Ieeepro techno solutions 2012 ieee embedded project - design & implementation of intelligent energy distribution management with photovoltaic system

340 IEEE Transactions on Consumer Electronics, Vol. 58, No. 2, May 2012
Contributed Paper
Manuscript received 04/15/12
Current version published 06/22/12
Electronic version published 06/22/12. 0098 3063/12/$20.00 © 2012 IEEE
Design and Implementation of Intelligent Energy Distribution
Management with Photovoltaic System
Insung Hong, Byeongkwan Kang, and Sehyun Park, Member, IEEE
Abstract — As increasing power consumption is becoming
a huge problem, renewable energy has been highlighted
recently. Many companies and research centers study this new
sustainable energy, and various products have appeared to the
public. However, this kind of researches concentrates on the
elemental technologies, and now a management system is
needed to manage these technologies to maximize energy
efficiency. In this paper, we propose the system of Intelligent
Energy Distribution Management (iEDM) to monitor fast-
changing environmental variables and manage solar power
flexibly. Compared with normal utility interactive systems, the
iEDM improves the energy efficiency up to 5.6 percent. 1
Index Terms — Energy Efficiency, Photovoltaic System,
Renewable Energy, ZigBee
I. INTRODUCTION
These days, even though there are many new IT services, these
cause another types of problems. There are increases in the
diversity of services and service quality, but there is also much
higher energy consumption. Furthermore, recent environment-
friendly technologies have some problems with efficiency, and it
is still premature to achieve any greatly positive effect.
At this point in time, renewable energy coincides with
sustainable growth. There are many studies and commercial
products involving photovoltaic and wind power, and these
have significant roles in propagating environment-friendly
technologies. In case of electric cars, meaningful, decreases in
energy consumption cannot be achieved since fossil fuel is
simply converted into electricity. Meaningful change means
that each car no longer consumes fossil fuel and every
thermoelectric power plant has to make more electricity to
afford cars’ energy needs. Moreover, a huge infrastructure of
electricity stations on every street is needed, and it is difficult
to catch up with the fuel efficiency of fossil cars immediately.
Due to these technical troubles, the importance of renewable
energy is getting bigger. In addition, the power market would
1
This research was supported by the MKE(The Ministry of Knowledge
Economy), Korea, under the HNRC(Home Network Research Center) –
ITRC(Information Technology Research Center) support program supervised
by the NIPA(National IT Industry Promotion Agency (NIPA-2010-C1090-
1011–0010) and by the Human Resources Development of the Korea Institute
of Energy Technology Evaluation and Planning (KETEP) grant funded by the
Korea government Ministry of Knowledge Economy (20104010100570).
Insung Hong is with the School of Electrical and Electronics Engineering,
Chung-Ang University, Seoul, Korea (e-mail: axlrose11421@wm.cau.ac.kr).
Byeongkwan Kang is with the School of Electrical and Electronics
Engineering, Chung-Ang University, Seoul, Korea (e-mail:
byeongkwan@wm.cau.ac.kr).
Sehyun Park is with the School of Electrical and Electronics Engineering,
Chung-Ang University, Seoul, Korea. (e-mail: shpark@cau.ac.kr)
become more important, and it is expected that the influence
of renewable energy as sustainable growth will be much larger
with the Smart Grid. However, it is not easy to be connected
to commercial electricity in these kind of generating systems,
because each type of renewable energy such as solar and wind
power, is slightly different and needs to be managed [1].
Furthermore, renewable energy can provide almost indefinite
power but it is hardly expected to be stable [2].
Related solutions are being developed and commercialized
by many companies but these products have a passive
property. That is, these kinds of solutions need to include
intelligent management because of passive operation
according to hourly variation or battery status. For example,
even though there are various variables such as future power
demands, generation status depending on weather conditions,
and current battery status, current solutions do not consider
these variables, so it is hard to expect high efficiency [3].
Therefore, for much higher efficiency of renewable energy, an
intelligent system is needed to monitor these statuses and
provide proper management services.
In this paper, we propose an intelligent energy management
system with a photovoltaic system. In other words, according
to each environment, it monitors various variables, and
performs optimal energy management to maximize efficiency.
We designed Intelligent Energy Distribution Management
(iEDM) middleware, implemented it in a test bed, and verified
its performance on how efficiently it manages energy.
Moreover, we also design two different management methods
according to service types.
To help understand the study trends of generation systems
and photovoltaic systems, section II reviews the characteristics
of photovoltaic systems and analyzes the need for energy
management systems. Section III describes the middleware
architecture of iEDM and the two analysis methods, and
Section IV and V shows the implementation of this system
and verify its efficiency. In the last section, concluding
remarks are made.
II. RELATED WORKS
Renewable energy systems such as photovoltaic power
generation, wind power generation and fuel cells are receiving a
huge attention globally. Eco-friendly power generation is the
best feature of renewable energy systems. Renewable energy
systems emit no pollution into the atmosphere when they
generate electricity. However, most power plants such as
thermal power generation and nuclear power generation plants
have produced most of the power supply. Thermal power plants
emit the carbon dioxide into the atmosphere, and nuclear power
plants have potential danger and discharge nuclear wastes. On
the other hand, renewable energy systems are very clean.

I. Hong et al.: Design and Implementation of Intelligent Energy Distribution Management with Photovoltaic System 341
Although renewable energy power generation unit cost is more
expensive than fossil fuel generation [4], this type of generation
will replace the existing power plants because it is an
inexhaustible resource. Therefore, it is considered as the key
solution to solve various energy problems.
Renewable energy has some disadvantages. Thermal plants
and nuclear plants can provide steady output power, but the
renewable power plants such as photovoltaic and wind power
systems cannot always maintain steady power, depending on
the season, hourly variation and weather conditions. In
general, a photovoltaic power system generates maximum
power at noon. In the case of a wind power system, it
generates minimum power at noon compared with in the
morning or at night [5]. According to time variation, the
efficiency of renewable energy makes a huge difference.
Furthermore, it is difficult to expect steady generation output
because of insolation and wind velocity factors, which are
changing every hour. Therefore, the renewable energy is
required to store unsteady generated energy in energy storage
systems [6]. Nevertheless, a renewable energy system is
needed to improve energy efficiency through efficient
management considering charging periods and times for use,
because of limitations of storage capacity. An inverter system
is also required to use stored or generated energy with the
existing electrical grid.
First of all, the characteristics of unsteady output power in
the renewable power generation systems cause some problems
with output voltage and frequency control. This problem is the
most significant problem to solve to connect renewable energy
systems to the existing power grid [7]. A high-efficiency
inverter and a high-capacity energy storage system maintain
the output voltage and frequency stably on equal terms with
the power grid network [8]-[10]. Therefore, high-efficiency
inverters and the high-capacity energy storage systems are
necessary to connect to the power grid. Reference [11] shows
the high-efficiency inverters. This study shows a prototype
inverter having 96.5% energy efficiency. It shows that the
power generated through a solar panel is converted to lossless
output power. Reference [12] proposes an operation system
for an inverter in grid-connected mode and stand-alone mode.
The proposed inverter generates stable output voltage and
frequency in two modes. Reference [13] shows research on
single-phase inverter control techniques in a micro-grid. The
proposed inverter controls the active power and the reactive
power that is supplied to the micro-grid from the renewable
energy source. Then, the result shows that it can be supplied to
the stable power in micro-grid. Reference [14] describes the
power quality control strategy of a grid-connected inverter
when the distributed power sources such as photovoltaic and
wind generation systems are connected to the power network.
Through these studies, various types of inverters have been
developed and researched to minimize energy loss in the
energy conversion process. However, as described above,
there are considered to be many variables to provide steady
generation output. In case of photovoltaic power systems,
weather and season have a critical influence on the amount of
generation [15][16]. Moreover, most renewable energy
systems require storage systems. However, the storage
systems have their own limitations, and are not easy to install
on a large-scale from the perspective of return of investment.
In this paper, we propose a management system to maximize
the efficiency of a photovoltaic power system in application's
aspect.
The combination of element technologies of renewable
energy with commercial electricity result in high efficiency
and positive results as described above. However, while
research on the element technologies have been studied well,
studies on energy management with renewable energy are not
relatively developed. In case of on-grid photovoltaic systems
connected to commercial electricity grids directly through
inverters like in figure 1, power consumption can be decreased
in buildings or homes, but there could also be energy loss
when power consumption is very low or electricity price are
cheap, and vice versa. To maximize the efficiency, an
intelligent management system is needed not only to monitor
the whole system but also to perform optimal management
according to ever-changing conditions such as weather,
season, and power consumption.
Fig. 1. Structure of General Photovoltaic System
III. SYSTEM ARCHITECTURE
The most important purpose of the iEDM is to determine
how efficiently generated power from solar panels can be
used. For this purpose, iEDM checks the status of a solar
batteries charge and infers future power consumption by using
specific methods to use solar power.
iEDM decides the time to use stored energy in a battery by
using power information, the residual amount of stored
energy, and web information. For example, iEDM considers
weather, which affects the efficiency of photovoltaic panels,
and power consumption, which is changing every hour, and
decides the best time to use the stored energy.
The structure of the iEDM consists of three main parts: the
Power Management System (PMS), which manages each node
and performs total energy management in the upper layer; the
Light Weight Power Management System (LWPMS) and
Repeater, which manage each node and deal with tasks in the
PMS for distributed processing; and the Flexible Power
Monitoring Device (FPMD) [16] and Power Monitoring
Device (PMD), which play the role of sensor nodes to collect
power consumption data.
As described above, the PMS collects information from the
LWPMS, manages the whole system, and provides the
collected information to users. The LWPMS has a structure of
light weight middleware suitable for smaller spaces, and
excludes the Rule-based Engine and Knowledge Repository
which are included in the PMS. Depending on the structure or

size of the places in which iEDM is implemented, LWPMS
can gather the information of the PMD and FPMD directly,
but it is also possible to gather it through Repeater.
The Power Monitoring Device (PMD) has three power
sockets to measure power consumption of devices, and a
ZigBee network module. By using these, it can transmit the
power consumption data or receive a control signal to control
the power of devices through ZigBee. The iEDM collects
power consumption data from the PMD and FPMD. The entire
system architecture is as follows.
The purple area in figure 2 is the area comprised of multiple
PMDs and the FPMD. Each node sends power measuring data
to an appointed Router or LWPMS through the ZigBee
network. In case of a specific area which is covered by one
FPMD and multiple PMDs according to the number of
devices, each node is classified by Node ID and Group ID.
The collected power consumption data are transmitted to the
LWPMS, analyzed, and used to perform energy management.
TABLE I
HARDWARE SPECIFICATION OF FPMD AND PMD
Classification FPMD and PMD
Communication Interface ZigBee
Rated Current 5, 20, 50, 100A (PMS: 20A only)
Number of Measurable Items 7
RF output 1mW(30m), 10mW(50m)
Figure 3 shows the hardware structure of the PMD and
FPMD. The MCU part is located in the middle, and the relay
control, ZigBee communication, power sensing, user interface
and power supply exist in the PMD. The PMD provides the
ability to sense power consumption, turn devices on or off,
and send data through the ZigBee network. However, FPMD
does not include the relay control part and user interface part.
Clamp-type CT sensors are used to make allow for easy
installation in a distribution panel without replacement of
modules. Generally, it is difficult to install a power metering
device in a distribution panel because of its structural
complexity. To minimize this inconvenience, the FPMD uses
the clamp type CT sensors to measure power consumption
easily. First, an analog value collected by the CT sensors is
converted to a current value that can be handled in the MCU
by the power metering IC, and the voltage value is changed.
Therefore, the FPMD and PMD can determine how much
power consumption is used from the current and voltage.
This power consumption value is stored in the internal
memory in the MCU, and the stored data are transferred
periodically to the Light Weight PMS or the Repeater. The
FPMD has a simple architecture because it only collects,
stores, and transmits data. The PMD also performs these
functions but additionally shuts off power in electrical
sockets if a monitoring value exceeds the default value or the
Fig. 2. Middleware Architecture and System Structure of the iEDM

value is changed by users. That is, the total permissible value
consists of (1):
max 1_ max 2_ max 3_ maxsocket socket socketP P P P   (1)
, and this value cannot be changed. However, the power
shutting-off value is able to be changed so that the power
shutting-off value of each power socket or all sockets can be
set to be applied in various environments. If a user wants to
save power consumption, he can change the value to be
applied in his own environment based on the user’s needs. By
using the PMD and FPMD, all used power in a specific space
can be monitored.
Fig. 3. (A) Power Monitoring Device hardware construction (B) Flexible
Power Monitoring Device hardware construction (C) Hardware
Architecture of FPMD and PMS
Moreover, the PMS also collects the current battery status
and generated power in a solar panel by using the FPMD. The
PMS gathers the measured power data from the PMD, FPMD,
and photovoltaic system module, and arranges these data with
time. According to middleware modules in the PMS, the types
of data are as follows:
Web Information Management: outdoor temperature,
humidity, weather and season information, and electric
charge through web-crawling
Renewable Energy Management: generated power and
battery charging condition in the solar power generator
Agent Management: power consumption in the PMD and
FPMD
The collected data are analyzed in the Smart Power
Management part in the PMS. In this paper, we propose two
power management methods, efficiency-oriented and user-
oriented methods.
Fig. 4. Rule Engine of the Web Information Management
A. Efficiency-Oriented Method
The main idea of the efficiency-oriented method is how
efficiently the PMS uses the solar power. There are two
important power factors: generated and charging solar power,
and current power consumption in this system. The efficiency-
oriented method focuses on the usage of the solar power.
As described above, the PMS collects the outdoor
temperature, humidity, and seasonal conditions by the Web
Information Management so that it can infer as follows. In
figure 4, the Web Information Analyzer receives the web
crawling data, and compares it with previous data stored in the
Knowledge Repository in the PMS. According to whether the
compared result is higher or lower than the previous data, the
Web Information Analyzer can predict whether power
consumption increases or decreases based on the rule-based
engine. For example, suppose that the current temperature
increases in summer. Because the temperature increases, the
Web Information Analyzer chooses the yellow 'summer' box
first in figure 4, and then the result would be 'Highly Increase'
or 'Slightly Increase'. The reason why it determines this action
is that if the temperature increases in daytime, we can infer
that users would need to adjust optimal temperature and use
air conditioning devices. In nighttime, users would also use air
conditioning devices, but the power consumption would be
less than daytime. Although this inference of the Web
Information Management cannot always be accurate, this
result is used with other measured power data to make more
accurate power predictions.
In the Renewable Energy Management, the generated power
and battery charging conditions are transmitted to the Smart
Power Management. First, the current battery condition is
compared with the current power consumption in the Smart
Power Management for understanding how much it can be
used and the electric charges according to season. The

gathered data from the PMD, FPMD, and photovoltaic system
module are converted into specific factors according to the
predefined table in the Rule-based Engine Management, and
these factors result in the following three equations:
_Web Info Weather Season Time   (2)
_ {( _ ) }PF A Web Info Generation Battery   (3)
_ (1 _ )PF B Consumption Web Info   (4)
( _ _ )
_
PF A PF B
PF C
Consumption

 (5)
The Smart Energy Management determines whether it uses the
battery through the three Power Factors: weather, season, and
time. Equation (3) describes the available battery power by
using Web Info in (2), weather, season, and current time as
variables. The variable 'Battery' is the wattage converted from
the current battery condition. Each variable (weather, season,
and time) has a specific value from 0 to 1, and those are
varying according to season, weather, and hourly variation.
Equation (4) is the expected power demand which is added to
current power consumption and the inferred power
consumption from the Web Information. Depending on the
Power Factor C in (5), the subtraction between the Power
Factors A and B, the Smart Power Management chooses to use
the charging battery.
 Power Factor C > Default Value: use the charging battery
 Power Factor C ≒ Default Value (margin of error of 3%):
use the charging battery with partial device controls
 Power Factor C < Default Value : do not use the charging
battery
We simplify and compose the rule as described above. The
Default Value can be changed by the user or other
experimental results so that the proposed system can be
applied in various environments. If the Power Factor C is
higher or the same as the Default Value, the PMS chooses to
use the battery. In the second case, the PMS finds the PMDs,
which are gradually used less and turns the power off to make
the Power Factor higher than the Default Value.
As we described above, the Default Value can be adjusted
according to the efficiency of the batteries and time. The
reason why the Default Value is able to be changed is that
charging a battery does not show linear characteristics and the
solar power module can be seriously influenced. In this paper,
we determine a specific value which shows the most
efficiency in a simulation as the Default Value, but it should
be studied further.
B. User-Oriented Method
The efficiency-oriented method only focuses on finding the
optimal time to use the charging battery for decreasing power
consumption and electric charges. However, although the
service quality of energy management increases, there is
another problem users have. For example, consider a device
which is always turned on such as a computer. If this device is
turned off by the power control of the PMS, users would feel
inconvenience, and this means that the service quality the
users feel decreases.
A user-oriented method is proposed to minimize this
problem. The PMD has four buttons, and one of the buttons
can give a priority among the three power sockets. Moreover,
the PMS infers which PMD or device plugged into a power
socket is used frequently based on data logs in the Knowledge
Repository. By using these two ideas, the PMS can control the
power except for devices which have priority or are used
frequently. That is, the PMS in the user-oriented method
manages the entire system for the user's convenience,
independently of the efficiency.
The user-oriented method has a much simpler algorithm
than the efficiency-oriented method. First, the PMS arranges
the power consumption according to the time slot on the table.
It chooses the specific time slot which has high power
consumption to use the charging battery. In case of the user-
oriented method, the PMS prevents decreases of the service
quality of the user's convenience because of shutting off
standby power indiscreetly. It maintains power to the special
devices, and uses the solar power to minimize cost burden of
the user.
IV. IMPLEMENTATION
The previous chapter describes the middleware architecture
of the iEDM. Based on this middleware, the PMS manages
each PMD and FPMD, and gathers power data from the two
devices and photovoltaic system through the ZigBee network.
This data are transmitted to the Light Weight PMS, and it
analyzes this data and sends it to the PMS. This distributed
structure helps the PMS handle many more nodes.
Furthermore, if the number of nodes is small, we design the
Light Weight PMS to perform most functions of the PMS so
that it can be applied in average homes with the one Light
Weight PMS, not the PMS and multiple Light Weight PMS.
Fig. 5. Photovoltaic Generation System (A) 4 solar panels (B) Inverter
(C) Battery (D) PV Charge Controller

Fig. 6. System Structure of Photovoltaic Generation System with the
iEDM
TABLE II
HARDWARE SPECIFICATION OF PHOTOVOLTAIC GENERATION SYSTEM
Classification Value
Maximum Power of Solar Panel (4) 100 W (Total 400W)
Maximum Output Power of
Inverter
600W
Battery Capacity (10) 120 Ah (360 Ah)
Battery Output Voltage 12V
Battery Power 14400 Wh
Figure 5 and 6 show how to generate the photovoltaic
system used in this paper. We install a solar panel system on
the side of a window, and it consists of an inverter, battery,
and PV charge controller which can manage the battery charge
efficiently. Then, this generation system is connected to a
power breaker in the PMS to be linked to the commercial
electricity grid. The FPMD with the power breaker and battery
measuring device in the solar panel controller sends power
data to the PMS regularly through the ZigBee network.
To test the efficiency of the iEDM including the PMS,
PMD, FPMD, and photovoltaic system, we implemented it in
the test bed. The test bed is used as a research space with TVs,
audio devices, and a washing machine, which are all normally
used in a home.
TABLE III
EXPERIMENTAL ENVIRONMENT OF TEST BED
Classification Value
Size 96 ㎡
The number of used devices 30
Monitoring time 6:00 ~ 23:00
V. EXPERIMENTAL RESULTS
We implemented the proposed system in the test bed similar
to a home space, and tested the two energy management
methods. The ten PMDs and one FPMD were installed, and
the PMS was located in the center of the test bed to minimize
wireless network problems. Furthermore, we also considered
the variation of types of devices to provide a general
experimental environment, and adjusted the number of devices
because it is difficult to handle power consumption in the test
bed by a limited capacity of batteries.
We tested three different methods: the general utility
interactive system method, the efficiency-oriented method,
and the user-oriented method. To decrease external variables
according to weather condition, the battery is charged up to
eighty percent in each experiment. In the user-oriented
method, five computers are given priority, and the PMS
chooses two PMDs to not turn off power based on the
previous power data. Over three days, each experiment was
performed in a sunny day to provide battery charge.
Figures 7 and 8 show the result of this experiment among
the three different environments. In figure 7, decreased
power consumption from 12:00 to 15:00 appears in the
efficiency-oriented and user-oriented methods. However,
there is a difference between the two methods, because the
efficiency-oriented method shows evenly declined power
consumption, but power consumption of the user-oriented
method is concentrated from 12:00 to 13:00. As explained,
both methods consider current battery status having limited
capacity so that the PMS manages the battery in specific
times.
In total power consumption, the efficiency method and
user method show 5.64 percent and 4.79 percent respective
improvement compared with the normal operation.
Moreover, it higher energy efficiency is expected with more
battery capacity and solar panels.
Fig.7. Experimental results according to energy management methods;
Power consumption according to time; bold line means the time when
the battery is used
(B)
Fig.8. Experimental results according to energy management methods;
Total Power consumption

VI. CONCLUSION
In this paper, the proposed system gave improved energy
efficiency compared with a normal utility interactive system.
If the limited storage capacity and the number of solar panels
are improved, the iEDM shows better performance.
Furthermore, more environmental factors are not included in
this paper for the sake of verification. We have studied each
factor that can influence this system and found other variables
to improve energy efficiency.
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BIOGRAPHIES
Insung Hong received his B.S and M.S degree in
Electrical and Electronics Engineering from Chung-Ang
University, Seoul, Korea, in 2009 and 2011. He is
currently a Ph.D. candidate at Chung-Ang University. His
current research interests include ubiquitous computing,
embedded system, and intelligent system and home
network.
Byeongkwan Kang received his B.S degree in the
school of Electrical and Electronics Engineering from
Chung-Ang University, Seoul, 2011. He is currently a
M.S. candidate at Chung-Ang University, Seoul, Korea.
His current research interests include renewable energy
system design, power management system design,
embedded system design and smart grid.
Sehyun Park (M’01) received the B.S. and M.S. degrees
in electronics engineering from the Chung-Ang
University, Seoul, Korea in 1986 and 1988, respectively,
and the Ph.D. from University of Massachusetts,
Amherst in 1998. From 1988 to 1999, he was a senior
research staff at ETRI, Korea. He is currently an
Professor of School of Electrical and Electronics
Engineering at the Chung-Ang University, where he has
established the Ubiquitous Computing and Cipher Internet Laboratory. He is
the head of Chung-Ang University HNRC (Home Network Research Center)-
ITRC (Information Technology Research Center) supported by the MKE
(Ministry of Knowledge Economy), Korea. His major research interests
include home networks, ubiquitous computing and network security.

Ieeepro techno solutions 2012 ieee embedded project - design & implementation of intelligent energy distribution management with photovoltaic system

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