Paper Final

Smart Grid Analysis of Centralized Cooling for an Urban
Community
Francinei Vieira, Jéssica Henriques, Larissa Soares, Leandro Rezende, Moises S. Martins
Raimundo R. Melo Neto and Donald J. Chmielewski*
Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL
* Corresponding author (312-567-3537, chmielewski@iit.edu)
This project will investigate three different methods to provide energy to
operate the cooling system for a new community that will have about 10,000
residents in an area formed only by residential buildings. First case we will study
a decentralized system, each home has its own residential electric chiller to
provide cooling. Centralized systems will be analyzed, the first one we will have
a big chiller for cooling. An absorption chiller will be used to produce the cooling
and it will use a natural gas combined cycle (NGCC) power plant to produce the
waste heat that it needs and to generate energy. In addition, by using smart grid
analysis and technology it will be decided when it will be profitable to produce
energy or turn the power plant off while the energy production does not provide
revenue. Finally, another centralized system case, a similar analysis will be
implemented, however a thermal energy storage (TES) will be added to the first
centralized system case.
I. INTRODUCTION
Energy consumption of buildings are responsible for 40% of the global primary energy
usage. The majority of energy consumption of buildings is caused by the operation of heating,

cooling and air ventilation systems (Kim, 2014). During the summer, the cooling system operates
almost all day long removing heat from buildings to maintain the inside environment at a
comfortable temperature. These thermal systems can be classified into two broad categories,
centralized and decentralized.
Centralized air conditioning systems serve multiple spaces from one base location. These
typically use chilled water as a cooling medium and use extensive ductwork for air distribution.
Decentralized air conditioning systems typically serve a single or small spaces from a location
within or directly adjacent to the space (Bathia, 2012). The following figures illustrate the
centralized and decentralized system.
To design and estimate costs of a cooling
system, it is necessary to know the cooling load,
which is the amount of heat that needs to be removed
from the buildings (see Figure 3). Looking at the
graphic of fig.4, it is possible to see that the cooling
load will vary during different periods of the day due
to disturbances provided by different heat sources.
Figure 1 – Centralized System Figure 2 – Decentralized System
Figure 3 – Different forms of heat.

Figure 4: Time-series of day-ahead electricity prices and cooling load for a single house.
The second curve on figure 4, illustrates the oscillation of the price of electricity during the
day. During the day, we need more cooling consequently the demand of electricity will increase,
through that we can correlate it with the graphic of the price of electricity, observing that when
the cooling is increased the price will also be high. The same concept is applied when there is a
decrease in the demand. Those fluctuations in the demand and price of electricity will be the
basic concept of the smart grid analyses, in which will be consider the cost of electricity to find

out what source of cooling must be used. For example, when the price of electricity is high, we
will use the absorption chiller since it requires just heat as source of energy, and when price of
electricity is low we can use the electrical chiller. This situation will be analyzed in three
different cases that will be exposed through this report.
The project is going to focus on two mainly chillers technologies that will be useful to study
solutions for this problem, the electrical and the absorption chillers. The electrical chiller uses
electricity as source of energy, this electricity will be purchased from the grid, and its price
oscillate during the day according to the demand. The absorption chiller uses waste heat as a
fuel.
Therefore, if a source of waste heat is available and the price of electricity is high a good
deal would be using absorption chiller instead of the electrical chiller. Many ways to provide
heat to the absorption chiller exist, however the one that is going to be studied for this project
will be the Natural Gas Combined Cycle Power Plant (NGCC).
Combined cycle plants are built around one or more combustion turbines, essentially the
same technology used in jet engines. The combustion turbine is fired by natural gas to rotate a
turbine and produce electricity. The hot exhaust gases from the combustion turbine are captured
and used to produce steam, which drives another generator to produce more electricity. By
converting the waste heat from the combustion turbine into useful electricity the combined cycle
achieves very high efficiency, comparing a single cycle to a combined cycle, the efficiency can
be about 10% higher (Kaplan, 2008).
Assembling two different thermodynamic cycles can provide more than a high efficiency, it
also reduce fuel costs. Common cycle used nowadays are the Brayton (gas turbine) in which
natural gas is burned, resulting in the production of heat used to generate steam in the Rankine

cycle and also provides heat to the absorption chiller.
The motivation for using a power plant is that the context of day-ahead electricity price for a
deregulate energy market can be used to change the system expenses into revenues. The power
plant will produce electricity that will be sold to the grid whenever the rescaled price of fuel
(natural gas) is lower than the price of electricity and the waste heat produced by the energy
generation process will be used to operate the absorption chiller.
Using those concepts a diagram of the first centralized case of study can be drawn:
Figure 5: Decentralized Cooling System with NGCC power plant diagram
From this model, using smart grid technology, the system is going to use the absorption

chiller to produce cooling when the price of electricity is high and during the night, when it is
low, the system purchase energy from the grid and will use the electrical chiller to produce
cooling.
To improve the cooling system, a thermal energy storage (TES) can be used to stock energy
produced when the price of electricity is high (see figure 6). Thermal energy storage has been
widely used in the commercial sector since the 1980s to shift HVAC cooling load out of the peak
demand period of the day (ASHRAE Journal, June 2013). The TES will help the system because
it is going to store energy when the price to produce it during the day is low and will use this
energy when the price is high.
Figure 6: Thermal Energy Storage Diagram

Figure 7: Decentralized Cooling System with NGCC power plant and TES diagram
An important concept that will be used to evaluate the profitability of the results in each case
of study is the Net Present Value (NPV) of each case that will be studied. For those cases the
initial cost, the annual operating costs and maintenance expenses will be considered. For the
decentralized systems the capital costs are installing the air conditioner for each building, and the
only operational cost is purchasing electricity from the grid. For centralized cases the initial costs
are implementing the power plant, the purchase of the absorption chiller and building the
network distribution, with the operating cost of the power plant and the Thermal Energy Storage
implementation when it is applied.
In this paper we present an economic analysis of three different cases of cooling system, the
first case is a decentralized system, where each building has its own electrical chiller, the second
case will be a centralized system using an industrial sized absorption chiller with a power plant
as source of energy, and the third case will be the same as the second, but with the addition of a

TES system. Consequently, the performance of each system was compared and validated using
NPV analysis.
II. DECENTRALIZED COOLING SYSTEM ANALYSIS
As in decentralized system each building has its own refrigeration system, the cooling load
must be calculated individually according to the construction materials and location to be able to
attend the cooling demand.
Cooling load (CL) is altered by any source of heat. For instance, the heat that is conducted
from the outside air through walls and especially sun light will make the temperature increase
during the day, however during the night, the cooling load will decrease because the outside
temperature will be smaller than the inside temperature. This oscillation occurs continuously and
can be estimated by adapting the Cooling Load Temperature Difference (CLTD) method
proposed by American Society of Heating, Refrigerating, and Air-Conditioning Engineers
(ASHARE) and described in the 1997 Fundamentals Handbook. The CLTD was developed
based on tabulated data obtained for different studies by Rudoy and Duran (1975) to simplify the
cooling load calculation to be done by hand. The CLTD tabulated values can be adjusted to most
situations as the Fundamental Handbook (ASHARE, 1997) provides instructions to adjust the
data according to the maximum and minimum temperature of a specific day and also according
to the building locations, orientation and materials of construction.
An algorithm can be develop to provide a times-series CL considered any period since some
historic weather data is available. Although this is not the most accurate method to be
considered, it is still used by some engineers and building designers to estimate rapidly the peak
load of a building. For designing and efficiency purposes it is recommended to use methods that
give results more precise, such as the Transfer Function Method (ASHARE, 2010).

From the highest CL value is possible to define what electric chiller will be able to attend the
cooling demand because the highest CL will lead the cooling capacity of the chilling system.
Each chiller has a feature called Coefficient of Performance (COP in ton/kW) that is used to
measure its efficiency and for electric chillers the COP is defined by the bellow equation:
Table 1: Packeged Compressor and Condenser Units (Electric Chiller)
Cooling capacity (ton) Total Cost ($)
1.5 1,700
5 4,400
10 7,775
20 16,600
This coefficient is extremely important because it helps to estimate the electricity
consumption according to the CL obtained. Therefore the power energy consumed by a regular
electric chiller is given by:
Where (ton) is the cooling load for the entire community and is in kW/h.
Once the CL is estimated hourly, electricity consumed is also an hourly time-series result,
similarly it is known that the electricity price is given as a function of time based on the
consumers demand, finally the day-ahead price is estimated by hour and the real-time price is

registered by the regional transmission organization as well.
When developing a model, it is expected that the variables price and electricity are
correspondents at time and have the same total of elements, consequently the cost for cooling a
building is achieved by multiplying these two variables, in addition the summation of costs gives
the operational cost for a decentralized CL:
Consequently, the operational cost of the decentralized system is basically the cost of
purchasing electricity from the grid, and it is used to estimate the Net Present Value (NPV)
The IC parameter corresponds to the capital cost of implementation of the cooling system.
For the decentralized system, the initial cost is the cost of installing the air conditioner in each
residence.
The PV parameter is the present value of all operational costs that applies. In the first case,
the only operational cost is the cost of purchasing electricity from the grid. The formula used to
calculate the PV is presented below:
Using the equations above, it is possible to define how much each project would be worth
nowadays.
Example 1:
This study analyzed a new residential community of 10,000 people that would be placed in
Chicago. Considering that Chicago has a density of 2.58 persons per household, the community
is assumed to have 3,876 residential buildings. (Census, 2013)

Weather and price data needed were based on the years 2005, 2008 and 2012. The historic
weather data was obtained on the database of National Oceanic and Atmospheric Administration
(NOAA) as this governmental organization has weather registers from the majors weather
stations situated in the U.S. since the year of 2005, thus the MIDWAY International Airport-
Chicago Weather station was chosen as reference to guarantee that the simulated cooling load
would have similar results to the actual cooling load for residential buildings in the area of
Chicago.
The selection criteria was kept while selecting the data of historic electricity price, therefore
the information was collected from the regional transmission organization responsible for the
Chicago power grid, the Pennsylvania, Jersey, Maryland Power Pool (PJM).
To estimate the cooling load it was assumed a house model that contains features similar to a
standard Chicago residence. By using MATLAB to run the simulation for a period of a year it
was possible to obtain the peak load value, which is equal to 3.89 ton, then a 5 ton electric chiller
will be able to attend the cooling demand of a single house and has a of 1.14 ton/kW
(Ulloa 2014), hence the energy power consumed as a function of time was obtained as a time-
series vector.
It is important to highlight that the heat load was neglected in this work, i.e. whenever the
cooling load was negative, it was changed to be zero. See figure 5.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
50
100
150
200
time [h]
EnergyCost[$/MWh]
Electricity
Natural Gas/
p
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
5
10
15
CoolingLoad[kTON]
time [h]
Figure 8: Day-ahead electricity & Cooling Load
In the next step, the historic hourly day-ahead electricity price for the PJM area during each
year was multiplied by the equivalent power energy data. The result is the cost of cooling each
building as a function of time ($/h) and represents the operational cost, the costs of installation
and equipment describe the capital cost for the electrical chiller, finally from these numbers the
NPV method was calculated based on different year and the results are presented on table 2.

Table 2: Cost analysis (in million) for the decentralized case
Year 2005 2008 2012
Capital Cost ($) 14 14 14
Operational Cost ($) 19 19,2 12
NPV ($) 33 33,2 26
It is possible to observe on figure 4 that exists a relation between electricity price and
cooling load, when the cooling demand is high, the cooling system will consume more
electricity, demanding the power plants that supply the grid to intensify their operation resulting
the increase of the energy price.
As the cooling system consumes a high amount of electricity when the price is high, an
alternative approach will be considered.
III. CENTRALIZED COOLING SYSTEMANALYSIS
From figure 5 that illustrate a centralized system it is possible to observe that there is a
balance between the cooling load demand and the cooling load provide by two different types of
chillers.
Where and represent the cooling load provided by the electric and absorption
chillers respectively and is the cooling demand that comes from all the buildings, the same
variable described in the previously section.
Also in figure 5 the energy balance is exhibited between the power plant and cooling load,

the amount of natural gas consumed is equal to the electricity produced plus the waste heat
generated in the process that feeds the absorption chiller.
Where is the mass flow of fuel (MMBTU/h), is the overall efficient of power plant,
is the electric energy (MW/h) produced by the power plant, and represents the heat
consumed by the absorption chiller (ton/kW(thermal)) and is the efficiency of the Rankine
Cycle in the power plant. It is important to know that all the features defined on the last two
equations are positive and have maximum values based on equipment capacity.
Additionally, it is important to highlight that , i.e. the plant can be turned off,
however, while operating, the power plant is not allowed consume less than the minimum fuel
defined. This statement is true if and only if:
Where is the power plant state, is the “on” switcher and is the “off” switcher.
The operational cost for the centralized structure will become dynamic allowing an
optimization to be applied in the perspective of smart grid. For example, the power plant will
stop produce electricity when energy price is to low and will consume fuel just to produce
enough heat to supply the absorption chiller. In addition, the centralized electric chiller will be

activated if and only if the absorption chiller reaches its maximum operation and is not able to
supply the cooling demand. Then, the operational cost is represented by the following three
components: cost of buying natural gas, plus the cost of purchasing electricity when the electric
chiller is running and m.
Where is the electricity price as described on the decentralized system analysis and is
the price of natural gas.
As the power plant will sell energy to grid, the NPV in a long term can represent revenue if
the power plant produce sufficient electricity.
Even that this economic approach provided can be solved by a computer, it is recommended
to avoid this implementation for a time-series prediction. The Economic Model Predictive
Control (EMPC) offers a better methodology to solve this category of smart grid problems.
On the following methodology presented, the index k represents a predictive time and the
index i represents the actual time:
|i

max
)|(0 gg PikP 
max
)|(0 ECEC QikQ 
0)|(
max
 ikEE ss
Where k = i … i + N – 1. This predictive model is then used within the following optimization
problem.
)()|(
1
)|()|(ˆ)|()|(ˆ)|()|(ˆmin
1
)|(),|(
),|(),|(
iEiiE
Niik
ikPikcikikcik
COP
Q
ikc
ss
Nk
ki
geff
AC
AC
e
ikEikP
ikQik
sg
ECf








 





Where )(iEs is the actual amount of energy at time i.
Example 2- Centralized System
To illustrate the statements presented in this section and help to visualize how the system
works, an example using sine wave as data was done. The result are exhibited on the plots
bellow.

Figure 9: Electricity price, fuel price and cooling load represented by a sine wave
Real electricity price and cooling load has a behavior similar to a sine wave, as can be
observed on figure 9, however a smooth signal can make easier the understanding of the
problem.
Figure 10: Cooling produced by electric and absorption chillers

There are two situations on this example where the centralized electric chiller had to be
operated, see figure 10. Whenever the absorption chiller reached its maximum level of operation
and whenever the price of electricity was lower than the price of natural gas. On this last
situation the power plant was switched off because it will not return a revenue selling electricity.
Figure 11: Mass flow of natural gas and power energy produced
Figure 12: State and switcher of the power plant
Figure 11 shows the electricity produced related to the fuel consumed by the process, while
figure 12 show the functionality of the power plant switchers.

Example 3- Centralized System- Real Data
By using the real data for energy price and features for the community from example 1, the
centralized system optimization was done. As can be observed in figure 12, the optimization
worked well when applying the real data. The electric chiller only operated when it was not
affordable to run the absorption chiller and the power plant produced energy only in high
electricity price periods, when it was profitable. The new capital costs are described in table 3
and the operational cost is described in table 4.

Figure 13: Graphs for the decentralized system.
4500 4520 4540 4560 4580 4600 4620
0
1
2
3
4
5
6
Elec.Chiller
[kTON]
time [h]
4500 4520 4540 4560 4580 4600 4620
4
8
12
Abs.Chiller
[kTON]
time [h]
4500 4520 4540 4560 4580 4600 4620
50
100
150
200
time [h]
EnergyCost[$/MWh]
Electricity
Natural Gas/
p
4500 4520 4540 4560 4580 4600 4620
4
6
8
10
12
14
CoolingLoad[kTON]
time [h]
4500 4520 4540 4560 4580 4600 4620
0
0.5
1
1.5
[Sk]
time [h]
4500 4520 4540 4560 4580 4600 4620
0
0.5
1
1.5
[OFF]
time [h]
4500 4520 4540 4560 4580 4600 4620
0
0.5
1
1.5
[ON]
time [h]
4500 4520 4540 4560 4580 4600 4620
0
100
200
300
400
PowerPlant
Fuel[mmBTUh]
time [h]
4500 4520 4540 4560 4580 4600 4620
0
10
20
30
40
50
60
Power[MW]
time [h]

The initial cost of the power plant includes:
 Building and Structures
 Instrumentation & Control Systems
 Accessory Electric Plant
 Cooling Water System
 Steam Turbine Generator
 HRSG, Ducting and Stack
 Combustion Turbine/Acessories
 Chemicals
The calculation of the Net Present Value was made using the data showed below:
Table 3: Initial Costs of equipment.
Initial Cost Value ( $ mi) More Information
Absorption Chiller 10.4 10 chillers of 1500 tons, ea
costs $1000. (Ulloa 2014)
Electrical Chiller 4.2 1 big chiller that costs $4400
(RSMeans 2011)
Distribution Network 63 -
Power Plant 20 Black, 2013
For the operational cost, three different periods were analyzed.
Table 4: Operational cost (in million) for the centralized cooling system
Year 2005 2008 2012
Electrical Chiller ($) 1.2 1.3 0.8
Fuel Cost($) 11 13 6.2

Electricity sold($) 11.7 12.6 6.2
Final Cost: ($) 0.6 1.7 0.8
Using the average of these results, the final value for the operational cost was $1.03 million,
obtaining the following NPV:
Table 5: NPV for the centralized cooling system
IV. CENTRALIZED SYSTEM WITH THERMAL STORAGE ENERGY
To improve even more the efficiency, a thermal energy storage (TES) can be added to the
centralized system. This thermal storage involves basic an insulated tank that will store chilled
water from the absorption chiller. See figure 6.
Still using the Smart Grid concepts to make decisions, the operational costs from the
previously section can be optimized. Whenever the power plant is not producing electricity
because of low energy prices and the cooling demand is low such the absorption chiller does not
operate at maximum rate, the thermal storage will be charged with chilled water. The chilled
water will be used in periods of high demand of cooling, when the absorption chiller reaches the
maximum operation level, chilled water from the TES will provide cooling to the residences. The
electric chiller will be triggered only if the TES is exhausted and the cooling demand is still high.
Capital Cost $98.3 million
Operational Cost $1.03 million
Present Value of OC $10.9 million
NPV $109.2 million

From these implementations, the equation for cooling balance will be altered.
+ =
Where
Where is the stored energy available and is the stored energy that will be needed
later.
Example 4
On this example, the sine wave data was used again to make the understanding and
interpretation of the problem easier, i.e. basically is a complementation of example 2.

Figure 14: Results for the centralized system with TES using replica data
The third curve from figure 14 illustrates the TES process, when the absorption chiller reached
its maximum level of operation and the price of electricity is high, the chilled water from the
TES is used to help to supply the demand. The TES will recharge when it does not worth to
produce electricity by the power plant, because of low prices of energy, therefore the electric
chiller supplied the demand while the absorption chiller recharged the TES.

Example 5
4500 4520 4540 4560 4580 4600 4620
0
2
4
6
8
Elec.Chiller
[kTON]
time [h]
4500 4520 4540 4560 4580 4600 4620
0
5
10
15
Abs.Chiller
[kTON]
time [h]
4500 4520 4540 4560 4580 4600 4620
-20
-10
0
StoredEnergy
[kTON/h]
time [h]
4500 4520 4540 4560 4580 4600 4620
0
50
100
150
200
250
300
350
PowerPlant
Fuel[mmBTUh]
time [h]
4500 4520 4540 4560 4580 4600 4620
0
10
20
30
40
50
Power[MW]
time [h]

Figure 14: Results for the centralized system with TES using real data
From examples 3 and 4 data and analysis is possible to understand the results for the
centralized system using the TES and real data. It is interesting to observe that the TES
utilization behaved quite different from the result expected, this is due the imperfection of data.
4500 4520 4540 4560 4580 4600 4620
0
0.5
1
1.5
[Sk]
time [h]
4500 4520 4540 4560 4580 4600 4620
0
0.5
1
1.5
[OFF]
time [h]
4500 4520 4540 4560 4580 4600 4620
0
0.5
1
1.5
[ON]
time [h]

For the third case, the same data for the capital cost was used but adding the cost of $ 8 mi
for the Thermal Energy Storage.
Table 6: Operational cost (in million) for the centralized cooling system with TES
Year 2005 2008 2012
Electrical Chiller ($) 1.3 1.3 0.8
Fuel Cost($) 7 8.2 4.1
Electricity sold($) 9.4 10 4.7
Final Cost: ($) -1.1 -0.5 -1.4
Using the average of these results, the final value for the operational cost was $-1.4 million,
obtaining the following NPV:
Table 5: NPV for the centralized cooling system
V. CONCLUSIONS
In this work, the smart grid optimization was explored to analyze three different system
configurations to provide cooling for a community. First it was demonstrated that the electricity
price is correlated to the cooling load demand. By analyzing the NPV from the examples 1, 3 and
5, it can be conclude that considering the designing of all equipment, the decentralized is still a
Capital Cost $106.3 million
Operational Cost $-1.4 million
Present Value of OC $-14.8 million
NPV $91.5 million

more suitable approach, this occurred because the capital cost for a decentralized is much lower
than the capital cost for the centralized system. An alternative to this problem is to increase the
power capacity of the NGCC power plant, however this is a solution to be analyzed wisely,
because by increasing the supply of electricity irresponsibly, the energy market can be affected
drastically.

REFERENCES
[Kingston, 2010] Building Construction Cost Data (68th ed.). (2010). Kingston, MA: R.S.
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[Kingston, 2011] Building Construction Cost Data (2011). Kingston, MA: R.S. Means.
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and Knovel (Firm). 1997. 1997 ASHRAE handbook: Fundamentals. SI ed. Atlanta, GA:
American Society of Heating, Refrigeration and Air-Conditioning Engineers
[Chmielewski, 2015] Feng, J., Brown, A., O’Brien, D., & Chmielewski, D. J. (2015). Smart grid
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[Roth, Zogg & Brodrick, 2006] Roth, K., Zogg, R., & Brodrick, J. (2006). Cool thermal energy
storage. ASHRAE journal, 48(9), 94-96
[Newnan, 1997] Newnan, D. G., Lavelle, J. P., Eschenbach, T. G. (1991). Engineering Economic
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[Black, 2013] Black, J. Cost and performance baseline for fossil energy plants. US Department
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[PJM, 2015] PJM Data Miner - Energy Pricing. (n.d.). Retrieved June , 2015, from
https://dataminer.pjm.com/dataminerui/pages/public/energypricing.jsf
[NCDC] NCDC: Quality Controlled Local Climatological Data - Chicago Illinois. (n.d.).
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[EIA, 2015] Illinois Natural Gas Prices. (n.d.). Retrieved June, 2015, from
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[Choi, 2014] Kim, M. K. Leibung, H. Choi, J. Energy and exergy analyses of advanced
decentralized ventilation system compared with centralized cooling and air ventilation
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212–222. Switzerland. 2014.
[Bathia, 2012] Bathia, A. Centralized Vs Decentralized Air Conditioning Systems. Continuing
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[Ulloa, 2014] Ulloa, Priscilla, Nickolas J. Themelis, and Bettina Kamuk. "“Potential for
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APPENDIX I
Name Knowledge gained Were expectations met?
Moises
Soares
Martins
Learned how to use Matlab to solve
optimization problems, such as in case 2,
and case 3. Learned how to understand an
optimization problems, and apply tools, for
example, Matlab, Yalmip, MPC to solve
them. Understood the concept of cooling
system to an urban community, including
all equipments that it needs to operate.
Learned the idea of smart grid, and how to
use in our daily life. Developed skills in
communication as in presentations.
I worked directly solving optimization
problems by using Matlab. I had to
program the code that creates all the
graphics.
I think our team achieved the main goal of
this project. Everyone contributed during
this research. Yes I met my expectations.
However, in the beginner I thought we
would work with equipments such as
machines related with cooling systems or
smart grid.
Jessica
Madruga
I could learn about analysis of costs during
an engineering project, that was something
that I didn’t have any idea because
generally I just work with simulations,
machines and algorithms but not the cost of
the project.
I work directly with the analysis of the
chillers, the electrical and the absorption. I
was responsible to talk to the professor
when the group was in need as a leader.
For our poster presentation I was
responsible to format the poster. .
Some of them. I could learn many thing
new but I was expecting to work more
with smart grid.
The research group was excellent,
everybody contribute during the whole
period.
The professor really cares about the group
and the research, he was always there to
help us when we didn’t know what we
should exactly do.
Leandro
Lima de
Rezende
The major knowledge I obtained was about
cooling systems. I could develop a
simplified model by using MATLAB to
estimate the cooling load for a house
according to our necessities. Additionally I
was responsible to obtain and organize real
data as a function of time and had to
estimate the distribution network. I also
learned about the market layer of smart
grid and about the concept of net present
Most of the expectations were met. I
could learn more about smart grid,
although I did not work with the technical
layer of smart grid and develop a little
more team work skills, this experience
was rewarding. The professor helped all
the time and understand the time available
was really short.

value.
Francinei
Vieira
I got a good experience with control theory
and optimization methods on MATLAB
such as predictive control and Yalmip. I
could learn more about power plants,
cooling systems, thermal energy storage,
on how they interact with smart grids and
how much they would cost. I worked
directly with the optimization problems,
solving them and plotting the results on
MATLAB.
Most of my expectations were met.
Although the project was too theoretical,
and not as exciting as I supposed initially,
I consider this experience useful for me,
and I had a good time working with my
peers. The professor has an excellent
domain over the subject and he provided
guidance to the group when necessary.
Larissa
Soares
One of the main knowledge that I can say I
could learn was about the uses of Net
Present Value and its applications for the
energy market. Another good experience
was about the team work, since that for the
calculations I always needed information of
someone else, and it happened in a good
way. For the last, I would say I learned
more about the Smart Grid market.
I think that most of my expectations were
met. I could learn about everything I was
pretending to, and the research group
really helped with this. The subject, Smart
Grid, is very interesting, what makes it
easier. Another important point was that
the professor was always helping us when
we needed, providing books, meetings,
and giving all the fundamental support to
the group.
Raimundo
Renato
Melo
I have gain more knowledge on the
development of an energy balance
equations used to make the energy
integration between the power plant and
absorption chiller.
My expectations were met, I have applied
the knowledge gained by the some of the
courses that I have done during my
exchange program.

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