Smart Grid and Renewable Energy, 2013, 4, 419-427
http://dx.doi.org/10.4236/sgre.2013.45048 Published Online August 2013 (http://www.scirp.org/journal/sgre)
419
Valuation Model for Adding Energy Resource into
Autonomous Energy Cluster
Ewoud de Kok1, Ebisa Negeri2*, Ad van Wijk1, Nico Baken2
1Future Energy Systems, Delft University of Technology, Delft, The Netherlands; 2Network Architectures and Services, Delft Uni-
versity of Technology, Delft, The Netherlands.
Received May 24th, 2013; revised June 24th, 2013; accepted July 2nd, 2013
Copyright © 2013 Ewoud de Kok et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
With the availability of distributed generation (DG), clusters that can autonomously manage their energy profile are
emerging in the power grid. These autonomous clusters manage their load profiles by orchestrating their energy re-
sources, such as DG, storage, flexible energy consuming appliances, etc. The performance of such an autonomous clus-
ter depends on the composition of its energy resources. In this paper, we study how the performance of a cluster is af-
fected by adding energy resources such as generating units, storage systems or consuming appliances. First, we charac-
terize the energy resources by parameters that describe their relevant properties. Afterwards, we describe a comprehen-
sive set of performance indicators of a cluster that capture the economical, environmental, and social aspects. We pre-
sent a model that shows how the energy resources influence the performance indicators of the cluster. We have tested
our model with a case study, revealing its effectiveness to evaluate the value added by an energy resource to a cluster.
Keywords: Prosumer; Autonomous Energy Clusters; Valuation Model
1. Introduction
The electricity power system is in transition. Driven by
the growing need for clean, reliable and affordable elec-
tricity supply, more renewable and distributed energy
sources are penetrating into the distribution power grid,
i.e., close to the end-consumers [1-3]. For instance, ac-
cording to European parliament, all new buildings that
will be built after 2019 will have to produce energy on
site [4]. In addition to the generation capability, distrib-
uted electricity storage systems are also becoming avail-
able [5-9]. Moreover, massive presence of electric
vehicles is anticipated, that will have huge impact on
the distribution grid [10-12]. In parallel with these
trends, significant efforts is being made to develop in-
telligent solutions that could help to coordinate the
system [13,14].
The availability of distributed generation, the flexibil-
ity provided by distributed storage and other flexible
devices, as well as the accessibility of intelligent mecha-
nisms to coordinate these resources make local matching
of supply and demand more appealing. With more re-
sources becoming locally available and with the growing
intelligence of coordination, the lower parts of electricity
power grid tend to become energy autonomous. Accord-
ingly, various types of autonomous clusters are develop-
ing in the power system, namely virtual power plants
[15], microgrids [3], autonomous networks [16], energy
communities [17], etc. Common to these forms of clus-
ters is that they autonomously manage their resources
and exchange power bidirectionally with the rest of the
power grid.
A synthetic neighborhood autonomous cluster is shown
in Figure 1. The cluster consists of different types of
energy resources. The energy resources include different
power sources, electricity storage systems, and different
types of appliances in and around the houses that con-
sume electricity. The energy resources in the cluster can
be coordinated using appropriate strategies to achieve a
desired performance. The autonomous cluster is also
connected to the external grid, that enables it to ex-
chang e power with the rest of the gr id in a bidirectional
way.
It is desirable to optimize the performances of au-
tonomous clusters with regards to economical, environ-
mental, and social values. The performance measures
depend on the composition of the energy resources in the
*Corresponding a uthor.

Valuation Model for Adding Energy Resource into Autonomous Energy Cluster 420
Figure 1. A neighborhood autonomous energy cluster.
cluster, since the energy resources have different contri-
butions to the different performance measures of the
cluster. Therefore, finding the right composition of the
energy resources plays a significant role to obtain the
desired performance of the cluster. To find the right
composition of the energy resources in the cluster, the
influence of each energy resource on the performances of
the cluster need to be clearly identified.
In this paper, we present a novel study that investi-
gates how the energy resources in an autonomous cluster
influence the performance of the cluster. We consider a
generic autonomous cluster. To do this, we identify the
characteristics of the energy resources that influence the
performance of the cluster. Further, we describe a com-
prehensive set of relevant performance indicators of the
cluster, and then model how these performance indica-
tors are influenced by the characteristics of the energy
resources. This enables us to model the value added to
the cluster by adding an energy resource.
The rest of this paper is organized as follows. We pre-
sent the related work in Section 2. After presenting the
characteristics of the energy resources in Section 3, we
present our model of the performance indicators of a
cluster in Section 4. In Section 5, we present our case
study that is used to test our model. Finally, the conclud-
ing remarks are presented in Section 6.
2. Related Work
With the trend of increasing availability of distributed
energy resources, various forms of autonomous clusters
have been proposed. A Virtual Power Plant (VPP) [15] is
a collectively managed cluster of distributed power
sources. A Microgrid [3] is a low voltage distribution
system comprising of distributed generations, storage
systems and controlled loads that are coordinated to
achieve a controllable operation either as an island or
connected to the power grid. Autonomous network [16]
is a part of the power grid but its behavior is more or less
independent from the rest, and its primary aim is opti-
mizing its normal operatio n. An energy community [9] is
a cluster of prosumers that exchange power with the rest
of the system as a single unit.
In autonomous clusters, desirable performances are
achieved by orchestrating their energy resources. Cost,
emission and reliability/robustness are common per-
formance indicators in the power system. There are a
couple of works in the literature that attempt to optimize
some of these performance indicators on specific systems,
a review of which is provided in [18]. However, a com-
prehensive model that evaluates the performance indica-
tors of a cluster in terms of the properties of its constitu-
ent energy resources is missing, and this work attempts
to fill this gap.
In this work, we propose a model that evaluates the
value gained by adding an energy resource to an au-
tonomous cluster. In addition to the common perform-
ance indicators mentioned before, we propose two rele-
vant performance indicators of an autonomous cluster,
namely independence and convenience, that also capture
other performance aspects of a cluster as will be de-
scribed later.

Valuation Model for Adding Energy Resource into Autonomous Energy Cluster 421
3. Characterizing the Energy Resources
In this paper, energy resource of a cluster refers to gen-
eration unit, energy storage system, or consuming appli-
ance that is part of the cluster. Energy resources have
different characteristics that influen ce the performance of
the cluster. In this section, eight characteristics are iden-
tified, namely cost, emissions, failure rate, responsive-
ness, controllability, predictability, availability, and con-
venience. These characteristics are described subse-
quently.
3.1. Cost
An energy resource has a fixed cost which represents the
investment cost incurred to install it. Over a given period
of time T, the fixed cost can be translated to depreciation
cost. Depreciation costs are the costs due to the value
degradation of the energy resources as a result of aging
and usage. Depreciation of an energy resource depends
on how intensiv ely it is used, i.e. if it is used more inten-
sively, it depreciates faster. Thus, the depreciation cost

dep
c of an energy resource over an interval of time T is
obtained by multiplying its fixed cost

f
c by its de-
preciation over

TD, as shown in Equation (1).
In addition, an energ y resource has a variable cost that
is associated with its operation. The variable cost of an
energy resource over a period

var
Tc can be obtained
as shown in Equation (2), where cv is its average cost of
supplying a unit energy, and E is the total amount of en-
ergy supplied by the energy resource in the time interval
T.
dep f
ccD (1)
var v
ccE (2)
3.2. Emission
An energy resource usually has green-house gas emission
associated with it, that could be divided into fixed emis-
sion and variable emission. The total fixed emission

f
m is the emission associated with manufacturing
and installation process of the energy resource. The va-
riable emission is the emission resulting from the opera-
tion of the energy resource.
Similar to the cost, the depreciation and variable emis-
sions over period T (dep m and var m) of an energy re-
source can be obtained as shown in Equations (3) and (4),
respectively, where
f
m is the fixed emission of the
energy resource, and v m is the emission of the energy
resource per unit of the en ergy it supplies.
dep f
mmD (3)
var v
mmE (4)
3.3. Failure Rate
Failure rate expresses the probability of failure of an en-
ergy resource. Given an expected rate of failure per year



, a continuous probability distribution function can
be used to model the failure probability. Commonly, the
exponential distribution function is used. Accordingly,
the probability that a failure occurs within a time dura-
tion of T can be expressed as:

0ed 1e
TT
FT




 
 (5)
3.4. Predictability
The predictability of an energy resource indicates how
accurately its power supply or demand can be forecasted.
Two parameters are important to describe predictability,
the coefficient of reliability

P

and the time


t.
The coefficient of reliability tells how reliable the predic-
tion is. Predictability is measured by the prediction un-
certainty interval from the expected value. The prediction
uncertainty interval is denoted by
P

, which depends o n
both
P

and t. For example,



,0.3,45
PP P
t   
means that the prediction is 30% confident that at 4t

the value will be within ±5 uncertainty interval from the
expected value.
Based on these, we quantify the predictability factor


r of an energy resource as shown in Equation (6),
where T is the length of the time period over which the
prediction is made, and U is the capacity of the energy
resource. r is computed by integrating the prediction un-
certainty interval


P

over the time period T and all
coefficients of reliability


P

, and then normalized by
U. The normalization is done so that r gives the amount
of uncertainty per capacity of the energy resource. A
lower predictability factor r indicates a higher predict-
ability.
 
1
00
1,dd TP
rTt t
U

  (6)
3.5. Availability
Availability of an energy resource tells whether it is
available for use when it is needed. This characteristic
also captures the usefulness of its availability. For exam-
ple, a power source that is available at periods of surplus
production but not available at the times when there is
deficiency of supply has low availability. When quanti-
fying the availability, two parameters of importance are
the expected availability



At

and the correspond-
ing uncertainty




At

. While

At
 tells when the
energy resource is available,

At
 represents the level
of uncertainty of the availability.
To capture the usefulness of the availability of an en-
ergy resource, we propose an availability factor that de-

Valuation Model for Adding Energy Resource into Autonomous Energy Cluster 422
pends on the situation in the cluster. We denote the situa-
tion in the cluster by S, which represents the amount of
extra power production/consumption needed based on
whether there is shortage/surplus of power generation in
the cluster, normalized by the largest instantaneous po-
wer demand in the cluster. In order to determine the
availability of an energy resource to supply power, S is
computed based on the need of extra power supply,
whereas to determine the availability of an energy re-
source to consume/store power, S is computed based on
the need of extra power consumption. Accordingly, we
propose to compute the availability factor

a over a
period of time T as shown in Equation (7).




0d
A
T
A
tSt
aT t
t



 (7)
3.6. Controllability
Controllability refers to the extent to which the power
supply/consumption of an energy resource can be con-
trolled. In our case, controlling means making it produce
or consume a required amount of electricity on demand.
For example, the charging rate of a battery storage can be
tuned below the maximum possible charging rate. The
controllability of an energy resources is subject to its
inherent constraints. For instance, charging of a storage
is constrained by its maximum charging rate, state of
charge, and storage capacity. Therefore, we propose to
measure controllability

b as the length of the interval
over which the power supply/consumption of an energy
resource can be varied, as restricted by its inherent con-
straints.
3.7. Responsiveness
Responsiveness represents the duration of time it takes
the energy resource to respond to a power production/
consumption request from the cluster. Some energy re-
sources respond in few seconds, while others do in few
minutes or more. For example, a battery storage can re-
spond to a request in few seconds, while a fuel cell re-
sponds in a couple of seconds to minutes. Thus, respon-
siveness of an energy resource, x, is expressed as the
length of the time interval between receiving the request
and respond ing to the request.
3.8. Convenience
Convenience refers to the perception of people about an
energy resource r egarding its disruption of their comfor t.
Comfort can have various dimensions such as noise, vis-
ual disturbance, etc. For example, installing wind tur-
bines in a residential neighborhood could lead to visual
disturbance. People can have different opinion about the
importance of a comfort dimension. The importance can
be rated with integers ranging from 0 to 3. An energy
resource can be evaluated against each comfort dimen-
sion with a score ranging from say 1 to 10. Therefore,
convenience can be measured by surveying the opinion
of the people about the importance and score of each
comfort dimension. Afterwards, convenience factor


v
is computed as shown in Equation (8), where
j
h and
j
l are the impo r ta n c e and s cor e, r e sp e ctiv e ly, o f th e th j
comfort dimension.

comfortdimension
comfortdimension
j
j
j
j
j
hl
vh





 (8)
4. Performance Indicators of a Cluster
In this work, a cluster is a general term that refers to a
part of the power grid that autonomously manages its
own resources and is capable of exchanging power bidi-
rectionally with the rest of the power grid. The perform-
ance of a grid cluster could be evaluated by comprehen-
sively considering the economical, environmental, socie-
tal aspects. This approach enables a holistic evaluation of
the cluster. Accordingly, we present a comprehensive set
of performance indicators that cover the economical,
environmental and societal aspects. These performance
indicators include cost, emission, robustness, independ-
ence, and convenience.
The value gained by adding an energy resource into
the cluster depends on the precedence of usage of the
energy resources in the cluster. For example, in case of
excess power production, using a flexible load to match
demand and supply could be given priority compared to
storing the excess power in a battery storage. Thus, add-
ing a flexible load to a cluster could alter the contribution
a previously existing battery storage makes to the cluster.
When the contribution of the energy resources change,
the performance indicators of the cluster might change as
well.
Next, we will present the performance indicators to-
gether with how they are influenced by the characteris-
tics of the energy resources.
4.1. Cost
Evaluating the cost of a cluster is very relevant because it
affects the payments of the consumers to purchase elec-
tricity. The cost of the cluster per kWh

C in time
interval T is computed as shown in Equation (9). The
terms in the square bracket make up the net cost of the
cluster in T. It consists of the total depreciation and vari-
able cost of all the N energy resources in the cluster (ob-
tained from Equations (1) and (2)), the cost of the yearly
electricity import


im
C, and the benefit obtained from
the yearly electricity export

ex
C. The total net cost is

Valuation Model for Adding Energy Resource into Autonomous Energy Cluster 423
then divided by the sum of the energy supplied from the
cluster, both for local use and for export

s
up
E, and the
imported energy

im
E in time interval T.

=1
1Ndep varimex
kk
supim k
CccCC
EE




 (9)
To evaluate the impact of adding a new energy re-
source on the cost of the cluster, Equation (9) should be
recomputed with the new energy resource incorporated
into the cluster. Thus, th e difference between the original
cost and the new cost represents the added value of the
new energy resource on the cost of the cluster.
4.2. Emission
In line with the growing environmental concerns, reduc-
ing the emission of green house gases associated with
electricity system needs to be minimized. Emission meas-
ures the cleanness of electricity from green house gases.
The emission of a cluster incorporates the emissions as-
sociated with its energy resources. We assume that a
cluster is responsible for the emission associated with
the energy it supplies both for local consumption and
export.
Accordingly, we quantify the emission of a cluster per
unit kWh

M
in time interval T as shown in Equation
(10). The quantity in the square bracket represents the
total emission in period T associated with the cluster. The
total emission is then divided by the sum of the energy
supplied from the cluster, both for local consumption and
export, in time interval T.

=1
1Ndep var
kk
sup k
Mmm
E




 (10)
The impact of adding a new energy resource on the
emission of the cluster can be evaluated by recomputing
Equation (10) with the new energy resource included, in
a similar way it was done for cost.
4.3. Robustness
An energy cluster needs to supply reliable power to the
end-consumers, hence it is desirable to minimize the
chance of power outage. We express robustness of a
cluster in terms of the chance of power outages the con-
sumers experience. We consider three possible causes of
power outage. The first is the scenario when a producing
energy resource fails and there are no other energy re-
sources to cope with the reduction in supply; the second
cause is a big and rapid fluctuation of the supply/con-
sumption from the expected values that the cluster
could not cope up with; and the third one is the situa-
tion when the demand is higher than the maximum
power supply.
We define three vulnerability measures of a cluster
corresponding to these causes of power outage, namely,
failure vulnerability, fluctuation vu lnerability and power-
shortage vulnerability. Th e failure vulnerability


failu re
 of a cluster depends on the probability of fail-
ure of each energy resources
 
i
F
t as well as the po-
tential impact of failure of each energy resource on pos-
sibility of power outage of the clus ter

i

, as shown in
Equation (11). i

represents the probability that the
failure of an energy resource i leads to power outage in
the cluster. Clearly, i

depends on the composition of
the cluster.

failure 0=1 d
N
T
ii
i
F
tt 


 





 (11)
The impact of simultaneous failures of multiple energy
resources can be obtained by multiplying the product of
the failure rates of the individual energy resources by the
probability that their combined failures lead to power
outage. This combined effect can be added to Equation
(11), but the chance of simultaneous failures of multiple
energy resources is practically very small.
The fluctuation vulnerability of a cluster depends on
its maximum fluctuation tolerance, which is determined
based on the technique used to overcome fluctuations. In
conventional power system, three stages are involved to
overcome big fluctuations, namely primary, secondary
and tertiary control stages [19]. When a fluctuation arises,
the primary control is initiated, whereby highly respon-
sive energy resources are used to cope with the fluctua-
tion within short period of time (a few to several sec-
onds). Afterw ards, the secondary contro l stage takes ov er
(in a couple of seconds to a minute) the primary control
using the less time responsive resources, and the re-
sources used in the primary stage are freed. Finally, the
tertiary control takes over and brings the system back to
an equilibrium position, thereby freeing the resources
used in the secondary control stage.
For each control stage, the system has a fixed assimi-
late capacity to absorb fluctuations. If the fluctuation
exceeds any of these assimilation capacities, then power
outage could result. Thus, the maximum absorbable fluc-
tuation


max
 can be expressed as the minimum of the
assimilate capacities



in the three stages (Equation
(12)). Thus, we compute the fluctuation vulnerability of
the cluster over a period T (Equation (13)) by integrating
over


T the probability that the fluctuation


t 
exceeds max
 . The fluctuation at time t,


t  , de-
pends on the profile of the entire energy resources in the
luster.


maxprimary secondary tertiary
min ,,   (12)

max
fluctuation 0>d
Tpr tt




 (13)
On the other hand, power-shortage vuln erability ov er a

Valuation Model for Adding Energy Resource into Autonomous Energy Cluster 424
given period T can be computed by Equation (14), inte-
grating over period T the probability that demand ex-
ceeds supply.
 
shortage0demand> supplyd
Tprtt t


 (14)
Finally, the overall vulnerability


 of the cluster is
obtained by adding th e individual vulnerab ilities together
(Equation (15)). Then, the overall robustness of the clus-
ter is computed as the inverse of th e overall vulnerability
(Equation (16)).
failure fluctuation shortage
  (15)
1
R (16)
A cluster can have a certain level of tolerance for the
occurrence of power outage. For example, a single power
outage per year could be tolerable in a cluster. We refer
to the maximum vulnerability that is tolerated by the
cluster as power outage tolerance


. Thus, the condi-
tion in Equation (17) should always b e maintained.
<

 (17)
When a new energy resource is added to the cluster,
the values of the p arameters in Equations (11)-(16) cou ld
change. For instance, the failure rate, availability, con-
trollability, responsiveness and predictability of the en-
ergy resource could affect the vulnerability of the cluster.
Hence, the change in robustness R gives the added value
of the new energy resource.
4.4. Independence
A cluster may depend on the rest of the power grid for
various reasons. When the imported electricity is cheaper
than the local electricity supply from its own power
sources, then the cluster might resort to importing elec-
tricity from the rest of the power grid even though the
demand can be supplied locally. We refer to this optional
kind of dependency as economical dependency. On the
other hand, when the local demand exceeds the maxi-
mum capacity of the local supp ly, the cluster is forced to
import electricity. We refer to this kind of dependency as
mandatory dependency. The independence performance
metric addresses the mandatory dependence of the cluster
on the rest of the grid.
There could be various reasons why a cluster would
minimize its mandatory dependence on the rest of the
power grid. For instance, if the cluster is largely depend-
ent on the rest of the grid, then disturbances in the rest of
the grid could have larger impact on the cluster. Accord-
ingly, we represent independence as one performance
indicator of a cluster. We employ two types of metrics to
capture the mandatory dependence of a cluster on the rest
of the grid, namely aggregate dependence and instanta-
neous dependence. Aggregate dependence


aggregate
D
refers to the volume of mandatory electricity imported
from the rest of the power grid

*im
E over a period of
time compared to the total electricity consumed in the
cluster over the same period

cons
E, as shown in Equa-
tion (18) .
*
aggregate
im
cons
E
DE  (18)
Instantaneous dependence

instantaneous
D captures the
dependence of a cluster on the rest of the grid in terms of
the instantaneous power imported. Let X be the maxi-
mum mandatory instantaneous power that is imported
from the rest of the grid in period T, and let Y be the av-
erage power consumed in the cluster in the same period.
Then, instantaneous
D is computed as the ratio of the two
(Equation (19)).
instantaneous
X
DY  (19)
The characteristics of the energy resources such as
predictability, controllability, responsiveness and avail-
ability affect the independence of the cluster. For exam-
ple, if a cluster has more predictable energy resources,
then the possible supply shortages can be predicted early
enough, and hence the controllable energy resources can
be appropriately managed to locally compensate the sup-
ply shortage, thereby reducing dependence on the exter-
nal grid. The impact of adding a new energy resource can
be computed in the same fashion as it was done for the
previous cluster performance indicators.
4.5. Convenience
Convenience of a cluster measures the perception of the
people about the suitability of the energy resources to
maintain their comforts as mentioned in Section 3.8. As
shown in Equation (20), the convenience of a cluster


V can be obtained by summing up the individual
convenience k
v of all the energy resources in the cluster,
that were calculated using Equation (8). Thus, the value
added by adding a new energy resource can be obtained
by recomputing Equation (20) with the new energy re-
source incorporated in the cluster.
1
1N
k
k
Vv
N


(20)
5. A Case Study
In order to verify the theoretical model developed in the
preceding section, we present a simplified case study.
The clusters used in our case study are modeled based on
the design of the green village project of the TUDelft
[20]. The green village project aims at building a sus-
tainable village at TUDelft campus based on green en-

Valuation Model for Adding Energy Resource into Autonomous Energy Cluster 425
ergy and intelligent technological developments.
For our case study, we make three variants of clusters
with different compositions, that are simplified versions
of the green village design. The first cluster, cluster1,
represents a regular cluster whose composition is shown
in Table 1. The second cluster, cluster2, is a modified
version of cluster1 which is obtained by removing the
battery. Similarly, the third cluster, cluster3, is obtained
by modifying cluster1 such that the quantity of both wind
turbine and solar PV are reduced by half, and the power
capacity of the battery is increased to 50 kW.
We evaluate the gain with respect to cost and robust-
ness by adding a storage system on the three clusters. As
stated earlier, the value gained by adding an energy re-
source into the cluster depends on the precedence of us-
age of the energy resources in the cluster. While different
precedence strategies are possible, we adopt a simple one.
The precedence of usage of resources assumed for the
three clusters is as follows. If there is shortage of su pply,
then power is supplied from storage. If the storage supply
alone cannot cope up with the shortage, then additional
power is supplied from the fuel cell. If the shortage ex-
ceeds the combined capacity of the storage and the fuel
cell, then the power is imported from the rest of the grid.
On the other hand, if there is surplus production of power,
then storage is used to store it. If the surplus production
exceeds the storage capacity, then power is exported to
the rest of the grid.
To accurately test our theoretical model, stochastic
data, such as the mean and the standard deviation, are
needed to model the distribution of the profiles of the
energy resources. Since these kinds of stochastic data are
difficult to obtain, we resort to a simplified alternative
method whereby the data about the profiles of the energy
resources are approximated based on empirical data. We
employ the Renewable Energy Grid Simulator (REGS)
[21] for this purpose.
The REGS tool takes as input the average load, the
average electricity from the wind turbine, and the aver-
age electricity from solar PV, and outputs the corre-
sponding time series profile of the load, wind energy
supply, and solar energy supply over a period of time.
The outputs of the simulator are tuned by intelligent pat-
tern learning from a rich empirical data about load and
renewable energy supply patterns in The Netherlands
from the year 2000 to 2010, which is obtained from
Tennet1. Using the outputs of the REGS as input to our
model, we apply the aforementioned precedence of the
usage of our resources.
Figure 2(a) shows the gain obtained on the cost of the
cluster by adding battery storages of different storage
capacities and power capacities to the three clusters de-
scribed before. The storage capacities used are 100 and
250 kWh, while different power capacities ranging from
1 to 80 kW are used. The power capacity of the battery
refers to the maximum charging/discharging rate of the
battery.
As can be observed from the figure, adding a battery
yields the largest gain in cluster2 (the cluster without
storage) compared to doing the same for the other clus-
ters. In cluster2, the imbalance in demand and supply is
compensated by the fuel cell and the transaction with the
external grid because it does not have a storage. After a
battery is added to this clu ster, the imbalance is primarily
compensated by the battery, thereby significantly reduc-
ing the expensive cost of fuel cells and the imported
power.
On the other hand, moderate cost gain is observed for
cluster1 (the regular cluster) after adding a battery. The
moderate gain stems from the fact that the cluster already
had a battery that could compensate part of the power
imbalance, and the remaining imbalance is compensated
by fuel cells and transactions with the rest of the grid.
Thus, the extra added battery will be used to cope with
the imbalance that remain after using the existing battery,
thereby leading to a smaller gain.
In both cluster1 and cluster2, the gain in cost first rises
rapidly with increasing the power capacity of the added
battery and later saturates even though the battery capac-
ity is increased further. Moreover, the gain in cost satu-
rates at a smaller power capacity when the battery stor-
age capacity is smaller, and vice versa. Thus, given a
fixed storage capacity of a battery, the benefit of the bat-
tery can be improved by increasing the power capacity of
the battery to a certain extent. However, increasing the
power capacity beyond a certain level does not yield fur-
ther gain because the storage capacity of the battery is a
constraint to the maximum power that can be stored.
Hence, a battery with optimal combination of storage
capacity and power capacity need to be chosen.
On the contrary, cluster3 (a cluster with renewable en-
ergy reduced by half and larger storage capacity) did not
show any gain by adding a battery. This cluster has lower
variability in the supply side because of its lower compo-
sition of the variable renewable sources. Thus, the com-
paratively small surplus production from the renewable
sources can already be completely absorbed by its larger
battery storage capacity, and then supplied later when
there is shortage of supply. Accordingly, there is no re-
maining potential to reduce the use of fuel cells and
power imports from the external grid. Therefore, adding
an additional battery d oes no t reduce co st as it will not be
used any way.
Figure 2(b) shows the effect of adding batteries (with
storage capacity of 250 kWh and different power capaci-
ties) on the robustiness of the three clusters under con-
sideration. Improvement in robustness is measured by the
1Tennet is the transmission network operator in The Netherlands.

Valuation Model for Adding Energy Resource into Autonomous Energy Cluster 426
Table 1. The composition of the cluster1.
Power
capacity
(kW)
Capacity
factor (%)
Storage
capacity
(kWh)
Fixed cost
(€/unit)
Variable
cost
(€/kWh)
Fixed
emission
(kg/unit)
Variable
emission
(kg/kWh)
Monthly
failure rate
Cycle
efficiency
(%) Quantity
Wind
turbine 50 0.33 150,000 0 100,000 0 0.001 2
Solar PV 0.23 0.24 575 0 1000 0 0.001 160
Fuel cell 35 350,000 0.15 19,250 0 0.001 1
Battery 20 100 650 €/kWh65 €/cycle1000 kg/kWh0 0.001 99 1
Simple
load 105 1
(a)
(b)
Figure 2. The impact of adding storage systems to the clus-
ters. (a) Improvement on cost (battery capacity: 100 kWh,
250 kWh); (b) Improvement on robustness (battery capac-
ity: 250 kWh).
increase in the number of days it takes before occurrence
of a power outage. As can be observed, adding a battery
did not improve the ro bu stness of cluster3. Given the low
composition of the v ariable renewable sources, the exist-
ing battery can already provide enough flexibility that
could be used to store the surplus productions of the re-
newable sources and reuse it later to improve the robust-
ness of the cluster. Hence, adding a new battery does not
improve the robustness because there is no extra surplus
production to store and reuse.
On the other hand, adding a battery yielded larger ro-
bustness improvement in cluster1 (the regular cluster)
than in cluster2 (the cluster with no battery). Although
this sounds counter intuitive, it can be explained as fol-
lows. Batteries are used to improve robustness if they are
not being used at full capacity when the events (failure,
fluctuation, or power-shortage) occur in the cluster. At
the occurrence of these events, the reserve capacity of the
batteries can be exploited to minimize the vulnerability
of the cluster. Cluster1 already has a battery, hence the
probability that the newly added battery is used at full
capacity is smaller. Hence, the new added battery will
have larger reserve capacity that could be used to im-
prove robustness of the cluster. Whereas, cluster2 did not
have a battery, and thus the newly added battery is more
likely to have smaller reserve capacity, thereby leading to
smaller robustness improvement.
The results in Figures 2(a) and (b) clearly confirm
that the value gained by adding an energy resource to a
cluster depends on the composition of the cluster, as well
as the precedence of the usage of its energy resources.
Thus, our proposed valuation model enables the operator
of the cluster to wisely choose the appropriate energy
resources that could be added to achieve the desired per-
formance improvement. Similar simulations could be re-
peated with the other performance indicators of the clus-
ter.
6. Discussions and Conclusions
In this paper, we have developed a valuation model for
evaluating the value gained by adding an energy resource
into an autonomous energy cluster. Our model presents a
characterization of energy resources using wide range of
parameters, namely cost, emission, failure rate, predict-

Valuation Model for Adding Energy Resource into Autonomous Energy Cluster 427
ability, availability, controllability, responsiveness, and
-convenience. Moreover, comprehensive set of perform-
ance indicators of a cluster, that relate to environmental,
economical and social values, are considered and model-
ed.
Based on this model, the impacts of adding an energy
resource into a cluster is analyzed. We also presented a
case study to test our proposed theoretical model which
endorsed the strength of the model to evaluate the value
an energy resource adds to a cluster. Our model also re-
veals that the value added by an energy resource depends
both on the composition of the cluster and the precedence
of the usage of energy resources in the cluster.
Developing appropriate stochastic data that better cap-
ture the behaviors of the energy resources could help to
analyze the benefits of the valuation model more thor-
oughly. Further, more realistic and synthetic test cases
could be employed to evaluate the proposed valuation mo-
del.
Our proposed valuation model can be used as a basis
to design optimal composition of a cluster, whereby cer-
tain energy resources are added to or removed from the
cluster depending on their impact on the desirable per-
formance indicators.
REFERENCES
[1] OECD/IEA, “Medium-Term Renewable Energy Market
Report,” 2012.
http://www.iea.org/Textbase/npsum/MTrenew2012SUM.
pdf
[2] International Energy Agency, “Distributed Generation in
Liberalized Electricity Markets,” 2002.
http://gasunie.eldoc.ub.rug.nl/FILES/root/2002/3125958/
3125958.pdf
[3] N. Hatziargyriou, N. Jenkins, G. Strbac, J. A. P. Lopes, J.
Ruela and A. Engler, “MICROGRIDS—Large Scale In-
tegration of Micro-Generation to Low Voltage Grids,”
CIGRE C6-309, Paris, 2006.
[4] European Parliament, “All New Buildings to Be Zero
Energy from 2019,” Committee on Industry, Research
and Energy, Brussels, 2009.
http://www.europarl.europa.eu/sides/getDoc.do?language
=en&type=IM-PRESS&reference=20090330IPR52892
[5] IEA-ETSAP and IRENA, “Electricity Storage Technol-
ogy,” 2012.
http://www.irena.org/DocumentDownloads/Publications/I
RENA-ETSAP%20Tech%20Brief%20E18%20Electricity
-Storage.pdf
[6] D. Kottick, M. Blau and D. EEdelstein, “Battery Energy
Storage for Frequency Regulation in an Island Power
System,” IEEE Transactions on Energy Conversion, Vol.
8, No. 3, 1993, pp. 455-459. doi:10.1109/60.257059
[7] G. Mulder, F. D. Ridder and D. Six, “Electricity Storage
for Grid-connected Household Dwellings with PV Pan-
els,” Solar Energy, Vol. 84, 2010, pp. 1284-1293.
doi:10.1016/j.solener.2010.04.005
[8] P. F. Ribeiro, B. K. Johnson, M. L. Crow, A. Arsoy and
Y. Liu, “Energy Storage Systems for Advanced Power
Applications,” Proceedings of the IEEE, Vol. 89, No. 12,
December 2001, pp. 1744-1756. doi:10.1109/5.975900
[9] E. Negeri and N. Baken, “Distributed Storage Manage-
ment Using Dynamic Pricing in a Self-Organized Energy
Community,” Self-Organizing Systems, Springer, Berlin
Heidelberg, 2012, pp. 1-12.
[10] International Energy Agency, “Technology Roadmap:
Electric and Plug-In Hybrid Electric Vehicles,” 2011.
http://www.iea.org/publications/freepublications/publicati
on/EV_PHEV_Roadmap.pdf
[11] J. P. Lopes, F. J. Soares and P. R. Almeida, “Integration
of Electric Vehicles in the Electric Power System,” Pro-
ceedings of the IEEE, Vol. 99, No. 1, 2011, pp. 168-183.
doi:10.1109/JPROC.2010.2066250
[12] E. Negeri and N. Baken, “Smart Integration of Electric
Vehicles in an Energy Community,” Proceedings of the
1st International Conference on Smart Grids and Green
IT Systems, Porto, Portugal, SciTePress, 2012, pp. 25-32.
[13] H. Farhangi, “The Path of the Smart Grid,” IEEE Power
and Energy Magazine, Vol. 8, No. 1, 2010, pp. 18-28.
doi:10.1109/MPE.2009.934876
[14] Y.X. Yu and W. Luan. “Smart Grid and Its Implementa-
tions,” Proceedings of the CSEE, Vol. 29, No. 34, 2009,
pp. 1-8.
[15] K. Dielmann and A. van der Velden, “Virtual Power
Plants (VPP)—A New Perspective for Energy Genera-
tion?” Proceedings of the 9th International Scientific and
Practical Conference on Modern Techniques and Tech-
nologies, April 2003, pp. 18-20.
[16] F. Provoost, J. Myrzik and W. Kling, “Setting Up
Autonomous Controlled Networks,” 39th International
Universities Power Engineering Conference (UPEC), Vol.
3, 2004, pp. 1190-1194.
[17] E. Negeri, N. Baken and M. Popov, “Holonic Architec-
ture of the Smart Grid,” Smart Grid and Renewable En-
ergy, Vol. 4, No. 2, 2013, pp. 202-212.
doi:10.4236/sgre.2013.42025
[18] A. Alarcon-Rodriguez, G. Ault and S. Galloway, “Multi-
Objective Planning of Distributed Energy Resources: A
Review of the State-of-the-Art,” Renewable and Sustain-
able Energy Reviews, Vol. 14, No. 5, 2010, pp. 1353-
1366. doi:10.1016/j.rser.2010.01.006
[19] J. B. machowski, “Power System Dynamics: Stability and
Control,” Wiley, Hoboken, USA, 2011.
[20] A. Van Wijk, “Welcome to the Green Village,” IOS Press,
Delft 2013.
http://www.thegreenvillage.org
[21] The Renewable Energy Grids Simulator Tool.
http://arnekaas.nl/REGS/?id=1