Software and information simulation complex of multinode integrated and autonomous power and heat supply systems
. A software and information complex for modeling multi-node integrated and autonomous power and heat supply systems is proposed. The main difference of the proposed software and information complex is the possibility of a detailed consideration of the influence of economic and technological paramet...
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| Дата: | 2023 |
|---|---|
| Автори: | , |
| Формат: | Стаття |
| Мова: | Англійська |
| Опубліковано: |
General Energy Institute of the National Academy of Sciences of Ukraine
2023
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| Онлайн доступ: | https://systemre.org/index.php/journal/article/view/806 |
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Репозитарії
System Research in Energy| _version_ | 1871104317191094272 |
|---|---|
| author | Denysov, Viktor Babak, Vitalii |
| author_facet | Denysov, Viktor Babak, Vitalii |
| author_institution_txt_mv | [
{
"author": "Viktor Denysov",
"institution": null
},
{
"author": "Vitalii Babak",
"institution": null
}
] |
| author_sort | Denysov, Viktor |
| baseUrl_str | https://systemre.org/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-07-18T12:57:47Z |
| description | . A software and information complex for modeling multi-node integrated and autonomous power and heat supply systems is proposed. The main difference of the proposed software and information complex is the possibility of a detailed consideration of the influence of economic and technological parameters contained in the power system of individual power units and nodes. These parameters can be presented both in the form of matrices on the sheets of the software and information complex, and in the form of separate attached files available for automated input by the software and information complex. The main advantages of the complex, which distinguish it from the known ones, include versatility, which makes it possible to study various models of energy systems in a short time. This versatility is ensured by the fact that the complex is developed using a combination of standard Microsoft Excel software and SolverStudio – an add-in for Excel 2007 and later versions on Windows, which allows you to explore a variety of optimization models using a large list of optimization modeling languages. With the SolverStudio add-in in the information package, the user can develop, edit, save, and debug an optimization model in an Excel workbook. The connection of source data, sets, parameters, constants and variables used in the model is conveniently organized. After editing the parameters and source data, the model is launched. Simulation results can be displayed both on model sheets and displayed as separate files. Another advantage of the software and information complex is the ability to conveniently compare many models, due to the fact that each of the worksheets can have its own model. The developed software and information complex makes it possible to calculate in detail the energy, technological and economic indicators of the optimal use of power system components, to determine the permissible limits of the operating parameters of power units of autonomous and integrated power systems. The results of these calculations make it possible to select appropriate measures for the future renewal of technologies for the production of electric and thermal energy. The ease of use and editing of both individual parameters and program texts used in modeling the development of power systems improves the quality of the resulting development scenarios. The proposed software and information complex can be used to study the prospects for short-term and long-term development of Ukraine, as well as the energy system integrated with the power systems of neighboring ENTSO-E member countries, which is relevant in these conditions. |
| doi_str_mv | 10.15407/srenergy2023.03.050 |
| first_indexed | 2026-03-24T02:03:08Z |
| format | Article |
| fulltext |
Системні дослідження в енергетиці. 2023. 3(74) 50
ISSN 2786-7102 (Online), ISSN 2786-7633 (Print)
https://doi.org/10.15407/srenergy2023.03.050
UDC 621.3:519.8
Viktor Denysov*, https://orcid.org/0000-0002-3297-1114
Vitalii Babak, Dr. Sci. (Engin.), Professor, https://orcid.org/0000-0002-9066-4307
General Energy Institute of NAS of Ukraine, 172, Antonovycha St., Kyiv, 03150, Ukraine
*Corresponding author: visedp@gmail.com
_______________________________________________________________________________________
SOFTWARE AND INFORMATION SIMULATION COMPLEX OF MULTI-
NODE INTEGRATED AND AUTONOMOUS POWER AND HEAT SUPPLY
SYSTEMS
Abstract. A software and information complex for modeling multi-node integrated and autonomous
power and heat supply systems is proposed. The main difference of the proposed software and
information complex is the possibility of a detailed consideration of the influence of economic and
technological parameters contained in the power system of individual power units and nodes. These
parameters can be presented both in the form of matrices on the sheets of the software and information
complex, and in the form of separate attached files available for automated input by the software and
information complex. The main advantages of the complex, which distinguish it from the known ones,
include versatility, which makes it possible to study various models of energy systems in a short time. This
versatility is ensured by the fact that the complex is developed using a combination of standard Microsoft
Excel software and SolverStudio – an add-in for Excel 2007 and later versions on Windows, which allows
you to explore a variety of optimization models using a large list of optimization modeling languages.
With the SolverStudio add-in in the information package, the user can develop, edit, save, and debug an
optimization model in an Excel workbook. The connection of source data, sets, parameters, constants and
variables used in the model is conveniently organized. After editing the parameters and source data, the
model is launched. Simulation results can be displayed both on model sheets and displayed as separate
files. Another advantage of the software and information complex is the ability to conveniently compare
many models, due to the fact that each of the worksheets can have its own model. The developed software
and information complex makes it possible to calculate in detail the energy, technological and economic
indicators of the optimal use of power system components, to determine the permissible limits of the
operating parameters of power units of autonomous and integrated power systems. The results of these
calculations make it possible to select appropriate measures for the future renewal of technologies for the
production of electric and thermal energy. The ease of use and editing of both individual parameters and
program texts used in modeling the development of power systems improves the quality of the resulting
development scenarios. The proposed software and information complex can be used to study the
prospects for short-term and long-term development of Ukraine, as well as the energy system integrated
with the power systems of neighboring ENTSO-E member countries, which is relevant in these conditions.
Keywords: software, information, complex, multi-node, integrated, autonomous, power, systems.
1. Introduction
The field of digital and industrial technologies progress [1] has a significant impact on all sectors of
the economy, including energy. They lead to a change in approaches to assessing and modeling sustainable
development and renewal paths. The grave challenges of recent years have shown the need to increase the
stability and flexibility of both technological development and the improvement of methods for forecasting
and modeling the modes of operation and renewal of energy systems. Given the global digitalization trend, it
is necessary to support the modeling of technological innovations in key sectors of industrial ecosystems
while maintaining their ability to develop safely and sustainably. European Commission, in its document
"Updating the New Industrial Strategy 2020: Building a Stronger Single Market for Europe's Recovery" [2],
supported the European Green Deal and the Action Plan on the Circular Economy, as well as the digital
strategies "Shaping Europe's Digital Future", "Data", "White Paper on Artificial Intelligence", "Digital
Decade of Communication".
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Energy systems have complex structures and contain a large number of elements. This leads to
difficulties in modeling, researching and managing the modes of their components. To overcome these
difficulties, complex multifunctional software is required, with the help of which the tasks of assessing the
current state and predicting the optimal operating modes of power units should be solved. To solve the
described problems, the authors [3] proposed a multi-agent approach.
The correct approach to determining the energy efficiency of complex energy systems is quite
problematic and requires the use of specialized methods [4]. Energy efficiency can be understood as the most
complete compliance of the technical and economic essence of the system with the requirements of the
consumer. The organization of the system of energy efficiency indicators can be carried out both on the
principle of "bottom up" and on the principle of "top down" with gradual aggregation or de aggregation of
macroeconomic indicators determined at each level of the system.
In [5] an economic and mathematical model of the production type is proposed. The use of the model
makes it possible to predict a balanced supply of carbon-containing fuels, electricity and heat from fossil
fuels and renewable energy sources. The model is built on the basis of open data sources and, unlike known
ones, uses subsystems for the production of abstract products. Selective aggregation procedures are also used
to construct various forms of interproduct balance matrices.
Integrated power systems with multiple energy carriers [6] provide high efficiency of power supply to
consumers. The possibility of transforming energy systems under the influence of internal and external
factors contributes to the expansion of their technological integration areas and the wider use of integrated
multi-carrier energy systems. To simulate such systems, the authors [6] proposed a concept and developed a
simulation model of the power unit. The implementation of this simulation model is made using
Matlab/Simulink. An example of the application of the simulation model is given. The simulation results
demonstrate the possibilities and prospects of using the simulation model of the power unit.
The task of energy management ILES [7], taking into account the energy balance and technological
limitations for the operation of each power unit, is aimed at realizing the system economic efficiency.
Mathematical model for solving the problem of energy management given. The use of it makes possible to
optimize operating costs.
The paper [8] emphasizes that the relevance of modeling energy systems is becoming increasingly
important in the process of planning the implementation of the "energy transition". At the same time, it is
necessary to take into account both the issues of cogeneration and the problems of regional and temporal
coordination. While in many modern reviews, models are grouped by type or energy carrier, the authors offer
a classification according to the quality of expert assessments. The advantages of a dynamic model of
cogeneration of heat and electricity are analyzed, in combination with traditional and advanced control
schemes.
In [9], the authors presented a recently developed model called MESS – Multi Energy System
Simulator and their assessment of the advantages and disadvantages compared to the optimization model
(Calliope [10]). They believe that using their simplified method for modeling energy systems at urban scales
gives satisfactory results. The advantage is that the simplified approach allows many alternative scenarios to
be explored in a relatively short period of time. At the same time, the authors acknowledge that with an
hourly modeling step, the use of an optimization approach seems more appropriate.
Paper [11] shows that existing models are inadequate to address grids with a high percentage of
renewables, and there is a challenge in integrating short-term temporal changes due to model complexity and
computational cost.
Given the rapid growth in renewable energy production, the authors of [12] devoted their review to
two types of energy storage (ES), taking into account the need for their widespread use to ensure the
flexibility and resilience of power grids. They distinguish between storage devices that use direct and reverse
energy conversion (e.g., heat and electricity) (GIES) to store energy, and those where this conversion is not
used. From their point of view, long-term models of electric power systems (LEPSM) are limited to
consideration of power systems with one type of ES without taking into account the GIES. The authors
believe that:
Системні дослідження в енергетиці. 2023. 3(74) 52
− existing models are inadequate for networks with a high percentage of renewables and ES;
− there is a problem of integrating short-term time changes into LEPSM due to model complexity and
computational cost.
The key feature of the new structure proposed by the authors is taking into account "agent-based
modeling" of consumer behavior, thereby reducing the number of scenarios for the functioning of
renewables.
All the above-mentioned works confirm the conclusion about the relevance of updating existing and
developing new software and information complexes for modeling modern complex energy systems.
2. Software and information complex
The software and information complex (SIC) proposed in this paper is aimed at overcoming most of
these difficulties. The main advantages of the complex, which distinguish it from the known ones, include
versatility, which allows you to explore various models of energy systems in a short time. This versatility is
ensured by the fact that the complex is developed using a combination of standard Microsoft Excel software
and the SolverStudio add-in – an add-in for Excel 2007 and later versions on Windows, which allows you to
explore a variety of optimization models using a large list of optimization modeling languages:
− PuLP – open-source COIN-OR modeling language based on Python;
− COOPR/Pyomo, which extends Pulp with abstract models, support for stochastic programming and a
wider range of solvers;
− AMPL – modeling language that supports running models in the cloud;
− GMPL (GNU MathProg Language) – open-source analogue of AMPL, developed as part of the
GLPK (GNU Linear Programming Kit). GMPL is part of SolverStudio;
− GAMS, – commercial modeling language;
− Gurobi, – commercial solver available in SolverStudio;
− CMPL, – an open-source COIN-OR modeling language.
− SimPy, – open-source Python modeling language that is included in the SolverStudio download.
Thanks to the presence of the SolverStudio add-in in the software and information package, the user
can develop, edit, save and debug the optimization model in an Excel workbook. The connection of source
data, sets, parameters, constants and variables used in the model is conveniently organized. It is possible to
edit the parameters and source data, and then run the model. Simulation results can be displayed on model
worksheets or output as separate files. Another advantage of the software and information complex is the
ability to conveniently compare many models, due to the fact that each of the worksheets can have its own
model.
The complex (Fig. 1) includes interacting blocks: service, control, information and analytical.
The service unit is designed to improve the convenience of working with SIC and user service. It
includes internal interface and user interface programs, control and dialog forms, provides opportunities for:
generating statistical reports on specified features, visualization of intermediate data, parameters of the
current state, results of calculations on request in a form convenient for the developer and user.
The main functions of the control unit are to coordinate the processes of entering initial data,
optimizing and monitoring the results obtained.
The information block is designed for collecting, storing and primary processing of data and includes a
set of databases (DB) that provide permanent storage and replenishment of data necessary for the functioning
of the SIC, supports standard data management functions:
− input and editing;
− organization of storage;
− connection (download) and disconnection (unloading) of databases necessary for modeling at the
current stage of work.
The information block, in addition to storing elements of the working database, allows you to perform
the functions of selecting modes and variable parameters of the optimization process.
Системні дослідження в енергетиці. 2023. 3(74) 53
Fig. 1. The information and software complex's structure
The analytical block is designed to solve the problems of intelligent data processing and includes
predictive and expert subsystems, as well as an optimization subsystem. The predictive subsystem allows
estimating the consequences of different input effects on the hierarchical controlled energy system based on
the scenario method. The optimization subsystem allows you to solve the problem of choosing the best
control options under given restrictions.
2.1 Service unit
An example of the SIC title page with the dialog form of the service block is shown in Fig. 2. The
dialog form of the service block (Fig. 3) allows you to select or modify possible the modeling process
configuration.
Fig. 2. SIC title page with the dialog form of the service block
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Fig. 3. The dialog form of the service block
You can edit the model text and observe the optimization process using the panel (Fig. 4).
Fig. 4. Model text editing and optimization process observation panel
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The parameters of the model processing program, which are specified with GMPL (GNU MathProg
Language), if they need editing, can be changed using the form of Fig. 5. Or these parameters can be left
unchanged if the user wants to leave the default values. For example, increasing the mipgap parameter,
which determines how accurately a solution is found, can reduce the search time for an integer solution.
Fig. 5. Form for selecting the model processing program parameters
The parameters that are most often subject to change, for example, the modeling date, scaling factors
and others, for ease of editing are placed in a separate form of parameters (Fig. 6).
Date K_PV К_WIND MAX PV MAX WIND
February 28, 2040 16.393 19.444 11000 7000
Fig. 6. Parameter editing form
In the form of parameters (Fig. 6):
– K_PV – PV generating capacity scaling factor for modeling forecast data;
– K_WIND – WPP generating capacity scaling factor for modeling forecast data;
– MAX PV – limitation of the maximum possible generating capacity of PV, (MW);
– MAX WIND – limitation of the maximum possible generating capacity WPP, (MW).
The service unit functions allow the developer to make changes to the optimization algorithms by
changing the values and binary variables in the appropriate forms (Fig. 7). So, in Fig. 7a provides examples
of import-export parameter management forms. Also on Fig. 7 presents the parameters for controlling the
participation of power units of PSPs, HPPs, storage facilities and TPPs for redundancy and redundancy
management using total generating and storage capacities.
In the form of parameters (Fig. 7, a):
– MaxImpPower – the maximum possible import capacity limited by the existing or projected capacity
of power lines (MW);
– MaxExpPower – the maximum possible export capacity limited by the existing or projected capacity
of power lines (MW);
– MinImpPower – the minimum possible import capacity, which is set by technological constraints,
(MW);
– MinExpPower – the minimum possible export capacity, which is set by technological constraints,
(MW);
– ImportPrice – unit cost of imported electricity, taking into account the market situation, ($ / MWh);
– ExportPrice – unit cost of exported electricity, taking into account the market situation, ($ / MWh);
– DailyBalanceExpImp – binary variable that determines whether the simulation will fulfill the
condition: 1 – zero balance of flows, 0 – do not comply with the condition of zero balance of flows.
In the form of parameters (Fig. 7, b):
– EachHydroPump – binary variable that determines whether the simulation will fulfill the condition:
1 – each hydraulic unit of the PSP must be started once, 0 – the condition is not met;
Системні дослідження в енергетиці. 2023. 3(74) 56
– PumpGenBalance – binary variable that determines whether the simulation will fulfill the condition:
1 – each hydraulic unit of the PSP must use all the accumulated water for the current day, 0 – the condition
can be ignored;
– MaxSumPumpPower – the maximum allowable level of power consumption for injection by all
PSPs, (MW);
– MaxSumGenPower – the maximum allowable level of power generation by all PSPs, (MW);
– PumpGenOperate – binary variable that determines whether the simulation will fulfill the condition:
1 – cannot be simultaneously pumped and generated, if in a certain period of time the hydraulic unit pumps
then all niches of the hydraulic aggregate cannot generate, 0 – it is possible;
– On_Off_HPPS_Reserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use PSP capacities in common redundancy, 0 – do not use;
– Include_HPPS_UpReserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use PSP capacities in reserve for load, 0 – do not use;
– Include_HPPS_DownReserve – binary variable that determines whether the simulation will fulfill
the condition: 1 – use PSP capacities in redundancy for unloading, 0 – do not use;
– Up_HPPS_ReserveLevel – the maximum possible capacity of participation of PSPs in reserve for
load, (MW);
– Down_HPPS_ReserveLevel – the maximum possible capacity of participation of PSPs in reservation
for unloading, (MW).
a) b)
c) d)
e) f)
Fig. 7. Forms of the import-export parameters management. And also, the parameters for controlling the participation of
power units of WPP, HPPS, storage facilities and TPPs in redundancy
а) import-export; b) HPPS; c) WPP; d) BUT;
e) TPP; f) total reservation
Системні дослідження в енергетиці. 2023. 3(74) 57
In the form of parameters (Fig. 7, c):
– On_Off_WPP_Reserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use HPP capacities in common redundancy, 0 – do not use;
– Include_WPP_UpReserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use HPP capacity in reserve for load, 0 – do not use;
– Include_WPP_DownReserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use HPP capacity in redundancy for unloading, 0 – do not use;
– Up_WPP_ReserveLevel – the maximum possible capacity of participation of hydroelectric power
plants in the reservation for loading, (MW);
– Down_WPP_ReserveLevel – the maximum possible capacity of participation of hydroelectric power
plants in reservation for unloading, (MW).
In the form of parameters (Fig. 7, d):
– On_Off_BUT_Reserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use the capacity of the drives in the general redundancy, 0 – do not use;
– BatteryBalance – binary variable that determines whether the simulation will fulfill the condition: 1
– battery charge must be greater than the discharge in the previous hours, 0 – battery charge must be greater
than zero;
– BatChargePrice – unit cost of battery charge, ($/MWh);
– BatDisChargePrice – unit cost of battery discharge, ($/MWh);
– DailyMaxStorage – the maximum possible amount of charge-discharge of drives, (MWh per day);
– Max_CHARGE_SumStoragePower – the maximum possible total power consumed when charging
the drives (MW);
– Max_DISCHARGE_SumStoragePower – the maximum possible total power given by all drives to
the system (MW).
In the form of parameters (Fig.7, e):
– On_Off_TPP_Reserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use TPP capacities in general redundancy, 0 – do not use;
– Include_TPP_UpReserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use TPP capacities when reserving for load, 0 – do not use;
– Include_TPP_DownReserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use the capacity of TPPs when reserving for unloading, 0 – do not use;
– RampUp_TPP_ReserveLevel – minimum permissible capacity of TPP participation in load
redundancy, (MW);
– RampDown_TPP_ReserveLevel – minimum permissible capacity of TPP participation in reservation
for unloading, (MW).
In the form of parameters (Fig. 7, f):
– On_Off_ALL_Reserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use the power system capacity in redundancy, 0 – do not use;
– Include_ALL_UpReserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use the power of the system when redundant for the load, 0 – do not use;
– Include_ALL_DownReserve – binary variable that determines whether the simulation will fulfill the
condition: 1 – use the capacity of the system when redundant for unloading, 0 – do not use;
– RampUp_ALL_ReserveLevel – minimum permissible capacity of general reserve for load, (MW);
– RampDown_ALL_ReserveLevel – minimum permissible capacity of the total reservation for
unloading, (MW).
Ways of displaying initial parameters and calculation results can be varied by editing the model text
(Fig. 4). For the convenience of visualization of input data and the results obtained, respectively, the
software blocks of visualization and output of results are responsible.
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2.2 Information block
As mentioned above, the information block is designed for collecting, storing and primary processing
of data and includes a set of databases (DB) that provide permanent storage and replenishment of data
necessary for the functioning of the SIC. It supports standard data management functions: their input and
editing; organization of storage; connection (download) and disconnection (unloading) of databases
necessary for modeling at the current stage of work. Depending on the problem being solved, the type of
power system to be modeled (electrical, thermal, autonomous, integrated), the necessary databases from the
database set are connected or disconnected (Fig. 8).
Fig. 8. Element of the working DB, actual capacities (MW) of Poland, Hungary, Romania power systems
In Fig. 8 in the columns are displayed:
– TIME_ALL – hour number since the beginning of the year;
– DATE_ALL – date and hour.
The following columns reflect the actual hourly values [13]:
– DEMAND_ALL_PL – actual hourly load schedule of the EC in Poland;
– DEMAND_ALL_HUN – actual hourly load schedule of the Hungarian EC;
– DEMAND_ALL_ROM – actual hourly load schedule of the EC in Romania;
– PV_PL – actual hourly schedule of capacity of PV in Poland;
– WIND_PL – actual hourly capacity schedule of wind farms in Poland;
– PV_HUN – actual hourly capacity schedule of Romanian PV.
The information block, in addition to storing elements of the working database, contains matrices of
economic, energy and climatic parameters of modeled power systems (Fig. 9–11). Changing the values of
the elements of the matrices of economic, energy and climatic parameters allows you to perform the
functions of selecting modes and parameters of the optimization process.
Fig. 9. Matrix of economic and energy parameters of TPPs of Ukraine
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In the columns of the economic and energy parameters matrix of TPPs of Ukraine Fig. 9:
– TPP – list of types of power units of TPPs of Ukraine;
– pTPP Units – number of power units of each type;
– pTPP InstCap – installed capacity of the unit, (MW);
– pTPP UnitMax – maximum possible power that the power unit gives to the system (MW);
– pTPP UnitMin – minimum possible power that the power unit gives to the system (MW);
– pTPP Unit Ramp – accepted for calculations permissible change in the capacity of a TPP power unit
when it is operating (MW);
– pTPP Unit Ramp Max – the maximum possible change in the capacity of a TPP power unit when it
is operating (MW);
– pTPP ProdCost – the cost of supplying electricity to the grid by a TPP unit, ($/MWh);
– pTPP NumStart – the number of starts of TPP power units during the day;
– pTPP MinUnits – minimum number of TPP power units involved in covering the load schedule;
– pTPP MaxUnits – the maximum number of TPP power units that can participate in covering the load
schedule;
– pTPP Time Operate – the permissible number of hours of operation of a TPP power unit during a
day.
Fig. 10. Matrix of economic and energy parameters of NPPs of Ukraine
In the columns of the economic and energy parameters matrix of NPPs of Ukraine Fig. 10:
– NP – list of types of power units of Ukrainian NPPs;
– pNPP InstCap – installed capacity of the unit, (MW);
– pNPPT – number of hours of continuous operation of NPP units;
– pNPP ProdCost – the cost of supplying electricity to the grid by a nuclear power unit, ($ / MWh);
– pNPP NumStart – number of NPP power units starts during the day;
– pNPP MinUnits – minimum number of NPP power units involved in covering the load schedule;
– pNPP MaxUnits – the maximum number of NPP power units allowed to participate in covering the
load schedule.
Fig. 11. Matrix of economic and energy parameters of HPSs of Ukraine
In the columns of the economic and energy parameters matrix of HPs of Ukraine Fig. 11:
– HP_number – serial number of the hydraulic unit of the HPS of Ukraine;
– HP – list of types of hydraulic units of HPSs of Ukraine;
– pP – power consumed by the HPS hydraulic unit in pumping mode;
– pG – power of the HPS hydraulic unit in generation mode;
– pHPGV – the cost of operation of the HPS hydrogenerator in the generator mode;
– pHPPV – the cost of operation of the HPS hydrogenerator in pumping mode;
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– pHPT – number of hours of continuous operation of HPS hydraulic units in pumping and generating
mode;
– pHPS – permissible number of starts of HPS hydraulic units in pumping and generating mode;
– pHPVol – maximum amount of energy that can be accumulated by the HPS hydraulic unit in
pumping mode, (MWh).
2.3 Analytical block
Due to the joint use and interaction of the service unit, control unit and information unit, the user
prepares a variety of information for the operation of the analytical unit. The analytical unit uses an
optimizer built into the Solver Studio platform [14]. The optimization subsystem allows solving the problem
of choosing the best options for controlling the power system under given constraints. After all the initial
information is prepared, the user presses the button "Finish data input and editing" (Fig. 13) and the control
unit forms a working data file and starts the optimization process. If the data is prepared correctly, the
optimizer finds an analog solution at the first stage, and then, taking into account the integrity and binarity of
individual variables, the optimal solution. The results of calculations are displayed by blocks of visualization
and output of results on worksheets (Fig. 13–15) of the software and information complex. Otherwise, the
optimizer displays a message stating that the solution does not exist (Fig. 12).
Fig. 12. Optimizer notification that a solution does not exist
Fig. 13. Worksheet with the results of calculating the optimal parameters of the IPS of Ukraine
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Fig. 14. Optimal values of cross-border flows during synchronous operation of power systems of Ukraine, Poland and
Hungary
Fig. 15. Optimal values of battery operation
3. Results and discussion
The developed SIC [15]–[19] allows you to calculate in detail the energy, technological and economic
indicators of the optimal use of components of energy systems, to determine the permissible limits of the
parameters of operation of power units of autonomous and integrated power systems. Based on the results of
these calculations, appropriate measures for the prospective renewal of technologies for the production of
electricity and heat could select.
The possibility of incorporating software implementations of diverse models of the power systems' life
cycle into the system allows for the collection of comparative characteristics of the most efficient technical,
financial, and environmental parameters of technologies for all permitted modes of their operation in power
Системні дослідження в енергетиці. 2023. 3(74) 62
systems. It also allows for comparisons of how efficient technologies are in operation and how they need to
be updated, which is important for building new power systems' development scenarios.
4. Conclusions
A software and information complex for modeling multi-node integrated and autonomous systems of
electricity and heat supply is proposed. The main difference of the proposed software and information
complex is the possibility of detailed consideration of the influence of economic and technological
parameters contained in the power system of individual power units and nodes. These parameters can be
presented both in the form of matrices on software and information complex worksheets, and in the form of
separate attached files available for automated input by the software and information complex.
The ease of use and editing of both individual parameters and program texts used in modeling the
development of power systems improves the quality of the resulting development scenarios. The proposed
software and information complex can be used to study the prospects for short-term and long-term
development of the energy system of Ukraine, as well as the energy system integrated with the power
systems of neighboring ENTSO-E member countries, which is relevant in these conditions.
References
1. Geidl, M., Koeppel, G., Favre-Perrod, P., Klockl, B., Andersson, G., & Frohlich, K. (2007). The energy hub – a
powerful concept for future power systems. Third Annual Carnegie Mellon Conference on the Electricity Industry,
13, 14.
2. Updating the 2020 New Industrial Strategy: Building a stronger Single Market for Europe’s recovery. SWD(2021)
351 final – SWD(2021) 352 final – SWD(2021) 353 final.
3. Voropai, N.I., Kolosok, I.N., Massel, L.V., Fartyshev, D.A., Paltsev, A.S., & Panasetsky, D.A. (2011). A Multi-
Agent Approach to Electric Power Systems. Multi-Agent Systems – Modeling, Interactions, Simulations and Case
Studies, Dr. Faisal Alkhateeb (Ed.), 369–394. ISBN 978-953-307-176-3. URL:
http://www.intechopen.com/books/multi-agent-systems-modeling-interactions-simulations-andcase-studies/a-
multi-agent-approach-to-electric-power-systems (Last accessed: 07.08.2023).
4. Eutukhova, T., Iliev, I., Novoseltsev, O., Nikitin, Y., & Shcherbyna, Y. (2023). Methodological platform for
determining energy efficiency indicators of a complex system. System Research in Energy, 2(73), 38–47.
https://doi.org/10.15407/srenergy2023.02.038
5. Kaplin, M., & Bilan, T. (2023). Aggregated energy supply model according to product energy balance data
structure. System Research in Energy, 2(73), 48–61. https://doi.org/10.15407/srenergy2023.02.048
6. Shulzhenko, S. (2023). Generation unit commitment mixed integer linear model for simultaneous heat and electric
daily load covering. System Research in Energy, 1(72), 25–34. https://doi.org/10.15407/srenergy2023.01.025
7. Voropai, N., Sеrdyukova, E., Gerasimov, D., & Suslov, K. (2022). Simulation modeling of integrated multi-carrier
energy systems. https://doi.org/10.5772/intechopen.99323
8. Huang, B., Wang, Y., Yang, Ch., Li, Y., & Sun, Q. (2021). A neurodynamic-based distributed energy management
approach for integrated local energy systems. International Journal of Electrical Power & Energy Systems, 128,
106737. https://doi.org/10.1016/j.ijepes.2020.106737
9. Fodstad, M., Crespo del Granado, P., Hellemo, L., Knudsen, B. R., Pisciella, P., Silvast, A., Bordin, Ch.,
Schmidt, S., & Straus, J. (2022). Next frontiers in energy system modelling: A review on challenges and the state
of the art. Renewable and Sustainable Energy Reviews, 160, 112246. https://doi.org/10.1016/j.rser.2022.112246
10. Diangelakis, N. A., & Pistikopoulos, E. N. (2017). A multi-scale energy systems engineering approach to
residential combined heat and power systems. Computers & Chemical Engineering, 102, 128–138.
https://doi.org/10.1016/j.compchemeng.2016.10.015
11. Pfenninger, S., & Pickering, B. (2018). Calliope: a multi-scale energy systems modelling framework. Journal of
Open Source Software, 3(29), 825. https://doi.org/10.21105/joss.00825
12. Bottecchia, L., Lubello, P., Zambelli, P., Carcasci, C., & Kranzl, L. (2021). The Potential of Simulating Energy
Systems: The multi energy systems simulator model. Energies, 14, 5724. https://doi.org/10.3390/en14185724
13. Lai, Ch. S., Locatelli, G., Pimm, A., Wu, X., & Lai, L. L. (2021). A review on long-term electrical power system
modeling with energy storage. Journal of Cleaner Production, 280, 1, 124298.
https://doi.org/10.1016/j.jclepro.2020.124298
14. ENTSO-E Generation Forecasts for Wind and Solar. URL:
https://transparency.entsoe.eu/generation/r2/dayAheadGenerationForecastWindAndSolar/show/ (Last accessed:
04.02.2022).
15. SolverStudio. URL: https://solverstudio.org/download-install/ (Last accessed: 17.09.2020).
16. Denysov, V., Zaporozhets, A., Nechaieva, T., Shulzhenko, S., & Derii, V. (2023). Improving the model of long-
term technological update of power system components. System Research in Energy, 2(73), 30–37.
https://doi.org/10.15407/srenergy2023.02.030
https://doi.org/10.15407/srenergy2023.02.048
Системні дослідження в енергетиці. 2023. 3(74) 63
17. Denysov, V.A. (2022). Evaluation of the parameters of optimized coating of load graphs in the modes of
synchronous operation of the power systems of Ukraine and Poland for characteristic days on the example of the
actual values of the established power and load graphs. The Problems of General Energy, 1-2(68-69), 50–57 [in
Ukrainian]. https://doi.org/10.15407/pge2022.01-02.050
18. Denisov, V. (2022, October 12–14). Integrated Power System multi-node model, taking into account the
nondispatchable of renewable energy sources. 2022 IEEE 8th international conference on energy smart systems
IEEE ESS-2022, 175–179. URL: https://crust.ust.edu.ua/server/api/core/bitstreams/942ceb1e-3eb7-443a-b843-
bb3e03993b4e/content (Last accessed: 07.08.2023).
19. Denysov, V. (2022). Software and information complex for district heat supply systems modeling. System
Research in Energy, 1(70), 38–45 [in Ukrainian]. https://doi.org/10.15407/srenergy2022.01.038
ПРОГРАМНО-ІНФОРМАЦІЙНИЙ КОМПЛЕКС
МОДЕЛЮВАННЯ БАГАТОВУЗЛОВИХ ІНТЕГРОВАНИХ І
АВТОНОМНИХ СИСТЕМ ЕЛЕКТРО- І ТЕПЛОПОСТАЧАННЯ
Віктор Денисов*, https://orcid.org/0000-0002-3297-1114
Віталій Бабак, д-р техн. наук, професор, https://orcid.org/0000-0002-9066-4307
Інститут загальної енергетики НАН України, вул. Антоновича, 172, м. Київ, 03150,
Україна
*Автор-кореспондент: visedp@gmail.com
Анотація. Запропоновано програмно-інформаційний комплекс для моделювання багатовузлових
інтегрованих та автономних систем електро- і теплопостачання. Основною відмінністю
запропонованого програмно-інформаційного комплексу є можливість детального розгляду впливу
економічних і технологічних параметрів окремих енергоблоків і вузлів енергосистеми. Ці
параметри можуть бути представлені як у вигляді матриць на аркушах програмно-
інформаційного комплексу, так і у вигляді окремих прикріплених файлів, доступних для
автоматизованого введення програмно-інформаційним комплексом. До основних переваг
комплексу, які відрізняють його від відомих, можна віднести універсальність, що дає можливість
вивчати різні моделі енергетичних систем в короткі терміни. Така універсальність
забезпечується тим, що комплекс розроблений з використанням комбінації стандартного
програмного забезпечення Microsoft Excel і SolverStudio – надбудови для Excel 2007 і пізніших
версій. Це дозволяє досліджувати різноманітні моделі з використанням великого переліку
оптимізаційних мов моделювання. За допомогою надбудови SolverStudio в інформаційному
комплексі користувач може розробляти, редагувати, зберігати та налагоджувати модель
оптимізації у книзі Excel. Зручно організувати зв’язок вихідних даних, множин, параметрів,
констант і змінних, використовуваних в моделі. Після редагування параметрів і вихідних даних
запускається модель. Результати моделювання можуть відображатися як на аркушах
програмно-інформаційного комплексу, так і у вигляді окремих файлів. Ще однією перевагою
програмно-інформаційного комплексу є можливість зручного порівняння багатьох моделей
завдяки тому, що кожен з робочих аркушів може мати свою модель. Розроблений програмно-
інформаційний комплекс дає можливість детально розрахувати енергетичні, технологічні та
економічні показники оптимального використання компонентів енергосистеми, визначити
допустимі межі робочих параметрів енергоблоків автономних та об’єднаних енергосистем.
Результати цих розрахунків дають можливість обрати відповідні заходи щодо майбутнього
оновлення технологій виробництва електричної та теплової енергії. Простота використання і
редагування як окремих параметрів, так і програмних текстів, використовуваних при
моделюванні розвитку енергосистем, підвищує якість одержуваних сценаріїв розвитку.
Запропонований програмно-інформаційний комплекс може бути використаний для дослідження
перспектив короткострокового і довгострокового розвитку енергосистеми України, а також
інтегрованої з енергосистемами сусідніх країн-членів ENTSO-E, що є актуальним у сучасних
умовах.
Ключові слова: програмно-інформаційний, комплекс, багатовузлові, інтегровані, автономні,
енергосистеми.
Надійшла до редколегії: 19.07.2023
|
| id | systemreorg-article-806 |
| institution | System Research in Energy |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-19T01:22:10Z |
| publishDate | 2023 |
| publisher | General Energy Institute of the National Academy of Sciences of Ukraine |
| record_format | ojs |
| resource_txt_mv | systemreorg/5d/4034588129ecbd980fa71bd87de6c25d.pdf |
| spelling | systemreorg-article-8062026-07-18T12:57:47Z Software and information simulation complex of multinode integrated and autonomous power and heat supply systems Програмно-інформаційний комплекс моделювання багатовузлових інтегрованих і автономних систем електро- і теплопостачання Denysov, Viktor Babak, Vitalii software, information, complex, multi-node, integrated, autonomous, power, systems. програмно-інформаційний, комплекс, багатовузлові, інтегровані, автономні, енергосистеми. . A software and information complex for modeling multi-node integrated and autonomous power and heat supply systems is proposed. The main difference of the proposed software and information complex is the possibility of a detailed consideration of the influence of economic and technological parameters contained in the power system of individual power units and nodes. These parameters can be presented both in the form of matrices on the sheets of the software and information complex, and in the form of separate attached files available for automated input by the software and information complex. The main advantages of the complex, which distinguish it from the known ones, include versatility, which makes it possible to study various models of energy systems in a short time. This versatility is ensured by the fact that the complex is developed using a combination of standard Microsoft Excel software and SolverStudio – an add-in for Excel 2007 and later versions on Windows, which allows you to explore a variety of optimization models using a large list of optimization modeling languages. With the SolverStudio add-in in the information package, the user can develop, edit, save, and debug an optimization model in an Excel workbook. The connection of source data, sets, parameters, constants and variables used in the model is conveniently organized. After editing the parameters and source data, the model is launched. Simulation results can be displayed both on model sheets and displayed as separate files. Another advantage of the software and information complex is the ability to conveniently compare many models, due to the fact that each of the worksheets can have its own model. The developed software and information complex makes it possible to calculate in detail the energy, technological and economic indicators of the optimal use of power system components, to determine the permissible limits of the operating parameters of power units of autonomous and integrated power systems. The results of these calculations make it possible to select appropriate measures for the future renewal of technologies for the production of electric and thermal energy. The ease of use and editing of both individual parameters and program texts used in modeling the development of power systems improves the quality of the resulting development scenarios. The proposed software and information complex can be used to study the prospects for short-term and long-term development of Ukraine, as well as the energy system integrated with the power systems of neighboring ENTSO-E member countries, which is relevant in these conditions. Запропоновано програмно-інформаційний комплекс для моделювання багатовузлових інтегрованих та автономних систем електро- і теплопостачання. Основною відмінністю запропонованого програмно-інформаційного комплексу є можливість детального розгляду впливу економічних і технологічних параметрів окремих енергоблоків і вузлів енергосистеми. Ці параметри можуть бути представлені як у вигляді матриць на аркушах програмноінформаційного комплексу, так і у вигляді окремих прикріплених файлів, доступних для автоматизованого введення програмно інформаційним комплексом. До основних переваг комплексу, які відрізняють його від відомих, можна віднести універсальність, що дає можливість вивчати різні моделі енергетичних систем в короткі терміни. Така універсальність забезпечується тим, що комплекс розроблений з використанням комбінації стандартного програмного забезпечення Microsoft Excel і SolverStudio – надбудови для Excel 2007 і пізніших версій. Це дозволяє досліджувати різноманітні моделі з використанням великого переліку оптимізаційних мов моделювання. За допомогою надбудови SolverStudio в інформаційному комплексі користувач може розробляти, редагувати, зберігати та налагоджувати модель оптимізації у книзі Excel. Зручно організувати зв’язок вихідних даних, множин, параметрів, констант і змінних, використовуваних в моделі. Після редагування параметрів і вихідних даних запускається модель. Результати моделювання можуть відображатися як на аркушах програмно-інформаційного комплексу, так і у вигляді окремих файлів. Ще однією перевагою програмно-інформаційного комплексу є можливість зручного порівняння багатьох моделей завдяки тому, що кожен з робочих аркушів може мати свою модель. Розроблений програмноінформаційний комплекс дає можливість детально розрахувати енергетичні, технологічні та економічні показники оптимального використання компонентів енергосистеми, визначити допустимі межі робочих параметрів енергоблоків автономних та об’єднаних енергосистем. Результати цих розрахунків дають можливість обрати відповідні заходи щодо майбутнього оновлення технологій виробництва електричної та теплової енергії. Простота використання і редагування як окремих параметрів, так і програмних текстів, використовуваних при моделюванні розвитку енергосистем, підвищує якість одержуваних сценаріїв розвитку. Запропонований програмно-інформаційний комплекс може бути використаний для дослідження перспектив короткострокового і довгострокового розвитку енергосистеми України, а також інтегрованої з енергосистемами сусідніх країн-членів ENTSO-E, що є актуальним у сучасних умовах. General Energy Institute of the National Academy of Sciences of Ukraine 2023-08-24 Article Article application/pdf https://systemre.org/index.php/journal/article/view/806 10.15407/srenergy2023.03.050 System Research in Energy; No. 3 (74) (2023): System Research in Energy; 50-63 Системні дослідження в енергетиці; № 3 (74) (2023): Системні дослідження в енергетиці; 50-63 2786-7102 2786-7633 en https://systemre.org/index.php/journal/article/view/806/702 Copyright (c) 2023 Viktor Denysov, Vitalii Babak https://creativecommons.org/publicdomain/zero/1.0 |
| spellingShingle | software information complex multi-node integrated autonomous power systems. Denysov, Viktor Babak, Vitalii Software and information simulation complex of multinode integrated and autonomous power and heat supply systems |
| title | Software and information simulation complex of multinode integrated and autonomous power and heat supply systems |
| title_alt | Програмно-інформаційний комплекс моделювання багатовузлових інтегрованих і автономних систем електро- і теплопостачання |
| title_full | Software and information simulation complex of multinode integrated and autonomous power and heat supply systems |
| title_fullStr | Software and information simulation complex of multinode integrated and autonomous power and heat supply systems |
| title_full_unstemmed | Software and information simulation complex of multinode integrated and autonomous power and heat supply systems |
| title_short | Software and information simulation complex of multinode integrated and autonomous power and heat supply systems |
| title_sort | software and information simulation complex of multinode integrated and autonomous power and heat supply systems |
| topic | software information complex multi-node integrated autonomous power systems. |
| topic_facet | software information complex multi-node integrated autonomous power systems. програмно-інформаційний комплекс багатовузлові інтегровані автономні енергосистеми. |
| url | https://systemre.org/index.php/journal/article/view/806 |
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