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]

En

erg

y C

ost

[$

/MW

h]

Electricity

Natural Gas/p

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

5

10

15

Co

oli

ng

Lo

ad

[k

TO

N]

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 SYSTEM ANALYSIS

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

|i) =

0)|(max

ikEE ss

max)|(0 ACAC QikQ

max)|(0 ECEC QikQ

max)|(0 loadload QikQ

max)|(0 gg PikP

max)|(0 SikS

|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)|()|(ˆmin1

)|(),|(

),|(),|(

iEiiE

Niik

ikPikcikikcikCOP

Qikc

ss

Nk

ki

geff

AC

AC

e

ikEikP

ikQik

sg

ECf

Where )(iEsis 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 46200

1

2

3

4

5

6

Ele

c. C

hil

ler

[kT

ON

]

time [h]

4500 4520 4540 4560 4580 4600 4620

4

8

12

Ab

s. C

hil

ler

[kT

ON

]

time [h]

4500 4520 4540 4560 4580 4600 4620

50

100

150

200

time [h]

En

erg

y C

ost

[$

/MW

h]

Electricity

Natural Gas/p

4500 4520 4540 4560 4580 4600 4620

4

6

8

10

12

14

Co

oli

ng

Lo

ad

[k

TO

N]

time [h]

4500 4520 4540 4560 4580 4600 46200

0.5

1

1.5

[Sk

]

time [h]

4500 4520 4540 4560 4580 4600 46200

0.5

1

1.5

[OF

F]

time [h]

4500 4520 4540 4560 4580 4600 46200

0.5

1

1.5

[ON

]

time [h]

4500 4520 4540 4560 4580 4600 46200

100

200

300

400

Po

wer

Pla

nt

F

uel

[mm

BT

Uh

]

time [h]

4500 4520 4540 4560 4580 4600 46200

10

20

30

40

50

60

Po

wer

[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 46200

2

4

6

8

Ele

c. C

hil

ler

[kT

ON

]

time [h]

4500 4520 4540 4560 4580 4600 46200

5

10

15

Ab

s. C

hil

ler

[kT

ON

]

time [h]

4500 4520 4540 4560 4580 4600 4620-20

-10

0

Sto

red

En

erg

y[k

TO

N/h

]

time [h]

4500 4520 4540 4560 4580 4600 4620

0

50

100

150

200

250

300

350

Po

wer

Pla

nt

F

uel

[mm

BT

Uh

]

time [h]

4500 4520 4540 4560 4580 4600 4620

0

10

20

30

40

50

Po

wer

[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 46200

0.5

1

1.5[S

k]

time [h]

4500 4520 4540 4560 4580 4600 46200

0.5

1

1.5

[OF

F]

time [h]

4500 4520 4540 4560 4580 4600 46200

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.

Means.

[Kingston, 2011] Building Construction Cost Data (2011). Kingston, MA: R.S. Means.

[ASHRAE, 1997] American Society of Heating, Refrigerating and Air-Conditioning Engineers,

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

coordination of a chemical processing plant. Chemical Engineering Science

[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

Analysis. 12th edition

[Black, 2013] Black, J. Cost and performance baseline for fossil energy plants. US Department

of Energy. September, 2013

[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.).

Retrieved June, 2015, from http://www.ncdc.noaa.gov/qclcd/QCLCD?prior=N

[EIA, 2015] Illinois Natural Gas Prices. (n.d.). Retrieved June, 2015, from

http://www.eia.gov/dnav/ng/ng_pri_sum_dcu_SIL_m.htm

[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

systems in the hot and humid climate. Energy and Buildings. Energy and Buildings, ed 79, pg

212–222. Switzerland. 2014.

[Bathia, 2012] Bathia, A. Centralized Vs Decentralized Air Conditioning Systems. Continuing

Education and Development Courses. New York. 2012.

[Ulloa, 2014] Ulloa, Priscilla, Nickolas J. Themelis, and Bettina Kamuk. "“Potential for

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Web. 21 Nov. 2014. http://www.seas.columbia.edu/earth/wtert/sofos/ulloa_thesis.pdf

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.