Multi-Objective Capacity Planning of a Pv-Wind-DieselBattery Hybrid Power System
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Saif, A. et al. “Multi-objective Capacity Planning of a PV-winddiesel-battery Hybrid Power System.” IEEE International Energy
Conference and Exhibition (EnergyCon), 2010. 217–222. ©
Copyright 2010 IEEE
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http://dx.doi.org/10.1109/ENERGYCON.2010.5771679
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2010 IEEE International Energy Conference
Multi-objective Capacity Planning of a PV-WindDiesel-Battery Hybrid Power System
A. Saif, K. Gad Elrab, H.H. Zeineldin, S. Kennedy
J.L. Kirtley
Masdar Institute of Science and Technology
Abu Dhabi, United Arab Emirates
Massachusetts Institute of Technology
Cambridge, MA, USA
Abstract—A new solution methodology of the capacity design
problem of a PV-Wind-Diesel-Battery Hybrid Power System
(HPS) is presented. The problem is formulated as a Linear
Programming (LP) model with two objectives: minimizing
total cost and minimizing total CO2 emissions, while capping
the Expected Unserved Energy (EUE). Total emissions
include, in addition to the direct emissions from burning fossil
fuel, the embedded emissions of all system components,
obtained using Life Cycle Assessment (LCA) techniques. The
proposed approach is applied on a case study which entails
designing a HPS for a city of 50,000 residents. Model inputs
were extracted from real environmental and technical data.
The results obtained were used to construct the Pareto front,
representing the best trade-off between cost and emissions
under different reliability conditions. The optimal system
configuration and operating plan were also found and the
results were interpreted.
I.
Ti = expected life of a generation/storage unit of type i
(days)
Cmax = maximum allowable charge of a battery (kWh)
DODmax = maximum depth of discharge allowable (%)
SRmin = minimum spinning reserve requirement as
percentage of the demand (%)
d = discharge efficiency of the battery (%)
sd = hourly self-discharge of the battery (%/hour)
wc = weight of the cost minimization objective function
in the combined objective function(%)
EUEmax = maximum allowed unserved energy (kWh)
D(t , m) = peak demand (kW)
Oi (t , m) = output of a RES generator (kW)
NOMENCLATURE
A. Subscripts and indexes
C. Decision Variables
dg diesel generator(s)
pv photovoltaic panel(s)
wt wind turbine(s)
bt battery module(s)
t 1,2,...,24 hours of a day
m 1,2,...,12 months of a year
Odg (t , m) = diesel generators power output (kW)
Dus (t , m) = unserved load (kW)
Chb (t , m) = charging power of all batteries (kW)
Dchb (t , m) = power supplied by all batteries (kW)
C (t , m) = energy stored in the batteries (kWh)
SR(t , m) = spinning reserve availed (kW)
N i = number of power generation/storage units
TDC = total daily cost of the system (US$/day)
TDE = total daily CO2 emission of the system (ton-
B. Parameters and Scalers
ICi = initial cost per power generation/storage unit of
type i (US$)
OM i = Daily Operations and Maintenance cost for a
CO2/day)
generation/storage unit of type i (US$/day)
FCdg = fuel cost per kWhe delivered by the diesel
generator (US$/kWh)
EEi = embedded emissions per power
generation/storage unit of type i (ton-CO2)
DEdg = direct emissions per kWhe delivered by the
diesel generator (ton-CO2)
RCi = rated capacity of a generation /storage unit of
type i (kW)
978-1-4244-9380-7/10/$26.00 ©2010 IEEE
II.
INTRODUCTION
The growing universal concern about the negative
impacts associated with power generation has urged the
pursuit for more sustainable energy sources and more
efficient and clean ways to use it [1]. A promising way for
reducing GHG emissions from energy generators is through
using Renewable Energy Sources (RES) such as solar, wind,
and geothermal in generating electricity. However, some
technical, economical and regulatory issues are still
hindering wider deployment of RES in power systems; one
217
is their uncontrollability and undispatchability. Electricity is
a commodity that is typically consumed almost
instantaneously once generated. And in order to meet the
fluctuating demand, system planners perform complex,
multistage planning processes that rely on the ability of
generators to deliver the agreed amount of power and
change their output promptly or in short notice.
One way to deal with the uncontrollability problem of
renewable energy generators is to use them in conjunction
with controllable generators and energy storage, a concept
known as ‘Hybrid Power Systems’. So far, HPS were
envisaged as a feasible alternative to serve only rural
communities such as frontier villages, islands and oases,
where it is prohibitively costly to extend power transmission
lines from the centralized grid to serve small loads.
However, this perception is changing now for two reasons
[2]. First, RES and storage units are getting bigger and less
expensive, allowing them to be used in the main grid scale,
so the grid itself is changing into a large HPS. Second, the
grid is becoming less centralized and more permeable to
distributed renewable technologies. The traditional
architecture of the grid hinging on centralized generation,
unidirectional power flow and three distinctive layers
(generation, transmission and distribution) is changing
gradually to a modular architecture consisting of
interconnected microgrids with distributed generation, smart
power flow controls and high penetration of RES.
The HPS capacity and operational planning was
traditionally formulated and solved as a single objective
problem: minimizing total cost, subject to maintaining
certain reliability [3]. Recently, other objectives were
incorporated, including maximizing reliability, minimizing
losses and minimizing emissions. Some of these objectives
are inherently contradictory, so the system designer/planner
may use Multi-Objective Optimization (MOO) techniques
to address the problem. In this case, the aim is usually to
find the Pareto front, a set of non-dominated solutions that
represent the frontier of the feasible region or the best
possible trade-off between the multiple objective functions
[4]. The decision maker then can choose a solution from the
Pareto Optimal frontier that suits his priorities and
preferences.
operating policies (e.g. which source shall be used first, the
battery or the diesel generator, to serve the demand that
cannot be served by RES) add unnecessary constraints in the
context of an optimization problem. And second, using
heuristic and evolutionary algorithms might result in suboptimal solution and, most importantly, the gap between the
solution arrived and the optimum one cannot be estimated
accurately since the ‘true’ optimum is not found. Therefore,
it is important to look for a “rule-free” exact optimization
approach for the HPS capacity design problem.
In this paper, an approach to simultaneously find the
optimal configurations and operating plan of a HPS is
presented. In the next section, the problem is formulated as a
Multi-objective Linear Programming (LP) optimization
problem. In section IV, the proposed approach is applied to
a case study of a stand-alone HPS similar to Masdar City.
The results are discussed in section V and a conclusion is
drawn in section VI.
III.
PROBLEM FORMULATION
Equations (1) to (12) constitute the mathematical
formulation of the problem. The following subsections
display in more details the elements of this model.
A.
Objective Functions
The combined objective to be minimized is:
Objective wc TDC (1 wc ) TDE
(1)
Where:
TDC ICi
OM i N i i dg , pv, wt Ti
24 12
ICb
Tb
Chb (t , m)
t 1 m 1
12 Cmax
24 12
FC dg Odg (t , m)
(2)
t 1 m 1
12
The sum of levelized capital cost and the operating cost
of all system components. And:
TDE N i .EEi Ti i dg , pv, wt HPS capacity optimization problem has been extensively
studied in the literature from different perspectives. An
interesting review [3] described the previous work in this
field and showed the guidelines on how to tackle such
problems. In most cases, the design strategy was to select
system configurations randomly, conduct system
simulations using static rules and pick the operating policy
that minimizes cost. System configurations were then
ranked according to their fitness, and the best ones are
utilized to generate a new set of solutions using evolutionary
algorithms, which are then compared with existing
solutions. This iterative process continues until the
incremental progress achieved becomes negligible [5], [6].
Several simulation tools such as HOMER [7], and
HYBRIDS [8] have been used to compare capacity
decisions and operating strategies in different case studies
[9-11].
The sum of the direct emissions and the levelized
embedded emissions. Embedded (indirect) CO2 emissions
are those produced during the life cycle of the power
generation/storage device, excluding operational emissions.
Those include emissions associated with extraction of the
materials used, manufacturing, transshipments, installation,
maintenance, and decommissioning of the generator.
Quantitative assessment of the embedded emissions for each
type of generator can be usually done using Life Cycle
Assessment (LCA) techniques [12] through one or a
combination of two approaches: Process Chain Analysis
(PCA) or input/output Analysis (I/O) [13].
The simulation approach recognizes the interdependency
between the capacity design and the operational planning
problems of HPS, nevertheless, it might not lead to the
optimal system configurations. First, imposing static
B. Constraints
The optimal solution must satisfy the following set of
constraints:
24 12
218
24 12
Chb (t , m)
Odg (t , m)
EEb t 1 m 1
DEdg t 1 m 1
12 Cmax
12
Tb
(3)
1. Supply-demand matching: at any time interval, power
demand is equal to power supply plus unmet load.
D(t , m) Chb (t , m) Odg (t , m) N pv Opv (t , m)
N wt Owt (t , m) Dchb (t , m) Dus (t , m) t , m
(4)
2. Diesel Generators output limit: Power output and
spinning reserve availed by the diesel generators cannot
exceed their gross rated capacity.
Odg (t , m) SR(t , m) Ndg RCdg
t , m
(5)
3. Battery State of Charge (SoC): An energy
conservation constraint linking the energy stored in the
battery at any time with the charging and discharging
processes.
C (t1 , m) C (t , m) (1 sd ) Chb (t , m)
Dchb (t , m)
d
t , m t1 t 1
(6)
4. Battery capacity limits: the battery has an upper limit
with respect to its SOC, and there is a maximum Depth of
Discharge (DOD) that can be reached within a cycle.
(1 DODmax ) Cmax N b C (t , m)
Cmax N b
t , m
(7)
Also, the electric power charged to or discharged from
the battery cannot exceed its rated capacity.
Chb (t , m) Nb RCb
Dchb (t , m) Nb RCb
t , m
t , m
(8)
(9)
5. Spanning Reserve (SR) requirement: For reliable
operations, diesel generators shall be able to promptly
respond to any drop in the power supply caused by failure of
any generating unit. For the sake of simplicity, Spinning
reserve requirement is assumed a percentage of the total
power demand.
SR(t , m) SRmin D(t , m) t , m
(10)
It has been assumed that the diesel generators can
change their output level instantaneously when needed with
no limits on their ramp up/ down.
6. Cycle repeatability: the optimal cycle shall be
repeatable, meaning that the SOC of the storage unit at the
beginning and the end of the cycle have to be identical.
C (24, m) (1 sd ) Chb (1, m)
Dchb (1, m)
d
C (1, m) m
(11)
7. Maximum Allowable Unserved Energy (EUE): EUE
is defined as the expected amount of energy not supplied by
the generating system during the period of observation, due
to capacity deficiency [14]. To maintain acceptable level of
service, this metric must not exceed a certain threshold.
24 12
D
us
(t , m) EUEmax
(12)
t 1 m 1
It is important to note that what defines reliability in the
context of this paper is the ability of generators and storage
units to cover the system demand, given that they operate
perfectly. Since the possibility of power interruptions
attributed to generator failures or line outages are
neglected, relying on the high spinning reserve assigned ,
perfect reliability (EUE = 0) can be achieved through
proper planning of resources.
IV.
CASE STUDY
The proposed approach is used to find the optimal
configurations of a stand-alone power system serving a
community of 50,000 inhabitants. This size is intentionally
chosen to represent a community similar to Masdar City in
Abu Dhabi, UAE. Masdar City is to be the first zero waste,
zero carbon city that relies entirely on RES to serve the
power demand of its residents [15]. The city power network
is currently connected to the main grid of Abu Dhabi, and
operating based on the “energy offsetting” concept, where
the city exports RES generated electricity equal to the fossil
fuel generated energy imported from the grid. However, if
the city decides to become entirely sustainable and selfsufficient without need for support from a conventional grid,
it needs to operate autonomously through utilizing its own
renewable resources. Indeed, this might be economically
unviable due to the high cost of clean energy, so the
decision maker has to make a trade-off between
sustainability and economic considerations through
accepting some carbon emissions while offsetting them
through carbon “cap and trade” mechanisms. The
methodology presented in this article helps the decision
maker to find the best attainable tradeoffs and to estimate
the price of sustainability.
A. Demand data
The daily demand pattern of the city is assumed constant
for each month of the year, so there are twelve demand
patterns. The demand pattern of the city is assumed similar
to the demand of Abu Dhabi Emirate. Demand data for
January and August 2008 published by Abu Dhabi Water
and Electricity Company (ADWEC) [16] was scaled down
to the 50,000 inhabitant population size of Masdar City.
Demand patterns for the rest of the months were estimated
using linear interpolation of the available data. Fig. 1 shows
the average daily demand patterns of January and August
used in the model.
B. CO2 emissions of system components
CO2 emitted during the manufacturing, assembly,
transportation and installation of the power generators was
sourced from the literature. In [17] the embedded emissions
for the PV panels and the wind generators were estimated
using the process analysis method. Whereas the embedded
emissions for the Diesel generator and the batteries are
calculated using the interindustry method. Despite the fact
that these two methods use different system boundaries and
are likely to give incomparable outcomes, we will use the
values provided in [17] as fairly reliable inputs to our model.
Table I depicts the values of embedded emission per kW
capacity installed of each generator type.
Except the diesel generators, the CO2 operational
emissions of all system components can be neglected. The
technical specifications of the diesel generator are sourced
from [18]. The rated capacity of this diesel generator is 500
kVA. Assuming, on average, it will operate at half load, it
will deliver 250kWh/h. Fuel consumption at that level is
219
71.175 liter/hour. So the generator will consume 0.2847
liter/kWh. A liter of diesel fuel generates 2.663 kg of CO2
[19]. Thus, the CO2 direct emissions for the diesel generator
are taken as 0.758 kg-CO2/kWh.
Figure 1. Demand Patterns for January and August
TABLE I.
Generator Type
PV Modules
Wind Turbibes
Diesel Generator
NaS Battery
D. Power output of RES generators
The time series data for the power output of a test PV
panel located at Masdar City PV test site was used to extract
the input data for our model. First, power output pattern for
every day in a month was plotted in a single graph. Each
day has 288 output readings with five minutes interval
between consecutive readings. These plots were then
averaged to get a one-day representative pattern for the
month. Thus, an average value for every time interval
throughout the month was obtained. The 24 readings falling
within each hour were then averaged out to get one value for
the power output every hour. This process was repeated for
all the months in the year. So, twelve stepwise PV output
patterns, one for each month, each consisting of 24 hours are
computed from the data.
Wind speed data at 50m were obtained from the data
recorded by the Metrological department at Abu Dhabi
International Airport. Then, wind speed at the hub height of
80 meters was calculated using a standard formula [1].
Subsequently, hub level wind speed was used to estimate the
wind turbine power output using the power curve provided
by the manufacturer. A 1.5 MW wind turbine manufactured
by GE was chosen as our standard wind turbine generator.
The same data processing methodology applied to the PV
power output was applied for the wind data. It is noted that
the wind data was recorded every 10 minutes, having
approximately 144 readings per day.
EMBEDDED CO2 EMISSIONS
Embedded Emissions (ton-CO2/kW capacity)
5.340
1.035
1.000
0.372
C. Cost and life of system components
Current cost for a typical commercial scale onshore wind
turbines is $1000/kW of capacity, excluding installation and
salvage costs, which are estimated to add 30% to this figure
[20]. Thus a 1.5 MW rated capacity wind turbine has a
capital cost of $ 1.95 M. Photovoltaic panel cost is assumed
$6 per Wp, in line with current market prices for monocrystalline PV panels, including the installation, inverter and
the stand cost. The cost of each diesel generator is taken as
$100,000. The capital cost of the Sodium- Sulfide (NaS)
battery is $3000/kW [21]. Each storage module has a rated
output of 50 kW and can store up to 300kWh of energy. In
general, the NaS battery has virtually no self-discharge, and
fairly high discharge efficiency (~89%) [22].
Annual O&M cost for wind turbines is assumed 1.5% of
their initial cost based on industry data. This is equivalent to
$80/day for a 1.5MW wind turbine. For the diesel
generators, the annual O&M and incidental expenses is
assumed 5% of the initial generator cost, so for a 500kW
diesel generator, this is equivalent to $13.7/day. PV panels
and batteries are virtually maintenance free. One major cost
component for the diesel generator is the fuel cost. In Abu
Dhabi, diesel price for bulk retailers is $3.515/gallon. Using
the fuel consumption data given earlier, the fuel cost per
kWh is $ 0.264
The life span of the diesel generators is taken as 15
years, with 2 major overhauls in the middle of its lifetime
(their cost is included in the maintenance). Wind turbines
have a manufacturer guaranteed life of 20 years [23]. In
most literature, the lifespan of a PV panel is taken as 25
years, and that is the value used in our case. Life span of the
battery ranges between 2,500 and 4,500 full cycles
depending on operating conditions, so it will be taken as
3,500 cycles.
V.
RESULTS
Upon applying the multi-objective optimization
approach proposed on the case under study, we were able to
obtain the configurations of the system and the operation
plans that simultaneously minimize both total levelized cost
and CO2 emissions. Fig. 4 presents the set of possible tradeoffs between the two objective functions bounded by the
Pareto front. The points lying on the Pareto front are nondominated solutions obtained from the optimization routine
and represent the best feasible trade-offs. The Pareto front
divides the solution space into a feasible solution zone
(above the line) which contains feasible, but not optimal,
solution points and infeasible solution zone (below the line).
Each point on the Pareto front is an optimal solution
associated with certain weights of the two objective
functions.
It is noted that the solution obtained in fig. 2 is at a
certain performance level of EUE of 0% (the supply
satisfies the demand at all cases and the probability of losing
the power is zero). By finding the Pareto set at different
performance levels, one can construct Pareto surface. Every
point plotted on the Pareto front is a weighted average of
cost and emissions. It is interestingly noticed that the
solution with the least emissions level (point a) is relatively
close to the solution with the least cost (point b), with less
than 1% difference in cost and 3% difference in emissions,
indicating that the system configuration that minimizes cost
also brings the emissions close to its minimum level
attainable, and vice versa. Table II shows the system
configurations that minimize cost and emissions. It can be
easily noticed that the wind-diesel- battery combination
achieves the best results in terms of cost and emissions, with
different number of wind turbines and batteries in each case.
In both cases, no PV panels were selected and the number of
Diesel generators has not changed. To understand the reason
220
behind the superiority of this combination, the average cost
and emissions per kWh energy delivered from each type of
generation or storage units is calculated, and the results are
depicted in table III.
Figure 2.
TABLE II.
Pareto front of the case study
SYSTEM CONFIGURATIONS FOR COST AND EMISSIONS
MINIMIZATION
Objective
ndg
npv
nwt
Nb
Cost minimization
Emission minimization
119
119
0
0
427
367
12,463
13,533
TABLE III.
constraints (represented by the percentage of expected
unserved energy) are shown in Fig. 3.
The first observation is that, when the energy storage
option is disregarded, the Pareto front shifted towards higher
cost and emissions under the same reliability requirement
(i.e. 0% EUE). Specifying zero storage capacity is
equivalent to imposing an additional constraint, causing the
solution to worsen, or at least not improve. Moreover, as the
reliability constraint is relaxed through accepting higher
EUE, both the cost and emissions decrease, as less system
components are needed. However, emission declines faster
than cost, as the number of diesel generators, a major cost
driver, needed to satisfy the spinning reserve requirement
will not change, although they will remain idle (i.e.
generating zero emissions). Compared to the system with
storage devices, the range of trade-off between cost and
emissions in this system is larger. For example, there is
more than 14% difference between the least possible
emission level and the emission level when cost is
minimized under perfect reliability condition, and the same
applies for cost. This is an indication of wider variation in
generation mix between the two objectives. Table IV depicts
the system configurations with different values of w, and an
EUE value of zero.
COST AND CO2 EMISSIONS PER KWH DELIVERED BY EACH
TYPE OF POWER GENERATION / STORAGE UNITS
Power generation /
storage unit type
Cost/energy unit
delivered
($/kWh)
Emissions/energy
unit delivered
(kg-CO2/kWh)
Diesel generator
PV module
Wind turbine
Battery
0.2706
0.1436
0.0326
0.1605
0.777
0.166
0.026
0.020
As can be observed from Table III, the wind turbine
generators have the lowest cost and emissions per kWh
energy delivered. When supplemented by large scale storage
they can satisfy the system demand in most times. However,
diesel generators are also needed beside the wind-battery
combination for two reasons: first, to supply the stipulated
spinning reserve requirements, and second to supply energy
in cases when storing wind-generated energy is infeasible.
For example, in certain months, wind speed remains below
the cut-off speed of the wind turbine for nine consecutive
hours, so massive storage is needed if dispatchable
generators are eliminated, adding to the cost and emissions
of the system.
Despite its promising future, the technology of Sodium
Sulfur (NaS) batteries, and utility scale electricity storage
devices in general, are still in their infancy and known to
have some technical challenges related to temperature
sensitivity and life-cycle performance deterioration [25]. To
the authors’ knowledge, they have not yet been used in
power systems of comparable scale with the system under
study, which raise concerns about their applicability and
reliability. Thus, system designer might opt to disregard the
option of energy storage and design the system as merely a
PV-Wind-Diesel HPS. Therefore, another set of
optimization runs was conducted while excluding the energy
storage option. The Pareto fronts under various reliability
Figure 3. Patero fronts under different reliability requirements for a HPS
without energy storage. Reliability is represented by the ratio of energy
demand not satisfied.
TABLE IV.
SYSTEM CONFIGURATIONS UNDER DIFFERENT WEIGHTS
OF THE OBJECTIVE FUNCTIONS
Weight of the cost
minimization objective
function
number of
Diesel
Generators
number of
wind
turbines
number
of PV
panels
0
50%
100%
281
299
341
896
711
306
31,058
2,278
0
Fig. 4 provides some insight on an application of
the Pareto front for the design of the system. If the carbon
cost is factored into the picture by the presence of carbon
credits or carbon taxes, a solution that minimizes total cost,
including carbon cost can be easily found, as shown in Fig.
7. A line with a slope of carbon price can be plotted on the
Pareto plot. Moving the line to get into tangency with the
curve will show the optimum solution directly. To explain
that, let’s examine the net effect of shifting from solutions II
to solution I, both are shown in Fig. 7. The system cost will
increase by the distance (aO), while the reduction in CO2
emitted will generate carbon credit that can be sold at a
221
value equal to (bO), so there is a net gain of (ab) resulting
from this shift. On the other hand, moving from solution III
to solution I will reduce the system cost by the distance
(dO), while the value of carbon credit units that have to be
purchased to offset the increase in CO2 emissions is (cO),
so the net gain is the distance (cd). In both cases there was a
gain for moving towards solution I, the tangency point, so it
is the most suitable solution if carbon price is taken into
consideration.
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
Figure 4.
Selecting the ‘suitable’ point on Pareto front based on the
price of Carbon
[12]
VI.
CONCLUSION
In this paper, a model for determining the Pareto optimal
front for a hybrid system consisting of photovoltaic panels,
wind turbines, diesel generators and batteries has been
developed. The main objective was to determine the
optimum techno–economic configuration with respect to
cost and emissions. The general trend in the results obtained
showed that using RES generators in conjunction with
conventional generators and/or storage capacity can achieve
high reliability and reasonable cost of energy. The analysis
also showed a need for taking embedded emissions into
account when comparing different generation options. This
is crucial to reach objective and comprehensive estimate of
their impact on the environment. Otherwise, we would be
making a sub-optimum decision by basing our optimization
on only operational emissions.
Multi-objective optimization is a useful tool in
designing and planning the operation of hybrid power
systems as it allows the decision maker to set its priorities
and select the solution that realizes them. We have thus used
a multi-objective optimization approach in solving the
problem. Throughout the optimization, we have used actual
wind and solar resource data, as well as performance
characteristics from commercially available sources to
improve the credibility of the model. We have developed a
general rule-free Linear Programming model which can be
used in optimizing Hybrid Power Systems efficiently and
precisely.
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
REFERENCES
[1]
J. Tester, E. Drake, M. Driscoll, M. Golay, and W. Peters,
Sustainable Energy: Choosing Among Options, The MIT Press, 2005
[24]
222
J. Paska, P. Biczel, and M. Kios, Hybrid power systems – An
effective way of utilising primary energy sources, Renewable
Energy, Vol. 34, 2009, Pages: 2414-2421
J. Bernal-Agustin and R. Dufo-Lopez, Simulation and optimization
of stand-alone hybrid renewable energy systems, Renewable and
Sustainable Energy Reviews, Vol. 13, 2009, Pages: 2111–2118
Y. Collette, and P. Siarry, Multiobjective optimization: principles
and case studies, 1st ed. Berlin: Springer, 2004
G. Seeling-Hochmuth, A combined optimisation concept for the
design and operation strategy of Hybrid̽PV energy systems, Solar
energy, Vol. 61, No. 2, 1997, pages: 77̽87
J. Bernal-Agustin, R. Dufo-Lopez, and D. Rivas-Ascaso, Design of
isolated hybrid systems minimizing costs and pollutant emissions,
Renewable Energy, Vol. 31, No. 14, 2006, Pages: 2227̽2244
T. Lambert and P. Lilliental, HOMER: the micro-power optimization
model,
NREL,
2004.
Can
be
downloaded
from:
www.nrel/gov/HOMER
T. Berrill, HYBRIDS, Solaris solar power home, 29 Burnett St.,
Wellington Point Qld, Australia, 2005
G. Dalton, D. Lockington, and T. Baldock, Feasibility analysis of
stand-alone renewable energy supply options for a large hotel,
Renewable Energy, Vol. 33, 2008, Pages 1475-1490
S. Shaahid , and M. Elhadidy, Technical and economic assessment of
grid-independent hybrid photovoltaic̽diesel̽battery power systems
for commercial loads in desert environments, Renewable and
Sustainable Energy Reviews, Vol. 11, No. 8, 2007, pages 1794̽1810
Y. Himri, A. Boudghene Stambouli, B. Draoui, and S. Himri,
Techno-economical study of hybrid power system for a remote
village in Algeria, Energy, Vol. 33, No. 7, 2008, Pages: 1128̽1136
Henrikke Baumann and Anne-Marie Tillman, The Hitch Hiker’s
Guide to LCA, ISBN 978-91-44-02364-9, Holmbergs I Malmo AB,
Sweden 2009
Voorspools K., Brouwers E. and D’haeseleer, Energy content and
indirect greenhouse gas emissions embedded in ‘emission-free’
power plants, Applied Energy Vol. 67 (2000), pgs 307-330
S. Fockens, A.J.M. van Wijk, W.C. Turkenburg, and C. Singh, A
Concise method for calculating expected unserved energy in
generating system reliability analysis, IEEE Trans. Power Systems,
Vol. 6, No. 3, August 1991.
S. Nader, Paths to Low Carbon Economy – The Masdar Example,
Energy Procedia, Volume 1, Issue 1, February 2009, Pages 39513958
Abu Dhabi Water and Electricity Company (ADWEC) Statistics,
Electricity
Charts,
can
be
downloaded
from:
http://www.adwec.ae/statistics/pdf/9808/9.0%20Electricity%20Chart
s.PDF
Y. Kemmoku, K. Ishikawa, S. Nakagawa, T. Kawamoto, and T.
Sakakibara,
Life
Cycle
CO2
Emissions
of
a
Photovoltaic/Wind/Diesel Generating System, Electrical Engineering
in Japan, Vol. 138, No. 2, 2002 (translated from Japanese)
Cummins© Diesel Generator Specification, Model DFEK, can be
downloaded
from:
www.cumminspower.com/www/common/templatehtml/technicaldoc
ument/SpecSheets/Diesel/na/d-3401.pdf
Ontario Ministry of the Environment, Guideline for Greenhouse Gas
Emissions
Reporting,
can
be
downloaded
from:
www.ene.gov.on.ca/publications/7308e.pdf
Danish
Wind
Industry
Association
http://www.talentfactory.dk/en/tour/econ/index.htm
www.ngk.co.jp/english/products/power/nas/index.html
K. Divya and J. Østergaard, Battery Energy Storage Technology for
Power Systems – An Overview, Electric Power Systems Research,
Volume 79, Issue 4, April 2009, Pages 511-520
E. Martinez, F. Sanz, S. Pellegrini, E. Jimenez, and J. Blanco, Life
Cycle Assessment of a Multi-megawatt Wind Turbine, Renewable
Energy, Volume 34, Issue 3, March 2009, Pages 667-673
S.C. Smith, P.K. Sen, and B. Kroposky, Advancement of Energy
Storage Devices and Applications in Electrical Power Systems, Proc.
2008 IEEE PES General Meeting, Pittsburgh, USA, 20-24 Jul 2008.