=Paper=
{{Paper
|id=Vol-3018/Paper_9
|storemode=property
|title=Information System for Decision-Making in the Management of Renewable Energy Sources in the Microgrid System
|pdfUrl=https://ceur-ws.org/Vol-3018/Paper_9.pdf
|volume=Vol-3018
|authors=Nikolay Kiktev,Volodymyr Osypenko,Oleksii Kalivoshko,Alexey Kutyrev
|dblpUrl=https://dblp.org/rec/conf/intsol/KiktevOKK21
}}
==Information System for Decision-Making in the Management of Renewable Energy Sources in the Microgrid System==
https://ceur-ws.org/Vol-3018/Paper_9.pdf
Information System for Decision-Making in the Management of
Renewable Energy Sources in the Microgrid System
Nikolay Kikteva, b, Volodymyr Osypenko c, Oleksii Kalivoshko d and Alexey Kutyrev e
a
National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony str., 15, Kyiv, 03041,
Ukraine
b
Taras Shevchenko National University of Kyiv, Volodymyrs’ka str., 64/13, Kyiv, 01601, Ukraine
c
Kyiv National University of Technologies and Design, Nemirivicha-Danchenka str., 2, Kyiv, 01011,
Ukraine
d
National Science Center “Institute of Agrarian Economics”, Heroiv Oborony str., 10, Kyiv, 03041, Ukraine
e
Federal Scientific Agroengineering Center VIM, 1-st Institutsky proezd, 5, 109428, Moscow, Russia
Abstract
The researches of scenarios of dynamic energy management in local Microgrid networks,
the mathematical instruments which a used for realization of algorithms are carried out.
Various elements of the intelligent system of cost-effective scheduling of energy islands
with a photovoltaic source, as well as the mechanisms of electricity price formation using
different generation sources in Microgrid systems are analyzed. The analysis of the
architecture of information systems and software in the management of renewable
electricity systems are carried out as well. The algorithm and software interface of the
decision-making information system for managing alternative energy sources are developed.
The design of a database and software that calculates the efficient distribution of energy to
the user, forecasts the efficiency of certain energy sources, building in real-time graphs is
described.
Keywords 1
Electricity, alternative sources, effective management, decision making, information
system,database, forecasting.
1. Introduction
Currently, power plants using alternative energy sources, such as solar power plants, wind power
plants, are becoming more common. The growing penetration of stochastic and uncertain distributed
energy resources, such as wind and photovoltaic, has a significant impact on the dynamics of the power
system, which raises concerns about reliability and sustainability. This requires innovation in modeling,
operation and management of the power system to address these new challenges. In addition,
coordinated control between different devices usually relies on communication systems. Connected
control and communication systems bring both opportunities and challenges for the future development
of renewable energy systems. The use of intelligent energy management systems can reduce
consumption and thus save money for consumers. The need for energy consumption must be met and
some benefits can be obtained if you use specific optimization algorithms. Thanks to the efficient use of
renewable sources and energy imported from the grid, intelligent and adaptive control systems are able
to meet the load needs and minimize all energy costs associated with the studied scenario. Based
on the idea of intelligent network (SMART-grid and Microgrid-systems), this study it is planned to
develop an intelligent scheme of renewable resources management in combination with the battery,
implemented using a dynamic database, forecasting methods and decision-making algorithm. Computer
simulation in the form of system testing confirms the effectiveness of this approach.
II International Scientific Symposium «Intelligent Solutions» IntSol-2021, September 28–30, 2021, Kyiv-Uzhhorod, Ukraine
EMAIL: nkiktev@ukr.net (A. 1); vvo7@ukr.net (A. 2); alek-k@ukr.net (A. 3) ; alexeykutyrev@gmail.com (A. 4)
ORCID: 0000-0001-7682-280X (A. 1); 0000-0002-1077-1461 (A.2); 0000-0003-0417-4529 (A.3); 0000-0001-7643-775X (A.4)
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
101
2. The state of the issue
In the article of Russian researchers [1] the basic approaches to creation of control systems of
complexes of alternative energy sources are considered, and also the algorithm of management of
forecasting of states is described. Its feature is forecasting the state of the complex of objects, dynamic
optimization of equipment operation modes, in addition, the authors analyze the needs of thermal
consumers. The paper performs numerical simulation of the algorithm and compares it with the cascade
algorithm.
In 2012-2013, scientists of the Volga branch of the Moscow Energy Institute (MEI) under the
leadership of prof. V.S. Kuzevanova created a unique landfill, which is also called a green research
landfill [2]. It allows us to study the efficiency of electricity and heat sources (non-traditional and
renewable). Energy in this project is produced by a set of alternative energy sources - solar modules and
wind turbines, it is used for hot water production, heating, air conditioning and electricity. At creation
of this complex the Master SCADA software was used, it allows to collect and process information
from various devices, and then displays data on technological processes and a condition of the
equipment on mnemonics of the operator with their subsequent registration and archiving in a database.
Ukrainian researchers from the Kyiv Polytechnic Institute have proposed a new approach to
choosing the optimal structure and characteristics of energy sources for a combined power supply
system of an industrial enterprise, taking into account the profile of its needs for different types of
energy resources [17]. This approach is based on modeling the optimal operating parameters of the
elements of the combined power supply system operating in the power hub mode. This approach allows
to assess the cost-effectiveness of different options for the structure of the system and choose the best
option. As a result, the optimal decision is made regarding the choice of energy sources for your own
power supply system. This will reduce the energy intensity of industry in the face of rising prices for
traditional energy resources.
The work of Ukraine researchers N. Kiktev, V Osypenko and others. [6] also applies to this topic
and is devoted to the grouping of meteorological data for further use in the control system of alternative
energy sources. In the work of N. Kiktev, N. Chichikalo, H. Rozorinov and others. [7] considered
decision-making algorithms to ensure the required ash content of coal and the creation of an info-
communication system for managing this process.
The works of Ukrainian scientists V. Kaplun and V. Osypenko [3, 6] consider the formation of a
specific conditional dynamic tariff (CDT), which is an integral indicator depending on time, taking into
account tariffs from both the general grids and renewable sources.
In [3] one of the approaches to solving the problem is presented, namely: intelligent modeling of
dynamic energy management, strategies in polygeneration micronetworks using different elements of
electricity supply, inductive system-analytical technologies. Modeling based on the collected statistical
data using an inductive algorithm was also performed. The results of this study can be used in
electricity pricing processes for microgrids or for dynamic management of smartgrid with renewable
sources. To model scenarios of dynamic energy management in micronetworks, data grouping using a
bicluster analysis algorithm is used [3].
In [4], modeling was performed on the basis of collected statistical data, the results of which can be
applied in the processes (algorithms) of electricity pricing for dynamic management of intelligent
networks with renewable sources. The authors of this innovative approach imply the further application
of the initial data of the model (optimal clustering) to dynamically estimate the total cost of energy
generated by its own components, taking into account the cost of the network involved in subsequent
periods of the day.
This article can be considered as a development of work [3, 6] in the direction of software
development of the described techniques, namely - a dynamic database and user interface.
An article by MIT researchers M. Roozbehani, M. A. Dahleh and S. K. Mitter [4] proposes a
structure for modeling and analyzing the dynamics of supply, demand and clearing prices in power
systems with real-time retail prices and information asymmetry. The study involves the transmission of
wholesale electricity prices in real time to end users. Real-time pricing creates a feedback loop between
the physical and market levels of the system.
The article [5] presents the concept of smart buildings in the context of the decarbonization of the
energy system. The authors do not focus on one specific energy carrier, it is about convergence,
alignment and synchronization between heat and electricity on the one hand and the developing energy
system on the other. It explored how individual buildings can profit from existing or innovative energy-
saving technologies.
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The work of Indian researchers [13] describes methodologies for modeling the components of hybrid
renewable energy systems, the designs of such systems and their assessment.
At the Swiss Federal Institute of Technology, a new concept, the so-called Energy Hub, has been
developed to solve optimal energy flow (OPF) problems for integrated energy systems with multiple
energy carriers [14]. In this document, the Energy Hub model is applied to the OPF problem, given the
availability of multiple renewable energy sources in the mixed consumption zone. The creation of the
energy hub was also carried out by Chinese scientists from the State Key Laboratory of Power Systems
Department of Electrical Engineering Tsinghua University (Beijing) in collaboration with researchers
from Denmark and the USA [15], in particular, in the electricity and heat distribution markets. They
proposed a new approach - a mathematical model of equilibrium constraints (MPEC) program to study
the strategic behavior of a profit-oriented energy center in the electricity and heat market against the
background of grid integration.
Scientists from Iran and Spain presented a two-level model of the stochastic programming problem
(BSPP) of decision making by an energy hub manager [16]. The two-level circuit is converted to an
equivalent single-level circuit using the Karush-Kuna-Tucker optimality conditions, although there are
two bilinear products related to electricity and heat. The bilinear product of heat is replaced by the heat
price curve, and the bilinear product of electricity is linearized using the strong duality theorem. In
addition, the notional value at risk is used to mitigate the adverse effects of uncertainties.
An article by British and American researchers [17] is devoted to the problem of managing the energy
imbalance in the microgrid. The problem is investigated from the point of view of the electricity market.
The study proposes a new pricing scheme that provides resilience to this intermittent supply of power.
The proposed scheme takes into account possible uncertainties about the marginal benefits and marginal
costs of the electricity market.
In the work of the Italian researcher P. Siano [18], an overview of smart electric grids is considered,
which can deliver electricity in a controlled and intelligent way from generation points to active
consumers. Demand Response (DR), by fostering customer engagement and responsiveness, can offer a
wide range of potential benefits for system operation and expansion, as well as for market efficiency.
This study examines the energy management system at the local level. The main task is to create
information technology that could select such scenarios and provide recommendations for the most
efficient use of a particular energy source in real time. Modern possibilities of using combined energy
systems (COM) for local facilities with an installed capacity of up to 15 kW are based on the use of
several sources (traditional and renewable with energy storage), including the main network (MN).
The mathematical apparatus that can be used for decision making and control in energy facilities is
described in [12, 19, 22-25]. In particular, the method of group accounting of the relative advantage of
alternatives in decision-making problems is proposed by Yu. Samokhvalov [12].
Some problems of intellectualization of decision support systems for smart electrical grids and in the
design of others have been considered in in articles by scientists from Jordan and Iran [21, 25]. The
research of scientists from Taras Shevchenko Nationality University of Kyiv is devoted to the choice of
alternatives in decision-making. The article [19] considers the form of forming the procedure for the
dynamic equilibrium of an alternative in a multi-agent environment when making decisions by a
majority of votes on the basis of Markov chains. The derived method for the formation of matrices
"state - probability of choice." was described The proposed model includes several parameters, one of
which affects the spread of values between the best and worst values, and the other is the degree of
agent's decisiveness. The Markov chain is used to model changes in agents' preferences.
A number of scientists have been involved in mathematical support of renewable energy sources.
Researcher from Cairo (Egypt) Hussein. A. Attia compiled a mathematical formulation of the demand
management problem (DSM) and outlined the ways of its optimal solution [22].
3. Statement of the problem
The purpose of this study is to create an information and control system for the implementation of
the algorithm for the most efficient use of all possible energy sources in the microgrid system.
Research objectives:
determine the input and output information on the management of renewable energy sources;
using previous research to develop an algorithm for determining the most efficient source of
electricity;
to create a dynamic database of electricity parameters for a certain period of the year;
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using forecasting methods to estimate the cost of using different types of electricity sources for
the next period;
issuing recommendations to the user to determine the source that should be used in a certain
period of time.
4. Materials and methods
Research is based on mathematical methods, modeling of energy processes, as well as the use of
modern information technology for information processing and decision making. The study was based
on the idea described in the works of Ukrainian scientists V. Osypenko and V. Kaplun [3].
This innovative approach can be applied in the future in complex energy systems. The results of this
project can be widely and effectively used in small energy facilities (farms, cottages, industrial plants)in
climatic zones with different natural conditions, which involve the use of solar energy, wind or other
sources of distributed generation, forming combined systems.
To control the production, accumulation and consumption of electricity in microgrid systems, a
deterministic daily schedule of electricity consumption is compiled from several sources - external
power system, wind and solar power plants, static sources with energy storage and autonomous power
plants with an internal combustion engine. They use management principles based on a conditional
dynamic tariff [3].
4.1. Calculation of electricity indicators.
The formula for calculating wind energy [1]:
(1)
Here k is the efficiency of the turbine, which takes into account the impossibility of installation at
100 %; R - air density, kg / m2; V - wind speed, m / s; S = π D2 / 4 - wind flow area, m². Formulas for
calculating solar energy [1]:
(2)
where E1 - energy production using solar panels;
E2 is insolation per square meter;
P1 is rated power of the solar cell;
η is the total efficiency of electric current transmission;
P2 is maximum solar power per square meter of the earth's surface.
The calculation of the cost of generated electricity is carried out according to the formula for the
specific dynamic tariff [3]:
(3)
where P - the amount of energy produced;
C - the cost of maintenance of the wind or solar panel;
t -the number of time intervals.
Table 1 shows the formation of the initial conditions for calculating the efficiency of using certain
energy sources and the formation of a database. The formation of the numerical results of the
efficiency of using wind generators and solarbatteries is presented in table 2.
Decision-making on the use of this energy source is carried out in accordance with the formula:
Dec = {R, C; CDT min}
where R is the amount of energy produced;
C is the ability of the system to satisfy the consumer;CDT is the cost of electricity. It is proposed to
use the ARIMA model to analyze and predict time series data. It will be better than other methods to be
able to track daily trends in the distribution of energy from its sources, which will allow you to more
accurately predict increased loads and level them during peak hours.
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Table 1
Formation of conditions reports
Field name Report Time Air density Wind Activetime Fuelcost Fuelcost Consumerenergy Day
number speed
Notation # t R V ta η $ P day
Units of Integers Minu-tes kg/m2 m/s Minu-tes Ratio USD W day
Measure
Limits 1-∞ 0:00- 200-2000 0-100 0-30 0.00- 0-100 0-10 000 1-31
23:30 1.00
Task User-defined
Measured by anemometer
Automatically by database
Measured by hydrometer
Determined by recording
Determined by the angle
Taken from the market
pergeliometerofsolar
value offuel price
Measured by
Record time
of thepanel
onobject
onobject
panel
time
Table 2
Formation of efficiency results of wind and solar generators
Field name Total number Wind Time Day the amountCost ofDecisionmade
ofthe result generator of energyelectricity
result number produced
Designation # # t day P CDT Dec
Unit of Integers Integers Minuts Days W USD A set of possible
measurement solutions
Limits 1-∞ 1-∞ 0:00- 1-31 0-10 000 0-100 Used, saved
23:30
Tasks Automatic ally Automatic ally Determi ned Deter- Dec =
bydatabase bydatabase by mined by {R,C;CDTmin}
recording recording
time time
ARIMA model - is expressed by the equation [19]:
Yt-1 = β1Yt-2 + β2Yt-3 +… + β0Y0 + εt-1
where, Yt-1 is the lag of 1 row, β1 is the lag coefficient 1, which estimates the model, and βp is the
interception time, also estimated by the model.
ARIMA model in words:
Predicted Yt = Constant + Linear combination Lag from Y (up to p lags) + Linear combination of
lagging forecast errors (up to q lags). The decision-making algorithm of the renewable energy
management system is presented in Fig.1.
4.2. Database architecture
Among the most typical databases (MySQL, PostgreSQL, MongoDB, Microsoft SQL Server,
SQLite) implementations differ in the organization of data at different levels. Currently, SQLite is the
most used database in the world, it will be used during the task. SQLite is a C-language library that
implements a small, fast, stand-alone, highly reliable, full-featured SQL database engine.
The structure of the database of the management system of renewable energy sources includes 9
tables:
1) Handbook of wind turbines;
2) reference solar panels;
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3) handbook of diesel generators;
4-6) results of calculations of electricity and its cost for wind turbines, solar panels and diesel
generators;
7) initial conditions;
8) general dynamic database;
9) forecast for the next period.
Launching the
program
Determination of
the amount (P)
and price (CDT) of
electricity
Is the No
amount of Turning on the
electricity diesel generator
sufficient?
Yes
Sorting power
sources at the
lowest price
Using the next
cheapest source
No Is the Yes End of the
consumer
program
satisfied?
Figure 1: Block diagram of energy use decision making
To demonstrate the system and user convenience, a graphical interface of the software application
was created using html with javascript and python elements (fig. 2). This form contains buttons, each of
which is responsible for part of the functionality used.
4.2. Software application development.
To develop the software product used:
object-oriented Python programming language;
PyCharm environment;
SQLite Server database management system.
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The main modules that perform certain functions of the software application contain the prefix
"main":
main_client.py - interacts with the user interface
main_graphics.py - builds graphs of calculated data
main_output.py - displays the results of calculations to the user
main_predict.py - performs forecasting
main_sql.py - interacts with the database
main_system.py - performs basic calculations of the software application
Figure 2: The form of the graphical interface
4.3. Testing the system.
When you click on the "Start calculations" button:
a system is launched that will calculate the generated electricity;
the source of energy which will be used first of all is chosen;
it is determined whether there is enough energy to meet the needs of the user;
if necessary, the diesel generator is started.
After that, the residual energy will be transferred to the batteries for use in the next time period.
All considered data are recorded in the database for further analysis. On the basis of the received data
schedules for each executed day are constructed (for example, 1stday, fig. 3).
Figure 3: Schedule for the 1st day
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The graphs show: blue - the required amount of energy to the consumer, green - the amount of
energy on batteries, red - the energy produced by wind power plants, yellow - the energy produced by
solar power plants, gray - energy produced by a diesel generator. Based on the results obtained, we
obtain a graph of the price of each watt of energy for a wind farm (fig.4). The Solar Panel Results menu
section displays the results of the calculations for each solar panel for a given day, where you can see
the amount of energy produced, its price per watt of energy, and information about whether the energy
was used by the user or stored on batteries. The menu section of the program "Forecast for the next day"
based on previous results from the database displays the forecast based on the ARIMA model (fig. 5).
4. Results of the studies
The scientific novelty of this study is to use the ARIMA method to predict the cost of electricity
produced by alternative sources, and to develop an algorithm for deciding on the use of a particular
source of renewable energy. The practical value of the study is to create a dynamic database of technical
and economic indicators of energy and combine it with the algorithm of decision-making and
forecasting in a single software application.
Figure 4: Schedule of the price of electricity for W
5. Discussion
The article examines the dynamic management of electricity using an intelligent component -
decision making, this is an innovative approach to load management on the demand side by the criterion
of the minimum cost of using a particular source in a given period of time.
This approach incorporates traditional energy management principles, representing all levels of
energy distribution, integrating them into a structure for optimal demand management to reduce peak
loads on the energy system. The study selected half-hour time intervals of samples for one day.
Modeling based on the collected statistical data, the results of which can be applied in the processes
(algorithms) of electricity pricing for Smart Grid dynamic management with renewable sources.
Energy storage technologies are identified as key elements for the development of electricity
generation using renewable energy sources. In this study, they were illustrated through two cases of
modeling, how they can help eliminate technical constraints that limit the contribution of renewable
energy sources to electricity grids. Examples of the use of wind and solar energy are offered. The
considered methods can be implemented in the real sector of photovoltaic power generation taking into
account the preliminary processing of data on solar insolation and photovoltaic production. The
simulation results are presented in a clear and easy to understand form of graphs and tables that
108
demonstrate the effectiveness of the proposed method. Remote control of alternative power sources
using the Internet of Things technology is also promising. To do this, you can apply the development of
the authors of the article using this technology in the agricultural sector.
Figure 5: Forecast of energy use the next day based on preliminary data
6. Conclusion
The energy storage technology segment needs new solutions every day. Coming to the electric car
market greatly contributes to such innovations. The aim of the article was to show that a dynamic
approach to charge and discharge management at the energy storage system level provides good quality
of service (energy efficient power reduction, power smoothing and uncertainty reduction) withreduced
storage capacity. The results of this study allow further application of the initial data of the model
(optimal clustering) to dynamically estimate the total cost of energy generated by its own components
of the program, taking into account the cost of the network, in our case within one day. Further
implementation of the research results will contribute to the improvement of mathematical and
information support of decision support processes in the management of hybrid power grids. Further
research can be aimed at improving the user-friendliness of the interface, the use of more efficient
mathematical methods for grouping (clustering) data and forecasting technical and economic indicators.
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