Development of methods for managing energy consumption and energy efficiency in a common system

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 2, April 2023, pp. 1306~1313
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i2.pp1306-1313  1306
Journal homepage: http://ijece.iaescore.com
Development of methods for managing energy consumption and
energy efficiency in a common system
Nigar R. Aslanova1
, Esmira J. Abdullayeva2
, Aleksandr V. Beloglazov3
1
Department of Economics and Business Management, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan
2
Department of Social Development Economics and Evaluation, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan
3
Electrical Engineering Faculty, Tomsk Polytechnical University, Tomsk, Russian Federation
Article Info ABSTRACT
Article history:
Received Jan 24, 2022
Revised Oct 11, 2022
Accepted Oct 31, 2022
The work aims to analyze and examine renewable energy sources (RES)
to develop interconnected energy efficiency and energy consumption
management system by integrating the software-defined machine-to-
machine (M2M) communication. The article’s objectives include analysis of
using RES as alternative raw materials for electricity production, the study
of intelligent technologies for integrating RES into monitoring and control
systems, research of devices and methods for monitoring energy production
and consumption, analysis of sensor application for automation of control
systems in the energy sector, a study of data transmission and information
processing rates. The study results showed that the data transfer rate was
delayed by 6 seconds to process 1,000 MB of information. It has been
proven that wind energy can be used most efficiently within a 12-hour daily
cycle, in contrast to tidal energy and solar energy. It is shown that due to the
cyclical nature of obtaining energy from renewable sources, they do not
fully provide energy to a large city, on the basis of which it is necessary to
additionally use other energy sources. Three different types of power
generation facilities were examined and compared. Wind farms were found
to have the highest potential for electricity generation, amounting to
1,600-1,700 kW.
Keywords:
Data center
Energy sector
Intelligent energy management
Machine-to-machine
communication
Technology integration
This is an open access article under the CC BY-SA license.
Corresponding Author:
Esmira J. Abdullayeva
Department of Social Development Economics and Evaluation, Azerbaijan State University of Economics
Instyglaliyat Str., 6, Baku AZ 1123, Azerbaijan
Email: esm_abdullayeva@rambler.ru
1. INTRODUCTION
Today the energy sector is a crucial element in the functioning and prosperity of economics in many
countries worldwide, with new prospects for a gradual transition from existing traditional to alternative
energy generation methods, particularly renewable energy sources (RES) [1], [2]. Successful implementation
of intelligent energy management (IEM) involves real-time information about the load profile, power supply,
and system operating conditions [3], [4]. However, the communication network needed for IEM is very
different from conventional human-centric telecommunications networks. This type of communication is
characterized by the omnipresent exchange of information between many intelligent machines like sensors,
actuators, intelligent electronic devices (IEDs), excluding the human factor. This resulted in the introduction
of machine-to-machine (M2M) communication technology [5], [6].
Cellular technology is a major driver of M2M due to the pervasive presence of cellular
infrastructure and the availability of high-speed access. The 3rd generation partnership project (3GPP) has
defined several methods to support M2M communication in modern long-term evolution-advanced

Int J Elec & Comp Eng ISSN: 2088-8708 
Development of methods for managing energy consumption and energy … (Nigar R. Aslanova1)
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(LTE-advanced) systems [7]. M2M devices may co-exist with human communications on the same network
and use a random-access channel to establish connections to centralized base stations. As a result, M2M
communication, with its self-organization, self-adjustment, and self-healing skills, should be a critical factor
in the reliable functioning of intelligent energy management.
The application-specific method requires full customization of M2M platforms for a specific
application scenario, which does not allow to adapt to a rapidly evolving demand. Managing a large number
of M2M devices is highly inefficient due to the increasing complexity of the system and the significant
heterogeneity of equipment, interconnections, and deployment scenarios. Second, the close interconnectivity
between applications and task-based hardware makes reusing existing M2M physical infrastructure for new
applications impossible. Different or even the same application by different operators require redundant
equipment, resulting in excessive capital and operating costs. Finally, power grid applications have various
quality of service (QoS) requirements regarding latency, packet size, bandwidth, and packet arrival rates. The
co-existence of protection, control, monitoring, and billing traffic in a single communication network creates
new challenges for effective resource allocation in M2M communications [8]. Network design, deployment,
management, and maintenance can be easily implemented on one central controller based on an open
standard rather than directly setting up many heterogeneous devices [9].
This study investigates the SDN-M2M switching relationship and explores the potential for
intelligent control using RES. This work aims to analyze and examine RES in developing a methodology for
energy efficiency and energy consumption management in the overall control system by integrating
software-defined M2M technology. The scientific novelty implies a rational application of RES in the energy
sector through the conversion of energy by wind turbines, solar panels, and hydropower plants based on
software inter-machine communication.
2. METHOD
This study analyzes the use of RES in grid-connected intelligent energy management systems using
software-defined M2M communication. The work investigates the operation of wind farms with sensors for
power generation and monitoring information with rational power distribution; solar panels with irradiation
sensors, solar panel temperature, and surge sensors; hydro power plant (HPP) for power generation with
special monitoring systems and power supply to the plant. The RES considered in this study is wind, solar,
and water.
2.1. Wind power plant
The wind power plant (WPP) is a complex of related equipment and facilities designed to convert
wind flows into energy to produce other types of energy like electricity and mechanical power. The
conversion of wind streams into electrical energy is due to the electric generator, which is contained in the
plant’s construction. A scheme to monitor wind power production is presented in Figure 1.
Figure 1. WPP with wind turbines and communication systems
It comprises three major components: sensors, low-power wide-area network (LPWAN) gateway,
data pathway, database, and visualization solution. Sensors are installed on or around generators that measure
voltage, current, phase, and other necessary parameters for electricity generation. Sensors can also measure

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 2, April 2023: 1306-1313
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and monitor infrastructure parameters such as temperature, vibration, and environmental parameters such as
wind speed, humidity, and lighting conditions [10], [11].
2.2. Solar panels and solar energy
A solar panel is a set of photovoltaic converters in the form of semiconductor devices that convert
solar energy into direct electric power. Figure 2 shows a system for monitoring electricity generation through
solar panels and parameter sensors. A low-cost Arduino voltage sensor module with voltage splitters can be
used to measure the output voltage. This module is based on the principle of resistance point pressure, and it
can make the input voltage of the red terminal decrease by a factor of 5 from the original voltage [12], [13].
Figure 2. Sensor-based solar panel control system
2.3. Tidal power plant
Tidal energy is a type of hydroelectric production that uses water as a raw material to generate
electrical energy converted by tidal motion using tidal generators. There are several ways of producing
energy, one of which is through tidal generators or energy converters (TECs). Tidal generators use kinetic
energy from tidal water movement to turbines [14]–[16]. Figure 3 shows HPP with tidal power generation.
Figure 4 shows the way to supply generated hydroelectric power to a small city through HPP through a
special wire transmitter. HPP with tidal hydropower can generate more than 2 MW of electricity.
Figure 3. HPP with tidal power generation

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Figure 4. Power supply to the city by HPPs
2.4. Intelligent energy management with SDN-M2M technology
IEM combines defined machine learning programs to allocate resources in fine granularity and
ensure QoS. Figure 5 shows the SD-M2M architecture comprising such layers as energy sources and control,
application, transmission, management, and administration of information. The data layer represents the
presence of programmable field equipment and network elements involved in M2M communication (sensors,
actuators, smart meters, gateways, switches, routers). They are needed to support autonomous data collection
and transmission with IEM.
The application layer includes IEM application networks (domestic energy management, corporate
energy management, building energy management, micro-grid energy management, electric vehicle energy
management). Using standard application programming interfaces (APIs) between the control layer and the
applications, intelligent energy management applications can communicate the requirements to the
appropriate controllers through the north-facing interface [17]–[19]. IEM using RES is an interconnected set
of equipment, devices, and data carriers to monitor and control energy supply systems and energy
consumption.
Figure 5. SDN-M2M architecture
Figure 6 shows different RES (hydropower, wind turbines, solar panels) data control sensors to
transfer information to DC for further monitoring and control of the equipment. The points in Figure 6 are the
nodes of energy distribution. The electrical substation in Figure 6 is designed to receive, convert, and
distribute electricity with the help of control devices and switchgear. The conversion sub-station serves to
convert the current type and its frequencies.

 ISSN: 2088-8708
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Figure 6. Renewable energy complex with data transmission and processing
3. RESULTS AND DISCUSSION
The results of transmitting the signal from the equipment are given in Figure 7. Thus, the data
transmission rate may slow because of the increase in the amount of information over time. The delay in the
transmission rate is because multiple calculation processes are executed simultaneously. The transmission of
data from wind turbines and HPPs, together with the calculation of the joint flow of solar panels, can lead to
a non-significant delay. In addition, natural factors, such as meteorological conditions and sufficiently low
temperatures below 20-25 ℃. The data center information technology determines the node where the power
generated by the plants is measured. It enables the distribution of electricity consumption based on the energy
produced by the power plants.
The rate of data processing in Figure 7 depends on the speed of data transmission through sensors.
They record actual production records of renewable energy equipment and, for their purposes, can monitor
the equipment’s operating status in the event of a malfunction. Figure 7 shows that the data rate is of the
order of 0.1 with data processing from 100 to 1,000 MB, indicating a good performance of information
technology (IT) equipment in DCs. M2M devices serve as phase measurement units integrated into
production and transmission equipment to continuously collect critical grid data such as voltage, current,
harmonics, and frequency. Using excel, we calculated equation that describes correlation between time of
data transmission and their capacity:
𝑇𝑖𝑚𝑒 = 0.0006 × 𝑆𝑖𝑧𝑒 + 0.0995; 𝑅2
= 0.9657 (1)
By incorporating RES and SDN-M2M technology, it is possible to calculate the average daily
production capacity at service stations of RES plants over 12 hours, as shown in Figure 8. The average daily
generation volume of WPPs is more efficient than solar panels and HPPs. The lowest power volume is
350-400 for HPPs for 3 hours due to low tidal activity. Solar panels have the lowest rate after 9 hours, where
the volume decreases from 1,300 to 500 kWh, while the power output of WPPs falls from 1,500 to 850 kWh
at the same time.
Data in Figure 8 allow concluding that the generation of energy from RES is insufficient for
electricity supply in a large city. Furthermore, it is characterized by unforeseen circumstances during the
operation of power plants, decreasing the overall efficiency. To increase energy efficiency and ensure
electricity consumption by consumers, the complex energy produced during the day must be distributed.

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Figure 7. Dependencies of data transmission and processing rates from metering devices to DC
Figure 8. Average daily generation volumes of electricity from RES
This study considers the possible integration of RES into a software-defined M2M communication
system and compares it with existing monitoring, control, and management techniques to identify the current
shortcomings. M2M communication is one of the most active methods for managing power quality with
sufficient equipment safety. It also enables the collection and processing of data at a distance, excluding
human factors. At the same time, it is necessary to analyze the impact of information technology and the
functioning of DCs operating using RES on the environmental situation.
Machine learning and artificial intelligence technologies are of equal importance. They may
anticipate the potential to generate renewable energy through workload planning and implementation to
improve alternative energy use energy efficiency. One such example is a scheduler called “GreenSlot” in
Goiri et al. [20]. It is designed to forecast solar energy production as closely as possible, followed by DC
workload planning to maximize the use of RES while meeting deadlines. This programming approach allows
the DC to consume much more renewable energy by reducing the cost of conventional energy.
Uihlein and Magagna [21], statistical methods or physical models performed wave forecasting for
tidal energy. Statistical approaches involve the application of neural networks and regression methods with
genetic programming. They are represented, for example, by the wave forecasting systems based on physical
models and the weighted average mark (WAM) wave model included in the integrated forecasting system
(IFS) of the European centre for medium-range weather forecasts (ECMWF) [21], where forecasts can be a
maximum of 48 hours ahead.
The integration of RES with DCs is now widely used in practice. Many IT companies have already
built and transitioned to new DC that can operate partly or fully with renewables. One such company is
Apple, which has created a 40 MW solar network for its data center in North Carolina to provide extra energy
[22]–[28].
We summarized our results in 4 findings: i) we have determined that with an increase in the amount
of data transferred, an increase in the duration of their transfer occurs. On average, an increase in the amount
of data by 1 MB leads to an increase in processing time by 0.0006 seconds; ii) an increase in the amount of
simultaneously processed data does not lead to an increase in the processing time; iii) we estimated the

 ISSN: 2088-8708
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change in the amount of energy received from wind, solar and tidal energy. It has been established that wind
energy can be used most efficiently within a 12-hour daily cycle, in contrast to tidal energy and solar energy;
and iv) it is shown that due to the cyclical nature of obtaining energy from renewable sources, they do not
fully provide energy to a large city.
4. CONCLUSION
This study analyzes the use of renewables in three ways with the possibility of combining
alternative energy generation methods into a grid-connected system. By integrating RES with the
software-defined communication technologies, a relationship was established between electricity generation
and subsequent transmission of information to DCs. It allows monitoring the electricity generation volumes,
which depend on the data transmission and processing rates from the sensor equipment to remote servers to
display necessary distribution parameters for power supply to the end consumers. The study results showed
that the data transmission speed has a delay of 0.6 seconds for processing information of 1,000 MB. Data
processing rate depends on the speed of data transmission received from the equipment’s sensors when the
speed indicator does not exceed 2 seconds.
The study of three different power plants revealed that wind farms provide the most significant
potential for generating electricity up to 1,600-1,700 kW. The need to combine plant complexes for more
rational electricity use by consumers has been established. Each type of power plant has disadvantages, and
thus, they are highly dependent on favorable conditions for the normal operation of the equipment. That is,
solar panels require sunny weather, wind streams are critical for WPPs, tides and water flows specify the
functioning of HPPs.
ACKNOWLEDGEMENTS
The authors received no financial support for the research, authorship, and/or publication of this
article. The authors declared no potential conflicts of interest with respect to the research, authorship, and/or
publication of this article.
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BIOGRAPHIES OF AUTHORS
Nigar R. Aslanova received the Ph.D. degree from Azerbaijan State University of
Economics (UNEC), Azerbaijan. Currently, she is Senior Lecturer at the Department of
Economics and Business Management of Azerbaijan State University of Economics (UNEC)
in Baku, Azerbaijan. Her research interests include managing energy consumption and energy
efficiency and managing methodology. She can be contacted at ng_aslanova301@rambler.ru.
Esmira J. Abdullayeva holds a Ph.D. from Azerbaijan State University of
Economics (UNEC), Azerbaijan. She is currently Senior Teacher at the Department of Social
Development Economics and Evaluation of Azerbaijan State University of Economics
(UNEC) in Baku, Azerbaijan. Esmira is an expert in energy sources, their integration into
grid-connected system, energy efficiency and consumption. She can be contacted at email:
esm_abdullayeva@rambler.ru.
Aleksandr V. Beloglazov is currently a high-regarded Power Engineer and
graduate of Tomsk Polytechnical University, Russian Federation. His research areas include
power energy source, renewable energy, sustainable development, energy efficiency. He can
be contacted at email: alksbeloglazov@rambler.ru.

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