ENVIRONMENTAL IMPACT OF DISPERSED GENERATION

679
ENVIRONMENTAL IMPACT
OF DISPERSED GENERATION
WORKING GROUP
C3.05
MARCH 2017

Members
Dr.-Ing. Thomas SMOLKA, Convenor DE
Masanobu KATAGIRI JP
André Luiz MUSTAFA' BR
Prof. Dr. Stefanie HELLWEG CH
Evanise MESQUITA BR
Stephen MARTIN AU
Yasuhide NAKAGAMI JP
Eva SZECHOWICZ DE
Thomas DEDERICHS DE
Dr. Christian CAPELLO CH
Melanie HAUPT CH
Lea EYMANN CH
WG C3.05
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ENVIRONMENTAL IMPACT OF
DISPERSED GENERATION
ISBN : 978-2-85873-382-8

ENVIRONMENTAL IMPACT OF DISPERSED GENERATION
TABLE OF CONTENT
1 INTRODUCTION ......................................................................................................5
1.1 AIM...................................................................................................................................................5
1.2 BACKGROUND..............................................................................................................................5
1.3 POLITICAL INSTRUMENTS FOR ENVIRONMENTAL IMPACT ASSESSMENTS.....................6
1.4 DEFINITION OF DISPERSED GENERATION ..............................................................................7
2 METHODS FOR ENVIRONMENTAL IMPACT ASSESSMENT..............................8
2.1 SUSTAINABILITY ASSESSMENTS ................................................................................................8
2.2 LIFE CYCLE ASSESSMENT (LCA) .................................................................................................8
2.3 ECO-EFFICIENCY ANALYSIS ....................................................................................................11
2.4 COMPREHENSIVE ASSESSMENT OF ENERGY SYSTEMS...................................................12
2.5 CONCLUSIONS..........................................................................................................................13
3 ENVIRONMENTAL IMPACTS OF DISPERSED GENERATION UNITS FROM A
COMPONENT PERSPECTIVE............................................................................................14
3.1 OVERVIEW OF ENVIRONMENTAL IMPACTS FOR DISPERSED GENERATION...............14
3.2 LCA RESULTS OF SINGLE DISPERSED GENERATION UNITS (COMPONENT
PERSPECTIVE).............................................................................................................................................15
3.2.1 LCA OF SOLAR POWER SYSTEMS (PHOTOVOLTAIC SOLAR POWER PLANT) ...........15
3.2.2 LCA OF WIND ENERGY CONVERTER....................................................................................18
3.2.3 LCA OF FUEL CELLS ...................................................................................................................20
3.2.4 LCA OF MOTOR ENGINE COMBINED HEAT-POWER (CHP) UNITS................................23
3.2.5 LCA OF SMALL HYDRO-PLANTS.............................................................................................26
3.2.6 LCA OF SMALL GEOTHERMAL POWER PLANTS.................................................................26
3.3 LCA OF SMALL SOLAR THERMAL SYSTEMS.........................................................................26
3.4 LCA OF ELECTRIC VEHICLES DEPENDING ON THE POWER GENERATION MIX..........27
4 METHODOLOGY FOR ASSESSING THE ENVIRONMENTAL IMPACTS OF
DISPERSED GENERATION IN DISTRIBUTION NETWORKS FROM A SYSTEM
PERSPECTIVE.......................................................................................................................30
4.1 ANALYSIS OF THE USE OF DG IN DISTRIBUTION NETWORKS.......................................30
4.2 MODEL FOR ENVIRONMENTAL ASSESSMENT OF ENERGY SUPPLY..............................32
4.2.1 LCA OF DISTRIBUTION NETWORKS......................................................................................33
4.3 DETERMINATION OF INFLUENCE FACTORS ON THE BALANCE OBJECT ......................35
4.4 SCENARIO ANALYSIS................................................................................................................36

4.4.1 METHODOLOGY OF THE SCENARIO ANALYSIS................................................................37
4.4.2 APPLICATION OF SCENARIO ANALYSIS ..............................................................................38
4.5 MODELLING AND IMPLEMENTATION OF THE PROCEDURE.............................................38
5 CASE STUDY OF THE IMPACTS OF DISPERSED GENERATION IN
DISTRIBUTION NETWORKS.............................................................................................40
5.1 CASE STUDY - COMPARISON OF FUTURE ENERGY SUPPLY SCENARIOS WITH
COMBINED HEAT POWER UNITS IN DISTRIBUTION NETWORKS [SMOLKA IV].........................40
5.1.1 APPLICATION OF SCENARIO ANALYSIS ..............................................................................40
5.1.2 ENERGY MODEL FOR THE OPERATION OF CHP SYSTEMS IN DISTRIBUTION
NETWORKS................................................................................................................................................46
5.1.3 CREDITS AND ALLOCATION PROCEDURES..........................................................................48
5.1.4 TRENDS IN ENERGY CONSUMPTION AND GENERATION IN GERMANY.....................49
5.1.5 SCENARIO ANALYSIS OF DIFFERENT ENERGY SUPPLY SCENARIOS IN DISTRIBUTION
NETWORKS................................................................................................................................................51
5.2 MODELLING AND IMPLEMENTATION OF THE CASE STUDY............................................53
5.2.1 EXEMPLARY RESULTS.................................................................................................................55
5.2.2 SUMMARY AND OUTLOOK.....................................................................................................58
6 SUMMARY, GUIDELINES AND RECOMMENDATIONS....................................60
7 REFERENCES ...........................................................................................................62

5
1. INTRODUCTION
1.1 AIM
The aim of the working group is to define procedures and methods to evaluate the environmental
impacts of Dispersed Generation (DG) in distribution networks.
The WG shall proceed by developing the steps that follow:
 Collection and analysis of practical experience (from technical literature and/or “case
studies”) about assessments of the environmental impacts of DG and of legislation and
technical standards in various countries.
 Synthesis and benchmarking of methods and experiences. Identification of critical issues.
 Definition of criteria and proposal of a standardised methodology for assessing
environmental impacts of DG
 Illustration of methodology in a case study
 Dissemination of conclusions (Target Groups: National and Local Authorities and
Agencies, Regulators, Manufacturers, Electric Utilities)
1.2 BACKGROUND
Dispersed Generation, based both on fossil sources and on renewables, is expected to experience
a large penetration in Power Systems but the issue of its impact on the environment is still open.
The assessment of the DG environmental impact should take into account different aspects, e.g.:
 fossil based DG produces pollutant emissions in densely populated areas, while
centralised power stations can be located away from cities and other residential zones;
 fossil based DG needs “cleaner” fuels than centralised generation, because it is more
difficult to apply sophisticated flue gas treatment systems;
 DG brings power generation closer to consumption points, with potential reduction of
transmission and distribution losses and need for network reinforcements;
 integration of DG with combined generation of heat and power (CHP) allows its diffusion
within tertiary and residential customers, i.e., near load centres, leading to higher energy
efficiency levels (with environmental benefits);
 DG fits very well with renewable energy sources (RES), with environmental benefits
(reduction of pollutant emissions, liquid discharges, wastes…), but comes with problems
like visual impact, land occupation, noise…
The working group is aware that there is no universal answer to the question, whether dispersed
generation is better or worse from an environmental point of view, as this depends on the specific
case and special conditions. The WG is aware that the life cycle assessment (LCA) approach for
assessing the environmental impacts of dispersed generation presented here is only one among

6
several decision dimensions, including e.g. economic considerations and net constraints.
However, need is felt to define a global procedure and relevant methods for the evaluation of
environmental impacts of DG, taking into account relevant site-specific key factors that may
influence this impact. This method shall - among others – tackle the following issues/questions:
1) Which impact categories and environmental indicators are relevant for different DG
technologies and what is the special extent of these impacts (i.e. the relevant area to
account for all the adverse and favourable impacts). Attention should be devoted to both
“local” and “global” impacts?
2) How to take into account “positive” impacts (e.g. loss reduction, co-generated heat …)?
3) How to model the system, considering for the whole lifecycle of the implied technologies?
i) From a component perspective: Lifecycle of a single DG unit
ii) From a system perspective: Usage of multiple DG units in a distribution network
1.3 POLITICAL INSTRUMENTS FOR ENVIRONMENTAL IMPACT ASSESSMENTS
CO2 trading
The European IPPC directive (integrated pollution prevention and control) requires BAT (best
available technology) to be applied for plants of above 50 MW (thermal); most DG units are below
these values.
The EU emission trading scheme for greenhouse gases is valid only for combustion installations
above 20 MW thermal output power. E.g. in Finland and Sweden smaller units are included as
well but most of DG units are not included so far. With respect to the EU emission trading scheme,
additional costs for DG can be noticed.
Various European research projects deal with the environmental assessment of power generation.
For instance, within the NEEDS project a Life Cycle Inventory database for electricity supply
systems, including a variety of DG technologies (photovoltaic, wind energy, fuel cells, wave
energy, bioenergy etc.), was set up (http://www.isistest.com/needswebdb/search.php).
Furthermore, The Project ECLIPSE (Environmental and Ecological Life Cycle Inventory for present
and future Power Systems in Europe) co-founded by European Commission and Swiss Federal
Office for Education and Science offers a database for environmental assessment of power
generation technologies. Some facts about the project
 Project co-founded: European Commission and Swiss Federal Office for Education and
Science
 Purpose: methodological guideline on how to carry a Life Cycle Inventory for electricity
systems.
 Focus: new and decentralised systems.
100 possible configurations of five technologies for power generation: photovoltaic, wind,

7
biomass, small combined cogeneration systems and fuel cells
Other instruments:
 Subsidies
 Ecolabel (Naturemade (CO2, renewable, … www.naturmade.ch)
 CDM (Clean Development). CER credits (certified emission reduction credits, international
scheme)
1.4 DEFINITION OF DISPERSED GENERATION
According to CIGRE WG 37-23 definition Dispersed Generation made, WG C3.05 defines Dispersed
Generation as:
 today not centrally despatched
 connected to the distribution network (MV, LV)
 smaller than 50 MW
 based on co-generation units (heat and electricity), renewable energies or other
conventional sources or electrical storage devices
Examples for DG are micro turbines, internal combustion engines, wind energy and photovoltaic
converters, mini hydropower systems, biomass and waste material power systems fuel cells, etc.

8
2. METHODS FOR ENVIRONMENTAL IMPACT ASSESSMENT
2.1 SUSTAINABILITY ASSESSMENTS
There are various methods that consider one or all three dimensions of sustainability, i.e. an
environmental, economic and social assessment. Table 1-1 shows the characteristic of a selection
of different approaches which are basically explained in the next chapters.
Table 1-1: Comparison of different approaches to life cycle assessment
2.2 LIFE CYCLE ASSESSMENT (LCA)
Principles of the Life-Cycle Assessment
DIN EN ISO 14040 (revision of 2005) and ISO 14044 abstract the principles for planning and
development of a LCA. Some characteristics are:
 Consideration of the Life-Cycle:
The entire Life-Cycle including raw material extraction and acquisition, energy and
material production, the utilisation phase and end of life treatment need to be
considered.
 Environmental focus
LCA focuses on environmental aspects and effects. Economic and social aspects are
outside the scope and may be assessed with the according complementary methods
(e.g. LCC, see table)
 Comprehensiveness:
LCA investigates all attributes and aspects of the natural environment, human health
and resources.
Term Application Areas of protection
considered
Life Cycle Assessment (LCA) Product or process
assessments (including energy
technologies and energy
systems)
Resources, human health, and
ecosystem impacts
Comprehensive Assessment of
Energy Systems
energy system,
energy technology
environment, human health,
accident risks, economy, social
aspects
Eco-Efficiency Analysis products and processes environment, economy and
sometimes social aspects

9
 Priority of scientific approach:
Decisions within an LCA are, if possible, based on natural findings. Should this not be
possible, other approaches such as economics can be applied.
Phases of an LCA
According to ISO 14040 an LCA is subdivided into four phases (Fig. 1-1). The four phases are:
 the goal and scope definition
 inventory analysis
 impact assessment
 interpretation
Inventory analysis
Goal and scope definition
Impact assessment
Interpretation
Direct Applications:
•Product development and
improvement;
•Strategic planning;
•Public policy making;
•Marketing
•Other
Life cycle assessment framework
Fig. 1-1: Life cycle assessment framework
The goal and scope definition
Within this phase the goal of the LCA is defined. In this phase, the intended application, the
reasons for carrying out the study and the intended audience should be clarified. It is determined
whether the results are appropriate for publication and for comparative statements. Also, the so-
called “functional unit” is defined, which is the unit of comparison (e.g. generation of 1 kWh of
electricity) to which all emissions and impact metrics refer to.
The scope must clearly be described. According to DIN EN ISO 14040 the scope contains inter
alia the following points: the product system to be studied, the function of the product system,
the system boundary, allocation procedures, data requirements, assumptions and limitations.

10
Life cycle inventory analysis
Life cycle inventory analysis contains data collection and the calculation procedure to quantify
relevant inputs and outputs of a product system. During the compilation of data new data
requirements or limitations may be identified, that require a change in the data collection
procedure. This can also lead to modification of the objective or to modifications of the scope
(see phase 1 above)
Life cycle impact assessment
In this phase, the results of the inventory analysis are evaluated and analyzed, in order to identify
potential environmental impacts. These environmental impacts are evaluated by their
expectations. The inventory analysis data will be assigned to impact categories and impact
indicators. This phase often includes a control of the goal and scope of the LCA.
Impact category Spatial extend Existing standard methods
Climate Change global IPCC 2007, De Schryver 2009
Respiratory effects Local and regional Hofstetter 1998, Goedkoop et al 2008,
Biodiversity and land use Local Köllner and Scholz 2007, Mila I Canals,
Biodiversity and water use Local or regional Pfister et al
Acidification Regional Van Zelm 2007,
Eutrophication Regional Huijbregts, Heijungs 1992
Toxicity Local, regional,
global
Rosenbaum et al. 2008,
Resource depletion, cumulative
energy or exergy demand
Regional and global vdi 4600 1997, Frischknecht, Bösch et al
2007
Noise local No standard method available (for traffic
noise Althaus et al.2009)
Odour local - (using key emissions as indicator)
Life cycle interpretation
In the last phase, the results of the inventory analysis and the impact assessment are jointly
analyzed and interpreted. These results should be consistent with the goal and scope of the LCA.
Through the analysis it should also be clarified that the results demonstrate the potential
environmental effects, but no real impacts on category endpoints, the exceeding of thresholds or
safety margins or risks. Here uncertainties, sensitivities and the completeness of the performed
analysis have to be discussed.

11
Assignment of LCI results (classification)
Calculation of category indicator results (characterisation)
Category indicator results, LCIA results (LCIA profile)
Optionale Bestandtei-
le
Calculation of the magnitude of category indicator results relative to refer-
ence information (normalisation)
Grouping
Weighting
Selection of impact categories, category indicators and characterization model
Fig. 1-2: Elements of the LCIA phase, after [ISO 14040 ff]
Several standard software systems facilitate the execution of a LCA study. These software
systems contain life cycle inventory databases with information on energy generation systems.
The most common software systems are Simapro (www.pre.nl/simapro/), Gabi (www.gabi-
software.com/), and Umberto (www.umberto.de/). Recently, also some open software has
become available (http://www.openlca.org/index.html).
2.3 ECO-EFFICIENCY ANALYSIS
There are numerous definitions and methods to assess the eco-efficiency of products (see for
instance special issue Journal of Industrial Ecology (9) 4, 2005). They all have in common that
they relate the financial costs and environmental impact of products over their complete life cycle,
beginning with the extraction of raw materials through the disposal or recycling of the product.
The main goal of eco-efficiency analysis is to quantify the sustainability of products and processes
by analysing total costs depending on environmental impacts of a product.
One approach which has become widely used in industry, particularly in chemical industry, is the
eco-efficiency method of BASF. By its own account the chemical company BASF became aware
of the responsibility towards human health and environment. Since the early 90’s BASF has
obligated itself to the positions of ”Sustainable Development“ and ”Responsible Care“. The
chemical company established the tool of eco-efficiency analysis to achieve these positions with

12
its own products.
The environmental impact is determined by the method of life cycle assessment. Economic data
are calculated using the usual business or national economical models.
The results of the environmental assessment are plotted against the total lifetime cost to generate
an eco-efficiency portfolio. Fig. 1-3 shows an example of portfolio plot.
Fig. 1-3: Portfolio plot for eco-efficiency by BASF for Indigo study, 0
The specific customer benefit is always in the centre of eco-efficiency analysis. In most cases,
the customer has a variety of alternative products or processes for selection. The analysis
compares the environmental and economic advantages and disadvantages of these products and
processes, thus the analysis provides a significant decision-making tool for the customer. In many
cases, also a social assessment is added.
2.4 COMPREHENSIVE ASSESSMENT OF ENERGY SYSTEMS
The method for the Comprehensive Assessment of Energy Systems was developed at the Paul
Scherrer Institute (PSI) in Switzerland. This method takes environmental and economic impacts,
health risks, and social aspects into account.
The aim of the Comprehensive Assessment of Energy Systems is to identify risks according to
their intentions. Different energy supply systems are the focus of this consideration. Furthermore
it should be shown which technical improvements of energy supply systems are available.

13
The life cycle assessment, ecosystem and health aspects are analyzed. In addition, a security and
risk assessment is carried out, in which the effects of serious accidents on the environment, health
and economic are considered. Finally, economic and social aspects are analyzed. The evaluation
is done with the assistance of various indicators. This allows analyzing both current and future
energy supply systems. In addition, the indicators also meet the demands of institutions such as
the OECD / NEA, or the UN / IAEA, which are provided for the assessment of energy systems.
The risk analysis is done through evaluation of the reports of any occurrence of events or with
the help of forecasting methods such as probability risk analysis. In the process the following
damages are considered: deaths, serious injuries, evacuations, contamination of air and water,
economic losses and loss of usable surface.
2.5 CONCLUSIONS
The Life Cycle Assessment approach has been chosen to be used for an assessment of the
environmental impacts of dispersed generation for this working group because it has become an
international ISO standard which is worldwide used and approved.
In the next chapters the environmental impacts of DG are analysed
i) From a component perspective: Lifecycle of a single DG unit (Chapter 3)
ii) From a system perspective: Usage of a DG in distribution network (Chapter 4)

14
3. ENVIRONMENTAL IMPACTS OF DISPERSED
GENERATION UNITS FROM A COMPONENT
PERSPECTIVE
3.1 OVERVIEW OF ENVIRONMENTAL IMPACTS FOR DISPERSED GENERATION
The table bellows shows a selection of the environmental impacts of dispersed generation that
should be considered in comprehensive environmental assessments by using LCA from the view
of this WG.
Technology Source (e.g. fuel) Environmental
impacts by
manufacturing,
operation and
disposal
Priority emissions of concern
micro turbines and internal
combustion engines
(Combined Heat & Power
systems) and fuel cells
Biomass (direct
incineration and
gasification)
1. Respiratory aspects
2. Climate change, bio
diversity, toxicity,
eutrophication
1. Particles
2. CO2, CH4, land and water
use, pesticide emissions,
nutrient emissions
combustion engines
systems)
Biomass (digestion) 1. Climate change and
eutrophication
2. Respiratory aspects,
bio diversity, toxicity,
odour
1. CH4, N-Emissions to air and
soil
2. Particles, CO2, land and
water use, pesticide emissions,
nutrient emissions
combustion engines
systems) and fuel cells
Natural gas, diesel,
oil
1. Climate change and
respiratory aspects,
resource depletion
2. Acidification
1. CO2, CH4 (losses by
transport), particles
Wind turbines Wind Manufacturing:
Global warming,
resource depletion
Operation:
Noise, land use, birds,
visual impacts
Solar power systems Sun Global warming ,
resource depletion,

15
Mini Hydropower systems and
tidal power systems
Potential energy Impact on biodiversity in
aquatic system and
occupied land
Geothermal power systems 1. Disturbance of
subsurface ecosystems
2. Global warming,
earthquakes
3.2 LCA RESULTS OF SINGLE DISPERSED GENERATION UNITS (COMPONENT
PERSPECTIVE)
Life Cycle Assessment studies on energy systems are readily available. One of the largest life
cycle inventory databases for energy conversion processes is the ecoinvent database (Swiss
Centre for Life Cycle Inventories, ecoinvent Version 2.2, www.ecoinvent.org, 2010). Below some
results of relevant dispersed generation units of this database are presented. It should be noted
that the LCA results of some types of energy conversion, in particular solar and wind, are very
much dependant on spatial conditions. Therefore, these results cannot be generalized and need
to be adapted according to the specific local conditions.
For solar power systems, wind turbines, fuel cells and combined heat and power (Sections 3.2.1
– 3.2.4), the data source was ecoinvent v2.2.
3.3 LCA OF SOLAR POWER SYSTEMS (PHOTOVOLTAIC SOLAR POWER
PLANT)
The environmental impact of photovoltaic electricity depends on the site of the installation (e.g.
exposure and intensity of solar irradiation, shading) as well as the technology (e.g. multi-Si,
single-Si), installation (e.g. angle) and operation. Figure 3-1 shows the average production mixes
of various countries, taking into account these differences and comparing them in terms of climate
change and resource depletion.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
BE GB DE CZ IE FI DK LU SE NO NL AT CH JP FR HU KR IT CA NZ GR AU PT TR ES US
kgCO2-equivalent
Photovoltaic electricity:Climate change (IPCC 2007)
[kg CO2-equivalent emissionsper kWhelectricity ]

16
Figure 3-1: Comparison of Photovoltaic production mixes in terms of Climate Change (IPCC
2007, above) and fossil resource depletion (Bösch et al. 2007, below): Different roof-mounted
photovoltaic production mixes in various countries (assumption: photovoltaic plant life time of
30 years). (Data source: ecoinvent v2.2)
The environmental impacts are not only depending on the location but as well on the technology.
Impacts of concern are energy-related impacts during the production of the cells and the use of
rare elements, contributing to resource scarcity. Figure 3-2 shows comparisons of different
technologies in terms of climate change and resource depletion. Photovoltaic cells are
investigated as panels and in a laminate design. While panels have their own supporting elements,
the laminated cells still need to be fixed at the building envelope. Single-Si means single crystalline
silicon cells, where multi-Si identifies multicrystalline cells. Additionally the figures show results
for ribbon-silicon (ribbon-si), amorphous silicon (a-si), Cadmium Telluride Photovoltaic laminates
(CdTe) and Copper Indium Selenide photovoltaic panels (CIS). The latter (CIS and CdTe) are thin
film modules. In Switzerland as well as in Europe, the photovoltaic fuel cells with multicrystalline
silicon cells, sold as panels and mounted at a slanted-roof hold a market share of over 30%
(Jungbluth et al. 2009 ).
0
0.2
0.4
0.6
0.8
1
1.2
BE GB DE CZ IE FI DK LU SE NO NL AT CH JP FR HU KR IT CA NZ GR AU PT TR ES US
kgMJ-equivalent Photovoltaic electricity:Resource depletion(CExD)
[MJ-equivalent non-renewable, fossilenergy resources per kWhelectricity]

17
Figure 3-2: Results for the impact category Climate Change (IPCC, above) and fossil resource
depletion (Bösch et al. 2007, below): Comparison of production technologies in Switzerland,
all with a capacity of 3kWp. (Assumption: photovoltaic plant life time of 30 years). (Data
source: ecoinvent v2.2)
The life cycle impact is determined by the infrastructure of the plant, as there are no impacts
during the operation phase. It should be noted that the environmental impacts will decrease in
the future, as photovoltaic technology is still in development and improving at a rapid pace. A
further decrease in the environmental impacts is expected due to advances in efficiency, improved
casting methods and a thinner wafer which will lead to a lower silicon demand (Penth 2005).
With regard to electricity from solar sources, parabolic troughs should be considered as well. The
sunlight is collected in mirrors and used to heat up water, which is used in steam turbines. CO2-
emissions of 13 g CO2-eq/kWh have been reported from facilities with a capacity of 80 MWel.
Parabolic troughs are mainly used in California and Spain. (Penth 2005, Viebahn 2004)
0
0.02
0.04
0.06
0.08
0.1
0.12
kgCO2-equivalent
Photovoltaic electricity:Climate change (IPCC 2007, above) andfossil resource depletion
(CExD, below)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
kgMJ-equivalent

18
The impact of photovoltaic electricity depends very much on the location, as with the same panels
more electricity can be produced in sunny locations than in less-sunny regions. In order to
calculate the environmental impact at a specific location, the spatial conditions need to be known.
There are various software programs available that contain historic weather data and allow for a
site-specific simulation of photovoltaic electricity conversion (e.g. Meteonorm). This information
can then be coupled with Life Cycle Inventory data of the production of photovoltaic panels, which
is readily available from databases (e.g. ecoinvent).
3.4 LCA OF WIND ENERGY CONVERTER
The wind conditions at the site (e.g. onshore/offshore) and the technology/size of the plant
influence the environmental impact. This can be seen in Figure 3-3, which shows the energetic
efficiency and thus the environmental impact per kWh generated in terms of climate change of
different wind turbines and at different locations. Figure 3-4 shows the impact on Climate Change
of the average European (2% offshore, 98% onshore) and Swiss wind power production mix.
Figure 3-3: Environmental impact in terms of Climate Change (IPCC 2007) per kWhel of various
wind turbines with different sizes and located at different sites. Assumption: life time of 40
years for fixed parts (for offshore plants only 20 years for foundation) and 20 years for mobile
parts; CF: Capacity factor. (Data source: ecoinvent 2.2)
0
0.01
0.02
0.03
0.04
0.05
0.06
30kW, CH,
(8% CF)
150kW, CH,
(9.5% CF)
600kW, CH y
(14% CF)
800kW, CH,
(14% CF)
800kW, EU, (20%
CF)
2MW, EU,
offshore
(30% CF)
kgCO2-eqivalent
Wind electricity: Climate change (IPCC 2007)
[kg CO2-equivalent emissions per kWh electricity ]

19
Figure 3-4: Results for the impact category Climate Change (IPCC 2007) of the European and
Swiss wind production mixes. While the European production mix is based on a wind power
plant producing 800 kWel, the electricity in the Swiss mix mainly comes from a 600 kWel-plant.
Roughly 40% are delivered by a 800kWel-plant and 2% by a 150kWel-plant. (Data source:
ecoinvent v2.2)
Similar to photovoltaic power plants, the life cycle impacts are determined by the infrastructure
of the plant and not the operation phase. In particular, glass fibre reinforced plastic for the rotor
blades, steel for the tower and gondola and concrete for the foundation are of high relevance
(the latter especially for offshore plants). Key factors determining the environmental impact are
the size of the plant, the life expectancy and the capacity factor, i.e. the available wind at the
particular location (Burger & Bauer 2007). These factors should be quantified in order to adapt
existing Life Cycle Inventory data to the specific conditions at the site of investigation.
Figure 3-5 illustrates the resource depletion and the land occupation of the different wind power
plants. As seen above, larger plants tend to have a smaller environmental impact. During the
operation phase, land use can be constrained close to the plant. However, agricultural use may
be unrestricted.
0
0.005
0.01
0.015
0.02
Production Mix Europe Production Mix Switzerland
kgCO2-equivalent
Wind electricity: Chlimate Change (IPCC 2007 )
[kg CO2-equivalent emissions per kWh electricity ]

20
Figure 3-5: Environmental impact of various wind turbines with different sizes and located at
different sites. Assumption: life time of 40 years for fixed parts (for offshore plants only 20
years for foundation) and 20 years for mobile parts; CF: Capacity factor. (Data source:
ecoinvent v 2.2)
3.5 LCA OF FUEL CELLS
The following figures show a comparison between proton exchange membrane fuel cells with 2
kWel (PEM FC) and solid oxide fuel cells with and without a micro gas turbine (SOFC and SOFC-
GT). The capacity of a single SOFC is 125 kWel. If the SOFC operates at elevated pressure, a
micro gas turbine with a capacity of 55 kWel can be added. This raises the efficiency of the total
system. All investigated fuel cells are not yet available as serial products and are therefore
indicated with the suffix future. The performance data is calculated either from field test or from
target values. (Primas 2007) In the below analysis, exergetic output was used to allocate
emissions and resource uses to electricity output.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
30kW, CH,
(8% CF)
150kW, CH,
(9.5% CF)
600kW, CH y
(14% CF)
800kW, CH,
(14% CF)
800kW, EU, (20%
CF)
2MW, EU,
offshore
(30% CF)
MJ-equivalent
Wind electricity: Resource depletion (CExD)
[MJ-equivalent non-renewable , fossil energy resources per kWh electricity ]
0.E+00
5.E-04
1.E-03
2.E-03
2.E-03
3.E-03
30kW, CH,
(8% CF)
150kW, CH,
(9.5% CF)
600kW, CH y
(14% CF)
800kW, CH,
(14% CF)
800kW, EU, (20%
CF)
2MW, EU,
offshore
(30% CF)
ReCiPepoints
Wind electricity: Total land occupation
[points per kWh electricity delivered]

21
The used fuel influences substantially the environmental impact of fuel cells, while the technology
is the second most important parameter. This can be seen in the following figures, which compare
the investigated types of fuel cells operated with natural gas or biogas in natural gas-quality
(Primas 2007). The biogas is produced from biowaste and sewage sludge. In figure 3-6 the fuel
cells are compared in terms of climate change and fossil resource depletion.
Figure 3-6: Comparison of fuel cells in terms of Climate Change (IPCC 2007, above) and fossil
resource depletion (Bösch et al. 2007, below). (Data source: ecoinvent v2.2)
In the case of fuel cells, the impacts of respiratory aspects, toxicity, eutrophication, acidification
and biodiversity (land use) should also be considered. In contrast to the above assessment, the
biogas performs worse than the natural gas with regard to these impact categories (Figures 3-7
0
0.2
0.4
0.6
0.8
biogas, at PEM
FC 2kWe
biogas, at SOFC
125kWe
biogas, at SOFC-
GT 180kWe
natural gas, at
PEM FC 2kWe
natural gas, at
SOFC 125kWe
natural gas, at
SOFC-GT
180kWe
kgCO2-equivalent
Fuel cells: Climate Change (IPCC 2007)
[kg CO2-equivalent emissionsper kWh electricity delivered]
0
2
4
6
8
10
12
biogas, at PEM
FC 2kWe
biogas, at SOFC
125kWe
biogas, at SOFC-
GT 180kWe
natural gas, at
PEM FC 2kWe
natural gas, at
SOFC 125kWe
natural gas, at
SOFC-GT
180kWe
MJ-equivalent
Fuel cells: Resource depletion (CExD)
[MJ-equivalent emissions per kWh electricity delivered]

22
– 3-8). However, within one fuel group (biogas from waste or natural gas) the ranking between
the various technologies remains the same for all impacts assessed.
Figure 3-7: Comparison of fuel cells in terms of ecotoxicity (Rosenbaum et al. 2008; above) and
human toxicity (Rosenbaum et al. 2008, below). The human toxicity includes carcinogenic and
non-carcinogenic emissions. (Data source: ecoinvent v2.2)
The toxicity is mainly caused by the production of biogas from biowaste and sewage sludge.
Concerning human toxicity, respiratory affects are assessed separatly with the impact assessment
method ReCiPe, particulate matter formation (Goedkoop et al. 2008).
0.00
0.04
0.08
0.12
0.16
biogas, at PEM
FC 2kWe
biogas, at SOFC
125kWe
biogas, at SOFC-
GT 180kWe
natural gas, at
PEM FC 2kWe
natural gas, at
SOFC 125kWe
natural gas, at
SOFC-GT
180kWe
CTU
Fuel cells:Ecotoxicity (USEtox)
[CTU per kWh electricity delivered]
0.0E+00
1.0E-08
2.0E-08
3.0E-08
4.0E-08
biogas, at PEM
FC 2kWe
biogas, at SOFC
125kWe
biogas, at SOFC-
GT 180kWe
natural gas, at
PEM FC 2kWe
natural gas, at
SOFC 125kWe
natural gas, at
SOFC-GT
180kWe
CTU
Fuel cells:Humantoxicity total (USEtox)

23
Figure 3-8: Comparison of fuel cells in terms of respiratory aspects with the indicator
“particulate matter formation” (ReCiPe; Goedkoop et al. 2008). (Data source: ecoinvent v2.2)
The land occupation is, for both fuels, very low, because biogas was assumed to be produced out
of sewage sludge and biowaste. This would be different in cases where biogas is produced from
primary agricultural products such as corn, grass and whey. In these cases land occupation will
be important and should be taken into account.
In contrast to photovoltaic power plants, the combustion of fuel and therefore the operation
phase determined the life cycle impacts.
3.6 LCA OF MOTOR ENGINE COMBINED HEAT-POWER (CHP) UNITS
Combined heat and power (CHP) integrates the production of electricity and heat in one process.
In the below analysis, exergetic output was used to allocate emissions and resource uses to
electricity output. The impacts result primarily from the operation of the plant, while the
production of the engine is of minor importance. Climate change is one of the major impacts to
consider. Depending on the fuel, also respiratory effects should be considered, as well as water
and land use in the case of biomass-fuelled CHP plants.
The impact of CHP plants depends largely on the fuel that is used (figure 3-9). On average, the
global warming potential of CHP’s with natural gas (NG) is more than twice as high as for the
systems that use biogas from waste (BG). The third possible fuel input that was analysed is diesel.
Although diesel engines are quite efficient, their environmental impact (expressed as global
warming potential) is relatively high.
For the combined heat and power plants that use natural gas as a fuel input, several technologies
are compared. Most systems run with a lean burn engine (i.e. λ > 1). Two of the analysed
technologies have a lambda of 1 (one is a non-specific plant that is typical for the CHP technology
models available in 2000, the other one is the specific CHP plant of Basel Jakobsberg that was
used as a reference). As this system contains a three-way catalytic converter it emits less NOx
0.0E+00
5.0E-04
1.0E-03
1.5E-03
2.0E-03
biogas, at PEM
FC 2kWe
biogas, at SOFC
125kWe
biogas, at SOFC-
GT 180kWe
natural gas, at
PEM FC 2kWe
natural gas, at
SOFC 125kWe
natural gas, at
SOFC-GT
180kWe
ReCiPepoints
Fuel cells:Particulate matter formation(ReCiPe (H/A))
[points per kWhelectricity delivered]

24
(Heck 2007).
For plants running with natural gas, larger plants tend to have a smaller global warming potential.
If biogas is used as a fuel input, the plant size is not the driving factor. More relevant is the fact,
whether or not the biogas plant has a covered stock and methane recovery. Biogas plants without
a covered stock cause higher methane emissions (Jungbluth et al. 2007). Moreover, the type of
biogas used influences the result.
While the global warming potential is smaller for biogas-based CHP’s, their impact on human
health (in terms of respiratory effects) may be considerable (figure 3-9, below).
Figure 3-9: Comparison of combined heat and power plants (CHP) in terms of Climate Change
(above) and particulate matter formation (below). BG: biogas, NG: natural gas. (Data source:
ecoinvent v2.2)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
kgCO2-eqivalent
CHP: Climate change (IPCC 2007, above) and particulate matterformation (ReCiPe (H/A), below)
0
0.001
0.002
0.003
0.004
0.005
0.006
0.007
ReiPepoints

25
Figure 3-10 illustrates the impacts of biogas-based CHP’s on human health and in terms
of ecotoxicity. Again, the impact of CHP plants with covered slurry storage is smaller.
Figure 3-10: Comparison of combined heat and power plants (CHP) in terms of ecotoxicity
(above) and human toxicity (below). BG: biogas. (Data source: ecoinvent v2.2)
The impact on eutrophication and land use is rather small because only biogas plants from
biowaste were considered. This might be different for biogas produced from primary agricultural
crops.
Figure 3-11 visualizes the impacts of the CHP using natural gas or diesel as fuel input in terms of
acidification and resource depletion. While in terms of acidification, the diesel engine performs
worse than the natural gas-based CHP’s, all the systems have a similar impact in terms of resource
depletion. A slight trend to lower impacts for larger plants can be observed.
0
0.01
0.02
0.03
0.04
0.05
50 kW, BG, CH,
covered, BG
mix (slurry,
fat/oil,
biowaste)
50 kW, BG, CH,
agric.
digestion
120 kW, BG,
CH, agr. Mix
160 kW, BG,
CH, covered,
agr. co-
digestion
160 kW, BG,
CH, agr. co-
digestion
160 kW, BG,
CH, BG mix
(biowaste,
sewage
sludge)
CTU
CHP: ecotoxicity above and humantoxicity below (USEtox)
0
4E-09
8E-09
1.2E-08
1.6E-08
50 kW, BG, CH,
covered, BG
mix (slurry,
fat/oil,
biowaste)
50 kW, BG, CH,
agric. digestion
120 kW, BG,
CH, agr. Mix
160 kW, BG,
CH, covered,
agr. co-
digestion
160 kW, BG,
CH, agr. co-
digestion
160 kW, BG,
CH, BG mix
(biowaste,
sewage
sludge)
CTU

26
Figure 3-11: Comparison of combined heat and power plants (CHP) in terms of Terrestrial
acidification (above) and Resource Depletion (below). NG: natural gas. (Data source: ecoinvent
v2.2)
3.7 LCA OF SMALL HYDRO-PLANTS
The impact on global warming potential for small hydro plants is about 10 g CO2-equiv./kWh
[Pehnt].
3.8 LCA OF SMALL GEOTHERMAL POWER PLANTS
The impact on global warming potential for small geothermal plants is about 38 g CO2-equiv./kWh
[Pehnt].
3.9 LCA OF SMALL SOLAR THERMAL SYSTEMS
The impact on global warming potential for small geothermal plants is about 13 g CO2-equiv./kWh
[Pehnt].
0
2
4
6
8
10
12
14
ca. 2 kW,
NG, CH
50kW, NG,
CH
160kW, NG,
λ=1,
Jakobsberg,
CH
160kW, NG,
λ=1,
typical, CH
200kW, NG,
CH
500kW, NG,
CH
1MW, NG,
CH
1MW, NG,
RER
200kW,
diesel, CH
MJ-equivalent
CHP: Resource depletion (CExD)
[MJ-equivalent emissions per kWh electricity delivered]
0.0E+00
5.0E-06
1.0E-05
1.5E-05
2.0E-05
2.5E-05
ca. 2 kW,
NG, CH
50kW, NG,
CH
160kW, NG,
λ=1,
Jakobsberg,
CH
160kW, NG,
λ=1, typical,
CH
200kW, NG,
CH
500kW, NG,
CH
1MW, NG,
CH
1MW, NG,
RER
200kW,
diesel, CH
ReCiPepoins
CHP: Terrestrial acidification (ReCiPe (H/A))
[points per kWh electricity delivered]

27
3.10 LCA OF ELECTRIC VEHICLES DEPENDING ON THE POWER
GENERATION MIX
Compared to the conventional vehicle with an internal combustion engine, the drive train of the
electric vehicle is much more energy efficient. The overall efficiency from charging the battery
until propelling the vehicle is around 85% for an electric vehicle, compared to an overall efficiency
of around 25% for the conventional vehicle. This means that the electric vehicle itself is very
energy efficient and therefore gives a good possibility to reduce the total energy consumption
per kilometre driven. However, the well-to-tank emissions which are produced during electricity
generation have an important contribution on the overall CO2 emissions of the whole chain. From
this perspective, it is really important how the energy is produced. There is a big difference in the
efficiency of the different electricity production processes, and especially a big difference between
the usage of renewable energy such as wind energy and the usage of coal or gas fired plants.
Usually, electric vehicles are using the electricity mix from the grid and thus do not derive their
energy exclusively from renewable energy sources (RES). The following figure 3-12 shows the
emissions for electric vehicles in comparison to an average conventional vehicle for various
countries when basing the calculation on a yearly average [Smokers]. Depending on the power
plant mix, the emissions per km vary from under 10g CO2/km in Norway (high percentage of
renewable energy sources especially water power) to over 170g CO2/km in Poland (power
generation mainly from coal-based power plants) [Szczechowicz]. The power plant mix in most
of the countries is a composition of different types of power plants as mentioned before.
Fig. 1-42: Well-to-wheel CO2 benefits of electric vehicles are different in different countries
[Szczechowicz]
A further refined methodology might however also take into consideration the fluctuation of the
CO2 emissions resulting from electricity generation and transmission, since the charging process

28
of EVs could easily be adapted to prefer times of low carbon emissions. Depending on the actual
power plant deployment and the highly volatile feeding of renewable energy into the grid in
particular, the specific emissions of the electricity mix vary in the daytime and have to be
considered for the ecological assessment electric vehicles [Szczechowicz i]. The following figure
3-13 depicts the daily fluctuations exemplified by the German electricity mix for a day in 2007.
Fig. 1-13: Time dependency of CO2 emissions of the electricity mix in Germany in 2009
[Szczechowicz i]
The shift towards sustainable vehicles will at some point involve drastic changes in propulsion
technology, potentially leading to significant changes in the life cycle impacts. In the case of
electric and plug-in hybrid vehicles especially the use of batteries and the use of electric motors
will have an impact. For fuel cell vehicles e.g. the use of platinum in the catalyst may significantly
change life-cycle impacts. The use of advanced lightweight materials, such as fibre reinforced
composites, to reduce vehicle weight may also be expected to significantly affect life-cycle
impacts. For batteries and other components of sustainable vehicles such impacts relate to
pollutant emissions, GHG emissions and waste produced in the production (incl. mining and
production of materials) and in the process of decommissioning (collection and disposal or
recycling). Producing batteries and e.g. electric machines also implies use of scarce materials and
possible depletion of resources. For electric machines the availability of rare earth metals may
become a problem. Such impacts from the production chain will have to be assessed in a Life
Cycle Assessment [Del Duce].
In view of this it is of paramount importance that policies that promote the deployment of battery-
driven vehicles also promote the set-up of a satisfactory battery collection and recycling system.
Environmental legislation applying to battery production, collection and recycling facilities should
also be reviewed and if necessary amended to avoid adverse impacts.
Currently the GHG emissions associated with production and recycling of vehicles (incl. mining
400
450
500
550
600
650
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
CO2emissionsingCO2/kWh
Hour
Diurnal variations - CO2
Average Workingday Weekend

29
and production of materials) are on the order of 10% of the total GHG emissions emitted in the
utilisation phase as a result of energy consumption for driving. This ratio may change in the
future, among other reasons this is due to the fact that vehicles will become more energy efficient
and will emit less CO2 during driving as a result of existing and upcoming legislation. Consequently
the energy used and amount of greenhouse gases emitted in production and recycling will become
a higher percentage of the energy consumed and GHG emission produced in the utilisation phase.
It should be noted here that developments that may contribute to meeting long term GHG
reduction targets may conflict with other environmental policies. An example in Europe is the
regulation for CO2 emissions from passenger cars vehicle efficiency and the Directive 2000/53/EC
on end-of-life vehicles. The latter directive requires that “no later than 1 January 2015, for all
end-of life vehicles, the reuse and recovery shall be increased to a minimum of 95 % by an
average weight per vehicle and year. Within the same time limit, the re-use and recycling shall
be increased to a minimum of 85 % by an average weight per vehicle and year.” Composites
used to reduce vehicle weight are intrinsically difficult to recycle. They can be collected and
incinerated with energy recovery, but according to the directive no more than 10% of the vehicle
weight may be processed in this way. As an all-composite structure could amount up to 20% of
a vehicle’s weight, the use of such materials would thus be in direct violation with Directive
2000/53/EC. Batteries should be designed to allow good recyclability in order to avoid conflicts
with the end-of-life vehicle directive.

30
4. METHODOLOGY FOR ASSESSING THE
ENVIRONMENTAL IMPACTS OF DISPERSED
GENERATION IN DISTRIBUTION NETWORKS FROM
A SYSTEM PERSPECTIVE
In this chapter a methodology for assessing the environmental impacts of dispersed generation
in distribution networks (system perspective) is described, which illustrates the application of Life
Cycle Assessment. The methodology was defined in a PhD thesis [Smolka iv] and has been slightly
modified within the work of the working group C3.05.
4.1 ANALYSIS OF THE USE OF DG IN DISTRIBUTION NETWORKS
The basic technical design of the model is shown in Fig. 1-5. It represents the supply of electrical
and thermal energy of the end customer in a distribution network. Essential elements are the
sub-systems to generate electricity and heat, which are coupled via electrical energy. The sub-
system of primary energy carriers can be considered as decoupled.
The system boundary of the model includes all material and energy flows of decentralized energy
supply to achieve the electricity and heat demand from the end customers, which belong to the
sectors of private households as well as commercial offices and service companies.
Fig. 1-5: Basic coupling of the subsystems in the model
Energy Supply of a
Distribution Network (MV/LV)
Thermal and Electrical
Energy Demand
of End-Consumer
Dispersed
Generation Units
<50MW
Large Power
Plant
(Thermal,
Nuclear, Hydro,
Solar, Wave
Power Plant
etc.)
>50MW
Output of the System:
· Thermal Energy
· Electrical Energy
Primary Energy Carrier (e.g. Coal,
Gas, Biomass, Wind, Sun)
Thermal Energy
Thermal Energy
Heat Supply System
(Heat Network)
Power Supply System
(Power Grid)
High Voltage
Network
(HV/EV)
Elec. +
therm.
Storage
Elec.
Storage
Elec.
Storage
Electrical Energy
Electrical Energy
DG Unit
<50MW
g CO2/kWhel
g SO2/kWhth
t CO2/a
....
Energy Supply of a
Distribution Network (MV/LV)
Thermal and Electrical
Energy Demand
of End-Consumer
Dispersed
Generation Units
<50MW
Large Power
Plant
(Thermal,
Nuclear, Hydro,
Solar, Wave
Power Plant
etc.)
>50MW
Output of the System:
· Thermal Energy
· Electrical Energy
Thermal Energy
Thermal Energy
Heat Supply System
(Heat Network)
Power Supply System
(Power Grid)
High Voltage
Network
(HV/EV)
Elec. +
therm.
Storage
Elec.
Storage
Elec.
Storage
Electrical Energy
Electrical Energy
DG Unit
<50MW
g CO2/kWhel
g SO2/kWhth
t CO2/a
....
g CO2/kWhel
g SO2/kWhth
t CO2/a
....

31
Sub-system for power supply in the service area
In the developed model the service area is connected to an overlaying power grid. Therefore it
is possible to obtain electrical energy from distributed energy resources and to realise energy
recovery to the overlaying grid.
In this model a differentiation has to be made between the global and the local electricity mix.
The global electricity mix consists of shares of electrical energy according to the power generation
from the power plants fleet. The local electricity mix within the balance area describes energy
from dispersed generation and energy ordered from the overlaying grid. Depending on the
dispersed generation the local electricity mix can deviate significantly from the global electricity
mix.
Fig. 1-6: Technical model of the electrical energy supply
Subsystem for the heat supply in the service area
The technical model of the heating system is shown in Fig. 1-7. According to the modelling goals
of the assessment of distribution energy resources, the heat supply is a priori provided by
decentralised CHP units in the model. In the case of insufficient coverage of the local CHP units,
a supply over the global heat mix is guaranteed.
Electrical Demand of the End Customer in the Model System
DG based on
Renewables
Environmental Impacts of
End-Consumers Electricity Demand
Gas Distr.
System
DG based on
fossil Energy
Carriers
Local Electrical Energy Mix
Balance Object
Electrical Energy
Supply of a Distribution
Network (MV/LV)
Global Electrical Energy Mix from
Large Power Plants of HV power Grid
Primary Energy Carrier (Coal,
Natural Gas, Oil etc.)
Renewables (Wind,
Sun, Water,
Biomass etc.)

32
Fig. 1-7: Technical model of heat supply
The heat mix represents the heat delivery which doesn’t belong to any CHP system in analogy to
the global electricity mix. The shares of the respective heat generation units at the heat mix are
implemented as arbitrary parameters.
4.2 MODEL FOR ENVIRONMENTAL ASSESSMENT OF ENERGY SUPPLY
The implementation of LCA in complex systems requires the use of assistive technology. The
model for the environmental assessment of distributed energy resources in distribution networks
can be realised in commercial LCA software tools. Among other things these software tools allow
material and energy flow analysis and LCA according to DIN EN 14040 for products or systems.
A system model for distribution energy resources in distribution networks would include an
analysis of the use of distribution energy conversion units in distribution networks and would
evaluate the associated impacts on the environment.
In addition the process chains (raw material production and fuel treatment) and manufacturing
of electrical resources and systems the electricity and heat generation processes are considered.
In 4.3 an LCA for an entire distribution network is shown in which specific life cycle stages have
influence on the ecological assessment.
Using the network model, it is possible to display the manufacturing and operation of electrical
resources and energy systems in distribution networks. Thus an environmental assessment of the
Thermal Demand of the End Customer
Heat Mix
Environmental Impacts of
End-Consumers Heat Demand
Secondary Energy from
Power Plant Fleet
Electr.
Power Grid
District
Heating System
Heat Pumps/ Direct Electr. Heating
Balance Object Electrical Energy Supply
of a Distribution Network (MV/LV)
DG based on
Renewables
Gas Distr.
System
DG based on
fossil Energy
Carriers
Primary Energy Carrier (Coal,
Natural Gas, Oil etc.)
Renewables
(Sun,
Biomass etc.)
Gas Distr.
System
Renewables (Wind,
Biomass, Geothermal
Sun, Water, Air etc.)
Primary Energy
Carrier (Natural Gas)

33
existing material and energy flows can be realised. To analyse the environmental impacts of a
dispersed energy supply, the components of the distribution network must also be considered.
4.3 LCA OF DISTRIBUTION NETWORKS
The method of LCA requires a review of the entire life cycle, as shown in Fig. 1-8. This also
includes a consideration of lifecycle parts manufacturing and disposal of the electrical resources
and systems. The influence of these lifecycle parts in the present model is considered by including
a life cycle assessment of energy supply in distribution networks. In the process the emissions
related to the manufacture and disposal are compared to the complete environmental impacts of
energy supply. Furthermore the LCA will include an accounting of current heat losses in power
grid as part of the model.
Manufacturing Disposal
Operation
Fig. 1-8: Entire life cycle
In [Mersiowsky] a comparative LCA of energy distribution of a medium-voltage power grid for a
rural and an urban distribution network was realised. There the existing current heat losses in
the distribution networks shown without taking into account the emissions from electricity
generation.
It can be seen that the current heat losses represent the dominant part of the environmental
impact. Approximately 73% to 92% of the total annual emissions in the relevant impact categories
consist of global warming potential, acidification potential and eutrophication.
An LCA of decentralized energy supply in exemplary distribution networks are shown in [9, 10].
The largest emissions in the distribution networks for the global warming potential category is
covered by the electricity itself as shown in Fig. 1-9. It is evident that the impacts of
manufacturing processes and emissions due to operating expenses (maintenance) are negligible.

34
The results show that the environmental impacts in all categories are expected to differ
significantly from the composition of power plant fleet and thus depend on the global electrical
power mix.
Fig. 1-9: Comparison of greenhouse gas emissions from energy
supply within the service area, according to [Smolka i]
In Fig. 1-10 the environmental impacts resulting from energy supply are faded out. Here the
current heat losses are dominant, followed by the environmental impacts caused by the
manufacture of large power plants and distributed generation units. Similar results were already
published in [Smolka, Smolka i, Smolka ii].

35
Fig. 1-10: Comparison of greenhouse gas emissions of a distribution network
without power consumption, according to [Smolka]
Based on these results, the subsequent environmental assessment is realised with a focused
consideration of the electricity and heating generation processes depending on the electricity and
heat demand by the end consumer. The upstream chains of the fuel supply are also considered.
Manufacturing and disposal processes of equipment and systems in distribution networks are not
considered, due to their limited influence in the context of this model.
4.4 DETERMINATION OF INFLUENCE FACTORS ON THE BALANCE OBJECT
The possible influencing factors on the balance object are varied for a particular subject. Thus, it
can be seen what influence raw materials production and fuel treatment, manufacture, handling
and disposal of equipment and facilities have on the balance object. As already discussed in
Chapter 4.3, under this model a focused consideration of the electricity and heating generation
processes is occurred. The contribution of the annual environmental impacts from production and
disposal are marginal.
The network structure of the balance object can be considered as another major influencing
factor, which cannot be categorised by individual factors in the context of a scenario analysis. To
define the influence of different network structures on the results of scenario analysis (cf. Chapter
4.5), an application of the valuation model in similar distribution networks has to be performed.
In this way possible influences of different network structures can be detected.
Other influencing factors are the different technologies to generate electricity and heat with their
individual efficiencies. The following types of power plants as well as distributed energy resources
should be implemented in the model:

36
Power plants:
· nuclear power plant
· lignite-fired power plant
· hard coal fired power plant
· gas-fired power plant
· hydroelectric power plant
· co-generation power plants
· geothermal power plants
· oil-fired power plants
Distributed energy resources:
· motor driven block heat and power plant
· fuel cell block heat and power plant
· photovoltaic units
· wind energy plant (onshore)
· Solar thermal
For an assessment of the environmental impacts of existing material and energy flows on energy
supply among to the various types of power plants the electrical equipment should be considered.
The following electrical installations and equipment are considered in the model system.
Electrical equipment:
· electric power substations between HV/MV and MV/LV
· transformers between HV/MV and MV/LV
· air and gas insulated medium voltage switchgear 10/20 kV
· power cables between 400 V and 110 kV
· overhead line between 10kV and 380 kV
The operational emission data from the large-scale power plants and most DG systems and their
heating systems can be derived from existing LCA databases (e.g. www.ecoinvent.ch). Different
procedures are used to evaluate the environmental impacts of the operation of CHP systems. The
allocation of emissions will depend on the combination used for the generation of heat and power,
cf. Chapter 5.4.
4.5 SCENARIO ANALYSIS
In many areas of business economics and public planning the scenario analysis has been used to
forecast future developments. The application of this analysis should help to show realistic
development opportunities in the distant future, depending on basic conditions. The developed
model for environmental assessment of future dispersed generation in distribution networks uses
these approaches for scenario analysis.

37
4.6 METHODOLOGY OF THE SCENARIO ANALYSIS
Scenario analysis is applicable especially for non-quantitative statements in areas where
uncertainties are large. The single scenario can depend on controllable as well as uncontrollable
factors [Zäpfel].
The result of scenario analysis indicates an image of a possible future by consideration of causal
relationships. Afterwards possible extrapolations of these factors are forecasted and combinative
future scenarios are created from possible development directions.
Individual scenario analysis has been used to forecast future developments. But there are many
disadvantages to using a single scenario to predict future outcomes. To compensate, individual
scenarios analysis’ will combined to create a composite scenario. In this way the impact of
significant events on the future development will emerge and then a range of possible future
developments can be reviewed. The scenario is the result of the described method and "goal" of
a development path [Zürni].
The approach for scenario analysis can best be described with the help of the scenario funnel
shown in Fig. 1-11. Two desperate scenarios will build the border of the funnel. This funnel spans
variations of an n-parameter or n-dimensional state space.
Analyzing
Actual Situation
Influencing Factors/ Trends/ Basic Conditions
Influencing Factors/ Trends/ Basic Conditions
Longitudinal Analysis
2010
2020
2030
Positive
Desperate Scenario
Negative
Desperate Scenario
Trend Scenario
Cross-Section Analysis
A
A1
Failure Decision Point / Correction
Scenario Developement of Scenario
Change of Developement
Pathby Failure
Fig. 1-11: Presentation of a scenario funnel, according to [Zürni]
Changes or faults in strategy are leading to scenarios with different results but these results are

38
all still within the state area. This process is a great advantage of the scenario analysis. Without
appearance of not forecasting events, which are outside the state area, it can be secured with
very high probability that in the future resulting scenario is pictured actual.
4.7 APPLICATION OF SCENARIO ANALYSIS
With the help of the cross-sectional analysis, for example, the differences between various
versions of the integration of dispersed generation or the use of various technologies are
displayed. Using the longitudinal analysis it is possible, to investigate the same development path
of a technology at different points in time.
As shown in Fig. 1-12, the usual approach of the scenario analysis can be considered on the basis
of five sub-steps. Please see case study for a detailed approach.
3.) Operationalization of the key factors respectively determining the relationship
of individual factors to each other as well as modeling of the object of
investigation.
2.) Determination of influence factors on the balance object and its future
development potential.
1.) Identification of the object of investigation and analyzing the actual situation
5.) Simulations of the scenarios for the balance object and evaluation of the
results.
4.) Elaboration of possible scenarios in the form of a complete analysis and
transfusion of the scenarios on the subject of investigation for the simulation of
the model system
Fig. 1-12: Steps of the scenario analysis by [14]
4.8 MODELLING AND IMPLEMENTATION OF THE PROCEDURE

39
As part of a material and energy flow simulation for the scenarios of distributed energy supply of
the studied service areas, a quantitative assessment of possible environmental effects must be
enabled. There are a lot of suitable model system software tools available to model the material
flow analysis for example GaBi, SimaPro, Umberto etc.

40
5. CASE STUDY OF THE IMPACTS OF DISPERSED
GENERATION IN DISTRIBUTION NETWORKS
5.1 CASE STUDY - COMPARISON OF FUTURE ENERGY SUPPLY SCENARIOS
WITH COMBINED HEAT POWER UNITS IN DISTRIBUTION NETWORKS
[SMOLKA IV]
5.2 APPLICATION OF SCENARIO ANALYSIS
With the help of the cross-sectional analysis, for example, the differences between various
versions of the integration of dispersed generation or the use of various technologies are
displayed. Using the longitudinal analysis it is possible, to investigate the same development path
of a technology at different points in time.
As shown in Fig. 1-12, the usual approach of the scenario analysis can be considered on the basis
of five sub-steps. Please see case study for a detailed approach following the following five steps:
1. Identification of the object of investigation and analyzing the actual situation
The scope of the work carried out in this scenario analysis is the task to supply electrical and
thermal energy demand of customers in distribution networks with different shares and
technologies in dispersed generation. The studies are realised based on different supply areas.
2. Determination of influence factors on the balance object and its future development
potential.
To identify the influence of different network structures on the results of the scenario analysis,
there will be an assessment of miscellaneous distribution networks presented in step four of the
scenario analysis. In regard to the analysis of the described model in Chapter 4.2 the following
factors are considered in the model Table 5-1:

41
Influencing
factor
Origin Controllability
Power
requirement
inside low
Heat
requirement
inside middle
Global power
mix
outside high
Heat mix outside middle
Fuel mix outside middle
Dimensioning
of CHP units
(kVA-factor)
inside high
Table 1-2: Exogenous influencing factors of the model system
For the deduction of policy recommendations also the controllability and the origin of the variables
in relation to balance volume are estimated. Controllability is understood as the opportunity,
through an active policy to influence the impact factor. The origin of a factor provides information
which factors are subject to local change and which factors can be influenced by actions outside
the balance volume.
power and heat demand
The influencing factors electricity and heat demand of the balance object (distribution network)
are available in the form of exemplary supply areas. The future development of the electrical
demand according to the forecasting development of step four of the scenario analysis will be
accepted. The development of heat demand depending on the electricity demand will be pictured
over the HPR-factor (Heat-to-Power-Ratio). The change of the heat demand will be estimated
using the HPR factor. The factor is defined as follows:
elVG
thVG
E
Q
HPR
,
,
 (1-1)
with:
QVG,th: complete heat demand within the distribution network
EVG,el: complete electricity demand in the distribution network
Both factors are inside the balance object. The factors show a low controllability for electricity

42
demand and a middle controllability for heat demand due to efficiency policies and aid programs
to reduce demand.
The global electrical energy mix describes the development of the power plant fleet on the basis
of predicted variants. With these variants a scenario funnel of potential development options is
spanned for the future power plant fleet. The influencing factor of the global electrical energy
mix is out of the balance object and a medium controllability is adopted depending on incentive
programs.
Heat mix
The heat mix indicates the heating structure of the different types of radiators for the heat
generation of the housing supply. The influencing factor heat mix is within the balance object
and has an average controllability due to politically aid programs for modification to more energy-
efficient or renewable heating systems.
Fuel mix
The influencing factor fuel mix is composed of the fuel mixture of the natural gas distribution
network to energy conversion in CHP units and condensing boilers. Here the distribution of various
supplier countries for fossil natural gas as well as potential future shares of biogas in the natural
gas network are considered. An analysis of the environmental impact of biogas use in
cogeneration units can be seen in [Smolka iii].
3. Operationalization of the key factors respectively determining the relationship of
individual factors to each other as well as modelling of the object of investigation.
The dependencies of the defined factors among each other based on the technical model are
shown in Figure 5-2.

43
Balance Object
Thermal
Supply of the
End Customer
Electrical
Supply of the
End Customer
Dimensioning
CHP units
+
Heating
Structure
System Boundary
Emissions
Fuel Mix
Local Power Mix
Heat Mix
Global Power Mix
Global Power Mix
Local Power Mix
Fig. 1-13: Dependencies of the influence factors
Here the coupling of the local and global electrical energy mix must be considered with the
thermal demand in the form of electrical heating systems. The fuel mix has a direct influence on
the heat mix and the local power mix because of his need as fuel for heating systems and
decentralized CHP plants.
The modelling of the object for investigation of distributed energy supply is realised by a technical
model with LCA software considering the dependencies of the influencing factors.
4. Elaboration of possible scenarios in the form of a complete analysis and transfusion of
the scenarios on the subject of investigation for the simulation of the model system.
The influencing factors are used according to the approach of the scenario analysis by using a
combination of extreme values and assumptions to span a state space. The variation of the input
parameters allows an assessment of the sensitivity of the parameters on the results of the holistic
assessment of the balance object depending on the selected scenario variant for energy supply.
To setup a scenario funnel it will be considered a reference scenario and two extreme scenarios
for the development of the power plant fleet. This allows an illustration with a wide range of
possible developments. The variants differed in the assumptions for the development of the
power plant fleet. The new building of power plants under consideration of age-related
decommissioning of power plants is a good basis to determine a reference scenario for the
development of the power plant fleet. The bases for this are economic and political conditions
and investment decisions, which are already taken. The development of power consumption is
equal in all variants to allow a comparison of the different variants. In the scope of this analysis
a possible development of the three scenario variants is presented using the example of Germany.

44
Development of the reference scenario
The identified construction of power plants using fossil fuels and the projected development of
wind turbines for the period up to 2030 results to a massive change of shares of various energy
sources at electricity generation in the reference scenario and is shown in Figure 5-3.
Key features of the reference scenario are:
· Termination of use of nuclear power by 2020
· Decline in power generation from lignite
· Strong growth in electricity generation from natural coal
· Strong growth in electricity generation from offshore wind turbines
Fig. 1-14: Generation capacity by energy sources in the reference scenario
Development of the scenario variant 1
The extension of the operating time of the nuclear power plants up to 40 years in the scenario
variant 1 leads to a reduced share of coal power plants on electricity generation. The development
of wind power follows the development of the reference scenario.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2020 2030
Sharesofelectricitygeneration
Nuclear Lignite Hard Coal Gas Oil
Hydro Wind Onshore Wind Offshore PV Biomass

45
Fig. 1-15: Generation capacity by energy sources in the scenario variant 1
Development of the scenario variant 2
In scenario variant 2, the use of nuclear energy expires in 2020 in accordance to political
guidelines. Due to adopted rising gas prices a trend to use existing primary energy carriers such
as lignite and natural coal is assumed. The generation capacity by energy sources in the scenario
variant 2 is shown in Figure 5-5.
Fig. 1-16: Generation capacity by energy sources in the scenario variant 2
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2020 2030
Nuclear Lignite Hard Coal Gas Oil Hydro Wind Onshore Wind Offshore PV Biomass
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2010 2020 2030
Nuclear Lignite Hard Coal Gas Oil
Hydro Wind Onshore Wind Offshore PV Biomass

46
Based on the input data of the share of energy carrier on electricity generation in these three
variants the global power mix can be estimated from the distributed system of each variant. The
global power mix represents the generation mix of the power plan network according to the
shares of similar power plant on the entire electricity generation. The individual variants represent
potential barriers to the development of the power plant fleet. Within the scope of the scenario
analysis, the identified specific emission values of electricity generation from large power plants
are accounted as global power mix, depending on the reference date as input data for the volume
of purchased electricity from the overlaying grid for the considered service area.
Besides the development of the different scenario variants the HPR factor must be determined.
The HPR factor depends on the structure of the supply area and the different composition of the
consumer structure. Representative supply areas have a heat to power ratio from 3.5 to 4.1.
The outcome of this is an analytical framework considering the extreme values of the influencing
factors. Figure 5-6 shows an example of an analytical framework.
CHP
Dimensioning
Primary Energy
Mix
Electrical
Energy Mix
Heat Mix HPR
Reference Case without CHP 0% Biogas
100% Natural Gas Worst Case Worst Case 4:1
CHP
(heat oriented)
Decentral 10% Biogas 90%
Natural Gas
Reference Reference 3:1
Local
Heating
District
Heating
MCFC/SOFC
Fuell Cell
20% Biogas
80% Natural Gas Best Case Best Case 2:1
Fig. 1-17: Example of an analytical framework of the scenario analysis
5. Simulations of the scenarios for the balance object and evaluation of the results.
The modelling and the action of the simulation for the created scenario variants are described in
the following chapter.
5.3 ENERGY MODEL FOR THE OPERATION OF CHP SYSTEMS IN
DISTRIBUTION NETWORKS
Operation management of CHP systems
By using a cogeneration system the electrical as well as the thermal consumer demand should
be satisfied. The load curve of the thermal and the electrical energy demand of each consumer

47
is uncorrelated. Because of the correlated electricity and heat generation within the CHP there
can be set no consistent operation mode of the cogeneration plants a prior. In principle two
modes of operation processes are distinguished:
· heat-operated
· power-operated
Within the scope of this model the simulated systems have no facility to conduct unnecessary
heat. Hence there has to be a necessary operation management of the CHP, which only operates
the CHP plant by existing thermal demand.
To maximise the coverage of heat through the operation of cogeneration plants, a heat-operated
application is assumed. In the summer, a operating of CHP beyond the thermal demands does
not make sense, since this leads to a reduction of the synergistic effects of CHP and increase the
environmental impacts.
During the summer and downtime of CHP there are possible potentials for delivery of balancing
energy. This applies if the dispersed CHP plants equipped with communication technology and if
they operate as virtual power plants.
Determination of electrical and thermal external procurement
Based on the identified and approved standard load profiles according to [Fünfgeld], the energy
recovery and external procurement of electrical energy are determined by the following formula:
FbelRelVbeldezel PPPP ,,,,  (1-2)
with:
VbelP , : power consumption of consumer at the node
RelP , : energy recovery of distributed CHP systems into the overlaying grid
FbelP , : external procurement of the grid
dezelP , : cogeneration power of distributed CHP systems at the node
In periods of insufficient demand or on rising over the electrical demand from CHP plants at the
nodes the necessary electrical energy will be provide from the overlaying grid or will be recovered
to this grid.
The heat supply of the nodes in the model system occurs depending on the scenario variant of
the existing distributed energy resources or from the heating structure of the heat mix. The
following equation must be fulfilled:

48
0,,  HeatMixthKWKthth PPP (1-3)
with:
KWKthP , : thermal heat output from CHP plant
HeatMixthP , : thermal heat capacity from the heating structure of the heat mix after
scenario variant
5.4 CREDITS AND ALLOCATION PROCEDURES
The system model includes a coupled electricity and heat production by CHP plants. There are
two methods for allocation the emissions to the cogeneration products electricity and heat:
· System expansion (credit procedure)
· allocation procedure
Credit procedure
The credit procedure assumes that the cogeneration products can substitute alternative energy
production methods. In this case all occurring emissions e.g. are allocated to the produced
amount of electrical energy. Then an emission credit can be given for the produced heat. At this
time it must be decided subjectively which alternative heat generation should be chosen. For
example, if the emissions can be substituted from a gas, oil or biogas burner.
Allocation procedure
Emissions and resource uses may be allocated to the two output products of CHP, i.e. electricity
and heat, according to physical-chemical relationships. Other alternatives for allocation are, for
instance, allocation according to the economic returns of the respective output products.
Within the scope of this assessment of CHP plants, the allocation is applied according to the
exergy content of the output products. Here the quality beside of the actual energy amount of
the cogeneration products is considered. This will provide a direct comparison of the specific heat
emissions of the different heating systems in various scenarios, cf. [Men98].
In this model the electricity-related allocation factor (AFel) is defined as:
)()(
)(
thel
el
el
kWhValuekWhValue
kWhValue
AF

 (1-4)
Because of the unlimited changeability in any other form of energy, the electrical energy, as well
as the chemical energy in fuels, will be given the valance “one”.
The valence of heat or the exergy content of a heat flow is depending on the temperature level
of the expiring carnot process and will be set to 0,17 CHP units.
The emissions related to electricity or heat are calculated in CO2-equivalents, depending on the

49
current related allocation factor and the total emission of fuel of coupled energy conversion
process in the model. Example of the calculated CO2-equivalents of the impact category GWP:
)1(**
**
elthelel
totalelel
electrical
AFQAFE
GWPAFE
GWP

 (1-5)
)1(**
*)1(*
elthelel
totalelth
thermal
AFQAFE
GWPAFQ
GWP


 (1-6)
with:
elE : generated amount of electrical energy
thQ : generated amount of thermal energy
GWPtotal: Total emissions of the coupled energy conversion process included the
consideration of upstream chain of fuel (in CO2 equivalents) to produce elE and
thQ
Procedure of energy recovery in overlaying grids
In the model system, all emissions from electricity and heat generation to meet the demand are
invoiced to the customer of the balance object “distribution network”. The amount of current
used in the balance object is charged with the emissions of the global electricity mix in case of
energy flow from the overlaying grid to the distribution network. So these emissions will be
considered in the calculation of the environmental impacts of the balance object.
In times of low electricity load, but high heat load energy recovery can be occurred, due to the
cogeneration in CHP plants. Other reasons for energy recovery are good weather conditions for
wind turbines and photovoltaic systems. The corresponding emissions of these exports are not
calculated to the environmental impacts of the balance object, because they may not be charged
to the end consumer within the balance object.
A credit against external factors is not allowed, because this would lead to a overreaching of
energy recovery. This credit preferred unilateral production systems with high electrical efficiency
and an operating strategy, which leads to a high energy recovery.
5.5 TRENDS IN ENERGY CONSUMPTION AND GENERATION IN GERMANY
The overall trend of electricity consumption from 2000 to 2030 in Germany can be estimated as
nearly constant by analyzing the sectors industry, private households, trade and commerce and
transportation as shown in Fig. 1-18.

50
Fig. 1-18: Trend of Electricity Consumption in Germany
Possible increases in electricity consumption caused by a higher use of air conditioning and
information and communication technologies will be compensated by higher energy efficiency.
In contrast to, the overall heat demand up to 2030 will reduced significantly. In the sector private
households the heat demand is predicted to be reduced up to 20% while the heat demand in the
trade and commerce sector could be downsized up to 30% due to better insulation of private and
business houses, as shown in Fig. 1-19.
Fig. 1-19: Trend of Heat Consumption in Germany
Based on the possible trends in energy consumption three possible scenarios in electricity
generation can be described. The reference case would be a trend development in electricity
0
50
100
150
200
250
2000 2002 2010 2015 2020 2025 2030
PowerConsumption[TWh]
Households Trade and commerce
Industry Transportation
Transportation incl. E-mobility
100
200
300
400
500
600
700
800
2000 2002 2010 2015 2020 2025 2030
HeatConsumption[TWh]
Households Trade and commerce

51
generation based on the status-quo following the most likely environmental - political parameters.
By extending the operating time of the existing nuclear power plants and assumed a high
integration of renewable in the future the “best case” scenario can be described. The “worst case”
scenario under ecological aspects for Germany would be a reduced development of electricity
generation by renewable energy combined with a high amount of new coal fired power plants
which would lead to the highest specific CO2-e. emissions of the future electrical energy mix in
Germany as shown in Fig. 1-20.
Fig. 1-20: Emissions of Electrical Energy Generation in Germany (three scenarios)
5.6 SCENARIO ANALYSIS OF DIFFERENT ENERGY SUPPLY SCENARIOS IN
DISTRIBUTION NETWORKS
To evaluate the ecological and technical impacts of future energy supply scenarios in distribution
networks a scenario analysis of possible trends has to be done. Figure 5-10 describes the chosen
system evaluation model including the considered influencing parameters specifying the model.
350
400
450
500
550
600
650
2010 2020 2030
EmissionsingCO2-equiv./kWh
Reference Best Case Worst Case

52
Balancing Object
Energy Supply of a local
Distribution Network
Heat Demand
Electricity
Demand
Dispersed
Generation
Units
Large Power
Plants
Emissions
Primary Energy
Carrier
Primary Energy Carrier
Electr. Energy
Therm. EnergyTherm. Energy
Electr. Energy
Heat Supply System
Electrical Power
Supply System
Elektr.
Energie
Electr. Energy
Fig. 1-21: Definition of the system model and description of the considered influencing
factors
The system model is described by the following selected influencing factors:
· Electrical Energy Demand
· Heat Demand
· Electrical Energy Mix
· Heat Mix
· Primary Energy Carrier Mix
· Dimensioning of the CHP units
· Heat to power ratio of the distribution network
By a variation of the influencing parameters and simulation of each scenario in the implemented
system model in the material and energy flow software Umberto the environmental impacts of
each scenario can be calculated. Table 5-2 presents the analytical framework of the scenario
analysis. For a detailed analysis of the model see [18].

53
CHP
Dimensioning
Primary Energy
Mix
Electrical
Energy Mix
Heat Mix HPR
Reference Case without CHP 0% Biogas
100% Natural Gas Worst Case Worst Case 4:1
CHP
(heat oriented)
Decentral 10% Biogas 90%
Natural Gas
Reference Reference 3:1
Local
Heating
District
Heating
MCFC/SOFC
Fuell Cell
20% Biogas
80% Natural Gas Best Case Best Case 2:1
Table 1-3: Analytical Framework of the scenario analysis
The scenario analysis is done for two different distribution networks described in table 5-3.
Distribution Network A B
Region Urban Rural
Voltage Level [kV] 10 10
Share of households [%] 0,63 0,66
Share of Trade an Commerce [%] 0,37 0,34
Peak Load [MW] 26,6 33,7
Electrical Energy Demand [GWh/a] 46,8 44,1
Heat Demand [GWh/a] 165,3 179,9
Heat to Power Ratio (HPR) 3,5 4,1
Table 1-4: Figures of the considered distribution networks
The scenarios with CHP units are divided in three variants, all dimensioned on the local heat
demand:
· CHP Decentralized: The CHP units are installed in each household (1-50kW).
· CHP Local Heating: Each CHP unit powers the underlying network of a medium voltage
transformer (50-1000kW).
· CHP District Heating: Only a few CHP units are installed in the medium voltage network
including a heat supply by using a district heating system (1-10MW).
5.7 MODELLING AND IMPLEMENTATION OF THE CASE STUDY
As part of a material and energy flow simulation for the scenarios of distributed energy supply of
the studied service areas, a quantitative assessment of possible environmental effects must be
enabled. A suitable model system and the corresponding data are realized in the LCA and material

54
flow analysis software Umberto [Umberto].
The simulation of the large number of considered scenarios and the required needs for
automation was realised by the development of an additional software module. Through this an
automated loop processing of several procedures will allow:
· Node based dimensioning of cogeneration plants in dependence of the CHP model
configuration and the local heat demand at the node
· Calculation of scenario variants after defaults of the analytical framework for a service
area and compilation of a data file for the simulation
· Determination of energy recovery and external procurements by the connection to the
overlaying grid
· Calling the Umberto computing core and batch based simulation of the scenario variants
based on a data file
· Interpreting the results from the internal Umberto database and building an output file
for further analysis
Modelling
The basic procedure in the model system is a scenario approach from three model components,
as shown in chapter four.
On the basis of identified electricity and heat demand data from the end customers of the balance
object different variants to the expansion of decentralized cogeneration plants are created. These
different variants are compared with a reference system based on a power supply from global
power plants and local decentralized heating supply without CHP. After the dimensioning of the
cogeneration plants there will be an automated calculation of the energy flow of the supply area
in the software Umberto to realise on the basis of these results an analysis of the simulated
scenario variants. A detailed modelling process can be found in [Smolka iv].

55
2.b) Interpretation of the scenario variant to the heat supply of the node
CHP dimensioning
Calculated for all
nodes?
Yes
No
3.) Simulation and analysis of scenarios
1.) Determination of energy supply in the balance object
Reference
dimensioning
without DG CHP
Heat supply by CHP?
2.a) Selection of the scenario variant depending on theHPR factor
(4:1, 3:1, 2:1)
YesNo
Dimensioning
complete
Power Supply Heat Supply
Fig. 1-22: Structure of the simulation model
5.8 EXEMPLARY RESULTS
Exemplary Results for the urban network (A)
The specific emissions in heat and electrical energy mix show the results of the scenario analysis

56
of CHP integration in the urban distribution network in figure 5-12.
Fig. 1-23: Specific Emissions of the different scenarios in the urban distribution network (A)
with HPR 3:1
The dominant scenarios are P2 (CHP with district heating) with lowest electrical energy mix and
heat mix emissions and P3. The scenario with no CHP units installed (P1) has the lowest specific
electrical energy mix emissions but higher heat mix emissions due to the use of conventional
heating systems instead of heat out of high efficient cogeneration units. It is obvious that the
scenarios with small CHP installed in each household have ecological disadvantages compared to
CHP units in a larger power range which have a higher electrical coefficient efficiency.
The overall installed CHP capacity in the distribution network lies below the maximum grid load
in most of the considered scenarios. The installation of fuel cells with a higher electrical coefficient
than motor CHP units lead for a HPR 3:1 and 4:1 to a - the maximum grid load exceeding -
installed overall capacity as shown in figure 5-13. In these scenarios it has to be analyzed if local
network transformer or other components get overloaded in peak load times and have to be
replaced.
0,05
0,1
0,15
0,2
0,25
0,3
0,35
0,35 0,4 0,45 0,5 0,55 0,6
GWP Electrical Energy Mix in kg CO2 -equi./kWh
GWPHeatMixinkgCO2-equi./kWh
no CHP CHP decentral CHP local heating CHP district heating Fuel Cell local heating
P1
P2
P3 x
HPR
m *
1

G1

57
Fig. 1-13: Installed CHP capacity in the urban distribution network (A)
Exemplary Results for the rural network (B)
The ecological impacts of the scenario analysis in a rural network show differing results compared
to the urban network. The dominant scenario with lowest electrical energy mix and heat mix
emissions is a scenario with no CHP units installed. Caused by a high amount of one-family houses
a proportion of 74% electrical heat pumps were installed in this scenario. Combined with the
lowest electrical energy mix of 380g CO2-equi. /kWh this scenario has the lowest overall emissions
as shown in figure 7.
The overall installed CHP capacity exceeds the maximum grid load in the scenarios with fuel cells
for HPR 3:1 and 4:1 and in the CHP district heating scenario with 4:1. Here it has to be analyzed
if local network transformers or other components (cables etc.) get overloaded in peak load times
and have to be replaced.
CHP-Decentral
CHP-Local Heat
CHP-District
Heat FC-Local Heat
Maximum Grid
Load
HPR 2:1
HPR 3:1
HPR 4:1
0
5
10
15
20
25
30
35
40
InstalledCHPPowerinMW
Variant

58
Fig. 1-24: Installed CHP capacity in the urban distribution network (B)
5.9 SUMMARY AND OUTLOOK
Different scenarios of CHP integration (decentralized, local and district heating with CHP units)
have been analyzed and compared to a centralized power supply with conventional local heating
systems. The integration scenarios are specified varying the identified influencing factors of the
power generation mix of centralized power plants, the structure of conventional heating systems
(heating mix), the fuel mix and the local heat-to-power ratio in distribution networks. Due to the
evaluation method developed it is possible to evaluate and compare multiple decentralized energy
supply scenarios considering ecological and technical circumstances. These are then compared
to an energy supply following the reference development without dispersed generation.
In highly populated areas a CHP-integration should be realized in combination with local or district
heating networks. Integration of decentralised CHP units into medium or highly populated areas
shows a higher CO2- reduction potential compared to an optimization of present large power
plants on the system level. Decentralized generation units with a high electrical efficiency are
necessary if assessing a significant reduction in heat demand in future scenarios. Assuming a CHP
unit dimensioning on the local heat demand – as today’s customary – leads to an installed capacity
which is much higher than the maximum electrical load. Here, feed in power in the overlaying
system is likely to occur during light load times. This variation requires dimensioning of the CHP
units according to the local power demand. Additional integration of electric consumer loads such
as heat pumps, electrical air-conditioning or electric vehicles can be an opportunity to reduce
generation into the overlaying system. Fuel cells do not have any ecological advantage compared
CHP-Decentral
CHP-Local Heat
CHP-District
Heat FC-Local Heat
Maximum Grid
Load
HPR 2:1
HPR 3:1
HPR 4:1
0
10
20
30
40
50
60
70
InstallierteBHKWLeistunginMW
Auslegungsvariante

59
to CHP units equipped with internal combustion using standardized natural gas. Only using
hydrogen based on renewable sources (e.g. surplus funds of wind energy) as combustion gas in
CHP plants leads to ecological advantages.
The integration of dispersed generation with CHP units is a local implemented method reducing
green house gases and increasing energy efficiency in distribution networks. In addition to the
promotion of CHP integration in distribution networks a national strategy has to be implemented
in order to achieve the targets of the formulated environmental production by reducing the
consumers’ energy demand and improving the electrical energy mix of the overlaying system.

ENVIRONMENTAL IMPACT OF DISPERSED GENERATION

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ENVIRONMENTAL IMPACT OF DISPERSED GENERATION