Improving the Model of Long-term Technological Update of Power System Components
An improved model of mathematical programming is proposed for the study of directions and optimal parameters of the technological renewal of energy system elements operating in energy associations, for the distant perspective and taking into account the perspectives of these elements. The model is a...
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General Energy Institute of the National Academy of Sciences of Ukraine
2023
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Репозитарії
System Research in Energy| _version_ | 1871103213776666624 |
|---|---|
| author | Denysov, Viktor Zaporozhets, Artur Nechaieva, Tetiana Shulzhenko, Sergii Derii, Volodymyr |
| author_facet | Denysov, Viktor Zaporozhets, Artur Nechaieva, Tetiana Shulzhenko, Sergii Derii, Volodymyr |
| author_institution_txt_mv | [
{
"author": "Viktor Denysov",
"institution": null
},
{
"author": "Artur Zaporozhets",
"institution": null
},
{
"author": "Tetiana Nechaieva",
"institution": null
},
{
"author": "Sergii Shulzhenko",
"institution": null
},
{
"author": "Volodymyr Derii",
"institution": null
}
] |
| author_sort | Denysov, Viktor |
| baseUrl_str | https://systemre.org/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-07-18T12:57:33Z |
| description | An improved model of mathematical programming is proposed for the study of directions and optimal parameters of the technological renewal of energy system elements operating in energy associations, for the distant perspective and taking into account the perspectives of these elements. The model is a combination and, at the same time, a separate case of two models: a model of a hierarchical controlled quasi-dynamic system and a stochastic quasi-dynamic model of the economic and technological impact of the life cycle of innovative technologies. The main difference of the proposed improved model is the explicit consideration of the influence of economic and technological indicators of the development of the national economy and production, presented in the form of stochastic quasidynamic functions. The conducted test calculations confirm the adequacy of the proposed model, the perspective of applying this approach and further development of the model to achieve the necessary levels of detail in the forecast scenarios of the development of energy systems of Ukraine. The performed test calculations made it possible to obtain numerical estimates of the potential that can be achieved by improving the model of long-term technological renewal of power system components. Consideration of the influence of economic and technological indicators of the development of the national economy and production in the form of stochastic quasi-dynamic functions expands the tools and possibilities of obtaining high-quality predictive scenarios of the development of energy systems in Ukraine. The use of efficiency coefficients LCOE, LCOS and LACE, which are widely used in modeling the development of energy systems, increases the quality of the conclusions obtained. Examples of calculations of values of parameters of power system components under different modes of use are given. 
  |
| doi_str_mv | 10.15407/srenergy2023.02.030 |
| first_indexed | 2026-03-24T02:00:58Z |
| format | Article |
| fulltext |
Системні дослідження в енергетиці. 2023. 2(73) 30
МОДЕЛЮВАННЯ, ОПТИМІЗАЦІЯ
ТА ПРОГНОЗУВАННЯ В ЕНЕРГЕТИЦІ
______________________________________________________________________
ISSN 2786-7102 (Online), ISSN 2786-7633 (Print)
https://doi.org/10.15407/srenergy2023.02.030
UDC 621.3:519.8
Viktor Denysov*, https://orcid.org/0000-0002-3297-1114
Artur Zaporozhets, Dr. Sci. (Engin.), Senior Researcher, https://orcid.org/0000-0002-0704-4116
Tetiana Nechaieva, PhD (Engin.), Senior Researcher, https://orcid.org/0000-0001-9154-4545
Sergii Shulzhenko, PhD (Engin.), Senior Research Scientist, https://orcid.org/0000-0002-7720-
0110
Volodymyr Derii, PhD (Engin.), Senior Research Scientist, https://orcid.org/0000-0002-5689-4897
General Energy Institute of NAS of Ukraine, 172, Antonovycha St., Kyiv, 03150, Ukraine
*Corresponding author: visedp@gmail.com
_______________________________________________________________________________________
IMPROVING THE MODEL OF LONG-TERM TECHNOLOGICAL UPDATE
OF POWER SYSTEM COMPONENTS
Abstract. An improved model of mathematical programming is proposed for the study of directions and
optimal parameters of the technological renewal of energy system elements operating in energy
associations, for the distant perspective and taking into account the perspectives of these elements. The
model is a combination and, at the same time, a separate case of two models: a model of a hierarchical
controlled quasi-dynamic system and a stochastic quasi-dynamic model of the economic and
technological impact of the life cycle of innovative technologies. The main difference of the proposed
improved model is the explicit consideration of the influence of economic and technological indicators of
the development of the national economy and production, presented in the form of stochastic quasi-
dynamic functions. The conducted test calculations confirm the adequacy of the proposed model, the
perspective of applying this approach and further development of the model to achieve the necessary
levels of detail in the forecast scenarios of the development of energy systems of Ukraine. The performed
test calculations made it possible to obtain numerical estimates of the potential that can be achieved by
improving the model of long-term technological renewal of power system components. Consideration of
the influence of economic and technological indicators of the development of the national economy and
production in the form of stochastic quasi-dynamic functions expands the tools and possibilities of
obtaining high-quality predictive scenarios of the development of energy systems in Ukraine. The use of
efficiency coefficients LCOE, LCOS and LACE, which are widely used in modeling the development of
energy systems, increases the quality of the conclusions obtained. Examples of calculations of values of
parameters of power system components under different modes of use are given.
Keywords: long-term technological renewal of power system components.
ACRONYMS
SS Battery Electric Storage System
IPS Integrated Power System
NPP Nuclear Power Plant
1. Introduction
The study of the development and improvement of energy systems is an urgent scientific problem,
which is given considerable attention [1]–[3]. An improvement of the model of mathematical programming
is proposed, which is based on the modification and development of a family of hierarchical models of step-
by-step optimization of trajectories of sustainable development of energy systems [4] and a stochastic quasi-
Системні дослідження в енергетиці. 2023. 2(73) 31
dynamic model of the economic and technological impact of the life cycle of innovative technologies [5].
The main difference of the proposed model is the explicit consideration of the influence of economic and
technological indicators of the development of the national economy and production, presented in the form
of stochastic quasi-dynamic functions. The calculations used the actual and predicted values of these
functions at the corresponding step of the modeling horizon. The modification and development of these
models is aimed at adapting the latter to study on their basis the directions and optimal parameters of
technological renewal of the components of energy systems operating within the framework of energy
associations in the long term, and taking into account their prospects.
Estimating the relative costs of upgrading energy systems that use different technologies is a difficult
task. The results of this assessment are critically dependent on location. For example, coal, given the current
situation, is and is likely to remain economically attractive in countries, where carbon emissions are taxed
low or not at all. Gas is also able to compete with base generation capacity, especially when combined cycle
plants are used. Nuclear power plants are expensive to build, but relatively cheap to operate. Waste disposal
and decommissioning costs are usually fully included in operating costs. In many places, nuclear energy
successfully competes with fossil fuels as a means of generating electricity. Accounting for the social, health
and environmental costs of using fossil fuels increases the competitiveness of nuclear power. Technology life
cycle assessments show that nuclear power is an economically viable source of electricity generation,
combining the benefits of safety, reliability and very low greenhouse gas emissions. Existing NPPs operate
reliably with a high degree of predictability. The operating costs of nuclear power plants are lower than those
of all fossil fuel competitors and also have a very low risk of operating cost inflation. Experience shows that
the life cycle of nuclear power plants is 60 years and may be extended in the future. The main economic risks
for existing nuclear power plants lie in competition both with subsidized renewable energy, whose
generation volumes are difficult to predict, and with low-cost gas generation, especially given the political
risks of increased specific taxation of nuclear power.
2. Methods and materials
At the moment, the following two performance factors are widely used in modeling scenarios for the
development of energy systems.
Levelized Cost of Electricity (LCOE)
Currently, the evaluation of the effectiveness of decisions made is often based on the calculation of the
levelized cost of electricity produced − LCOE, which is used as a convenient generalizing indicator of the
overall competitiveness of various generating technologies [6]. It represents the cost per kilowatt-hour (with
currency discounts) to build and operate a generating set over its expected life cycle, including
decommissioning. The main inputs for the calculation are: capital costs, fuel prices, fixed and variable
operating costs, maintenance costs, financing costs and the estimated utilization rate for each type of
generating plant. The weight of the factors varies depending on the technology. For technologies such as
solar and wind that have no fuel consumption and relatively small operating costs, the LCOE is roughly
proportional to the estimated cost of capital for generating capacity. For technologies with significant fuel
costs, fuel consumption and operating costs have a significant impact on LCOE. The presence of various
incentives, including state or federal tax credits, may also affect the LCOE calculation. As with any design,
there is uncertainty about all of these factors and their importance may vary regionally and over time as
technology advances and fuel prices change. It is important to consider that the actual investment costs
depend on the specific technological and regional features of the project, which are influenced by many other
factors.
Levelized Avoided Cost of Electricity (LACE) as an additional indicator
Because projected utilization rates, existing resource mixes, and the amount of capacity required can
vary greatly in different regions where new generation capacity may be required, direct comparison of LCOE
technologies as a method of assessing the economic competitiveness of various generation project
alternatives is often problematic and misleading. Conceptually, a better estimate of economic
competitiveness can be achieved by taking into account cost avoidance, namely the cost of generating
Системні дослідження в енергетиці. 2023. 2(73) 32
resources that already exist, the electricity that is supposed to be generated by a new generation project, as
well as its LCOE. Cost savings, providing a proxy for the annual economic value of a candidate project, can
be estimated over the horizon of its financial life cycle by dividing on the project's average annual generation
to determine the unit cost of electricity avoided (LACE) [7]–[8]. Then, the LACE value of the candidate
project can be compared with the LCOE value, and thus determine whether the benefit received from the
project exceeds its cost. If several competing technologies are considered, a comparison of each project's
LACE with its LCOE can be used to select the most efficient project. A LACE estimate is more complex
than an LCOE estimate because it requires information about how the system would perform without the
modification. The LACE calculation is based on forecasting the marginal cost of energy and capacity, as a
result of the use of existing technology or the technology being developed, for a certain future date and
allows the investor to estimate the potential value of the project implementation. Long-term forecasts use
both LACE and LCOE indicators, which is generally more representative than using LCOE alone. However,
it should be noted that LACE and LCOE estimates are simplifications of modeled decisions and cannot fully
account for all decision-making factors or comparisons of simulation results. Calculations based on LACE
and LCOE estimates, which are usually performed using an assumed set of capital and operating costs, are
subject to uncertainty due to the use of projected values of fuel prices and possible administrative acts. This
should be taken into account when analyzing technology choices in the electricity sector based on estimated
LCOE and LACE.
Model formulation
The main difference of the proposed step-by-step optimization model is taking into account explicitly
the impact of economic and technological indicators of the development of the national economy and
production, presented in the form of stochastic quasi-dynamic functions. Therefore, the model allows the
study of the optimum volume of technological renovation of combined power systems. The model is
represented as a hierarchy of scenarios. On the top-level of the hierarchy involved aggregated technology of
energy provision. For example, aggregated technology k = 1, is an aggregation of several coal technologies.
Aggregated technology k = 2, is the aggregation of gas technologies, etc. The matrix of the system state
k reflects the structure of the volumes of generation, delivery and consumption of energy at 𝜏τ step of the
modeling horizon. All 1, ,k K= used technologies are involved in ensuring the balance of energy
supplied
SE
and consumed
CE
at each τ step. The main limitations of the model – keeping the right
balance of the total power P
, maneuverable power
mP
and the volumes of generated
GE
, supplied
SE
and consumed
CE
energy, when restrictions, that all parameters belong to the set of possible states
performed. The measure k of the inconsistency of supplied and consumed energy vectors is introduced.
The initial information for modeling are:
− target sequence of annual total energy consumption
1
K
Consum
C k
k
E E
=
= , at time 𝜏, 𝜏 = 0,1,2, … , 𝑇;
− the initial state of the vector of total supply 0SE
;
− the vector of integral parameter differences (k k kL LACE LCOE− for each of the aggregated
technologies involved in the calculation;
− functional of economic and technological influence F[PPF(τ),ET(k,τ),FCF(k,τ),EGR(τ)], where
PPF(τ) – regional purchasing power factor, ET(k,τ) – efficiency of technology, FCF(k,τ) – final cost
factor, EGR(τ) – economy growth rate;
− forecast cost of technology components:
cap
kC – investment, $/kW,
co
kC – constant operating
expenses, $/kW,
vo
kC – variable operational costs, $/kWh.
Системні дослідження в енергетиці. 2023. 2(73) 33
The set of admissible states ( ) ( ) ( ) ( ) ( ) , , , , , ,k k k kL LACE LCOE F PPF ET k FCF k EGR −
for the development trajectory of the aggregated technology k is calculated. Solved the problem of
calculating the development scenario of the such supply vector
( ) ( ) ( ) ( ) ( ) , , , , , ,S k k k kE L LACE LCOE F PPF ET k FCF k EGR − ,
that the measure
of the total inconsistency of the supplied and consumed energy vectors minimized. The
objective function of minimizing the total inconsistency of supplied and consumed energy vectors during the
forecast period with the observance of mandatory restrictions is as follows:
( ) ( ) ( )
1
, , , ) ,S CE E u k k min
=
= − →
where: SE
, CE
– total volumes of supplied and consumed energy; u(τ,k) – vector of control actions for
aggregated technology 𝑘 at the time τ; ξ(τ,k) – vector of random external actions for aggregated technology k
at the time τ. Optimal target trajectory for supply vector
( ) ( ) ( ) ( ) ( ) , , , , , ,S k k k kE L LACE LCOE F PPF ET k FCF k EGR − , τ=1,2,…,T
determined on the basis of forecasts of the overall development of the economy and related forecasts of
consumption structure
1
K
k
C C
k
E E
=
= . The values of the total supply obtained at the upper level and the
component value of the vector
1
K
k
S S
k
E E
=
= for each of the aggregated technologies are the source data for
the following calculations: the required balance of total power P
; the necessary structure of maneuver
capacities mP
; volumes of total and component generation
1
K
k
G G
k
E E
=
= ; volume of necessary investments
1
K
cap cap
k k
k
C C
=
= , $/kW; volumes of constant operating expenses,
1
K
co co
k k
k
C C
=
= $/kW; amounts of variable
operating costs,
1
K
vo vo
k k
k
C C
=
= $/kWh.
At the following simulation levels, similar calculations are performed within each of the aggregated
technologies, that is, the components of the development scenarios for the respective vectors of each of the
aggregated technologies are calculated. Returning to the above examples of coal and gas technology, all the
calculations listed above, which include the components of the aggregated coal and gas technology
components, are fulfilled. Further application of this approach ends with the achievement of required or
specified by the developer level of the simulation detail. Perhaps the use of the model in the opposite
(bottom-up) direction. The performed test calculations confirm the adequacy of the proposed model, the
promising application of this approach and the further development of the model to achieve the necessary
levels of detail of forecast scenarios for the development of energy systems in Ukraine.
As described above, aggregate technologies were used at the top level of the hierarchical model for
stepwise optimization of the sustainable development of the power system. At the following levels of
modeling are performed similarly calculations for each of the aggregated technology, that is, the components
of the scenarios for the corresponding vectors for each of the aggregated technologies are calculated. The
disaggregated (detailed in modeling) technology k of electricity production with a battery electric storage
system described below, is considered as a set (combination) of several technologies that reflect the
functions of its components. The purpose of this combination is the reduction of the cost of maintaining the
balance of the network peak loads and the risk of overload in current mode and emergency modes. The set is
equipped with the necessary control device, which monitors voltage and frequency in the network, converts
Системні дослідження в енергетиці. 2023. 2(73) 34
AC to DC and Vice versa, stores the electric energy received from the system in batteries and uses it if
necessary, correcting poor frequency, voltage and other, to increase energy efficiency. The operation is in
automatic mode or at the command of the dispatcher, using:
− frequency controller (FC), which in real time changes the frequency of electricity in the network
and issues commands to the charging or discharging of the battery;
− system of energy conversion, which receives signals from the FC, calculates the necessary
degree of charge or discharge of the battery and converts the direct current of batteries into
alternating current network, and vice versa;
− battery management system, which in real time monitors the state of charge in the system, makes
a decision and gives commands to the charge/discharge/holding and monitors the temperature of
the batteries.
In the simulation, a battery electric storage system (SS) is submitted in the form of two power
generating technologies, one primary and one that models the energy from SS at the stage when it is
discharged. In addition, the SS included the technology of consumption, which reflects energy consumption
at the stage of charge of the SS. For each of these technologies are calculated target sequence of annual total
volumes consumed and supplied energy and the initial state of the volume of electricity. To calculate the
vector of differences of integral parameters ( )k k kL LACE LCOE− , pre-calculates SSLCOS – reduced cost
of storage [9] according to the formula:
( )
( )
( )
1
,
1
cap co vo
T SS SS SS
SS
T
SS
C C C
i
LCOS
E
i
+ +
+
=
+
where: SSE – stored energy in each year, kWh; i – discounted rate (%);
cap
SSC − capital investment, $/kW,
co
SSC − constant operating expenses, $/kW,
vo
SSC − variable operational costs, $/kWh.
Further, calculations are made similar to the aggregated top-level technologies.
Calculations
Considering the foregoing, below are examples of calculations comparative assessment NPPs
parameters in modes of basic generation –
base
NPPLCOE , load following – N P
LF
PLCOE and with ESS – set of
several storage system –
Σ
NPP ESSLCOE + . The results of all subsequent test calculations are summarized in
Table 1.
The base generation
base
NPPLCOE was calculated by the formula (1) [3]:
( )
( )
( )
( )
& &
_
1
, 1
1
NPP
NPP
cc FO M VO M
T NPP NPP NPP
base
NPP An base
T
NPP
C C C
i
LCOE
E
i
+ +
+
=
+
where:
_An base
NPPE – annual NPP generation in base mode, kWh; i – discounted rate (WACC), %;
cc
NPPC –
capital costs, $/kW;
&FO M
NPPC − annual fixed costs, $/kW-yr;
&VO M
NPPC − variable costs, $/kWh; NPPT – NPP
life cycle, years. Test calculations for WACC 3, 5, 7 and 10% confirm the model adequacy.
In load following (LF) mode, N P
LF
PLCOE calculated using the formula similar to (1). Based on the
hourly load distribution of the IPS of Ukraine components [10], the model hourly power balance and the
annual generation of NPP in the LF mode –
LF
NPPE were calculated. The results of the test calculations of
N P
LF
PLCOE and the ratio of /LF base
NPP NPPLCOE LCOE are also given in Table 1.
Системні дослідження в енергетиці. 2023. 2(73) 35
Table 1. Results of the test calculations
WACC % 3 5 7 10
base
NPPLCOE $/kWh 0,058 0,071 0,084 0,105
N P
LF
PLCOE $/kWh 0,066 0,080 0,096 0,120
LF/BASE 1,138 1,127 1,143 1,143
ESST
ESSLCOS $/kWh 0,279 0,279 0,280 0,280
Σ
1 100NPP ESSLCOE + − $/kWh 0,061 0,073 0,087 0,107
Σ
3 100NPP ESSLCOE + − $/kWh 0,066 0,078 0,092 0,112
Σ
5 100NPP ESSLCOE + − $/kWh 0,071 0,083 0,097 0,117
Σ
7 100NPP ESSLCOE + − $/kWh 0,075 0,087 0,101 0,122
The NPP with a set of several storage systems (ESS)
Σ
NPP ESSLCOE + is calculated according to the
formula (2), which is a development of the formula (1):
( )
( )
( )
( )
& & &
Σ
_
1
, 2
1
NPP
cc FO M VO M IIc O M Charg
T NPP NPP NPP ESS ESS ESS
NPP ESS An base
T
NPP
C C C C C C
i
LCOE
E
i
+
+ + + + +
+
=
+
where:
IIc
ESSC – total initial installed costs, $/kWh;
&O M
ESSC – operating costs, $/kW-yr;
Charg
ESSC – charging
cost, $/kWh.
In the first phase, it was performed a test calculation of levelized cost ESST
ESSLCOS for a storage
system ESS-100 with power of 100 MW and capacity of 400 MWh, throughout the life cycle ESST = 20 years
[11]. The results of these calculations confirm the adequacy of the model. In the second phase
Σ
1 100NPP ESSLCOE + − for a two advanced nuclear power unit AP1000 with a total capacity of 2200 MW (NPP-
2200) and one ESS–100 during the life cycle of 40 years was calculated. Next, the model hourly balance of
NPP-2200 is calculated, which, with the required amount ESSN of ESS-100, will allow leveling the daily
energy consumption schedule. The required ESSN is determined taking into account the limit on the
maximum annual permissible charge / discharge power, which, according to passport data, should not exceed
140 000 MWh. Accordingly, the charge / discharge energy required to completely equalize the load schedule
and the NPP's operation in the base generation mode is 936 437 MWh and ESSN = 7.
Calculations of the comparative cost of a system consisting of NPP-2200 and several (3, 5, 7)
ESS-100 are performed. The results are shown in Table 1 and Figure 1. Calculations showed, that for this
particular configuration, the system is economical until 𝑁𝐸𝑆𝑆 does not exceed 3, and total ESS power does
not exceed 15% of NPP power.
3. Results and discussion
The results obtained during test calculations made it possible to obtain numerical estimates of the
potential for improving the model of long-term technological renewal of power system components. The use
for obtaining estimates, of efficiency coefficients LCOE, LCOS and LACE, which are widely used in
modeling the development of energy systems, provides an opportunity to develop high-quality development
scenarios. Consideration of the influence of economic and technological indicators of the development of the
national economy and production, presented in the form of stochastic quasi-dynamic functions, to study on
their basis the directions and optimal parameters of the technological renewal of the energy systems
Системні дослідження в енергетиці. 2023. 2(73) 36
components, that operate as part of energy associations, in the long term and taking into account their
prospects, expands tools and possibilities of qualitative forecasting.
Fig. 1. The generation system comparative cost
4. Conclusions
In order to adapt mathematical programming models to study the directions and optimal parameters of
technological renewal of power system components operating in power interconnections, in the long term
and taking into account their prospects, an improved model is proposed. It is based on the modification and
development of the family of hierarchical models of step-by-step optimization of trajectories of sustainable
development of energy systems and of the stochastic quasi-dynamic model of economic and technological
impact of the life cycle of innovative technologies. The main difference of the proposed model is the explicit
consideration of the influence of economic and technological indicators of the development of the national
economy and production, presented in the form of stochastic quasi-dynamic functions. The use of efficiency
factors LCOE, LCOS and LACE, widely used in modeling the development of power systems, improve the
quality of the resulting development scenarios. The simulation results can be used to study the prospects for
the short-term and long-term development of the energy system of Ukraine, which is relevant in these
conditions.
References
1. Lai, C.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).
https://doi.org/10.1016/j.jclepro.2020.124298
2. Gaur, A.S., Das, P., Jain, A., Bhakar, R., & Mathur, J. (2019). Long-term energy system planning considering
short-term operational constraints. Energy Strategy Reviews, 26. https://doi.org/10.1016/j.esr.2019.100383
3. Izui, Y., & Koyama, M. (2017). Future energy and electric power systems and smart technologies. IEEJ
Transactions on Electrical and Electronic Engineering, 12(4), 453–464. https://doi.org/10.1002/tee.22436
https://doi.org/10.1016/j.esr.2019.100383
Системні дослідження в енергетиці. 2023. 2(73) 37
4. Denisov, V. (2017). Family of hierarchical models of sequential optimization of the energy systems sustainable
development Renewable energy and energy efficiency in the 21st century. Materials of the XVIII international
conference, Kyiv, 119–123.
5. Denisov, V. (2017). A stochastic quasi-dynamic model of the economic and technological impact of the life cycle
of innovative technologies. Materials of the conference "Problems of regulatory and legal support of innovative
activity and ways to solve them", Kyiv, 35–37.
6. Projected Costs of Generating Electricity 2020 Edition. (2020). International Energy Agency and Organisation for
Economic Cooperation and Development/Nuclear Energy Agency. URL: https://www.iea.org/reports/projected-
costs-of-generating-electricity-2020 (Last accessed: 28.03.2023).
7. Levelized Cost of Electricity and Levelized Avoided Cost of Electricity Methodology Supplement. (2013). U.S.
Energy Information Administration. URL:
https://www.eia.gov/renewable/workshop/gencosts/pdf/methodology_supplement.pdf (Last accessed: 28.03.2023).
8. Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2017.
(2017). U.S. Energy Information Administration, 21 p.
9. E-storage: Shifting from cost to value. (2016). © World Energy Council, 27 p. URL:
http://www.worldenergy.org/publications/ (Last accessed: 28.03.2023).
10. Hourly balance of power of the IPS of Ukraine. https://map.ua-energy.org/en/resources/8998f2ed-379f-4fa9-9076-
88782b32ee4f/ (Last accessed: 28.03.2023).
11. Lazard’s levelized cost of storage analysis — version 7.0. 2021.
УДОСКОНАЛЕННЯ МОДЕЛІ ДОВГОСТРОКОВОГО
ТЕХНОЛОГІЧНОГО ОНОВЛЕННЯ КОМПОНЕНТНИХ
ЕНЕРГОСИСТЕМ
Віктор Денисов*, https://orcid.org/0000-0002-3297-1114
Артур Запорожець, д.т.н., ст. досл., https://orcid.org/0000-0002-0704-4116
Тетяна Нечаєва, к.т.н., ст. досл., https://orcid.org/0000-0001-9154-4545
Сергій Шульженко, к.т.н., ст. наук. співр., https://orcid.org/0000-0002-7720-0110
Володимир Дерій, к.т.н., ст. наук. співр, https://orcid.org/0000-0002-5689-4897
Інститут загальної енергетики НАН України, вул. Антоновича, 172, м. Київ, 03150,
Україна
*Автор-кореспондент: visedp@gmail.com
Анотація. Запропоновано удосконалену модель математичного програмування для дослідження
напрямів та оптимальних параметрів технологічного оновлення елементів енергосистем, що
працюють в енергооб’єднаннях, на віддалену перспективу та з урахуванням перспектив цих
елементів. Модель є об’єднанням і в той же час окремим випадком двох моделей: моделі
ієрархічної керованої квазідинамічної системи та стохастичної квазідинамічної моделі
економіко-технологічного впливу життєвого циклу інноваційних технологій. Основною
відмінністю запропонованої удосконаленої моделі є явне врахування впливу економіко-
технологічних показників розвитку національної економіки та виробництва, представлених у
вигляді стохастичних квазідинамічних функцій. Проведені тестові розрахунки підтверджують
адекватність запропонованої моделі, перспективність застосування даного підходу та
подальшого розвитку моделі для досягнення необхідних рівнів деталізації прогнозних сценаріїв
розвитку енергосистем України. Виконані тестові розрахунки дозволили отримати числові
оцінки потенціалу, що може бути досягнутим за рахунок вдосконалення моделі довгострокового
технологічного оновлення компонентів енергосистем. Розгляд впливу економіко-технологічних
показників розвитку національної економіки та виробництва у вигляді стохастичних
квазідинамічних функцій розширює інструменти та можливості одержання якісних прогнозних
сценаріїв розвитку енергосистем України. Використання коефіцієнтів ефективності LCOE,
LCOS і LACE, широко використовуваних при моделюванні розвитку енергосистем, підвищує
якість одержуваних висновків. Наведено приклади розрахунків значень параметрів компонентів
енергосистем при різних режимах використання.
Ключові слова: довгострокове технологічне оновлення компонент енергосистем.
Надійшла до редколегії: 30.03.2023
https://www.iea.org/reports/projected-costs-of-generating-electricity-2020
https://www.iea.org/reports/projected-costs-of-generating-electricity-2020
https://www.eia.gov/renewable/workshop/gencosts/pdf/methodology_supplement.pdf
http://www.worldenergy.org/publications/
https://map.ua-energy.org/en/resources/8998f2ed-379f-4fa9-9076-88782b32ee4f/
https://map.ua-energy.org/en/resources/8998f2ed-379f-4fa9-9076-88782b32ee4f/
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| id | systemreorg-article-167 |
| institution | System Research in Energy |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-19T01:04:38Z |
| publishDate | 2023 |
| publisher | General Energy Institute of the National Academy of Sciences of Ukraine |
| record_format | ojs |
| resource_txt_mv | systemreorg/84/04872b3caa3b709b884c8f807bca0084.pdf |
| spelling | systemreorg-article-1672026-07-18T12:57:33Z Improving the Model of Long-term Technological Update of Power System Components Удосконалення моделі довгострокового технологічного оновлення компонентних енергосистем Denysov, Viktor Zaporozhets, Artur Nechaieva, Tetiana Shulzhenko, Sergii Derii, Volodymyr long-term technological renewal of power system components. довгострокове технологічне оновлення компонент енергосистем. An improved model of mathematical programming is proposed for the study of directions and optimal parameters of the technological renewal of energy system elements operating in energy associations, for the distant perspective and taking into account the perspectives of these elements. The model is a combination and, at the same time, a separate case of two models: a model of a hierarchical controlled quasi-dynamic system and a stochastic quasi-dynamic model of the economic and technological impact of the life cycle of innovative technologies. The main difference of the proposed improved model is the explicit consideration of the influence of economic and technological indicators of the development of the national economy and production, presented in the form of stochastic quasidynamic functions. The conducted test calculations confirm the adequacy of the proposed model, the perspective of applying this approach and further development of the model to achieve the necessary levels of detail in the forecast scenarios of the development of energy systems of Ukraine. The performed test calculations made it possible to obtain numerical estimates of the potential that can be achieved by improving the model of long-term technological renewal of power system components. Consideration of the influence of economic and technological indicators of the development of the national economy and production in the form of stochastic quasi-dynamic functions expands the tools and possibilities of obtaining high-quality predictive scenarios of the development of energy systems in Ukraine. The use of efficiency coefficients LCOE, LCOS and LACE, which are widely used in modeling the development of energy systems, increases the quality of the conclusions obtained. Examples of calculations of values of parameters of power system components under different modes of use are given.    Запропоновано удосконалену модель математичного програмування для дослідження напрямів та оптимальних параметрів технологічного оновлення елементів енергосистем, що працюють в енергооб’єднаннях, на віддалену перспективу та з урахуванням перспектив цих елементів. Модель є об’єднанням і в той же час окремим випадком двох моделей: моделі ієрархічної керованої квазідинамічної системи та стохастичної квазідинамічної моделі економіко-технологічного впливу життєвого циклу інноваційних технологій. Основною відмінністю запропонованої удосконаленої моделі є явне врахування впливу економікотехнологічних показників розвитку національної економіки та виробництва, представлених у вигляді стохастичних квазідинамічних функцій. Проведені тестові розрахунки підтверджують адекватність запропонованої моделі, перспективність застосування даного підходу та подальшого розвитку моделі для досягнення необхідних рівнів деталізації прогнозних сценаріїв розвитку енергосистем України. Виконані тестові розрахунки дозволили отримати числові оцінки потенціалу, що може бути досягнутим за рахунок вдосконалення моделі довгострокового технологічного оновлення компонентів енергосистем. Розгляд впливу економіко-технологічних показників розвитку національної економіки та виробництва у вигляді стохастичних квазідинамічних функцій розширює інструменти та можливості одержання якісних прогнозних сценаріїв розвитку енергосистем України. Використання коефіцієнтів ефективності LCOE, LCOS і LACE, широко використовуваних при моделюванні розвитку енергосистем, підвищує якість одержуваних висновків. Наведено приклади розрахунків значень параметрів компонентів енергосистем при різних режимах використання. General Energy Institute of the National Academy of Sciences of Ukraine 2023-06-09 Article Article application/pdf https://systemre.org/index.php/journal/article/view/167 10.15407/srenergy2023.02.030 System Research in Energy; No. 2 (73) (2023): System Research in Energy; 30-37 Системні дослідження в енергетиці; № 2 (73) (2023): Системні дослідження в енергетиці; 30-37 2786-7102 2786-7633 en https://systemre.org/index.php/journal/article/view/167/125 Copyright (c) 2023 Viktor Denysov, Artur Zaporozhets, Tetiana Nechaieva, Sergii Shulzhenko, Volodymyr Derii https://creativecommons.org/publicdomain/zero/1.0 |
| spellingShingle | long-term technological renewal of power system components. Denysov, Viktor Zaporozhets, Artur Nechaieva, Tetiana Shulzhenko, Sergii Derii, Volodymyr Improving the Model of Long-term Technological Update of Power System Components |
| title | Improving the Model of Long-term Technological Update of Power System Components |
| title_alt | Удосконалення моделі довгострокового технологічного оновлення компонентних енергосистем |
| title_full | Improving the Model of Long-term Technological Update of Power System Components |
| title_fullStr | Improving the Model of Long-term Technological Update of Power System Components |
| title_full_unstemmed | Improving the Model of Long-term Technological Update of Power System Components |
| title_short | Improving the Model of Long-term Technological Update of Power System Components |
| title_sort | improving the model of long-term technological update of power system components |
| topic | long-term technological renewal of power system components. |
| topic_facet | long-term technological renewal of power system components. довгострокове технологічне оновлення компонент енергосистем. |
| url | https://systemre.org/index.php/journal/article/view/167 |
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