The 11th International Symposium on District Heating and Cooling,
August 31 to September 2, 2008, Reykjavik, ICELAND
METHODOLOGY AND STRUCTURE OF COMPUTATIONAL MODEL TO ESTIMATE
ECONOMICAL AND TECHNICAL PROFITABILITY OF SMALL SCALE COMBINED HEAT AND
POWER PLANTS
E. Latõšov1
A. Siirde1
1
Department of Thermal Engineering
Tallinn University of Technology, Tallinn, ESTONIA
ABSTRACT
This paper presents a methodology for creation of a
computer program to estimate the applicability of
combined heat and power plants (CHP) based on
renewable bio fuels, recognizing the high primary
energy efficiency and low CO2 emission from such
plants. In this paper, the main initial data classes and
interaction between them as well as economical
calculation methods and requirements to modeling
principles are discussed.
INTRODUCTION
This paper is draws on ongoing PhD thesis ‘Analysis
on the technical and economical consequences by
CHP systems based on renewable energy in new
areas with lowered useful heat demand or after
energy conservation measures in areas with older
buildings’ within the NER’s partly financed project
‘Primary Energy Efficiency’ which contribute to the
effort of enhancing the primary energy efficiency
PEE and reducing CO2 emissions in the energy
sector.
The paper considers the model structure, and
main model related subjects for a creation of
computational program to estimate economical and
technical profitability to build a combined heat and
power plant based on renewable fuels in
Nordic/Baltic Sea Region countries taking into
account the local conditions.
The paper is structured as follows. After an
overview of the methodology and methodology
related subjects, the paper will provide an overview
of the model main components and their distinctive
features in view of composition and usage of the
model. The last section will provide an overview of
data collecting and interpretation techniques, which
will help to estimate the initial data for the program
databases and single case calculations.
METHODS
The calculations are based on a range of initial data
classes:

local climate data (temperature duration
data),

local energy prices and forecasts,

local indexes and forecasts,

consumer heat consumption and forecast,

consumer electricity consumption,
duration and forecasts,

project initial finance data.

CHP technologies related data
load
These data are then used in the main calculations
to generate payback times, internal rate of return and
other measures for evaluating investments. Data
types and interactions between them are given in
Appendix B as concept visual structure of a planed
computer program.
Evaluation of the economical and technical
profitability of chosen small scale CHP concept is a
complex of optimization processes with aim to
achieve an optimum between CHP electrical
capacity (technical component) and one of the
measures for capital budgeting (economical
component).
Program has to take into account both electricity
based and heat led operation strategies for further
calculations.
Proposed model is based on assumption that
electrical capacity is main technical component and
all the other technology related and economical
figures are basically in correlation with it. This
approach seems approved, because handbooks and
studies mainly represent values of CHP typical
figures in the same way.
Heat load, MW
P48
P7
3,5
P36
2,5
P5
P24
1,5
P3
P12
0,5
P1
00
0
2000
4000
6000
8000
Hours
Figure 1 – Typical district heating area heat load
duration curve
Primary Energy Efficiency (PEE) is a PhD project in Nordic Energy Research
The 11th International Symposium on District Heating and Cooling,
August 31 to September 2, 2008, Reykjavik, ICELAND
Considering heat led operating strategy,
theoretically it is possible to choose CHP nominal
heat load capacity, which is evaluating thought the
nominal electrical capacity, within the range shown in
Fig. 1.
Increasing of nominal electrical capacity will have
effect on the other CHP technical, economical and
operational data. At once it will have an influence on
cash flow formation, which is a basis for calculating
of NPV, IRR and other measures for evaluating
investments (Table 1).
Table 1 - Heat led operation strategy; change of
calculation components value and its effect on cash
flow in case of CHP capacity increase
Calculation components Component Effect on
cash flow
value
Fixed O&M costs
Variable O&M costs
Specific investment costs
Heat production
Electricity production
Fuel costs
Technologies for smaller CHP applications are
more expensive (specific price) and less efficient
than larger CHP, while usage of large scale CHP
units is restricted to heat load availability (income
from heat and electricity production). The
summarized effect of CHP scale factor on project
cash flow may be both positive and negative.
In Fig. 2 can be seen a theoretically proposed
IRR and NPV values of CHP project, where IRR and
NPV levels are in accordance to CHP nominal heat
production, which is equal to district heating area
heat loads shown in Fig. 1.
The shape of above shown IRR and NPV curves
depends on all the initial data used for calculations
and it may vary significantly.
In current example P2 capacity is the most
attractive for investor for getting maximum IRR. At
the same time maximum NPV is achieved at P5
capacity, but the investor risks are higher.
In addition to economical indices during planning
of heat supply system based on biofueled CHP some
other aspects has to be considered. And of them is a
PEE and CO2 emission reduction.
Enhancing PEE by expanding the market for
district heating based on energy production by CHP
has a great potential since a great part of the
electricity in the EU is still produced from fossil fuels
in condensing power plants.
In Fig. 3 can be seen a typical plots of total
annual electrical efficiency and PEE potential in
accordance to CHP nominal heat productions (equal
to district heating area heat loads shown in Fig. 1).
Shape of proposed PEE curve is closed to CO 2
saving curve.
In this example PEE is calculated as ratio
between energy of primary fuel consumed for useful
energy production (heat and electricity) in the district
heating area and energy of primary fuel used for
generating the same amount of heat in the boiler
house and electricity in the condensing power plants
separately.
0,88
EFF
PEE8
0,77
EFF
PEE7
0,6
EFF
6
PEE6
0,5
EFF5
PEE5
0,4
EFF
4
PEE4
0,33
EFF
PEE3
0,58
IRR
NPV8
0,22
EFF
PEE2
0,4
IRR
NPV7
0,11
EFF
PEE1
0,3
IRR
6
NPV6
0,2
IRR
5
NPV5
0,1
IRR
4
NPV4
IRR03
NPV3
7
-0,1
IRR
2
00 1000
P 1 2000
P 2 3000
P 3 4000
P 45000
P 56000
P 6 7000
P 7 8000
P8
Heat load, MW
-0,2
NPV2
NPV1
IRR1
-0,3
- NPV
- IRR
Figure 2 – IRR and NPV curves according to CHP
nominal heat capacity
00
0
1000
P 12000
P 23000
P 34000
P 45000
P 56000
P 6 7000
P 7 8000
P8
Heat load, MW
- PEE, primary energy efficiency
- EFF, annual electrical efficiancy
Figure 3 – Annual electrical efficiency and PEE
potential (CO2 saving) curves according to CHP
nominal heat capacity
Fig. 3 indicates that annual electrical efficiency is
poor for small CHP nominal capacities and increases
up to some optimum level whereupon it begins to
diminish, but then PEE factor may still grow up.
Primary Energy Efficiency (PEE) is a PhD project in Nordic Energy Research
The 11th International Symposium on District Heating and Cooling,
August 31 to September 2, 2008, Reykjavik, ICELAND
Taking into account stated above descriptions of
the optimum levels between CHP electrical capacity
and other figures it is important to mention, that:

Since the biomass CHP units are relatively
expensive, before making the investment
decision the CHP is mainly selected in such a
way that its annual utilisation time and working
time at nominal load would be possibly long. This
approach is the most commonly used for
selecting of CHP capacity and the most rational
choice for private entrepreneurs, which guaranty
higher rate of return.

Since the expansion of CHP is limited by the
demand for the useful heat it is important to
consider for each district heating area maximum
PEE and CO2 reduction potential. Mostly it is
possible to achieved higher PEE at higher CHP
capacities.
TECHNOLOGIES UNDER CONSIDERATION
There are numerous CHP technologies that can be
theoretically used for small scale CHP systems
(Fig. 4).
Figure 4 – Typical district heating area heat load
duration curve
Market
Demonstration plant
Pilot plant
Laboratory testing
CHP technologies
Steam turbine
Steam engine
ORC process
Stirling engine
Gas engine (gasification, pyrolysis)
Gas turbine (gasification, pyrolisis)
Fuel cell (gasification)
In this program it is important to consider first of
all market ready solutions, such as steam turbine,
steam engine and ORC technology. It is reasonable
to start preparation of databases for technologies
which are close to reach market level, as an example
gas engine technology based on gasification or
pyrolysis of solid biofuels.
Each technology should be determined in model
by:

capital cost,

operating cost per kWh electric produced,

fixed operating and maintenance costs of
technology (€/kW),

overall efficiency,

nominal electrical efficiency,

electrical efficiency decrease working on
partial load.
ECONOMICAL CALCULATIONS
The revenues of a CHP company are generated
from the heat and electricity sales and they must
cover the costs of the company completely. The
costs include:
 fuel cost;
 capital costs (investments made, repayment of
loans and interests);
 variable and fixed operation and maintenance
(O&M) costs.
For calculating the capital cost, the interest rate,
length and terms of repayment and the plant lifetime
have to be also considered. The source of financing
is not important at the investment appraisal, i.e., from
the point of view of project appraisal, it is not
important whether the project is financed from the
owner’s equity or by a bank loan.
The bio CHP investment cost can be input either
as a constant or a function of the electrical capacity
of the CHP. In small scale CHP plants
interdependence between electrical capacity and
specific investment cost is considerable.
While planning future investments the most
complicated item is forecasting the price changes,
because the reimbursement of planned investments
depends highly on the fuel prices at the repayment
period. The prices on fossil fuels are mainly
developed in the international fuel market and for the
biofuels also, the international trade is increasingly
influencing their prices.
The fuel prices are more and more influenced by
environmental taxes, but it concerns mainly fossil
fuels and improves the competitiveness of biofuels.
For the evaluation of the economic feasibility of
investments it is useful to consider the O&M costs
separately as fixed and variable costs. The fixed
costs include costs that do not depend on the heat
output, and are approximately proportional to the
plant nominal value. The fixed O&M costs include,
for example the salaries of employees. Since the
heat loss in DH pipelines does not depend on the
amount of supplied heat, the heat loss in pipelines
can also be considered as the fixed costs.
The profit can be understood and treated
differently – in many countries the profit is a financial
source used for investments, technology upgrading,
mitigation of environmental impact and improvement
of labor conditions. The owner’s profit from the
power company’s activities, i.e., dividends from the
shares, may be either allowed or prohibited.
In some countries (e.g., in Denmark) the DH
companies are usually owned by local authorities
and earning the profit for owner is forbidden. In other
countries most of DH companies are privately owned
and the owners’ interest in earning reasonable profit
provides better management.
Primary Energy Efficiency (PEE) is a PhD project in Nordic Energy Research
The 11th International Symposium on District Heating and Cooling,
August 31 to September 2, 2008, Reykjavik, ICELAND
HEAT LOAD SIMULATION AND CLIMATE DATA
For the heat load simulation heat demand data
and climate data is necessary.
An important part of simulating CHP plants is the
integration of the temperature duration data to the
heat load duration data simulation.
In order to provide a realistic picture of
temperature duration the trustworthy and up to date
source of datasets should be used.
One of the sources is data from European
Climate Assessment & Dataset project, where part of
the dataset is freely available for non-commercial
research.
The heat demand can be established in several
ways. Regarding consumers connected to the district
heating (DH) system to which the heat has been
supplied for many years already with their actual
heat consumption being measured, the heat demand
of previous years can be taken for the basis. It is
recommended to use the data of the last three years
at least. The data on a very long period is not
expedient to use either, because in this case the
heat supply could be significantly influenced
(reduced, as a rule) due to the renovation/insulation
work made in the building in the meantime.
Thereby, the consumption for heating should be
normalized with using the degree hours or degree
days calculation methods.
It must be underlined that when estimating the
future demand based on the earlier consumption
level, evident decrease of heat load in the future has
certainly to be taken into account. The reason for the
reduction can be additional thermal insulation of
buildings, more flexible control of heat consumption,
introduction of energy saving appliances and
changes in the consumption habits, etc. The rising
prices (both heat and water) and for example
installation of water meters in each flat can also
reduce consumption.
In order to estimate the heat demand correctly,
possible accession of new consumers must certainly
be considered too. The respective information should
be available in the detailed plans of the district, if
these have been developed by local authorities. The
situation is even better when a development plan of
energy supply is available.
For the consumers who are going to be
connected to the DH system in the near future, the
project documentation related to heat supply can be
used for establishing the heat demand in case of a
new building. When the new consumer is the one
who has used the local heating before, the data on
the earlier consumption can be used. For the
consumers who are going to be connected to the
system later, the demand could be estimated more
simply based on the volume of buildings, number of
inhabitants, etc.
Above mentioned heat load simulation approach
is appropriate for planning new district heating areas,
and renewing old district heating systems, where no
continuous measurements of heat production are
executed. Moreover program has to leave
opportunities to use continuously collected heat
production data, which will more exactly define
district heating area heat load and consumption.
DATA COLLECTING AND INTERPRETATION
Several techniques are available to help to estimate
the cost and performance data in the project. Based
on the project’s scope, the purpose of the estimate,
and the availability of estimating resources, the
estimator can choose one or a combination of
techniques.
Specific Analogy Technique
Specific analogies depend upon the known cost of
an item used in prior systems as the basis for the
cost of a similar item in a new system. Adjustments
are made to known costs to account for differences
in relative complexities of performance, design, and
operational characteristics.
Parametric Technique
Parametric estimating requires historical data
bases on similar systems or subsystems. Data is
derived from the historical information or is
developed from building a model scenario. Statistical
analysis is performed on the data to find correlations
between cost drivers and other system parameters,
such as design or performance parameters. The
analysis produces cost equations or cost estimating
relationships that can be used individually or
grouped into more complex models. This technique
is useful when the information available is not very
detailed.
A critical consideration in parametric cost
estimating is the similarity of the systems in the
underlying database, both to each other and to the
system which is being estimated. A good parametric
database must be timely and accurate, containing
the latest available data reflecting technologies
similar to that of the system of interest
Expert Opinion Technique
When other techniques or data are not available, this
method may be used. Several specialists can be
consulted repeatedly until a consensus value of
parameter or functional dependence estimate is
established.
CONCLUSIONS
The novelty of planed computer program is an
orientation to small scale (under 5 MW el) distributed
CHP units, based on existing district heating
networks.
This paper highlights the importance of initial data
which is connected to local conditions of supposed
CHP construction place. They are meteorological
data, predicted heat demands and types of heat
Primary Energy Efficiency (PEE) is a PhD project in Nordic Energy Research
The 11th International Symposium on District Heating and Cooling,
August 31 to September 2, 2008, Reykjavik, ICELAND
consumers, local economical figures, CO2 emission
permit prices, fuel prices, environmental fees, bio
fuel prices and availability, electricity prices and
taxes. Above mentioned trustworthy and up to date
initial data is a strong basis for construction of heat
load duration curves and economical calculations in
cooperation with CHP technology related initial data.
CHP technology-related initial data classes,
which include CHP specific prices, electricity and
heat efficiencies (also the partial load values),
specify conditions for optimization and choice of the
most cost-efficient and CHP technology and CHP
plant capacity. Computer program have to calculate
PEE and CO2 reduction potential at different CHP
nominal capacities. This information may be
important for CHP developer as well as for institutes
for grant setting.
Methodology related calculations and initial data
handling shall be based on well known thermal
engineering and economical principles. The
accuracy of received results much depends on initial
data values, which is especially important in small
scale CHP plant calculation.
NOMENCLATURE
…………………
REFERENCES
…………………
Primary Energy Efficiency (PEE) is a PhD project in Nordic Energy Research
The 11th International Symposium on District Heating and Cooling,
August 31 to September 2, 2008, Reykjavik, ICELAND
APPENDICES
Appendix.1. Concept visual structure of a planed
computer program.
Location
Energy prices and forecasts
CHP location
DB: Electricity price, €/MWh
DB: Heat price, €/MWh
Consumer heat demand
DB: Steam price, €/MWh
Heat consumption for heating and ventilation, MWh
DB: Electricity price forecasts, -/+ % year
Domestic hot water consumption, MWh
DB: Heat price forecasts, -/+ % year
Steam consumption, MWh
DB: Steam price forecasts, -/+ % year
Heat transmision losses, MWh
Indexes
Heat components forecasts, -/+ % year
DB: Inflation, % year
Climate data
Electricicy production
DB: Temperature duration data
Annual electricity production duration data
Heat consumer annual heat load duration data
Electricity production forecasts, -/+ % year
Annual heat productions, MWh
Annual electricity productions, MWh
DB: CHP overall efficiency, %
DB: Electrical efficiency drop working on partial load = f (actual electrical capacity), %
DB: CHP minimum/maximum fuel load, %
CHP
DB: CHP nominal electrical efficiency = f (nominal electrical capacity), %
Technology
DB: CHP specific investment costs = f (nominal electrical capacity), %
Nominal electrical
capacity, MW
DB: Fixed and variable O&M costs
Fuel
Price, €/MWh
Specific CO2 emision, t/MWh
PEE
CO2 saving
Time
Begining of the project, date
CHP start up, date
Project duration, years
Economical calculations
Loan properties
Cash flows, €/year
Loan interest, %
IRR, %
Loan duration, years
NPV, €
Loan type
Payback times, years
Sources of Financing
Sensitive analysis
...
Equity, % of total funding
Grant, % of total funding
Loan, % of total funding
Else
Depreciation time, years
SPECIFICATION
- Initial data, input
- Subsidiary intermediate data and calculation results
- Output
- Optimizing
- Direction of components interaction
DB:
- Database availability
Primary Energy Efficiency (PEE) is a PhD project in Nordic Energy Research
The 11th International Symposium on District Heating and Cooling,
August 31 to September 2, 2008, Reykjavik, ICELAND
Primary Energy Efficiency (PEE) is a PhD project in Nordic Energy Research