
break even at about200 g/kWh (= fgas)
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Output: CO2 savings
fgrid is the deciding factor
CO2 reductions increasewith higher fgrid
break even at about200 g/kWh (= fgas)
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
CO2 emissions analytically I
Conventional emissions ecfrom boiler and from grid:
ec = egrid + eboi
egrid = fgrid ∗ Eel ,dem
eboi = fgasEth,dem
ηboi
Emmissions with CHP eCHP fromboiler, grid and CHP:
eCHP = egrid + eboi + eCHP
egrid = fgrid(Eel ,dem−Eel ,CHP)
eboi = fgas(Eth,dem−Eth,CHP )
ηboi
eCHP =Eel,CHP
ηel,CHP
Solving the break even point results in possible savings if
ec > eCHP → fgridfgas
>1− ηth
ηboilηel
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
CO2 emissions analytically II
this results consequently in the following threshold curves:
ηth > ηboi (1− ηel ∗ fgridfgas
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Discussion point: is fgrid the right factor
fgrid represents average grid emission factor. I.e.
fgrid =∑
mCO2∑Eel,prod
This may seen valid for imported (bought el. energy)
Exported electrical energy may on the other side replacepower plants with far higher specific CO2 emissions (e.g. coal)
Therefore the usage of an emission factor for the replaced el.energy could render better results
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Output: Operation cost savings
(R=1)
ri =cgas
cel,imp, [ri ] =
EUR/kWhEUR/kWh
re =cgascel,exp
, [re ] =EUR/kWhEUR/kWh
Financial benefits dependmainly on the price ratios
for “good” price ratios,higher el. powers arepreferable
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Table of Contents
1 Introduction
2 Input data and Case studies
3 Results: HD operation
4 Results: EF operation
5 Conclusions
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Input: Demand distribution I
High share ofdemand with lowpower
Even for highdemand household,more than 60%below 1 kW
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Input: Demand distribution II
Illustration of coverable demand for systems with different nominalpower (a minimal power of 30 % nominal power was assumed)
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Output: CO2 savings
Emission reductionsdepend strongly onoperation mode
Turning point at about200 g/kWh
Standby: Reductionsincrease with Pel
Shut down: Reductionsdecrease with Pel
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Output: CO2 savings
Emission reductionsdepend strongly onoperation mode
Turning point at about200 g/kWh
Standby: Reductionsincrease with Pel
Shut down: Reductionsdecrease with Pel
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Output: Change in operation costs
(Standby operation, Pel = 1kW )
Cost benefits increase monotonwith ηtot
Benefits depend strongly oncountry-specific price structure
Benefits increase with ηel
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Table of Contents
1 Introduction
2 Input data and Case studies
3 Results: HD operation
4 Results: EF operation
5 Conclusions
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Questions?
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Introduction Input data and Case studies Results: HD operation Results: EF operation Conclusions
Thank You for your attention
Niklas Griessbaum, Yannick Mermond EIFER
FC Eurogrid
Programme Review Day Brussels, 28/29 November 2012
FC‐EuroGridSummary of Findings
Robert Steinberger‐WilckensU i it f Bi i hUniversity of Birmingham
The Good News FirstThe Good News First ….
- microCHP based electricity generation has its place in the European energy systemf l ll d li i i d ti i- fuel cells can deliver emission reduction, primary energy savings and operating cost reduction in most European countries
- in most cases, heat following mode will be preferred –unless virtual power plants are consideredtheir performance indicators partly need further- their performance indicators partly need further improvement to high total and electrical efficiencies, power-to-heat ratio, cycling capability etc.
- sensitivity analysis provides the break-even points where microCHP improves the emission and energy balance and how to prioritise technical developmentp p
Technology ChoiceTechnology Choice
Efficiency Peak TotalCompany Name Capacity
Efficiency el
Peak boiler
Total efficiency
[kWel] [kWth] [%] [kWth]mCHP for residential applicationsapplicationsGas engine SenerTec Dachs 5.5 12.5 27 93
ecoPOWER ecoPower 4.7 1.3 - 4.7 4 - 12.5 24 96Vaillant/Honda ecoPower 1.0 1 2,8 22,5 12 - 30 90
Stirling engine EHE Wispergen 1 5.5 - 7.5 11 14 5 90Stirling engine EHE Wispergen 1 5.5 7.5 11 14.5 90BDR eVita 0.9 7 13 18 105
Fuel Cell Baxi Innotec Gamma 1.0 0.3 - 1 0.5 - 1.7 32 15 85Hexis Galileo N 1 2 30 20 90
CFCL Bl G 0 2 0 3 160 (at
20 85CFCL Blue Gen 0 - 2 0.3 - 1(
1.5kW)20 85
Average Efficiency
System ComparisonSystem Comparison
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Effective Emissions
Thank You for your Attention !Thank You for your Attention !