Energy and Power Engineering, 2013, 5, 352-362 Published Online July 2013 (
A Financial Approach to Evaluate an Optimized
Combined Cooling, Heat and Power System
Shahab Bahrami*, Farahbakhsh Safe
Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
Email: *
Received January 7, 2013; revised February 8, 2013; accepted February 16, 2013
Copyright © 2013 Shahab Bahrami, Farahbakhsh Safe. This is an open access article distributed under the Creative Commons Attri-
bution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
Iran’s removing subsidy from energy carrier in four years ago leads to spike electricity price dramatically. This abrupt
change increases the interest on distributed generation (DG) because of its several benefits such as lower electricity
generation price. In Iran among all type of DGs, because of wide natural gas network infrastructure and several incen-
tives that government legislated to support combined cooling, heat and power (CCHP) investors, this type of technology
is more prevalent in comparison with other technologies. Between existing CCHP technologies, certain economic
choices are to be taken into account. For different buildings with different load curves, suitable size and operation of
CCHP should be calculated to make the project more feasible. If CCHP does not well suited for a position, then the
whole energy efficiency would be plunged significantly. In this paper, a model to find the optimal size and operation of
CCHP and auxiliary boiler for any users is proposed by considering an integrated view of electricity and natural gas
network using GAMS software. Then this method is applying for a hospital in Tehran as a real case study. Finally, by
applying COMFAR III software, useful financial parameters and sensitivity analysis are calculated.
Keywords: Combined Cooling Heat and Power (CCHP); Energy Hub; Optimal Size; Financial Analysis
1. Introduction
The development of poly-generation smart grids repre-
sents an interesting solution to satisfy electricity and heat
demand and emission reduction [1,2]: poly-generation
smart grids generate electricity, heating and cooling
thermal power close to end users, solving the main dis-
advantages of the centralized generation approach, due to
energy transmission [3]. In fact, the distributed genera-
tion approach has several benefits over the others, such
as: 1) reduction of transmission and distribution costs:
about the 30% of the costs related to electricity supply
relates to these costs. Local connections do not generate
high capital costs and energy losses for long distances to
be wired with overhead facilities; 2) decrease of energy
dissipation: piping and conversion devices dissipate al-
most 6% of produced energy, [4] increasing costs and
emissions; in a smart grid, these kinds of losses are
avoided; 3) increase of energy efficiency: the simultane-
ous supply of electrical and thermal demand allow to
reduce energy waste, improving system global efficiency;
since thermal energy is less easily transported than elec-
tricity, distributed generation approach (production close
to users) is essential. Because of mentioned benefits de-
regulation has evolved in all three sectors of the power
system (i.e. generation, transmission, and distribution)
from centralized to a decentralized status. One of the
main concepts in deregulation is Microgrids which are
used at the distribution level [1]. Microgrid, with its de-
centralized electricity generation, combined with on site
production of heat, could provide reliable and electric
power as well as heat and cooling to its consumers at an
economic cost. Nowadays, following the expansion of
natural gas networks and also benefits of this energy car-
rier such as lower emission level and prices, CCHP
technologies have attained unprecedented level of popu-
larity as one of the most important distributed energy
resources. The US Environmental Protection Agency
(EPA) found that the primary applications for CHP in the
commercial sectors are those building types with rela-
tively high and coincident electric and hot water demands
such as hotels, universities and hospitals. Use of CHP
thermal output for absorption cooling could increase the
efficiency of CHP system in commercial sectors, this
integrated system which provides these demands named
*Corresponding a uthor.
opyright © 2013 SciRes. EPE
combined cooling heat and power (CCHP).
One of the major factors for users to choose a CCHP
system is the overall cost of CCHPs which is largely de-
pendent on its size [1]. Hence finding the optimized size
of a CCHP is economically important.
Generally, an optimized CCHP can be evaluated by
analyzing two main factors: costs and benefits. Cost is
one of the main components in nearly all DG financial
analysis, but is inadequate for complete evaluations.
Furthermore, reliability enhancements [5], power cost
saving, power loss and emission reduction [6] are also
key elements in deciding which CCHP should be in-
The cost of generation of electricity, heat and cooling
from a CCHP can be classified into capital investment
cost, operation and maintenance (O&M) costs, fuel cost
and depreciation cost. On the other hand, the benefits from
the CCHP placement can be classified into power loss re-
duction, significantly decreasing the expected energy not
supplied which is a favorable effect in a power system.
CHP can inject its power directly into distribution
feeders and by alleviating transmission losses the bene-
fits of power loss reduction become quite clear [6].
Moreover, reliability enhancement has received substan-
tial attention as it reduces the costs of losses incurred by
utility customers as a result of power failures [7].
All of these costs and benefits are calculated in terms
of Present Value Factor (PVF), accumulated over the
economic life of the respective equipment. It is a com-
mon practice for a decision maker to translate future cash
flows into their present values.
From a number of recent publications [1-9], it can be
seen that in a deregulated power system, each individual
distribution company may wish to determine the costs
and benefits of DG planning under different circum-
stances. It is difficult to find a single planning method
that satisfies all objectives simultaneously. In this pa-
pe r , a value-based planning method for CCHP placement
based on the energy hub concept is proposed. The pro-
posed method takes the benefits and costs of CCHP
placement into account and determines the optimal sizing
for CCHP placement. Test results show that with proper
size selection, CCHP placement can be used to improve
service reliability, and reduce power loss and emission
costs [8].
The survey of previous literature on DER (Distributed
Energy Resource) planning as well as optimal DER de-
ployment in the radial (conventional) as well as meshed-
type distribution systems indicates that a number of
similar studies [9-16], encompass sensitivity analysis to
modern soft computing techniques, such as genetic algo-
rithms (GAs), evolutionary programming (EP), DER-
CAM, etc. Special mention can be made to [9] and [14].
Reference [9] proposes a method for distributed genera-
tor planning based on GAs and considers customer in-
terruption cost (CIC) as the benefit of distributed gen-
erators placement but the benefit of waste heat recovery
is not considered. Sheikhi et al. [10] proposed an opti-
mization model to find the optimal size and operation for
combined cooling, heat and power systems, in order to
reduce power loss and enhance service reliability of the
system. Reference [11] gives the economic analysis of
the microgrid, which evolves from the existing low-
voltage (LV) network, on the basis of cost and benefit of
potential reliability improvements. Reference [12] ex-
amines some successful experiences of DG in Central
Virginia Electric Cooperative. References [13,14] use the
DER-CAM technique to minimize the cost and proposes
a model for location and optimal selection of DER system,
which could work parallel to the macrogrid. Reference
[15] uses the evolutionary-algorithmic (EA) approach to
optimize placement of DG in a meshed microgrid. Ref-
erence [16] focuses on the optimal distributed generator
placement in a conventional radial distribution system.
The contents of this paper are organized into the fol-
lowing six sections. Determination of the optimum op-
erational point, the energy hub concept and a brief over-
view of the energy hub modeling are presented in Section
2. Section 3 discusses the potential benefits of deploying
energy hubs. Section 4 provides detailed formulations of
the problem and case studies are debated in detail in sec-
tion 5. Finally, conclusions are drawn in “List of Sym-
bols and Abbreviations”.
2. Energy Hub Concept and Modeling
Some conceptual approaches for an integrated view of
transmission and distribution systems with distributed
generation have been published. Besides “energy-ser-
vices supply systems” [17], “basic units” [18], and “mi-
cro grids” [19], so-called “hybrid energy hubs”, are sug-
gested, where the term “hybrid” implies the use of multi-
ple energy carriers [20,21]. An energy hub is considered
a unit where different energy carriers can be converted,
conditioned, and maybe stored. It represents an interface
between different energy infrastructures and/or loads.
Some worth mention works in this field are listed in Ta -
ble 1. Figure 1 demonstrates an example of an energy
The CHP device couples the three energy systems at
the same time that produces electricity, heat and cooling
from natural gas. The absorption chiller converts wasted
heat from CHP or produced one from boiler to cooling
Consider a converter device as depicted in Figure 2
converts an input energy carrier α into β. Input and out-
put power flows are not independent; they are considered
to be coupled,
Copyright © 2013 SciRes. EPE
Copyright © 2013 SciRes. EPE
which defines the coupling between input and output
energy flow. For a simple converter device with one in-
put and one output, the coupling factor corresponds to
the converter’s steady state energy efficiency.
Lc P
 
 (1)
where Pα and Lβ are the steady state input and output
energy flows respectively, cαβ is the coupling factor
A general model covering all types of couplings can be
stated all power inputs ,,,PP P
and outputs
,,,LL L
in vector form and enables the formula-
tion of a multi-input, multi-output power conversion as
follow [7]:
 
 
 
3. Proposed Optimization Method
Investigating the best size of CCHP, auxiliary boiler,
heating and electricity storage devices of an energy hub
system have a substantial effect on the users’ benefits.
This section proposes an analytical method to determine
the most advantageous selection.
Figure 1. An energy hub c ontaining an electr ic transformer,
a CCHP, a boiler (B), battery and heat storage.
To find the best ener gy hub elements between existing
Figure 2. Model of energy converter.
Table1. A summary of major works on Energy Hub (EH).
Author Approach Publish year
Andersson [22] A modeling framework for future e ne rgy systems 2007
Favre-Perrod [23] VoFEN (EH concept) 2005
Hemmes [24] EH concept 2006
Geidl [25] EH modeling 2007
Kopple [26] EH reliability 2012
Galus [27] PHEV in EH 2011
Kienzle [28] DSM &Uncertainty in EH 2011
Arnold [29] Renewable &MPC in EH 2011
Kraus [30] A country as EH 2011
Schuzle [31] Renewable modeling &Pricing in EH 2011
Parisio [32] A robust optimization on uncertainty environment in EH 2012
Carradore [ 33] Voltage regulation in EH 2009
Robertson [34] EH for multiple energy carrier 2009
Ramiraz-elizen do [35] Unit commitm e nt in EH 2011
Velez [36] Control strateg y for EH 2011
Chehreghani [37] Mathematica l optimization modeling of EH 2008
Syed [38] EH for plug-in hybrid fuel cell vehicle 2010
Sheikhi [1] Financial ana
lysis &optimal size & operation of EH 2012
Haghifam [39] Multi objective electric distribution system expansio n planning 2009
choices the value based planning is employed. The costs
of energy hub placement include investment, operation
and maintenance cost (O & M) of CHP, absorption
chiller, auxiliary boiler and heat and electricity storage
devices. To find the benefit term for CCHP placement, it
is assumed that the outputs of CCHP are fully sold.
Emission reduction and reliability enhancement are other
major terms that would be added to form the total benefit.
This planning method attempts to realize the minimum
cost solution where the overall benefits can be maxi-
The total input flow Pg splits up to different converters
such as CCHP and auxiliary boiler in Figure 1. Dispatch
factor, γ, specifies how much of the total input power Pg
flows through the CCHP. At the same time, absorption
chiller uses heating power to generate cooling.
show the proportion of heating power that is produced by
auxiliary boiler and CHP, consumed by the chiller. To
investigate optimal value of this parameter, an appropri-
ate objective function which is considered the net benefit
for the energy hub system has to be formed.
For modeling CO2 emission effects on power genera-
tion two factors,
e and
g, are introduced. These pa-
rameters show how many dollars you have to pay as
penalty more than the electricity and gas price to com-
pensate their harmful results for emitting greenhouse
Based on the social costs of carbon emissions, it is as-
sumed that the price of carbon is $30 per ton ($0.03 per
kg) which needs to increase with inflation rates [40]. With
these extensions, multi-period multi-carrier optimal power
flow and limitations can be stated as nonlinear program-
ming (NLP) structur e:
 
 
ein out
ee eNgeg
 
 
 
 
gfgh g
outh inh
 
 
 
11 1
ac goutcac
 
 
Equality constraints which describe the electricity, heat
and cooling flow through the hub are given in (3 - 5)
KK(N), CC(N) and H(N) indicates the amount of ex-
ported electricity, cooling and heat in each hour.
 
SE NSE NSENSEout N  (6)
 
0.9851 sinhSN SNN
south Nsoutc N
in m
SE NSE (8)
out m
SE NSE (9)
SE NSE (10)
sinh20 kwhN (11)
,15ksoutc Nsouth Nwh
The amount of heat in the heating storage devices and
electricity storage units are as in (6, 7). The coefficient
0.985 in Equation (7) shows 1.5% of stored heat gets lost
per hour. Constrains which describe in (8-12) indicate the
maximum and minimum input and output of battery and
heat storage. Sm in (13) is the maximum capacity of heat
PN P (14)
PN Pem
:0, ,1NNNN
Limitation of the dispatch factors in (16) has to be re-
garded as well.
2sinh hT
N southN soutcNP  (17)
ge e
PN g xPNeNX  (18)
Equation (17) is the price of pumping heat auxiliary
boiler or CHP in to the heat storage and from heat stor-
age to the heating load. PhT is the price of pumping heat
for each kWh.
 
 
Benf i t
ggh g
gh ac g
old OPT
LNeN XkkNeN Xb
 
 
 
 
 
costcapital costrunning costCPVF (20)
capital cost
costCHP $ kwcap
costBoiler$ kw
costChiller$ kw
Heating storogecost
Electircity storog cost
Fixed Cost
cap Boiler
cap chiler
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runing cost30maintenanceCostZZ (22)
360 maintenance cost per kWh
Cm is the maintenance cost per year. The main part of
maintenance cost is related to CHP then other mainte-
nance costs have been ignored. Equations (17)-(23) for-
mulate the benefit and cost in details.
 
ge g
gf g
NP NCapBoiler
 (25)
 
1 capChiller
gh gac
 
 
 (26)
 
seM M
Inequalities (24-26) show constrains for maximum
capacity of CHP, chiller and auxiliary boiler. Maximum
allowable values of heating, cooling and electrical power
for sale are given by (27).So the objective function of the
problem can be expressed as:
BMCBenefit Cost (28)
Using CHP to produce electricity eliminates the cost
of transmission and this is one of the important factors
that make CHP as a financially attractive option to pro-
duce electricity. Decreasing the cost of transmission dose
not benefit the end users directly and is a beneficial fac-
tor for governments. To make end users share th is benefit,
governments provide some bonus schemes for electricity
producers by distributed generation. This bonus has been
added to the base price of electricity exported to the grid.
Finding this added value needs some calculations and
suppositions .The effective efficiency considered as fol-
lows and it must be more than the mean efficiency of
conventional power generation.
 (29)
ηE : Effective efficiency.
ηe: Electrical efficiency.
ηt: Thermal efficiency.
μ: Percentage of the used heat of CHP.
The amount of saving natural gas when CHP is used to
produce 1 kWh of electricity is calculated as follow:
860100 100
 
SG: saving natural gas when 1kWh of electricity gen-
erated by CHP instead of conventional power generation
system [m3].
HVg: Heating value of natural gas [kcal/m3].
avg: mean efficiency of conventional power genera-
L: percentage of transmission loss of electrical grid.
Multiplying the natural gas price by the above value
results in the bonus th at would be added to the base price
of electricity:
natural gas pricebSG
Obviously, efficiency and size of energ y hub elements
considerably affect the optimum value of parameters (Pe,
The main objective in this paper is to calcu late the op-
timum size of CHP, absorption chiller and auxiliary
boiler in an energ y hub.
CPVF is the compounding present value factor. ir, if
and EL are respectively the per unit (p.u) interest rate, p.u
inflation rate and economic life of the equipment.
Fixed cost term in (22) consists of the cost of the cen-
tral controller, load controllers, interfacing equipment
and low voltage circuit breaker [41,42].
By introducing a CHP system, the reliability of sup-
plyingan electrical load increases substantially. This in-
crease depends on the size of CHP. To explain the effect
of CHP on power system reliability, Expected Energy
Not Supplied (EENS) shows up in th e (21). EENS is one
of the most important indices in generating capacity
adequacy evaluation. The EENS for one year can be cal-
culated using the following equation:
EENSMWh year
where Pk is the probability of having a capacity outage
equal to Ok; Ok, the magnitude of the capacity outage;
and Ak, the energy not supplied because of the capacity
outage Ok.
The Value of Lost Load (VoLL) in (19) is the esti-
mated amount that customers receiving electricity with
bilateral contracts would be willing to pay to avoid a
disruption in their electricity service [41].
4. Financial Analysis
The financial analysis of investment projects is typically
carried out using the technique of discounted cash flow
(DCF) analysis. This section introduces concept of DCF
analysis for the derivation of project performance criteria
Copyright © 2013 SciRes. EPE
such as net present value (NPV) and internal rate of re-
turn (IRR).
Discounted Cash Flow (DCF) analysis is the tech nique
used to derive financial performance criteria for invest-
ment projects. Cash flow analysis is simply the process
of identifying and categorizing of cash flows associated
with a project or proposed course of action, and making
estimates of their values. Discounted cash flow analysis
is an extension of simple cash flow analysis and takes
into account the time value of money. A number of crite-
ria are used in DCF to estimate project performance in-
cluding Net Present Value (NPV), and Internal Rate of
Return (IRR) and Dynamic Payback Period (DPP) [43].
4.1. Net Present Value
The net present value (NPV) is the sum of the discounted
annual cash flows.
A project is regarded as financially desirable if the
NPV is positive [43].
4.2. Internal Rate of Return (IRR)
The internal rate of return (IRR) is the interest rate such
that the discounted sum of net cash flows is zero. Gener-
ally speaking, the higher a proj ect's internal rate of return,
the more desirable it is to und ertake the project. As such,
IRR can be used to rank several prospective projects a
firm is considering. Assuming all other factors are equal
among the various projects, the project with the highest
IRR would probably be considered the best and under-
taken first [44].
The valu e of IRR such that
4.3. Dynamic Payback Period
The dynamic payback period (DPP) is the number of
years for the projects to break even, i.e., the number of
years for which discounted annual net cash flows must be
summed before the sum becomes positive (and remains
positive for the remainder of the project’s life). The dy-
namic payback period indicates the number of years until
the investment in a project is recovered. It is a useful
criterion for a firm with a short planning horizon, but
does not take accoun t of all the information availab le, i.e.,
the net cash flows for years beyond the payback period
5. Case Study
The high thermal to electrical ratio of hospitals shows
that hospitals have the potential to capture the waste heat
generated by a CCHP system. Healthcare inpatient facili-
ties can use the thermal energy for space heating and hot
water for laundry and kitchen facilities. The high values
of many key criteria show that healthcare inp atient facili-
ties offer great potential for large capacity CCHP sys-
tems. Many hospitals also proactively look for cost ef-
fective energy solutions because of their energy costs.
The potential to meet the high power quality an d reliabil-
ity needs with a CHP system is also of great interest to
The hospital which is con sidered as case stud y, Figure
3, has 10,000 square-meters comprised of three main
sections. The hospital operates 24 hours a day all year-
round (8760 hours per year). To increase the energy effi-
ciency of this hospital, a detailed energy audit has been
done for this hospital and installing a CCHP system was
introduced as one of the key solutions to decrease energy
price effectively because of its location, it is not possible
to sell heat and cooling energy and also there is no stor-
age device in this case study.
Energy load profiles (Figures 4-6) and energy price
(Figure 7) of this hospital are depicted as follow. The
price of cooling is considered to be 1.2 times more than
electricity price. Note that in Figures 5 and 6 there are
two load profiles. One of them denotes winter and autumn
day load sample and the other indicates load profile of
summer and spring days. Note that prices of electricity,
Figure 3. Hospital main building.
Figure 4. Electricity consuption in a normal day for the
hospital [summer and winter].
Copyright © 2013 SciRes. EPE
Figure 5. Heating energy consuption in a normal day for the
hospital [summer and winter].
Figure 6. Cooling energy consuption in a normal day for the
hospital [summer and winter].
Figure 7. Energy price.
cooling and heat have an escalation rate of 14% per year.
In this case, since extra recovered heat could not be sold,
the extra heat is passed to a heat dump radiator. The heat
dump radiator is cooled by electrically driven fans.
In this study, all efficiencies are independent of power
and have a constant value. The typical energy distribu-
tion for internal combustion engines is provided [46]. It
shows that 30% of the fuel energy is converted to heat
energy rejected through the coolant and another 30% of
the fuel energ y is rejected as heat thr ough the exh aus t gas.
The total efficiency of heat exchangers for the coolant
and exhaust gas is estimated to be 0.85, and the total
fuel-to-thermal-energy conversion efficiency (i.e., total
heat recovered from the engine) is then calculated to be
(30% + 30%)·(0.85) = 51%.
The boiler thermal efficiency (
gf) is assumed to be
90%. The total efficiency of the cooling components
(chiller efficiency) was estimated by considering the Co-
efficient of Performance (CoP), amount of heat moved
per unit of input work required, of an absorption chiller
and the efficiency of an air handling unit. A CoP of 0.7 is
used for the absorption chiller and an efficiency of 0.85
is used for the air handling unit. The total efficiency of
cooling components is then calculated to be (0.7) × (0.85)
×100 = 60%. The total efficiency of the heating compo-
nents is estimated at 85% which is the efficiency of the
air handling unit.
The thermal energy losses due to energy transport/
transmission in the network are neglected in this simula-
tion because the pipes are well insulated in the facility.
CHP and boiler costs depend on their sizes. Figures 8
and 9 depict these relations.
A summary of energy hub elements’ efficiency infor-
mation for the algorithm and the data needed for optimi-
zation problem are listed in Table 2. In Table 3, fix and
variable cost of absorption chiller are shown [30 ]. Bonus
for selling electricity to the grid is calculated by (22).
Furthermore, the boundary conditions are shown in Ta-
ble 4. Note that the all parameters are positive.
Size (kW)
Figure 8. CHP cost.
Figure 9. Boiler cost.
Table 2. Performance characteristics of CCHP and auxil-
iary boiler.
Cost ($/kWh) CHP
0.01 35% 40% 90% 98% 60%
Table 3. Cost of heating storage devices and absorption
Absorption chiller
Fixed cost ($) 20,000
Variables cost ($/kW) 115
Copyright © 2013 SciRes. EPE
The interest rate (ir) is 0.20 p.u., the inflation rate (if)
is 0.12 p.u., the economic life cycle (EL) of all equip-
ment is considered to be 15 years [47]. For Tehran Xe=
1.32 $/kWh and Xg=0.6 $/kWh [48,49]. In this case study,
analysis on Tehran Natural Gas shows that it has the
Higher Heating Value (HHV) of 10.35 kWh/Nm3.
As it was described in section 3 installing CHP as a
secondary supply for electricity, decreases EENS. In this
case study, since the hospital equipped with two standbys
1 MW diesel generator, this effect could be overlooked
[5]. To solve the above problem, GAMS software is used
and the best size of energy hub elements is evaluated.
Table 4 demonstrates optimal size of energy hub ele-
ments and Table 5 summarizes some important financial
Figures 10 and 11 depict imported and exported elec-
tricity in a winter and summer days. Between 10:00 a.m.
and 1:00 p.m. as electricity price is too high, CHP works
full loaded and exports electricity to the grid. Figure 12
shows the installed CHP system in the hospital which is
connected to the electricity network. Figures 13 and 14
depict how the payback period and IRR will be affected
according to variation in interest rates and component
prices. Figure 15 implies that if discount rate is less than
20%, NPV would soar dramatical l y .
Figure 10. Exported electricity to the grid.
Figure 11. Imported electricity from the grid.
Table 4. Optimized value of energy hub elements.
CHP Capacity
(kW) Auxiliary Boiler Capacity
(kW) Absorption Chiller Capacity
1750 4400 3000 kW = 850
refrigeration tons
Figure 12. Installed CHP system in the hospital.
Figure 13. Sensitivity of payback period.
Figure 14. Sensitivity of IRR.
Figure 15. Sensitivity of NPV.
Table 5. Financial parameters.
Internal Rate of Return
(IRR(%)) Net Present Value
(NPV(million $)) Dynamic Payback Period
64% 9.22 3
Copyright © 2013 SciRes. EPE
Finally, the net price of 1 kWh electricity which is
generated by CCHP calculates as follow:
Electricityprice by CCHP
Natural gas price
Totalelectricity generated by CCHP
O & M CostDepreciationCost
Totalelectricity generated by CCHP
Totalelectricity generated by CCHP
Benefitofheatrecovery for cooling
and heating
Totalelectricitygenerated by CCHP
4.36 CentkWh
[1] A. Sheikhi, A. M. Ranjbar, M. Mahmoody and F. Safe,
“CHP Optimize Selection Methodology for an Energy
Hub System,” 10th International Conference on Envi-
ronment and Electrical Engineering, Rome, 8-11 May
2011, pp. 1-5.
[2] US EPA, “Inventory of US Greenhouse Gas Emissions
and Sinks: 1990-2001,” EPA 430-R-03-004, US Environ-
mental Protection Agency, Washington DC, 2003.
[3] J. McDonald, “Adaptive Intelligent Power Systems: Ac-
tive Distribution Networks,” Energy Policy, Vol. 36, No.
12, 2008, pp. 4346-4351.
[4] V. H. Mendez, J. Rivier, J. I. De La Fuente, T. Gomez, J.
Arceluz and J. Marin, “Impact of Distributed Generation
on Distribution Investment Deferral,” International Jour-
nal of Electrical Power & Energy Systems, Vol. 28, No. 4,
2006, pp. 244-252. doi:10.1016/j.ijepes.2005.11.016
[5] R. Billinton and R. N. Allen, “Reliability Evaluation of
Engineering Systems, Conceptand Techniques,” 2nd Edi-
tion, Plenum Press, New York, 1992.
[6] R. Graham and W. Chow, “Technical and Economic As-
sessment of Combined Heat and Power Technologies for
Commercial Customer Applications,” EPRI Project Man-
ager, 2003.
[7] H. Cho, R. Luck, S. D. Eksioglu and L. M. Chamra,
“Cost Optimized Real Time Operationof CHP System,”
Energy and Buildings, Vol. 41, No. 4, 2009, pp. 445-451.
[8] A. Sheikhi, B. Mozafari and A. M. Ranjbar, “CHP Opti-
mize Selection Methodology for a Multicarrier Energy
System,” Power Tech Conference, Trondheim, 19-23 June
2011, pp. 1-7.
[9] J. Teng, Y. Liu, C. Chen and C.-F. Chen, “Value-Based
Distributed Generator Placements for Service Quality Im-
provements,” International Journal of Electrical Power
and Energy Systems, Vol. 29, No. 3, 2007, pp. 268-274.
[10] A. M. Sheikhi and R. H. Oraee, “Financial Analysis and
Optimal Size and Operation for a Multicarrier Energy
System,” Energy and Buildings, Vol. 48, 2012, pp. 71-78.
[11] A. Sheikhi and A. M. Ranjbar, “Optimal Dispatch of a
Multiple Energy Carrier System Equipped with a CCHP,”
ICREPQ Conference, Las Palmas de Gran Canaria, 2011.
[12] P. J. Mago and L. M. Chamra, “Analysis and Optimiza-
tion of CCHP Systems Based on Energy, Economical,
and Environmental Considerations,” Energy and Building,
Vol. 41, No. 10, 2009, pp. 1099-1106.
[13] R. Hashemi, “A Developed Offline Model to Optimal
Operation of Combined Cooling, Heating and Power Sys-
tems,” IEEE Transaction on Energy Conversion, Vol. 24
No. 1, 2009, pp. 222-229.
[14] P. M. Costa and M. A. Matos, “Economic Analysis of
Microgrids including Reliability Aspect,” 9th International
Conference on Probabilistic Methods Applied to Power
System, Stockholm, 11-15 June 1, 2006, pp. 1-15.
[15] H. Asano and S. Bando, “Operational Planning Method
and Economic Analysis of Microgrid with Intermittent
Renewable Energy and Battery Storage,” 29th IAEE In-
ternational Conference, Potsdam, 7-10 June 2006.
[16] A. R. Abdelaziz and W. M. Ali, “Dispersed Generation
Planning Using a New Evolutionary Approach,” IEEE
Bologna Power Tech Conference Proceedings, Bologna,
23-26 June 2003, 5 Pages.
[17] R. Dugan, T. E. McDermott and G. J. Ball, “Planning for
Distributed Generation,” IEEE Industry Applications Maga-
zine, Vol. 7, No. 2, 2001, pp. 80-88.
[18] H. M. Groscurth, T. Bruckner and R. Kümmel, “Model-
ing of Energy Service Supply System,” Energy, Vol. 20,
No. 9, 1995, pp. 941-958.
[19] I. Bouwmans and K. Hemmes, “Optimising Energy Sys-
tems—Hydrogen and Distributed Generation,” 2nd In-
ternational Symposium on Power System Market Aspects,
Stockholm, 2-4 October 2002.
[20] R. H. Lasseter and P. Piagi, “Microgrid: A Conceptual
solution,” Proceedings of IEEE 35th Annual Power Elec-
tronics Specialists Conference (PESC), Aachen, 20-25
June 2004, pp. 1-6.
[21] R. Frik and P. Favre-Perrod, “Proposal for a Multifunc-
tional Energy Bus and Its Interlink with Generation and
Consumption, Diploma Thesis,” Power Systems and High
Voltage Laboratories, Zurich, 2004.
[22] G. Andersson, “A Modeling Framework for Future En-
ergy Systems,” Project Home Page, 2006,
[23] P. Favre-Perrod, M. Geidl, G. Koeppel and B. Klockl, “A
Vision of Future Energy Network,” IEEE Inaugural Con-
ference and Exposition in Africa, Dur ban, 11-15 July 2005,
pp. 13-17.
Copyright © 2013 SciRes. EPE
Copyright © 2013 SciRes. EPE
[24] K. Hemmes, J. L. Zachariah-Wolf and G. Andersson,
“Towards Multi-Source Multi-Product Energy Systems,”
Hydrogen Energy, Vol. 32, No. 10-11, 2007, pp. 1332-
[25] M. Geidl, “Integrated Modeling a nd Optimization of Multi-
Carrier Energy Systems,” PhD Thesis, Eidgenössische
Technische Hochschule Zürich, Zürich, 2007.
[26] G. Koppl and G. Andersson, “Reliability Modeling of
Multicarrier Energy Systems,” Energy, Vol. 34, No. 3,
2009, pp. 235-244. doi:10.1016/
[27] M. D. Galus and G. Andersson, “Power System Con-
siderations of Plug-In Hybrid Electric Vehicles Based on
a Multi Energy Carrier Model,” IEEE Power & Energy
Society General Meeting, Calgary, 26-30 July 2009, pp.
[28] F. Kienzle, P. Ahcin and G. Andersson, “Valuing Invest-
ments in Multi-Energy Conversion, Storage, and De-
mand-Side Management Systems under Uncertainty,”
IEEE Transaction on Sustainable Energy, Vol. 2, No. 2,
2011, pp. 194-202.
[29] M. Arnol and G. Andersson, “Investigating Renewable
Infeed in Residential Area Applying Model Predictive
Control,” IEEE Power & Energy Society General Meet-
ing, Minneapolis, 25-29 July 2010, pp. 1-8.
[30] T. Krause, F. Kienzle, Y. Liu and G. Anderssson, “Inter
Connected National Energy Systems Using an Energy
Hub Approach,” IEEE PowerTech, Trondheim, 19-23 June
2011, pp. 1-7.
[31] M. Schuzle, L. Friedric h and M. Gautschi, “Modeling and
Optimization of Renewables: Applying the Energy Hub
Approach,” IEEE Conference on Sustainable Energy Tech-
nologies, Singapore City, 24-27 November 2008, pp. 83-
[32] A. Parisio, C. D. Vecchio and A. Vaccaro, “A Robust
Optimization Approach to Energy Hub Management,”
Electrical Power and Energy Systems, Vol. 42, No. 1,
2012, pp. 98-104.
[33] L. Carradore and R. Turri, “Modeling and Simulation of
Multi-Vector Energy Systems,” IEEE Conference on
Power Tech, Bucharest, 28 June-2 July 2009, pp. 1-7.
[34] E. M Robertson, et al., “Outline for an Integrated Multi-
ple Energy Carrier Model of the UK Energy Infrastruc-
ture,” 44th International Universities Power Engineering
Conference (UPEC), Glasgow, 1-4 September 2009, pp.
[35] L. Forbes, S. T. Galloway and G. W. Ault, “An Approach
for Modeling a Decentralized Energy System,” 45th In-
ternational Universities Power Engineering Conference
(UPEC), Cardiff, 31 August-3 September 2010, pp. 1-5.
[36] L, M, Ramirez-Elizondo and G. C. Paap, “Unit Commit-
ment in Multiple Energy Carrier Systems,” IEEE Con-
ference, Starkville, 4-6 October 2009, pp. 1-6.
[37] V. Velez, L. Ramirez-Elizendo and G. C. Paap, “Control
Strategy for an Autonomous Energy System with Elec-
tricity and Heat Flows,” IEEE Conference, Hersonissos,
25-28 September 2011, pp. 1-6.
[38] F. Syed, “Analysis of a Clean Energy Hub Interfaced with
a Fleet of Plug-In Fuel Cell Vehicles,” Master Thesis,
University of Waterloo, Waterloo, 2011.
[39] M. Setayeshnazar and M. R. Haghifam, “Multi Objective
Electric Distribution System Expansion Planning Using
Hybrid Energy Hub Concept,” Electric Power Systems
Research, Vol. 79, No. 6, 2009, pp. 899-911.
[40] M. Geidl, “A Green Field Approaches for Future Power
Systems,” Cigre Session 41, Paris, 2006.
[41] A. Sheikhi, A. M. Ranjbar, H. Oraee and A. R. Moshari,
“Optimal Operation and Size for an Energy Hub with
CCHP,” Energy and Power Engineering Journal, Vol. 3,
No. 5, 2011, pp. 641-649.
[42] A. K. Basu and S. Chowdhury,” Impact of Strategic De-
ployment of CHP-Based DERs on Microgrid Reliability,”
IEEE Transactions on Power Delivery, Vol. 25, No. 3,
2010, pp. 1697-1705.
[43] D. G. Newnan, T. G. Eschenbach and J. P. Lavelle, “En-
gineering Economic Analysis,” 9th Edition, Oxford Uni-
versity Press, Oxford, 2004.
[44] M. Y. Khan, “Theory & Problems in Financial Manage-
ment,” McGraw Hill Higher Education, New York, 1993.
[45] M. A. Main, “Project Economics and Decision Analysis,
Volume I: Deterministic Models,” 2nd Edition, PennWell
Publishing, Tulsa, 2002.
[46] American Society of Heating Refrigerating and Air-Con-
ditioning Engineers, “Ashrae Handbook—Hvac Systems
and Equipment,” 2009.
[47] A. Sheikhi, et al., “CHP Optimized Selection Methodo-
logy for a Multi-Carrierenergy System,” International of
Review Electrical Engineering (I.R.E.E), Vol. 6, No. 4,
2011, pp. 1-7.
[48] H. Cho, “Evaluation of CCHP Systems Performance
Based on Operational Cost Primary Energy Consumption,
and Carbon Dioxide Emission by Utilizing an Optimal
Operation Scheme,” Applied Energy, Vol. 86, No. 12,
2009, pp. 2540-2549.
[49] B. Rolfsman, “CO2 Emission Consequences of Energy
Measures in Buildings,” Building and Environment, Vol.
37, No. 12, 2002, pp. 1421-1430.
List of Symbols and Abbreviations
N Time intervals of optimization
Electrical energy demand in the time interval
LNHeating energy demand in the time interval
LNCoolingenergy demand in the time interval
eNElectricity price $kwh
Gas price $kwh
ir Interest rate
EL Inflation rate
Economic life of the equipment [year]
CMaintenance cost $kw h
C Yearly maintenance cost $year
Effective efficiency of CHP
Transformer efficiency 20kV/440kV
Electrical efficiency of CHP
Auxiliary boiler efficiency
Thermal efficiency of CHP
Absorption chiller efficiency
Emission factor for average power generation
$kw h
Emission factor for gas power generation
L Value of lost load $kw h
HHV Higher heating value
CoP Coefficient of performance
P Pumping heat price [cent/kWh]
Pu rchased electricity [kWh]
g Purchased nat ural gas [ kWh]
P Maximum purchased natural gas [kW]
P Minimum purchase d nat ural gas [kW]
eM Maximum purchased electricity [kW]
P Minimum purchased electricity [kW]
Dispatched factor for natural gas inlet
Dispatch factor for auxiliary boiler
Dispatch factor for CHP
inh NS Inp ut rate of heat storage kWh
outh Output rate of heat storage kWh
S Nominal capacity of heat storage kW
in NSE Input rate of bat t ery kWh
out Output rate of battery kWh
Benefit(X) Benefit of using the element X [$]
Nominal capacity of battery kW
Cost(X) Cost of using element X [$]
Cap(X) Capacity of element X [kW]
BMC Benefit minus cost [$]
KK(N) Exported electricity to the grid [kWh]
Z1 Price of heat transfer from CHP, heat storage and
auxiliary boiler to the load [$]
Z2 Price of purchased electri c i t y and gas [$]
H(N) Exported heat [kWh]
CC(N) Cooling exported [kWh]
Maximum heat exported [kWh]
CM Maximum cooling exported [kWh]
SeM Maximum exported electricity from CHP to the
grid [kWh]
EENS Expected energy not supplied
Pk The probability of having a capacity outage equal to
Ok Outage capacity
Ak The energy not supplied because of the capacity
outage Ok
ak Annual net cash flow
DCF Discounted cash flow
IRR Internal rate of return
DPP Dynamic payback period
NPV Net present value
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