=Paper=
{{Paper
|id=Vol-2922/paper011
|storemode=property
|title=Mathematical model of parametric virtualization of technocenosis data
|pdfUrl=https://ceur-ws.org/Vol-2922/paper011.pdf
|volume=Vol-2922
|authors=Viktor I. Gnatyuk,Oleg R. Kivchun,Sergey A. Dorofeev,Elena V. Bovtrikova
}}
==Mathematical model of parametric virtualization of technocenosis data==
https://ceur-ws.org/Vol-2922/paper011.pdf
Mathematical model of parametric virtualization of
technocenosis data*
Viktor I. Gnatyuk1, Oleg R. Kivchun2*, Sergey A. Dorofeev3, Elena V. Bovtrikova4
1
Kaliningrad State Technical University, 1, Sovetskiy prospect, Kaliningrad, 236000, Russian
Federation,
2
Immanuel Kant Baltic Federal University, 14, st. A. Nevskogo, Kaliningrad, 236016, Russian
Federation,
3
Limited Liability Company Kaliningrad Innovation Center "Technocenosis", 1, Sovetskiy
prospect, Kaliningrad, 236000, Russian Federation,
4
Russian New University, 22, st. Radio, Moscow, 105005, Russian Federation,
oleg_kivchun@mail.ru
Abstract. The article discusses a mathematical model of parametric virtualiza-
tion of technocenosis data. The basis of the model is the methodology of rank
analysis, which is aimed at studying complex technical systems. The implemen-
tation of the parametric data virtualization model allows you to create a subject-
oriented information database that can be used for the functioning of digital
platforms and services, as well as to complement the architecture of the Internet
of Energy. The database serves as a data storage, includes a primary digital data
layer and secondary digital layers of the first, second and third stages. The pri-
mary data layer is the results of processing and verification of the initial re-
source values. The secondary layer of the first stage contains the results of static
modeling procedures, and the secondary layer of the second stage contains the
dynamic and bifurcation models of the rank analysis methodology. The secon-
dary layer of the third stage stores data on the performance indicators of the
rank analysis methodology procedures. The information of each layer is com-
bined into an OLAP cube, which allows you to fully describe the parametric
virtualization of the digital platform or service data. The practical implementa-
tion of the proposed model was carried out in the hardware and software com-
plex for monitoring the power consumption of the power grid company. Based
on the OLAP-cube, automated workstations for verification and data process-
ing, short-term and long-term forecasting, trend detection and construction of
typical electrical load graphs have been developed and implemented. The eco-
nomic effect from the implementation of the model can amount to more than
3000 thousand rubles per year.
Keywords: model, virtualization, parameter, data, technocenosis, digital plat-
form, digital service, OLAP-cube.
*
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribu-
tion 4.0 International (CC BY 4.0).
1 Introduction
The modern pace of development of infocommunication technologies around the
world has enabled to create technological basis for the social and economic spheres of
human life. As a consequence, a new type of economic activity has appeared which is
called the digital economy. Nowadays many platforms and services of the digital
economy are being actively developed and implemented. As for the energetic field,
digital power engineering is presented as an element of the digital economy. Its main
task is to manage technical and socio-economic subsystems of power systems during
generation, distribution and consumption of energy resources using digital platforms,
services and automation tools.
Analysis of the research into the scientific field of digital power engineering
showed that now there is a fairly high number of concepts for its development [1-5].
The content of these concepts presents solutions for the problems of increasing the
reliability of power supply, modernizing electrical installations as well as reducing
energy losses and number of accident situations. However, little attention is paid to
development and creation of digital platforms and services for the interaction of a
consumer (individuals or legal entity) with the power system.
Recently a group of the scientific and technical initiative “Energinet” has devel-
oped the concept of the Internet of Energy within the framework of digital power
engineering. The premises for the elaboration of this concept are based on the fact that
at the present moment the energy systems that have been developed on the basis of a
traditional centralized structure are becoming less efficient. This is mainly due to the
development of new infocommunication technologies, changes in the socio-economic
and political situation in the world as well as the shift of consumer demand.
“At its core, the Internet of Energy is a decentralized electric power system, which
implements intelligent distributed management, carried out through energy transac-
tions among its users” [3].
It can be used by individuals and legal entities which have electrical installations
that allow generating, accumulating, distributing and consuming electric power. Sub-
jects that provide various services to the owners of electrical installations are also
considered as users [6-8].
Thus, taking into account the key points of the concept of the Internet of Energy, it
can be concluded that digital power engineering should include new digital platforms
and services to ensure the sustainable operation of the energy system. On the other
hand, it should maintain the highest energy efficiency and minimize energy losses due
to a high-quality energy management process. In this regard, one of the main tasks is
to develop the mathematical model for the virtualization of data on the power con-
sumption of the technocenosis.
2 The concept of constructing a model of parametric
virtualization of technocenosis data
Currently, scientists and engineers This is one of the global markets of the National
Technology Initiative “EnergyNet” developed the concept of Internet energy [3]. The
article proposes to supplement this architecture with a mathematical model of virtual-
ization of data on power consumption at the user level (individuals or legal entities),
which further is served as a basis for developing power consumption monitoring ser-
vice.
The methodology of rank analysis of technocenoses became the basis for the de-
velopment of the mathematical model. From the practical point of view, technoceno-
sis is viewed as an energy system operating on the basis of the structure of the Inter-
net of Energy. From the theoretical point of view, it is considered as an intercon-
nected set of individual objects with non-Gaussian properties, having unified man-
agement and logistics system. More detailed information about the concept of «tech-
nocenosis» and the methodology of rank analysis can be found in the following scien-
tific works [1; 4-6]. The rank analysis methodology suggests the implementation of
static, dynamic and bifurcation models of optimal power consumption management,
which include a number of rank analysis procedures [9-11].
Virtualizing data on power consumption at the first stages, a certain subject-
oriented information database on power consumption is formed (Figure 1). Basically,
it is data storage. One of its main functions is decision support for using digital ser-
vices or platforms.
Fig. 1. Structure of the data storage for the model.
Thus, parametric data virtualization should be understood as a method of creating a
digital twin of the object under study (technocenosis) based on software that uses the
values of the data storage. Computational modules that implement rank analysis pro-
cedures are used as software [1].
At the initial stages of virtualization, a rank parametric distribution is constructed
based on the initial «raw» data, which presents the following function:
[{Wk }nk 1
f :W R
{Rk }nk 1 ]
Approx
W f ( x), (1)
{Wk }nk 1 – range of resource value;
n
{R }
k k 1 – range of ranks;
W ( x) – rank function;
x – rank measure.
Before ranking, the set {Wk }nk 1 is subjected to verification, on the results of which a
set of verified values {WkVER }nk 1 is formed. This operation is based on algorithms for
eliminating erroneous, equal and zero values {WkVER }nk 1 for power consumption.
Next, the values {WkVER }nk 1 of the set are compared with the values of the set of
topological ranks {Rk }nk1 in descending order. After the ranking, the ranged values are
approximated. The approximation method is set by a researcher. Figure 2 shows a
graphical view of the rank parametric distribution.
Fig. 2. Rank parametric distribution.
So, rank parametric distribution is a numerical function, which belongs to the range
of {WkRAN }kn 1 . Developing the rank parametric distribution on power consumption can
be presented in the following way:
{W RAW }n {W VER }n ;
kVER nk 1 Verific k k 1
{W }
k k 1 Rangin {Wk
RAN n
} k 1 ; (2)
RAN n
{Wk }k 1
Approx
{WkAPP }kn1 ,
{WkRAW }nk 1 – range of «raw» values of energy consumption;
{WkVER }nk 1 – range of verified values;
RAN n
{W k }k 1 – range of ranged values;
APP n
{W k }
k 1 – range of approximated values.
From Figure 2 it can be seen that the primary layer of the data storage consists of
four sets (Figure 3) [1].
Fig. 3. Primary data layer of the data storage for the model of parametric virtualization.
The implementation of a static model of power consumption enables to form the
secondary layer of the first stage of the data storage. It includes the values of the di-
flex parameters that are recorded during the examination of anomalous objects, the
results of short-term, medium-term and long-term forecasts, norms and limits for
power consumption established as a result of the rationing and potentiation proce-
dures. Figure 4 shows the structure of the first stage of the secondary layer [1].
Fig. 4. Secondary layer of the first stage of the data storage for the model of parametric
virtualization.
During the implementation of the dynamic and bifurcation models of the methodol-
ogy of rank analysis of the technocenosis, the values of the additional resources of MS
and DC analyzes modeling in various ways are imported into the data storage. Such
values in the data storage form a secondary layer of the second stage (Figure 5) [1].
Fig. 5. Secondary layer of the second stage of the data storage for the model of parametric
virtualization.
Thus, the digital data layer is the structural unit of the storage. It can be represented
as a two-dimensional or three-dimensional array. The values of the digital layer are
identified by the index, the number of the time intervals and the parameters of the
results of the rank analysis models.
3 Cubing data
The final operation of the mathematical model of parametric virtualization is the
creation of an OLAP data cube which is a multidimensional array of values of energy
consumption, located for a long time in the data storage (Fig. 6).
Fig. 6. OLAP-cube of data for parametric virtualization [1].
Mathematically, the digital data layer on the power consumption parameter in an
OLAP-cube can be described as following [1]:
[ RAW ]kt [ DIF ]kt [ IPK ]kt [ AMC ]kt
p fix [VER]kt [ PRO ]kt [ IPZ ]kt [ AMD]kt
WktOLAP
k 1..n ; (3)
t 1..
[ RAN ]kt [ NOR ]kt [ IPE ]kt [ BIF ]kt
[ APP ]kt [ LIM ]kt [ DFU ]kt [ MOD]kt
WktOLAP – sequence of OLAP-cube of data;
k – rank;
t – time interval;
– number of time intervals.
In order to clarify the elements (3), it should be reminded that data aggregators are
created and implemented when data is cubed. Aggregators can be primary and secon-
dary. Their purpose is to provide interoperability among the digital layers of the data
storage [1]. For a complete description of parametric virtualization of technocenosis
data, the OLAP-cube should be supplemented with additional secondary layers (Figure
7).
Fig. 7. Secondary layers of the third stage of data storage for the model of parametric
virtualization.
Then the mathematical formulae of the OLAP-cube will take the following form.
Parametric OLAP cube of technocenosis data on power consumption:
[ RAW ]kt [ DIF ]kt [ IPK ]kt [ AMC ]kt
k 1..n [VER ]kt [ PRO ]kt [ IPZ ]kt [ AMD]kt
WktOLAP
t 1..
; (4)
[ RAN ]kt [ NOR ]kt [ IPE ]kt [ BIF ]kt
[ APP ]kt [ LIM ]kt [ DFU ]kt [ MOD]kt
primary aggregators: secondary aggregators:
w :{[ RAW ],[VER ],[ RAN ]} [ APP ]; w :{[ APP ],[ DIF ],[ PRO ]} [ POT ];
w :{[VER ],[ RAN ],[ APP ]} [ DIF ]; w :{[ APP ],[ DIF ],[ POT ]} [ IPK ];
w :{[VER ],[ RAN ],[ APP ]} [ PRO ]; w :{[ APP ],[ DIF ],[ POT ]} [ IPZ ];
w :{[VER ],[ RAN ],[ APP ]} [ NOR ]; w :{[ APP ],[ IPK ],[ IPZ ]} [ IPE ];
w :{[VER ],[ DIF ],[ PRO ]} [ LIM ]; w :{[ APP ],[ DIF ],[ IPE ]} [ DFU ];
w :{[ APP ],[ DIF ],[ PRO ]} [ AMC ]; w :{[ APP ],[ DIF ],[ IPE ]} [ DAM ];
w :{[ APP ],[ DIF ],[ PRO ]} [ AMD ]; w :{[ APP ],[ IPE ],[ DAM ]} [ PLN ];
w :{[ APP ],[ DIF ],[ PRO ]} [ BIF ]; w :{[ DFU ],[ DAM ],[ PLN ]} [ MOD ],
WktOLAP – sequence of OLAP-cube of data.
The practical implementation of the mathematical model of virtualization of data
on the power consumption of the technocenosis was carried out in the software and
hardware complex (HSC) for monitoring the power consumption of the regional
transport network complex AO “Yantarenergo”.
4 Implementation of the model in the software and hardware
complex for monitoring power consumption of the regional
transport and network complex of AO “Yantarenergo”
The HSC database and storage were developed in the MS SQL Server 2019. Oper-
ating panels of automated workplaces are written in C # using the WPF platform. The
use of this software made it possible to implement OLAP analysis based on the
mathematical model of data virtualization on energy consumption of technocenosis.
HSC includes the main window, which contains an interactive map with objects of
OA “Yantarenergo”, AWP for data processing and verification, AWP for short-term
and long-term forecasting of power consumption, AWP for building a trend and typi-
cal graphs of electrical load. Figure 8 shows fragments of HSC elements [6].
The computational operations of AWP for data processing and verification are
based on the use of the system (2), and the values of the primary digital layer of the
OLAP data cube were used as the initial data (Figure 6). The work of the rest of the
AWP was carried out on the basis of (3) and (4), using secondary digital data layers
on the power consumption parameter of the OLAP-cube.
Implementation of the HSC at the facilities of OA “Yantarenergo” made it possible
to significantly increase the efficiency of power consumption management at facilities
by reducing routine operations for accounting and storing billing data on electricity
consumption, cleaning them from errors and replenishing lost data [6].
In addition, based on the analysis of secondary digital layers of OLAP-cube data,
the quality of fixing objects with abnormal power consumption, the accuracy of fore-
casting, fixing the range of normal power consumption based on the analysis of the
trend in the power consumption of objects and typical graphs of electrical load have
significantly improved.
Fig. 8. Fragments of elements of the software and hardware complex monitoring power
consumption of regional transport network complex of AO “Yantarenergo” [6].
The use of the HSC during the year, due to the implementation and updating of the
data of the digital layers of the OLAP cube, will account for:
1. Decrease in costs when paying fines for excessive deviations of electricity val-
ues in the wholesale market (approximately 1,800 thousand rubles per year).
2. Effective economic benefit due to the implementation of OLAP analysis tech-
nologies (approximately 1,200 thousand rubles per year).
5 Conclusion
The mathematical model for parametric virtualization of technocenosis data makes it
possible to form a subject-oriented information database on energy consumption: data
storage. One of its main functions is decision support when using digital services or
platforms. The digital data layer is the structural unit of the storage.
The theoretical basis of the model is the methodology of rank analysis of techno-
cenoses which involves the implementation of static, dynamic and bifurcation models of
optimal control of power consumption. Based on the results of these models, digital data
layers are formed, which are then combined into an OLAP cube of technocenosis data.
The developed model can be implemented as digital services and platforms, situ-
ational centers, artificial intelligence systems, etc. As shown by its practical imple-
mentation in AO “Yantarenergo”, the economic effect can reach approximately more
than 3000 thousand rubles per year.
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