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Proceedings ArticleDOI

A methodology to consider combined electrical infrastructure and real-time power-flow impact costs in planning large-scale renewable energy farms

01 Nov 2010-pp 674-678

Abstract: The U.S federal government's strategic vision encouraging renewable energy production has motivated several new energy generation projects. Among them are large-scale renewable energy farm building efforts, where one considers the renewable resource potential along with land, equipment, and installation costs. The goal in the planning phase of these efforts is to maximize the return on investment and resource utilization. The challenge, which is specific to integrating new generation is the need to include the operational cost (both construction as well as run-time) of introducing power to the existing infrastructure. In this paper, we propose a methodology to account for and include energy transmission line proximity (a construction time cost) as well as thermal-overload, and voltage out-of-range (an infrastructure cost) factors when we plan to “tap” into an existing infrastructure. We present results for a study over regions in Texas, Kansas, Colorado, New Mexico and Oklahoma and discuss the findings.
Topics: Feed-in tariff (64%), Energy planning (58%), Renewable energy (55%), Return on investment (53%), Strategic planning (50%)

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A Methodology to Consider Combined Electrical Infrastructure and Real-Time Power-Flow
Impact Costs in Planning Large-Scale Renewable Energy Farms
Sreenivas R. Sukumar
1
, Mallikarjun Shankar
1
, Mohammed Olama
1
, James Nutaro
1
,
Sergey Malinchik
2
and Barry Ives
2
1
Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37830.
2
Lockheed Martin Corporation, 3 Executive Campus, 6th floor, Cherry Hill, NJ, 08002.
Email: {ssrangan@ieee.org, shankarm@ornl.gov, olamahussemm@ornl.gov, nutarojj@ornl.gov,
sergey.b.malinchik@lmco.com, barry1.ives@lmco.com}
Abstract -- The U.S federal government's strategic vision
encouraging renewable energy production has motivated several
new energy generation projects. Among them are large-scale
renewable energy farm building efforts, where one considers the
renewable resource potential along with land, equipment, and
installation costs. The goal in the planning phase of these efforts
is to maximize the return on investment and resource utilization.
The challenge, which is specific to integrating new generation is
the need to include the operational cost (both construction as
well as run-time) of introducing power to the existing
infrastructure. In this paper, we propose a methodology to
account for and include energy transmission line proximity (a
construction time cost) as well as thermal-overload, and voltage
out-of-range (an infrastructure cost) factors when we plan to
“tap” into an existing infrastructure. We present results for a
study over regions in Texas, Kansas, Colorado, New Mexico and
Oklahoma and discuss the findings.
Index Terms-- renewable energy integration, power-flow
costs, energy planning.
I. INTRODUCTION
Resource abundance drives the spatial feasibility of
renewable energy farms more than energy demand and
construction convenience. This means that we have to
transport the power generated from such sites of resource
abundance into existing energy transmission interconnects.
As transmission line costs can go up to $1 million for a mile
of 345KV line [1], the distance to the infrastructure becomes
a key cost factor.
As is also well known, resources like wind and solar
energy are intermittent (variable) means of energy
production. There may be several days in a year when the
energy production falls below expected production efficiency
and times with spurts of higher than expected production
within the same day. On the other hand, the system must be
designed for the maximum power scenario with the existing
transmission and control infrastructure, which is expected to
operate within certain voltage and power specifications. A
typical transmission bus is designed for operation between ±
6% of its voltage rating [2]. Exceeding or falling below the
range must be avoided as it may severely damage company
and customer equipment. Therefore, before adding the new
power into the transmission network we have to make sure
that expected line and transformer loadings are upgraded to
handle spurts of energy production without violating ratings
of the installed equipment. In other words, the existing
infrastructure at the point of the “tap” must sustain the
electrical and thermal limits of the newly injected power.
Infrastructure improvements to prevent overload is thus
another critical cost factor.
The distance and infrastructure costs can separately and
jointly dictate site feasibility. For example, a nearby bus may
not be appropriate to connect new generation (even if it
reduces the transmission line construction cost) if the power
network topology is such that the new generation causes an
overload. In a different example, the risk of thermal overload
may be minimal, but the cost of installing a new transmission
line can overwhelm the allocated budget.
This paper is organized to address the need to consider the
combined transmission and infrastructure costs in the
planning phase. The question that we address is how can we
systematically quantify and call out the cost of new electrical
transmission construction as well as system topology
infrastructure improvements to handle the influx of
renewable energy during the site planning phase.
II. R
ELATED WORK
Large-scale renewable energy generation has been in the
wish list of several developed and developing countries over
the last three decades. Several books and research papers [3-
6] document issues associated with siting renewable energy
generation facilities bringing forth factors such as project
scale, technical feasibility and complexity, independent
investment and operation risks, environmental concerns and
demographic impacts. Some of the solutions presented in
these papers have been made available as software tools.
These software tools implement different models concerning
renewable energy integration like supply–demand prediction,
seasonal forecast, optimization, and emission estimation.
Conolly et al. presents a comprehensive survey of these
software tools in [7].
Recently, Vajjhala [8] conducted a spatial analysis for
understanding the promises and pitfalls of siting renewable
energy farms to conclude that green energy generation is
challenged by economic, environmental, and infrastructure-
978-1-4244-5287-3/10/$26.00 ©2010 IEEE 674

support hurdles. Her study also revealed th
most demand for energy within the U.
S
scarcity in renewable resources or have to a
d
and infrastructural siting issues.
Furthering analysis done in [8], Vajjha
l
[9] identify the need for quantifying econ
o
construction, and perception indicators of
They formulate each of these indicator
s
variability in the consumption market, p
h
separating generation and demand, additio
n
demands such as new transmission lines, an
d
opposition. Grijalva et al. [10-11] analyze t
of new generation plants with respect to
present methods that can estimate operat
i
construction and run-time) for siting renewa
b
Our emphasis in this paper is on thermal an
d
cost considerations, similar to the continge
n
presented in [11]. Although, we share simi
the papers [8-11], the methods we pres
e
estimate hidden costs of siting renewable
investor's decision-support standpoint rath
e
level policy and electrical-feasibility viewpo
III. P
ROPOSED APPROA
C
A typical site planning effort for r
e
generation
b
egins with identifying regions
renewable resource potential. In Figure 1, w
e
polygons as a set of potential sites S = {s
j
considered for investment in a region o
v
Kansas, Colorado, New Mexico, and Okl
a
that our approach requires in addition to si
t
renewable energy is the topology of the exi
s
We represent the grid network as a networ
k
E> where V is the (vertex set {v
i
}) of bus s
t
geographically associated with a latitude a
n
(x
i
, y
i
) . E (the edge set) is the set of trans
m
between generators and substations. The int
e
Fig. 1. We use the knowledge of the energy interconne
c
this figure are user selected regions for the study presen
at states with the
S
ironically face
d
dress geographic
l
a and Fischbeck
o
mic, geographic,
siting difficulty.
s
to capture the
h
ysical distances
n
al infrastructural
d
societal/cultural
t
he location value
grid security to
i
onal costs (both
b
le energy farms.
d
voltage overload
n
cy suppor
t
costs
lar motivation as
e
nt in this paper
energy from an
e
r than the state-
ints.
C
H
e
newable energy
with dependable
e
present the pink
j
} for j = 1,2..N
s
v
erlapping Texas,
a
homa. The input
t
es of interest for
s
ting electric g
r
id.
k
graph G = < V,
t
ations/generators
n
d longitude pair
m
ission line links
e
rconnected green
lines in Figure 1 is a Google Earth
network G representing the electri
c
the figure represent transmission li
n
those line segments are generators
o
Our goal in this section is to
p
r
e
p
roximity and infrastructure costs t
o
The idea is that if we have an
allowable budget associated with
cover for the land, equipment, and
g
methods can help eliminate and ra
n
integration feasibility and convenie
n
A. Proximity costs
Given G and S, we compute
a
questions, namely: (1) How far aw
a
its nearest transmission bus in G?
(
the vicinity of s
j
so that a cluster o
f
to a single substation? (3) How
m
specified radius of s
j
to accept extra
We compute and store the dista
n
site and the nearest bus in V (E
q
specifications in [12] to convert t
h
data to a Cartesian system of th
r
ordinates. The function d in Equ
a
distance between two points in the
t
is the nearest bus to a site of interes
t
)
d( min )( V,ssd
j
v
jN
i
b
=
,d( min arg )(
V
j
v
jb
ssN
i
=
We leverage tools and software
d
for the popular Google Earth
v
conduct our spatial analysis.
formulation of the proximity co
implementation in other commerci
a
ArcGIS [14].
c
ts to compute the transmission and powe
r
-flow costs to inject renewab
l
n
ted in this paper. The selections are based on the land and resource avai
visualization of the graph
c
grid. The green lines in
n
es and the end points of
o
r sub-stations.
e
sent methods that assign
o
these use
r
-selected sites.
estimate of a maximum
each of these regions to
g
rid-integration costs, our
n
k the sites based on grid-
n
ce.
a
nswers to three specific
a
y is each site s
j
in S from
(
2) Are there other sites in
f
these sites can be linked
m
any buses are within a
power?
n
ce in miles between each
q
uation 1). We used the
h
e latitude and longitude
r
ee-dimensional (3D) co-
a
tion 1 is the Euclidean
t
ransformed 3D space. N
b
t
s
j
.
)
(1)
)
V
(2)
d
evelopment kits available
v
isualization platform to
However, the generic
sts lends itself to easy
a
l mapping tools such as
l
e energy. The regions shown in
lability.
675

The result of the nearest-bus assign
m
illustrated in Figure 2a. In some cases, es
p
near populated cities that have s
u
infrastructure capacity, we might actually b
e
distributing the power generated from a sin
g
bus nodes in proximity while still bounde
d
budget. We include such favorable
conditions by quantifying grid-resource av
a
site as the amount of extra generation that
c
to nodes within a distance threshold. As ill
u
2b, we look within the radius of a budg
e
threshold and compute R(s
j
) as the maximu
m
set of bus nodes within the specified radi
u
handle.
The next measure that we compute is ba
s
clustering of potential sites in S. The logic
sites is that it may be more economical to
node and required transmission capabilitie
s
rather than try and pump the energy into a
n
spending on several parallel transmission
l
the iterative winner-take-all clustering appr
o
[13] to suit our purpose. Instead of us
i
Euclidean distance as the threshold
p
aramet
e
separation, we define a 'effective-tran
s
measure as shown in Equation 3.
+
=
=
),(),,(*min)(
1
V
n
k
kjjgjeff
ssdsdnsd
g
where n
g
is the number of sites in each clust
e
As we iterate through the clustering pro
c
favours site clusters that reduce the cost of
n
lines. We show results of clustering using t
h
in Equation 3 in Figure 2c. The "Group
shown in the figure acts as the label for th
e
are able to see that it is economical to leave
on their own when they are close to an e
x
capable of handling the expected power f
r
also observe that it is beneficial to treat a
g
when the intra-cluster distances are small
bus and cluster-center distance. The results
suggest that what may be an expensive prop
o
site of interest can turn feasible when cons
i
of sites.
We combine the three proximity r
e
visualize the extent of siting-difficulty a
s
Figure 2d. Some sites in S can already b
e
consideration if the physical distance c
h
transmission requirements. We will be
information about the nearest bus
w
simulations to estimate the run-time infrastr
u
m
ent to sites is
pecially for sites
u
rplus electrical
e
able to consider
g
le site to several
d
by the allocated
siting-feasibility
a
ilability for each
c
an be distributed
u
strated in Figure
et
-driven distance
m
new power the
u
s of the site can
s
ed on the spatial
behind clustering
create a new bus
s
to the new bus
n
existing bus and
l
ines. We modify
o
ach described in
i
ng the standard
e
r for inte
r
-cluster
s
missio
n
-distance'
+
),( V
j
sd
(3)
e
r-group within S.
c
ess,
t
his measure
n
ew transmission
h
e distance metric
ID" assignment
e
n
g
clusters. We
certain sites to be
x
isting bus that is
r
om the site. We
g
roup as a cluster
compared to the
using this metric
o
sition as a single
i
dered as a group
e
lated costs and
s
a bar graph in
e
eliminated from
h
allenges electric
leveraging the
w
hile conducting
u
cture costs.
(a)
(b)
(c)
(d)
Fig. 2. The proximity module compute
s
infrastructural connections for renewable en
Distance from the nearest bus. (b) Number
Clustering of sites to optimize transmission
l
for each proposed site visualized as bars.
s
the cost of making the
e
rgy to the existing grid. (a)
o
f buses within a radius. (c)
l
ine costs. (d) Minimum cost
676

B. Infrastructure costs
Using our distance analysis, we identified the nearest sub-
stations that are candidates to receive the generated power.
We now have to understand the impact that the new power
will have on the existing infrastructure. We considered the
topology of the entire U.S electric grid consisting of
approximately 65000 bus, 10500 generators, and 85000
transmission lines [15,16] to run a power-flow simulation
treating the site as a new generator adding extra power. We
use the true values of generator and power line capacity
preserving the geographic locations within our simulations.
We note here that simulations using the real topology of the
electric transmission system produces analysis along the lines
of real-time transmission contingency planning. The results
presented in this paper are based on the power-flow solver
provided as part of the Power World simulator [17,18]. Other
commercial and open-source tools like Siemens PSS/E [19]
and ORNL's THYME [20] used in energy transmission
planning may be leveraged for this computation. We chose
Powerworld solver for its simplicity and functionality in
providing base-case overloads with contingency analysis
considerations similar to [11]. The process flow we followed
in quantifying these results is summarized in Table 1.
TABLE I
P
ROCESS FLOW TO ESTIMATE POWER-FLOW COSTS WHILE ADDING NEW
RENEWABLE ENERGY
.
Step 1: Determine the expected power output at each
potential farm site (based on average resource
potential and energy conversion efficiency of
equipment).
Step 2: Inject the total power generated for the farm (from
step 1) to the closest geographical bus N
b
(s
j
).
Step 3: Run power-flow solver.
Step 4: Check for transmission lines carrying power more
than its thermal limit in the simulator's solution.
Step 5: Check for buses that carry voltage more or less than
6% of its specifications in the simulator's solution.
Step 6: Repeat Steps 2 to 5 by adding generated power for a
different sites and site clusters.
We analyzed the output of the simulation and identified
buses that would be forced to operate over or under-voltage
as well as lines operating beyond their maximum capacity
limits. We present results from a few test cases by simulating
power-flow in the existing grid infrastructure in Figure 3. In
each figure, the red pin denotes the bus that receives the
renewable power. The yellow pins and the orange pins are the
result of our power-flow simulation representing under-
voltage and over-voltage buses respectively. The red lines
denote overloaded lines.
The results in Figure 3a and 3c suggest that building a new
bus to handle a group of proposed farms may be more
economical. On the other hand, Figure 3b indicates that it
may be sufficient to upgrade the 3 overloaded lines to higher
capacity.
With market rates for a new bus, a new transmission line or
an upgrade to a higher capacity transmission line available to
us [1], these violations are converted into a quantifiable cost
for the renewable energy integration. Again, the costs here
are a new layer in the spatial area of interest akin to those
developed in Section 2.1 and can be visualized similar to
Figure 2d.
(a)
(b)
(c)
Fig. 3. The power-flow effect of adding new power into existing
infrastructure. (a) Adding 831 MW of renewable energy from all the nearby
sites results in 8 under-voltage, 15 over-voltage buses and 24 over-loaded
lines (not all visible). (b) An isolated site generating 134 MW new power
overloads 3 lines (only two are visible). (c) Another example when 773 MW
of renewable energy is generated from several potential sites in proximity.
677

IV. SUMMARY
We have focused on building a computational framework
for estimating two types of costs associated with introducing
renewable generation as: (i) the investment required for the
upgrade of equipment (to handle the new power injected)
and (ii) the proximity costs (as the amount needed to
transport the power from the farm to the grid). We presented
the two-pass approach that allows for the spatial optimization
of renewable energy sites. The simulations that revealed the
number of over-loaded lines and number of under- and over-
voltage buses helped us identify installation sites more likely
for successful cost-effective integration while considering
electrical stability. The transmission costs captured the
feasibility from an infrastructural viewpoint. The
transmission costs combined with the power-flow costs
helped us assess the financial aspects of integrating
renewable energy beyond just the equipment purchase and
installation.
In studying close to 500 potential sites for renewable
energy farming, we observe that the integration and
transmission costs can be as exorbitant as the cost of the
renewable energy equipment themselves. With a mile of
transmission line costing close to a $1M, upgrades to lines
costing about $0.5M, and building a new sub-station costing
a few millions of dollars, the importance of having to
consider integration and transmission costs cannot be over-
emphasized.
The contributions of this paper are two-fold: (i) we have
described a methodology to integrate electrical infrastructure
related costs together with the proximity costs while planning
for renewable energy investment in an evaluation study
considering an actual US electric grid network, and (ii) we
have demonstrated the construction of the two constituent
cost layers - spatial proximity cost and power-flow cost to
quantify and anticipate the impact of installing new energy
generation capabilities.
This methodology lends itself to systematic inclusion of
land cost, resource potential, policy considerations, etc., that
feed into an optimization program [21,22] for feasibility
evaluation and investment enabling the integration of diverse
cost measures.
A
CKNOWLEDGMENT
This manuscript is authored by employees of UTBattelle,
LLC, under contract DE-AC05-00OR22725 with the U.S.
Department of Energy. Accordingly, the United States
Government retains and the publisher, by accepting the
article for publication, acknowledges that the United States
Government retains a non-exclusive, paid-up, irrevocable,
world-wide license to publish or reproduce the published
form of this manuscript, or allow others to do so, for United
States Government purposes.
R
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678
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Benjamin F. Hobbs1Institutions (1)
TL;DR: This review is to examine how the needs of utility planners for optimization models have changed in response to environmental concerns, increased competition, and growing uncertainty.
Abstract: Electric utility resource planning is the selection of power generation and energy efficiency (conservation) resources to meet customer demands for electricity over a multi-decade time horizon. Because investments in these resources are large, electric utilities became one of the earliest users of optimization methods. The industry is now an eager consumer of the operations researcher's wares, and is a continual source of stimulating problems. The first purpose of this review is to examine how the needs of utility planners for optimization models have changed in response to environmental concerns, increased competition, and growing uncertainty. The second purpose is to survey the range of models that have developed in response to those needs, and to identify gaps requiring further research.

355 citations


"A methodology to consider combined ..." refers background in this paper

  • ..., that feed into an optimization program [21,22] for feasibility evaluation and investment enabling the integration of diverse cost measures....

    [...]


Book
01 Jan 1980-

218 citations


Journal ArticleDOI
Qiong Zhou1, Janusz Bialek1Institutions (1)
Abstract: Research into transmission pricing and congestion management in interconnected power systems, such as those found in USA and Europe, requires an appropriate benchmark system to test different methodologies. Creation of a realistic benchmark system is difficult as utilities are often unwilling to disclose details of their own systems because of commercial sensitivity and security reasons. This paper presents development of an approximate model of a European interconnected system which could be used to study the effects of cross-border trades. In creating the load-flow model, only publicly available information was used. Comparison of simulation results conducted on the test system with the published cross-border flows and power transfer distribution factors showed a very good correlation, exceeding 90%.

169 citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20131
19821