Smart Grid and Renewable Energy, 2009, 23–28
Published Online September 2009 (http://www.SciRP.org/journal/sgre/).
Optimised Model for Community-Based Hybrid Energy
System
ABSTRACT
Hybrid energy system is an excellent solution for electrification of remote rural areas where the grid extension is
difficult and not economical. Such system incorporates a combination of one or several renewable energy sources such
as solar photovoltaic, wind energy, micro-hydro and may be conventional generators for backup. This paper discusses
different system components of hybrid energy system and develops a general model to find an optimal combination of
energy components for a typical rural community minimizing the life cycle cost.
The developed model will help in sizing hybrid energy system hardware and in selecting the operating options. Mi-
cro-hydro-wind systems are found to be the optimal combination for the electrification of the rural villages in Western
Ghats (Kerala) India, based on the case study. The optimal operation shows a unit cost of Rs. 6.5/kW h with the selected
hybrid energy system with 100% renewable energy contribution eliminating the need for conventional diesel genera-
tor.© 2006 Elsevier Ltd. All rights reserved.
Keywords: Hybrid Energy System; Micro-Hydro; Solar PV; Wind
1. Introduction
India has an enormous renewable energy potential of
about 100,000 MW, which is mostly untapped [1]. It is
estimated that 40% of villages in the country are not
electrified through grid electricity mainly due to capacity
shortage and difficult terrain and environmental consid-
erations. It becomes necessary to take up electrification
of remote villages through non-conventional energy
sources such as solar, micro-hydro and wind systems.
Standa- lone units are already in operation at many plan-
tations/colonies though the availability of solar, hydro or
wind energy is not continuous. Isolated operation of
these power units may not be effective in terms of cost,
efficiency and reliability. A viable alternative solution is
by combining these different renewable energy sources
to form a hybrid energy system [2]. A system using a
combination of these different sources has the advantage
of balance and stability that offers the strengths of each
type of sources that complement one another. The main
objective is to provide 24 h grid quality power in remote
communities. Hybrid energy systems are pollution free,
takes low cost and less gestation period, user and social
friendly. Such systems are important sources of energy
for shops, schools, and clinics in village communities
especially in remote areas. Hybrid systems can provide
electricity at a comparatively economic price in many
remote areas.
In order to obtain electricity from a hybrid system re-
liably and at an economical price, its design must be op-
timal in terms of operation and component selection.
Many attempts have been tried to explore a relatively
simple method for designing hybrid energy systems. An
algorithm based on energy concept to optimally size so-
lar photovoltaic (PV) array in a PV/wind hybrid system
was reported [3]. Different system developments in hy-
brid energy system for Thailand were published [4]. A
simple numerical algorithm was used for unit sizing and
cost analysis of a stand-alone wind, solar PV hybrid sys-
tem [5]. A linear programming technique was developed
for optimal design of a hybrid wind/ solar PV power sys-
tem for either autonomous or grid-linked applications [6].
Different aspects of PV, wind diesel and battery-based
hybrid system including optimal sizing and operation has
been detailed [7,8]. A probabilistic performance of stand
alone Solar PV, wind energy system with several wind
turbines of same or different sizes, with PV models and
storage batteries has been presented [9]. A hydro-based
system was discussed in synchronized operation with
wind energy [10]. Pre-feasibility study on hybrid energy
based on wind and fuel cell system was published [11].
This paper attempts to develop a general model to find
an optimal hybrid system among different renewable
energy combinations for a rural community, minimizing

Optimised Model for Community-Based Hybrid Energy System 24
the total life cycle cost while guaranteeing reliable sys-
tem operation. Solar PV, wind, micro-hydro with diesel
and battery backup are considered in the model. A case
study is conducted in a typical remote village of Western
Ghats in Kerala, India. The objective is to find the suit-
able component sizes and optimal operation strategy for
an integrated energy system. The results will lead to the
design and planning of an optimal hybrid energy system
ensuring reliable and economical power supply to the
village community.
2. Methodology
The hybrid energy system integrates various renewable
combinations of micro-hydro, PV, wind energy units, etc.
applicable depending on availability. In general, there
can be 2n-1 combinations, where ‘n’ is the type of re-
newable energy resources. A parallel hybrid configura-
tion of these components is used in this approach with
battery storage and diesel generators as backup sources.
The criterion of selecting the best hybrid energy system
combination for a proposed site is based on the trade-off
between reliability, cost and minimum use of diesel gen-
erator sets. For different renewable combinations, the
output of the optimal sizing and operation is a set of
component sizes for a given application together with
recommendations for system operation. The component
sizes are restricted to that available in the markets. From
the cost comparisons of different combinations, the most
economical system is selected which ensures power sup-
ply continuity. Basic power modules of hybrid energy
system considered for the rural community in Kerala are
micro-hydro, wind and solar PV with diesel or battery
backup. Micro-hydro units considered here are very
small in size below 100 kW.
3. Optimal Hybrid Energy System
While planning, designing and constructing a hybrid en-
ergy system, the problem becomes complex through un-
certain renewable supplies, load demand, non-linear
characteristics of components and the fact that the sizing
and operation strategies of hybrid systems are interde-
pendent. This calls for an optimized hybrid energy sys-
tem with the objective of minimizing the life cycle cost
while guaranteeing reliable system operation. As the
component sizes and operation are interdependent, dif-
ferent set of component configurations are analyzed in
each hybrid combination to get an optimal hybrid system.
3.1 Unit Sizing
Numerical iterative algorithm is used for unit sizing of
hybrid energy system, minimizes the capital cost for 2n–1
combinations of renewable sources. Timely availability
of energy resources, load demand-supply balance, mini-
mum–maximum operating limits of the units are the ba
sic constraints. Since the energy density of the mi-
cro-hydro and wind, far exceeds that of a single solar PV
module, the number of wind turbines and micro-hydro
are fixed as one each and number of PV modules is in-
cremented until the system is balanced. The optimal op-
erating strategy and annual life cycle costs of this
configuration is determined. These steps are repeated
with the number of wind turbines with incremental steps.
In the same manner, the entire procedure is repeated for
all the seven combinations. The combination with lowest
cost, minimal use of diesel generators and service reli-
ability is selected as the optimal one. The battery bank is
sized with a capacity equal to the difference between
positive and negative peaks of the energy curve.
The unit sizing for a PV-wind-micro-hydro hybrid
combination is to minimize the total capital cost Cc given
by
11111
g
hw sb
N
NN NN
chwsg
hw sg b
CCCCC

 
b
C


, (1)
where Nh; Nw; Ns; Ng; Nb are the total no. of micro-hydro,
wind, solar PV, diesel generator and battery units, re-
spectively, and Ch; Cw; Cs; Cg; Cb are the corresponding
capital costs.
As detailed models of hydro, wind and PV systems
have already been published, only the final expressions
for the electrical power output of these components are
given below. The electrical power generated by the mi-
cro-hydro unit is given by
hyd waterneth
PgHQ


, (2)
where hyd

the hydro efficiency as obtained from the
quadratic fit to the manufacturers’ data, water

the den-
sity of water, g the acceleration due to gravity, hnet the
effective head, Q the flow rate.
The wind power output,
3
ga
0.5 V
wt P
PC  
r
A

 , (3)
where Vr the wind velocity, air

the factor to account
for air density, Cp the power coefficient of wind turbine
depends on design, A the wind turbine rotor swept area,
t

,
g

the wind turbine and generator efficiency, re-
spectively, as obtained from the quadratic fits to the
manufacturers’ data.
The hourly output power of solar PV array,
pvpvpvppv pv
PNVI



 , (4)
where pv

the conversion efficiency of a PV module,
Vpv the module operating voltage, Ipv the module operat-
ing current, Npvp, Npvs , the number of parallel and series
solar cells, respectively.
The use of diesel generator is common in many hybrid
combinations to ensure supply continuity and battery

Optimised Model for Community-Based Hybrid Energy System 25
model accounts for energy storage.
From experimental tests it has been found that for a
diesel generator, a linear function fits for light load
working conditions, while in proximity of rated power, it
asks for quadratic expression. For an interval, the rate of
fuel F, consumed by the diesel generator delivering the
power P, is expressed in general as
2
F
aPbP c, (5)
where a, b, c the coefficients of diesel generator as ob-
tained from the manufacturers’ data.
The state of charge of battery can be calculated from
the following equations:
Battery discharging,
()(1) (1)(()/())
bb hil
Pt PtPtPt

  , (6)
Battery charging,
()(1) (1)(()()/)
bb hli
PtPtPt Ptb

  , (7)
Where , the battery energy at the begin-
ning and the end of interval t, respectively, Pl(t) the load
demand at the time t, Ph(t) the total energy generated by
PV
array, micro-hydro unit and wind generators at the
time t,
(1)
b
Pt()
b
Pt

the self-discharge factor and
b

,
i

the
battery charging and inverter efficiency, respectively, as
obtained from the manufacturers’ data .
As per the recommended practice to ensure sufficient
lifetime, batteries are cycled between a minimum and
maximum levels of rated capacity. Renewable sources
like biomass and fuel cells are not modeled, as they are
not commonly used for electricity production in Kerala.
Total hybrid power generated at any time t,
111
()
hw s
NN N
hw
hw s
PtP PP



s
, (8)
3.2 Optimal Operation
Optimal dispatch strategy of hybrid energy system is to
find the most economical schedule for different combina-
tions of renewable generators with diesel and battery
backup, satisfying load balance, resource availability and
equipment constraints. The dispatch strategy is such that
the battery charges, if the renewable energy is in excess
after meeting the demand and discharges, if load exceeds
the renewable energy. Diesel generator is used as part of
the system to respond to the emergency cases where re-
newable generation and stored energy are not sufficient
to meet the load.
The hourly operation strategy of the different hybrid
configurations is determined with the use of non-linear
constrained optimization energy are not sufficient to meet
the load. The renewable energy source constraints are
such that they should be used as much as possible.
The optimal operation strategy for a solar PV/wind/mi-
cro-hydro combination so as to minimize the annual op-
erating cost Co computed based on the operating cost for
the interval t in a day as shown below.
365 24
otoh owos og ob
11
{ (()()()()())}
t
C CtCtCtCtCt



(9)
subjected to the constraints as expressed in Equations.
(2–7). Coh(t); Cow(t); Cos(t); Cog(t), Cob(t) are the opera-
tional costs of micro-hydro, wind turbine, solar PV, die-
sel generator and battery units for the hourly interval t
(t=1–24), respectively. Operational costs are calculated
on the basis of component characteristics, size and
efficiency.
Total annualized life cycle cost of the system incorpo-
rating components of both capital and operating cost,
an cot
(. ).CCCRFC

 (10)
Unit cost of electricity by hybrid energy
system,
an
oe ,
l
C
CE  (11)
where El the load served in kW h/year and CRF the capi-
tal recovery factor for the system with expected discount
rate.
The optimization model developed above can be
solved by non-linear constrained optimization techniques.
The solution yields optimal combination with unit sizing
of energy components minimising the total life cycle cost.
The flowchart for finding an optimal hybrid energy sys-
tem is shown in Figure 1.
4. Case Study
The evaluation is based on a typical farming village of
Western Ghats in Kerala, India. It is around 110 km
away from the nearest local town. The mode of trans-
portation is limited to jeeps (that too to certain areas
only) and distance to the existing grid line is around 15
km. It is expensive and complex to extend the grid due
to hilly terrain and nature of landscape. The village has
120 families with a population of over 600. A 6.25 kVA
community-based diesel generator (DG) unit supplies
power to about 35% of the population. DG set operates
six hours a day during peak hours. During emergencies
and festival seasons, the DG operation is extended to
remaining hours of the day. Individuals for their typical
household applications own other small units of diesel
generators of ratings 1 kVA and less. About 40% of the
population is deprived of electricity. The principal de-
mand of electricity is for lighting and radio. In this study,
the electrical appliances in the village include 11 and 20
W compact fluorescent lamps, 60 W fan and 35 W radio
set. From the detailed study, data collection and
survey conducted in the site with the assistance of a
non-governmental organization [12], it is estimated that

Optimised Model for Community-Based Hybrid Energy System 26
Figure 1. Flowchart to find optimal hybrid system
there exists potential of renewable sources like several
water streams flowing down hills, wind and solar energy.
The load profile of the population was estimated consid-
ering 10% demand growth rate.
4.1 Proposed Scheme
Even though the potential of renewable sources are high,
the application of renewable generators as stand alone
units will not be sufficient to provide a continuous power
supply due to seasonal and non-linear variation of re-
newable resources. To ensure a balance and stable power
output, different renewable generators might be installed
and integrated to form a hybrid energy system. An opti-
mal combination of a decentralized hybrid energy system
will eliminate the resource fluctuations, increase overall
energy output and reduce energy storage. The existing-
diesel generators may be used during emergencies or
contingencies to add to the security of the system. Annu-
alized life cycle cost of the proposed hybrid energy sys-
tem is computed based on capital and operating costs of
different energy components. The capital cost of mi-
cro-hydro, wind, solar PV and DG units are Rs. 90/W, Rs.
125/W, Rs. 200/W, and Rs. 15/W and the operating costs
of micro-hydro, wind and solar PV units are Rs. 0.15/kW
h, Rs. 0.1/kW h and Rs. 0.05/kW h, respectively [13].
The operating cost of DG units includes fuel cost (Rs.
24/l) and operation and maintenance cost of Rs. 0.2/kW h.
The project lifetime is taken as 20 years with 15% dis-
count rate.
From the hourly load profile of the village, after con-
sidering the demand growth rate, the daily energy demand
is taken as 317 kW h. The average hourly load profile for
the typical day is shown in the Figure 2. The design flow
for the micro-hydro scheme is 35 l/s. The lowest recorded
flow measured is 60 l/s during dry period with the meas-
ured head 60 m. The average daily solar radiation based
on annual measurement is 6.53 kW/m2/day with varia-
tions shown in Figure 2. The average hourly variations of
ambient temperature (°C) and wind speed based on the
measured data are also plotted in Figure 2. The wind
blows in northwesterly direction for most of the year. The
average wind velocity is estimated as 9.1 m/s.
Locally available standard micro-hydro, wind and so-
lar PV units suitable to the resource measurements are

Optimised Model for Community-Based Hybrid Energy System27
Table 1. Iteration results (partial) of hybrid energy component sizes and cost
No.
Micro
hydro
(15 kW)
Wind
unit
(5 kw)
SPC
(120 kW)
DG set
(kW)
Batt. No.
(360 Ah,
6 V)
Coe (Rs./
kW h)
Renewable
(%)
DG
(% )
1 1 1 26 0 32 7.09 100 0
2 1 1 0 10 24 6.3 98.4 1.6
3 1 2 0 0 28 6.5 100 0
4 1 0 50 10 32 9.33 89.44 10.56
5 0 1 332 15 72 26.49 75.57 24.43
6 0 2 268 10 68 22.05 82.01 17.99
7 0 3 191 10 56 17.9 88.25 11.75
8 0 4 113 5 48 13.47 94.22 5.78
9 0 5 26 0 40 9.9 100 0
10 1 0 0 10 12 7.05 79.82 20.18
11 0 1 0 20 8 28.9 18.12 81.88
12 0 2 0 15 8 19.9 37.77 62.23
13 0 3 0 15 8 13.42 57.45 42.55
14 0 4 0 15 16 10.15 77.26 22.74
15 0 5 0 10 32 8.68 97.27 2.73
16 0 6 0 0 40 9.85 100 0
17 0 0 359 15 76 31.4 76.8 23.2
Table 2. Cost comparisons between different energy supply schemes
Description Daily hours of
operation
Population
electrified (%)
Cost of electricity
(Rs./kW h)
Proposed micro-hydro-wind hybrid system
Grid supply extended
Diesel generator units
24
24
6
100
100
40
6.5
7.11
10.12
Figure 2. Hourly load and renewable energy data for the
village
selected for unit sizing for the benefit of service and
maintenance. 15 kW, 3 Phase Kaplan-Induction-type
micro-hydro turbo units, 5 kW 3 Phase AEP 5000 asyn
chronous wind units, 120 W BP Solar 33 V, 3.56 A PV
units, 5 kW 3 Phase DG sets and 360 AH, 6 V battery
units are used for calculation. Their characteristics are
obtained from manufacture’s data [13].
The optimization model developed is applied for the
village with the above data to determine the optimal num-
ber of different renewable energy units and also to find
the optimal schedule. Quasi-Newton algorithm is used to
Figure 3. Hourly power output under optimal hybrid en-
ergy system
solve the optimization problem [14].
4.2 Results
With the three renewable sources considered, the analy-
sis will be to determine the optimal one out of seven
combinations for the village. Iterative results of certain
components are shown in Table 1. The optimization does
not choose PV system, naturally due to high capital cost.
Even though the second configuration with single wind
unit is cheaper, it requires diesel generator backup to
meet the demand during the peak hours. The third

Optimised Model for Community-Based Hybrid Energy System 28
configuration with two wind turbines eliminates the need
for the diesel generator but with a small battery backup.
So this configuration contributing to 100% renewable
energy and reduced emission is recommended. Here, the
surplus energy stored in the battery unit meets the peak
load instead of DG. The hourly output under optimal
operation of the hybrid system is shown in Figure 3.
The micro-hydro contributes 78% and wind 22% of
the total supplied energy system. The battery throughput
is 85 kW h/day. The unit cost of electricity for the above
hybrid system is Rs. 6.5/kW h.
The proposed hybrid system can supply the entire vil-
lage population with 24 h quality power eliminating the
need for peak load diesel generators. The existing diesel
power units can supply only half of the population on an
average six hours operation in a day. A 25 kVA diesel
generator is required to supply the whole population with
unit costs of Rs. 11.63/-. The cost comparisons of differ-
ent schemes are given in Table 2. Cost of grid charge is
arrived using annualised grid extension charge of Rs.
50,000/km and grid energy cost of Rs. 4/kW h. With the
installation of the proposed micro-hydro/wind hybrid
energy system in the village, the entire population will
have reliable electricity for their basic needs.
5. Conclusions
A general optimization model for finding an optimal
combination of community-based hybrid energy systems
is developed for Indian conditions. This compatible
model is applicable to renewable power generation in
any rural village. A decision support system designed
and developed using this model to help a designer in siz-
ing the hybrid power system hardware and in selecting
the operating options on the basis of overall system per-
formance and economics when site specific conditions
and load profiles are known.
From the case study, a micro-hydro/wind hybrid en-
ergy system is found to be the optimal combination for
the rural community. This hybrid system with battery
backup will provide 24-hour electric supply to every
household in the village at the unit cost of Rs. 6.5/ kW h.
The total renewable energy fraction of electricity is
100%, which eliminates the need for conventional diesel
generator.
6. Acknowledgment
The author are thankful to M/s People’s School of Energy,
Kannur for valuable information and assistance in data
collection and survey in the remote village of Kerala.
REFERENCES
[1] Annual report 2004 of Ministry of Non Conventional
Energy Sources (MNES), Govt. of India, (www.m-
nes.nic.inwww.mnes.nic.in)
[2] Nayar C.V, Lawrance WB, Phillips SJ. Solar/ wind/ die-
sel hybrid energy systems for remote areas. Energy Con-
version Engineering Conference IECEC–89, Vol. 4. 1989.
p. 2029–34.
[3] Borrowsy BS, Salameh ZM. Optimum photovoltaic array
size for a hybrid wind/PV systems. IEEE Trans Energy
Convers 1994;9(3):482–8.
[4] Kruangpradit P, Tayati W. Hybrid renewable energy sys-
tem development in Thailand. Renewable Energy
1996;8(1–4):514–7.
[5] Kellogg WD, Nehrir MH, Venkataramana G, Gerez V.
Generation unit sizing and cost analysis for stand alone
wind, photovoltaic and hybrid wind/PV systems. IEEE
Trans Energy Convers 1998;13(1):70–5.
[6] Chedid R, Rahman S. Unit sizing and control of hybrid
wind-solar power system. IEEE Trans Energy Convers
1997;112(1):79–86.
[7] Elhadidy MA, Shaahid SM. Optimal sizing of battery
storage for hybrid (wind+diesel) power systems. Energy
1999;18(1):77–86.
[8] Shaahid SM, Elhadidy MA. Prospects of autono-
mous/stand-alone hybrid (photo-voltaic + diesel + battery)
power systems in commercial applications in hot regions.
Renewable Energy 2003;29(2):165–77.
[9] Karaki SH, Chedid RB, Ramadan R. Probabilistic per-
formance assessment of autonomous solar-wind energy
systems. IEEE Trans Energy Convers 1999;14(3):766–72.
[10] Jaramill OA, Borja MA, Huacuz JM. Using hydropower
to complement wind energy: a hybrid system to provide
firm power. Renewable Energy 2004;29(11):1887–909.
[11] Khan MJ, Iqbal MT. Pre-feasibility study of stand-alone
hybrid energy systems for applications in Newfoundland.
Renewable Energy 2005;30(6):835–54.
[12] Data from People’s School of Energy, Kannur, Kerala.
[13] Data collected from various manufacturers and utilities.
[14] Rao SS. A text book on Engineering optimization, theory
and practice, 3rd ed. New York: Wiley; 1996.