Towards 2050: Evaluating the Role of Energy Transformation for Sustainable Energy Growth in Serbia

Abstract

This paper aims to investigate the outlook of energy generation by means of transformation within the context of sustainable energy development. An analysis is conducted to assess the stability of energy systems so to implement cutting-edge energy production models at the national level, with a focus on a contemporary approach to energy modeling. Considering the energy transition and the existing constraints within the energy industry, the model assesses the feasibility of the practical advancement of renewable energy sources. The bottom-up energy model was used to determine how the components of energy development sustainability can be applied until the year 2050. To perform comparison testing with the reference state scenario, the LEAP energy model was used. This instrument was selected because of its ability to provide flexible and advanced options for selecting suitable parameters for energy transformation prediction. A progressive reduction in environmental pollution can be achieved by the deployment of current methods of energy generation by transformation until the year 2050 in Serbia, as indicated by the findings. The research highlights the significance of utilizing green energy sources for the continuing development of energy and the gradual reduction in environmental pollution through value co-creation.

Full text

Citation: Backovi´c, N.; Ili´c, B.;
Radakovi´c, J.A.; Mitrovi´c, D.;
Milenkovi´c, N.; ´
Cirovi´c, M.; Raki´cevi´c,
Z.; Petrovi´c, N. Towards 2050:
Evaluating the Role of Energy
Transformation for Sustainable Energy
Growth in Serbia. Sustainability 2024,
16, 7204. https://doi.org/10.3390/
su16167204
Academic Editor: Brantley T. Liddle
Received: 15 July 2024
Revised: 13 August 2024
Accepted: 20 August 2024
Published: 22 August 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Towards 2050: Evaluating the Role of Energy Transformation for
Sustainable Energy Growth in Serbia
Nemanja Backovi´c , Bojan Ili´c , Jelena Andreja Radakovi´c * , Dušan Mitrovi´c , Nemanja Milenkovi´c ,
Marko ´
Cirovi´c , Zoran Raki´cevi´c and Nataša Petrovi´c *
Faculty of Organizational Sciences, University of Belgrade, 11000 Belgrade, Serbia;
nemanja.backovic@fon.bg.ac.rs (N.B.); bojan.ilic@fon.bg.ac.rs (B.I.); dusan.mitrovic@fon.bg.ac.rs (D.M.);
nemanja.milenkovic@fon.bg.ac.rs (N.M.); marko.cirovic@fon.bg.ac.rs (M. ´
C.); zoran.rakicevic@fon.bg.ac.rs (Z.R.)
*Correspondence: jelenaandreja.radakovic@fon.bg.ac.rs (J.A.R.); natasa.petrovic@fon.bg.ac.rs (N.P.)
Abstract: This paper aims to investigate the outlook of energy generation by means of transformation
within the context of sustainable energy development. An analysis is conducted to assess the stability
of energy systems so to implement cutting-edge energy production models at the national level,
with a focus on a contemporary approach to energy modeling. Considering the energy transition
and the existing constraints within the energy industry, the model assesses the feasibility of the
practical advancement of renewable energy sources. The bottom-up energy model was used to
determine how the components of energy development sustainability can be applied until the year
2050. To perform comparison testing with the reference state scenario, the LEAP energy model was
used. This instrument was selected because of its ability to provide flexible and advanced options
for selecting suitable parameters for energy transformation prediction. A progressive reduction in
environmental pollution can be achieved by the deployment of current methods of energy generation
by transformation until the year 2050 in Serbia, as indicated by the findings. The research highlights
the significance of utilizing green energy sources for the continuing development of energy and the
gradual reduction in environmental pollution through value co-creation.
Keywords: energy economics; energy transformation; energy efficiency; bottom-up modeling; LEAP
modeling tool
1. Introduction
The 1980s saw the end of the first and second global crises in the oil derivatives
market, which changed the terms of production and trade in energy products with a focus
on market-oriented reforms. This made energy efficiency an especially important topic
of study for economists. The conversion of primary energy into secondary energy and
the subsequent generation of economically viable tertiary energy is accompanied by the
multiplier effect of meeting the energy needs of end-users. This leads to a rational decrease
in the proportion of energy-intensive products in the gross domestic product and a shift in
emphasis towards the service industry. The constant reduction in energy supply technology
costs has led to increased efficiency in terms of reducing the amount of energy utilized per
unit of product or service given [1].
It is also possible to view the quantification of energy efficiency in the context of
changes in energy intensity on a global scale. As isolated indicators, productivity and
energy intensity can overestimate the cause of progress in the field of efficiency. This is
due to the fact that efficiency is directly dependent on climate conditions, the growth of
the total population, and structural changes in the economy [
2
]. Productivity and energy
intensity are important measures of the advancement of energy efficiency. Productivity is
a measure of how efficiently resources, especially energy, are used to generate goods and
services. Energy intensity, on the other hand, measures the quantity of energy required
Sustainability 2024,16, 7204. https://doi.org/10.3390/su16167204 https://www.mdpi.com/journal/sustainability

Sustainability 2024,16, 7204 2 of 22
to generate one unit of economic output. A drop in energy intensity and an increase in
productivity usually reflect a gain in energy efficiency, since more output is produced
with less energy. The feedback effect within the framework of microeconomic analysis
occurs when end-users try to use more energy services due to the drop in the costs of those
services. This would consequently mean that the final energy consumption and energy
intensity will decrease more slowly because of the growth in energy efficiency [3].
The areas of emerging research aimed at enhancing energy efficiency can be catego-
rized as follows [4]:
1.
Quantifying the decrease in emissions of hazardous gases to determine the diversity
of fuel options.
2. Assessing the quality of energy services.
3.
Improving the measurement and assessment of the increase in efficiency resulting
from energy policy laws.
4.
Strengthening the empirical foundation of transaction costs, discounts, and feedback
effects in order to bring them in line with regulatory policy.
5.
Enhancing the understanding of customer preferences and promoting the use of effec-
tive technologies through the strengthening of a research identity that encompasses
multiple disciplines.
The arguments supporting energy efficiency regulation can be condensed into the fol-
lowing four crucial and interrelated factors: (1) cost savings for the government,
(2) decreased
reliance on energy sources, (3) the alleviation of the impacts of greenhouse gas emissions, and
(4) the promotion of sustainable economic growth [
5
]. The rapid rise of the global population
and the increasing energy demands of developing nations will undoubtedly hinder efforts
to achieve greater energy efficiency. Therefore, it is predicted that, during the next several
decades, global prosperity will need to be achieved at a rate twice as fast as the current rate,
while using just half of the energy and natural resources currently being utilized [6].
The worldwide shift towards alternative energy sources, driven by the urgent neces-
sity to address the carbon emissions issue, climate change, and to provide energy stability,
presents substantial prospects for producing effective renewable energy [
7
,
8
]. It is projected
that renewable energy sources will overtake fossil fuels by 2050 [
9
]. During that period, re-
newable energy sources will account for more than half of global energy
production [8–10]
.
Also, “the focus on renewable energy utilization is growing sharper, due to its sustainabil-
ity and contribution to greenhouse gas emissions reduction” [
11
]. The objective of this
transition is to replace fossil fuels with renewable energy sources while enhancing the
sustainability and responsiveness of the power system. The core principle of generating
shared value is pivotal in this process, highlighting the cooperation of many stakeholders.
Additionally, it must be noted that a key idea for accelerating the transition to green
energy is co-creation [
12
]. Co-creation in renewable energy projects enables stakeholders to
collaborate and leverage their collective knowledge, resources, and creativity to enhance
project outcomes and community benefits [
13
,
14
]. The global transition towards alternative
energy sources, motivated by the pressing need to tackle climate change and provide energy
security, offers significant opportunities to generate efficient renewable energy. The essence
of this transformation is in the concept of creating shared value, which emphasizes the
necessity of collaboration among many stakeholders, such as governments, companies, and
local communities, in order to collectively achieve sustainable development and its goals.
Energy models provide guidance for making investment decisions in expanding the
electricity generation capacity. They accomplish this by outlining several ways to satisfy
future energy needs while also achieving environmental protection objectives [
15
]. The
authors Heuberger and others [
15
] also argue that energy models may provide a clear
economic rationale for technology in the power system and identify the most favorable
investment location. Modeling current energy systems has significant difficulty in including
the extensive unpredictability and complexity of the energy system, while also accounting
for all of the utilized technologies. Hence, this article presents an analysis of the effects

Sustainability 2024,16, 7204 3 of 22
of energy production development via transformation on the sustainable development of
energy and the overall stability of energy systems in Serbia.
The overall number of energy models and their complexity are continually increasing
as a result of the improved capabilities offered by computers and computer programming.
The models vary significantly in terms of their structure and the range of their application,
while the complexity of the data acquired sometimes poses a difficult challenge for analysts
in the stated subject. The significance of energy models is also evident in the execution
of energy decarbonization initiatives. Currently, there is no mention in the scientific
literature of the utilization of sophisticated energy models for forecasting energy growth
in Serbia. This work aims to address a specific gap in the existing scientific research at
the national level. The gap pertains to the analysis of the burden of the energy sector in
relation to incentive purchase prices for preferential producers in the field of renewable
energy sources (RESs). In addition, the potential effects of the energy transformation on the
overall stability of energy systems are examined, offering stakeholders valuable insights
into potential advancements and financial opportunities for the adoption of contemporary
energy technology.
2. Energy Models
The presence of two separate methods for energy modeling frequently results in con-
tradictions within research and uncertainty among analysts when selecting an appropriate
methodology. The lack of adequate information provided by energy firms is considered
a constraining element when examining energy in a situation of market failure. Energy
models are categorized into top-down and bottom-up approaches based on the analytical
method. This fundamental categorization became especially significant in the 1980s and
1990s as a result of the growth of the discourse surrounding the energy efficiency gap.
The bottom-up model’s assumptions are determined in relation to the spread of
technology, investments, and the operational expenses of power plants [
16
]. Given their
ability to explain the causes of specific outcomes in the energy sector and their reliance
on intricate programming, these models can accurately forecast the adoption of new
energy production technologies. Their purpose is to provide information on novel support
mechanisms for regulatory energy policy [
17
]. The notion of economic equilibrium can
adequately depict the end result of incorporating new production technologies into the
energy system. It demonstrates how the system settles into a balanced state when the
new technologies are implemented. However, this notion has the following limitation: it
does not account for the dynamic, step-by-step process that led to the adoption of these
technologies. In other words, while economic equilibrium can depict the final result, it
cannot explain the intricacies and changes that occurred along the transition.
Bottom-up models analyze energy efficiency by comparing the energy consumption
of a specific technology or device to a reference scenario with the goal of reducing energy
use. Compared to the top-down strategy of estimating energy demand, performing ex
post research on household income elasticity by incorporating economic and structural
variables greatly aids in predicting the amount of energy used per unit of activity [18].
The top-down approach refers to process-oriented models [
19
]. Top-down energy
models, in contrast to bottom-up models, utilize aggregated data to perform synergy
analysis across sectors [
20
]. The models mentioned encompass an examination of the entire
economy, taking into account ongoing market distortions, financial spillovers, and income
consequences for various economic entities. These models also consider the significant
endogeneity of economic activity during the energy crisis period [
21
]. This means that
economic activity is influenced by both external factors such as energy prices and internal
elements within the economy. Additionally, the indicated models have various drawbacks.
If the data collection is not conducted appropriately, the model may yield a poor level
of accuracy. Furthermore, when the top-down approach yields outcomes that diverge
considerably from the anticipated results, it indicates that the process, technology, or
equipment responsible for the variation cannot be identified [22].

Sustainability 2024,16, 7204 4 of 22
Recently, there have been efforts to develop models that can utilize both analytical
techniques to energy modeling. These models aim to integrate a macroeconomic model,
which takes a broad perspective, with at least one component of a bottom-up model
that focuses on the ultimate energy consumption sector. Hybrid scenarios necessitate
the incorporation of both qualitative and quantitative information to effectively combine
the distinct fields of engineering, natural sciences, and social sciences. These disciplines
often possess contrasting ontologies, epistemologies, and methodologies [
23
]. However,
after extensively examining the available literature, the authors determined that, for a
particular aspect of energy development research, choosing a bottom-up model would be
the most appropriate approach. This is primarily due to the model’s flexibility in adjusting
the load parameters of energy systems and its impact on the stability and growth of the
energy sector.
Bottom-up models are frequently employed to evaluate the financial viability of energy
systems in a broader perspective, strategies for mitigating the release of noxious gases, and,
overall, for implementing significant system transformations [
24
]. The bottom-up modeling
technique effectively predicts the behavior of the annual energy efficiency supply function,
allowing for the analysis of how energy efficiency responds to unforeseen fluctuations in
energy demand [16].
With the limited and seasonal use of renewable energy sources and the deregulation
of the electricity market, energy models need to incorporate factors such as seasonal
demand fluctuations, price changes, weather predictions, and other variables. This is
performed to enhance the competitiveness of energy companies [
24
]. Due to the gradual
nature of technological advancements, the bottom-up method is prone to overestimating
the economic benefits of fully implementing energy-saving technologies. Grubb and
others [
25
] have found that bottom-up investigations of practical application indicate a
higher potential for reducing emissions of hazardous gases and total costs compared to the
top-down model’s extrapolation of energy consumption.
Certain bottom-up models incorporate macroeconomic feedback, whilst others cal-
culate microeconomic behavioral characteristics for the selection of energy production
technology [
26
]. According to the same authors, specific top-down models have included
technical complexity in the energy supply sectors. Some instances exist where the parame-
ters of endogenous technological change are defined in order to link energy productivity
with energy policy that encourages research and development in the area of reducing
greenhouse gas intensity [
27
]. When comparing the deterministic and stochastic models
in the TIMES energy model, it becomes evident that the stochastic interpretation is more
accurate in estimating the overall costs of the energy system. This is because it considers
uncertain parameters that are specific to the model itself [28].
The models given are depictions of the partial equilibrium of the energy sector. They
incorporate numerous discrete energy technologies to replace outdated primary and sec-
ondary energy processes with the goal of enhancing energy efficiency [
21
]. Within this
framework, Koopmans and Te Velde [
29
] identify the following three primary domains for
research in bottom-up database management:
1.
Forecasting the demand for energy and energy services, considering the shift from
conventional fuel-based technology to electricity-based technology.
2.
The level of energy efficiency is contingent upon the chosen energy development
strategy, which can involve either investing in current technology or completely
replacing them with more advanced ones.
3.
The model may not accurately assess the rate at which the current energy system can
adjust to achieve optimal efficiency after the fact. This aspect should be enhanced in
future versions of the model.
At the same time, power grid systems worldwide are confronted with several issues,
such as escalating demand, the rising use of renewable energy sources, and mounting
infrastructural pressure. When faced with this problem, multi-stakeholder participation
plays a crucial role in establishing appropriate incentives and facilitating intelligent elec-

Sustainability 2024,16, 7204 5 of 22
tricity consumption. Successful collaboration between various stakeholders, including
residents, is crucial for the generation of value in sustainable energy. This collaboration
aims to maximize the efficient use of smart power usage. In the energy sector, effective
methods of co-creating value require utilizing both resource advantages and strategic
network positions to actively involve residents. Collaboratively developing intelligent
electricity services is essential for maintaining equilibrium in power system demand and
encouraging the adoption of sustainable energy consumption [30].
3. Energy Modeling Tool Used for the Empirical Research
The Low Emissions Analysis Platform (LEAP) is a comprehensive tool used for model-
ing energy processes using the scenario method approach [
31
], as well as a tool for creating
and evaluating “alternative scenarios by comparing their energy requirements, social costs,
benefits, and environmental impacts” [
32
,
33
]. It is classified as an Integrated Assessment
Model (IAM), which is a type of model that analyzes the relationship between energy and
climate change. Developed by the Stockholm Environment Institute, this tool is utilized by
numerous organizations in over 190 countries worldwide. The LEAP software 2020.1.0.64
model is primarily used in developing countries to facilitate the integrated resource plan-
ning of the energy system. Its main objectives include evaluating the reduction in harmful
gas emissions from energy sources and devising sustainable strategies for the utilization of
renewable energy sources. LEAP’s ability to model environmental, economic, and social
impacts helps stakeholders understand the potential benefits and trade-offs, making it
essential for informed decision making in energy transformation projects.
Another indication of the complexity of the modeling based on an integrated assess-
ment is the fact that multidisciplinary analysis of energy is so advanced in today’s world
that it frequently requires several hours of labor for the instrument to calculate all of the
scenarios that are provided. Integrated assessment models, in the context of game the-
ory, employ a sophisticated mathematical technique to address inquiries regarding the
environmental consequences of the energy sector [34].
LEAP supports bottom-up process modeling for energy demand, as well as enabling a
top-down approach from a macroeconomic analysis standpoint [
31
]. When considering
the extension of the current energy capacity, this model allows for the utilization of a
wider range of accounting and simulation techniques, as well as the optimization of
the energy system. The analyst constructing the model has the freedom to choose the
methodology, which supports the iterative analytical approach. LEAP does not provide
specific guidelines for the data needed to interpret the results. Instead, the analyst assesses
the model’s complexity and calculates the necessary volume of data. This emphasizes the
significance of using an intuitive approach to analyze complicated energy systems and
visually represent the underlying structure of the model.
LEAP evaluates alternate scenarios and their effects on the energy sector’s self-
sufficiency and the country’s ecosystem. It can also be used to study possible energy,
environmental, and cost effects of various energy usage under rapid economic growth and
successful energy policy implementation. The defined scenarios are analyzed by assessing
the model’s energy requirements, key assumptions, energy production costs, energy sector
environmental impact, and economic aspects of sustainable energy system management.
The following four fundamental methods for setting parameters for energy generation
through transformation are described in LEAP [35]:
•
Disregard the capacity constraints, which is useful for users without power plant
capacity information or who do not consider it significant for calculations.
•
The user can choose which manufacturing capacity to include and when. This method en-
ables the independent input of external capacitances for various
manufacturing technologies
.
•
The user chooses the production capacity to incorporate, while LEAP sets the imple-
mentation time intervals. This technique gives the user full control over resource
additions, including power plant operation length and cost. The key difference is

Sustainability 2024,16, 7204 6 of 22
the ability to use endogenous technologies in case of shortages from planned energy
transformation capacities.
•
In the fourth approach, LEAP decides which production capacities to include and at
what intervals. This gives it full control over the allocation of resources for energy
production through transformation.
An integral component of the LEAP instrument is the assessment of the process
efficiency of power plants, which is crucial for evaluating the impact of a specific energy
source on energy production through transformation. This assessment was conducted
using data provided by the Ministry of Mining and Energy of Serbia [
36
]. The utilization of
process efficiency by LEAP is illustrated in Figure 1below.
Sustainability 2024, 16, x FOR PEER REVIEW 6 of 23
• Disregard the capacity constraints, which is useful for users without power plant ca-
pacity information or who do not consider it significant for calculations.
• The user can choose which manufacturing capacity to include and when. This
method enables the independent input of external capacitances for various manufac-
turing technologies.
• The user chooses the production capacity to incorporate, while LEAP sets the imple-
mentation time intervals. This technique gives the user full control over resource ad-
ditions, including power plant operation length and cost. The key difference is the
ability to use endogenous technologies in case of shortages from planned energy
transformation capacities.
• In the fourth approach, LEAP decides which production capacities to include and at
what intervals. This gives it full control over the allocation of resources for energy
production through transformation.
An integral component of the LEAP instrument is the assessment of the process effi-
ciency of power plants, which is crucial for evaluating the impact of a specific energy
source on energy production through transformation. This assessment was conducted us-
ing data provided by the Ministry of Mining and Energy of Serbia [36]. The utilization of
process efficiency by LEAP is illustrated in Figure 1 below.
Figure 1. Standard process of energy production by transformation in the LEAP model. Source:
Heaps [32].
Extensive model testing is necessary to analyze energy production through transfor-
mation and accurately estimate the future development of the energy sector. This category
allows users to input comprehensive data regarding the projected movement of overall
expenses for power plants, including capital costs, variable costs, historical costs, remain-
ing loan repayments, and various other interconnected economic variables. These capaci-
ties are expected to enhance the direction of energy exports and promote a shift towards
increased investments in the energy sector [37]. The impact on the technological advance-
ment of the nation is clearly seen via the rejuvenation of current production systems and
the implementation of contemporary energy systems, such as the co-generation program
(CHP—Combined Heat and Power). This significantly decreased the burden of energy
imports.
The LEAP system determines which production capacities to add and when they will
be added. Within the fourth approach, LEAP has complete control over the allocation of
available resources for energy production through transformation. This allocation is ini-
tially determined by the system’s cost optimization methodology. The optimization can
only be applied to one of the predefined situations, allowing for a comparison with an-
other scenario that the user can still manipulate in their modeling process. The provided
flexibility simplifies the decision-making process regarding the prioritization of
Figure 1. Standard process of energy production by transformation in the LEAP model. Source:
Heaps [32].
Extensive model testing is necessary to analyze energy production through trans-
formation and accurately estimate the future development of the energy sector. This
category allows users to input comprehensive data regarding the projected movement of
overall expenses for power plants, including capital costs, variable costs, historical costs,
remaining loan repayments, and various other interconnected economic variables. These
capacities are expected to enhance the direction of energy exports and promote a shift
towards increased investments in the energy sector [
37
]. The impact on the technological
advancement of the nation is clearly seen via the rejuvenation of current production sys-
tems and the implementation of contemporary energy systems, such as the co-generation
program (CHP—Combined Heat and Power). This significantly decreased the burden of
energy imports.
The LEAP system determines which production capacities to add and when they will
be added. Within the fourth approach, LEAP has complete control over the allocation
of available resources for energy production through transformation. This allocation is
initially determined by the system’s cost optimization methodology. The optimization
can only be applied to one of the predefined situations, allowing for a comparison with
another scenario that the user can still manipulate in their modeling process. The provided
flexibility simplifies the decision-making process regarding the prioritization of alternative
energy sources. Regarding data availability, the primary emphasis lies in the construction
costs, maintenance costs, energy costs, and operational costs of power plants.
Certain additional advanced bottom-up optimization models, such the MARKAL
(Market and Allocation) model, rely on the idea that energy consumers would typically
choose the best course of action. The challenge in modeling arises from customers frequently
making the wrong assumptions due to unreliable information and illogical decision making,
hence hindering the creation of a realistic model [
38
]. The disparity between the MARKAL
and LEAP models is notably substantial when evaluating these advanced features. While

Sustainability 2024,16, 7204 7 of 22
acknowledging the significance of the MARKAL model, it was concluded that LEAP would
be the most appropriate choice for implementing the model in this research.
In order to generate scenarios in this research, multiple tests were conducted to explore
various ways of combining factors with the aim of optimizing the entire system. Factors
such as the prevailing technological trends, the state’s economic capacity to adopt new
technologies, and macroeconomic projections until 2050 were considered. Modeling the
integration of renewable energy sources into the energy system while considering the
existing limits is particularly difficult. The model ruled out the prospect of achieving a
scenario where renewable energy completely replaces conventional energy sources by 2050,
concluding that this process is impossible to achieve within a reasonably short timeframe.
The analysis of fiscal risks and issues faced by public firms in the energy sector of
Serbia, as well as delays in the construction of new energy facilities and deficiencies in the
Electric Power Industry of Serbia distribution network, are receiving significant attention.
After setting the parameters within the framework of energy production by trans-
formation, the optimal solution for creating alternative scenarios was reached. This was
accomplished by referring to the following: (1) the energy dispatch rule, (2) the process
efficiencies of power plants, (3) the percentage participation of power plants, and (4) the
maximum availability capacity.
The energy dispatch rule is a crucial element in the modeling of energy operations in
LEAP. The instrument itself allows for the specification of how unmet requirements will be
addressed. The current energy situation in Serbia has led to the establishment of distinct
criteria for the scenario of the moderate use of renewable energy sources, SCOIE1, and the
scenario of intensive use of renewable energy sources, SCOIE2, which are presented in
the accompanying figures for the purpose of this research. In the reference scenario (REF),
the criterion “Meet with Imports” was approved due to the consistent trajectory of energy
development, which remains unaltered compared to the present situation (Figures 2and 3).
Sustainability 2024, 16, x FOR PEER REVIEW 8 of 23
Figure 2. A summary of functions for meeting energy requirements in the LEAP model—SCOIE1
scenario. Source: the authorsʹ analysis, based on [31].
Figure 3. A summary of functions for meeting energy requirements in the LEAP model—SCOIE2
scenario. Source: the authorsʹ analysis, based on [31].
The decision to use the LEAP instrument for modeling and optimizing the growth of
the energy sector in Serbia was justified, as it successfully incorporated both macroeco-
nomic and microeconomic analyses. The specified software tool allowed for the input of
all crucial assumptions, as well as the endogenous and exogenous variables of the model.
LEAP was chosen for the research because it can be applied to various levels of the energy
system. It is particularly useful for assessing how energy production affects the overall
costs of the energy sector based on the current prices for purchasing electricity from
Figure 2. A summary of functions for meeting energy requirements in the LEAP model—SCOIE1
scenario. Source: the authors’ analysis, based on [31].

Sustainability 2024,16, 7204 8 of 22
Sustainability 2024, 16, x FOR PEER REVIEW 8 of 23
Figure 2. A summary of functions for meeting energy requirements in the LEAP model—SCOIE1
scenario. Source: the authorsʹ analysis, based on [31].
Figure 3. A summary of functions for meeting energy requirements in the LEAP model—SCOIE2
scenario. Source: the authorsʹ analysis, based on [31].
The decision to use the LEAP instrument for modeling and optimizing the growth of
the energy sector in Serbia was justified, as it successfully incorporated both macroeco-
nomic and microeconomic analyses. The specified software tool allowed for the input of
all crucial assumptions, as well as the endogenous and exogenous variables of the model.
LEAP was chosen for the research because it can be applied to various levels of the energy
system. It is particularly useful for assessing how energy production affects the overall
costs of the energy sector based on the current prices for purchasing electricity from
Figure 3. A summary of functions for meeting energy requirements in the LEAP model—SCOIE2
scenario. Source: the authors’ analysis, based on [31].
The decision to use the LEAP instrument for modeling and optimizing the growth of
the energy sector in Serbia was justified, as it successfully incorporated both macroeconomic
and microeconomic analyses. The specified software tool allowed for the input of all crucial
assumptions, as well as the endogenous and exogenous variables of the model. LEAP was
chosen for the research because it can be applied to various levels of the energy system.
It is particularly useful for assessing how energy production affects the overall costs of
the energy sector based on the current prices for purchasing electricity from preferential
producers. The support mechanisms for renewable energy sources, namely, the incentive
purchase price, play a crucial role in establishing the model, defining restrictions, and
specifying the assumptions for the efficient operation process of power plants, particularly
in the context of Serbia. In order to generate the three different scenarios, the authors of
this paper input data on the total amount of incentive purchase prices, using the official
data provided by the Government of the Republic of Serbia [
36
,
39
]. The model will be
discussed from the perspectives of rationalizing electricity consumption, analyzing the
costs and benefits of using new energy production technologies, examining the relationship
between economic growth and the sustainable operation of business entities, the active
participants in the electricity market, and assessing the model’s influence on the operations
of specific stakeholders in the energy sector group.
The socio-technical system has a significant impact on the formation, maintenance,
and stabilization of the energy demand, even if it is based on economic decisions and
consumer preferences [
40
]. The constraints could also act as an obstacle to the adoption of
next-generation technology. Not only would this increased cooperation among companies
maximize the deployment of renewable energy sources as a strategy, but it would also
lessen the knowledge imbalance between power generators and consumers. Moreover, the
likelihood of encountering unmet energy needs in the specified model parameters would
be diminished.
4. Empirical Data and Scenario Analysis
To investigate the development of the Serbia’s energy system until 2050, three alter-
native scenarios have been established: the REF scenario, the SCOIE1 scenario, and the
SCOIE2 scenario, as follows:

Sustainability 2024,16, 7204 9 of 22
1.
The REF scenario was developed as a research model and is an altered version
of the state reference scenario of Serbia, as outlined in the “Energy Development
Strategy of Serbia until 2025, with forecasts until 2030” by the Ministry of Mining
and Energy of Serbia in 2015 [
36
]. The reference scenario examines the feasibility
of improving the nation’s power system without introducing new energy policy
measures, additional reductions in energy use, or actions to improve energy efficiency.
The reference scenario includes measures that can enable a coordinated approach to
setting regulated circumstances for the energy market. However, it mainly excludes
innovative combinations of regulatory policies that would change the existing path of
the country’s development of energy.
2.
The SCOIE1 scenario was developed independently to forecast the future develop-
ment of the energy sector in Serbia until 2050. The scenario includes data up to 2020,
which is the latest year for which conclusive data on the energy industry are provided.
The LEAP model enabled the advanced modeling of a comprehensive scenario, in-
corporating both the macroeconomic and microeconomic analyses of future events.
The purpose of constructing the SCOIE1 scenario was to examine the incorporation
of renewable energy sources (RESs) into the energy system of Serbia. This analysis
will also consider the possible obstacles, limitations, and challenges involved with
this approach.
3.
The SCOIE2 scenario is the last of the three different scenarios that were created for
this research. The SCOIE2 scenario integrates data from the SCOIE1 scenario and then
modifies them to align with the particular criteria of the study. The SCOIE2 scenario
is a projection of Serbia’s energy sector development until 2050. The analysis explores
the feasibility of integrating renewable energy sources to the maximum extent feasible,
taking into account the constraints of the existing energy system.
In order to construct the energy development model of Serbia from 2011 to 2050, an
extensive quantity of data were gathered from the official sources of both national and
international entities. The year 2011 serves as a reference year, whereas 2012 is the initial
year for which results are computed for different scenarios. To effectively evaluate the
model, historical data from 2012 to 2019 were used for annual comparison. The validation
of the model for energy production through transformation from 2012 to 2019 has also
proven that the model satisfies the criteria for the long-term forecasting of the energy
industry. The year 2011 was selected as the initial year for inputting data into the model, as
it coincided with the most recent census of the population, households, and apartments in
Serbia. Subsequently, 2012 was designated as the starting year for the scenario. The model’s
reliability was tested and any discrepancies during projection were minimized by using
the period span until the final published energy balance from 2023. The data presented
are sourced exclusively from the official energy balance reports published by the Statistical
Office of the Republic of Serbia. Model validation and the comparison of historical data
with the modeled data are shown in Table 1.
The disparities between the official historical data and the data generated by the
produced model can be attributed to the utilization of a bottom-up modeling technique. If
the model is not properly configured, there is a high probability that the distribution of the
input data and the amounts of energy produced in each observed year would be disrupted.
By redistributing or reallocating the generated energy to other subcategories, namely, to
power plants that have not fulfilled the energy demands, it is possible to achieve outcomes
that may mislead analysts regarding the trajectory of energy system development. Model
validation is conducted to determine if there were any disruptions in the model that might
subsequently impact future years. The selection of energy sources in Serbia was based on
crucial groupings that have the highest contribution to electricity production.

Sustainability 2024,16, 7204 10 of 22
Table 1. Model validation—comparison of historical data with the model data (in TJ).
Year 2012 2013 2014 2015 2016 2017 2018 2019
Type of powerplant
Thermal powerplant official 94,590 103,032 79,463 97,678 98,392 96,140 89,911 91,966
Thermal powerplant model 94,579.8 103,005.2 79,465.5 97,678 98,389.8 96,128.9 89,890.6 91,942.1
Deviation −0.0001% −0.0002% 0.00003% 0% −
0.00002%
−0.0001% −0.0002% −0.0002%
Coal official 9134 9134 4801 5852 7708 9035 7508 6439
Coal model 9127.2 9127.2 4814.8 5861.5 7703.7 9043.5 7494.4 6447.7
Deviation −0.0007% −0.0007% 0.0028% 0.0016% −0.0005% 0.0009% −0.0018% 0.0013%
Oil refinery official 96,917 131,242 134,837 145,700 150,925 159,153 168,902 154,319
Oil refinery model 96,924.4 131,256.2 134,835.9 145,700.6 150,934.1 159,140.3 168,895.5 154,325.4
Deviation 0.00007% 0.00010% −
0.000008%
0.000004% 0.00006% −
0.00007%
−
0.00003%
0.00004%
Wood pellet official 1448 2011 2814 1809 2224 4294 4267 4427
Wood pellet model 1444.4 2009.7 2805.2 1800.3 2223.6 4291.5 4270.5 4438
Deviation −0.0024% −0.0006% −0.0031% −0.0048% −0.0001% −0.0005% 0.0008% 0.0024%
Wood chips official 238 311 329 152 371 326 302 254
Wood chips model 251.2 314 334.9 150.7 371 334.9 293.1 251.2
Deviation 0.0525% 0.0095% 0.0176% −0.0085% 0% 0.0265% −0.0294% −0.0110%
Source: authors’ analysis.
During the testing of the model for energy production, a discrepancy was noticed
compared to the official data from the energy balances of Serbia for the period spanning
from 2012 to 2019. The variance ranged from
−
0.0310% to 0.0229%. Therefore, it can be
concluded that the model is reliable and fulfills the requirements for generating plausible
alternative scenarios for the long-term energy sector. Between 2020 and 2024, consistent
slight deviations were observed for the specified model, providing confirmation that reliable
research utilizing the scenario technique with the LEAP instrument can be conducted.
5. Results and Discussion
The strong correlation between the official and modeled data demonstrates the LEAP
model’s ability to accurately replicate Serbia’s energy system. Precision in this matter is
essential for stakeholders to make well-informed decisions on energy policies, investments,
and strategies, thus improving the process of value co-creation. The analysis could clarify
the possible advantages and obstacles of different energy transformation trajectories by con-
trasting the reference scenario (REF) with the moderate (SCOIE1) and intensive (SCOIE2)
renewable energy scenarios. This allows for the identification of the most sustainable and
successful strategies, promoting collaboration among stakeholders. By involving various
stakeholders in the modeling process, the study encourages a common comprehension
and collaborative effort towards the advancement of sustainable energy. The data-driven
method enables an extensive assessment of the environmental, economic, and social conse-
quences of various energy scenarios. A clear comprehension of this matter is crucial for
co-creating values that blend diverse interests and fosters sustainability.
The production of energy through transformation was selected as the research area,
since it aims to promote the rational use of energy and the improvement of energy effi-
ciency, as well as the promotion of positive climate changes in the field of energy and the
improvement of the coordination of the activities of all energy companies.
The energy production projection through transformation inside of the LEAP model
is intricate due to the various parameters involved and the complexity of the modeling
process assumptions. Because scenario generation is performed in a bottom-up manner, it
is best to enter all available information about power plants’ exogenous and endogenous
capacities, together with the rules governing the order in which different energy kinds
should be sent. The placement of the transformation categories is crucial, as it might lead to
significantly varied and contradictory outcomes if not performed correctly. The computa-
tion methodology begins by assessing the potential losses during the energy transmission
and distribution, followed by evaluating the electricity output through transformation.
This allows the model to compute losses using a bottom-up approach and evaluate the
necessity of utilizing the input power plant capacity.

Sustainability 2024,16, 7204 11 of 22
To ensure an accurate representation of the current energy system and provide a real-
istic projection of future energy sector development, the model defines energy production
by transformation based on historical production data from the year prior to the scenario’s
base year, specifically, 2010 (referred to as Historical Production in the model parameter).
The transformation module in LEAP allows for calculations based on feedback on the
energy flow, facilitating the adaptation to more intricate energy systems [
31
]. Hence, the
model can accurately compute the allocation of energy produced by the power plant for its
own consumption. By making iterative adjustments to the system, we can fulfill the unmet
energy needs that arise from energy flows coming from upstream sources.
The model has employed the energy dispatch rule for simulation since 2011. Conse-
quently, the significance of establishing these rules is explained in a distinct portion of the
text. In the SCOIE2 scenario, the choice was made to input the percentage of participation
for each type of power plant. According to this scenario, thermal power plants are not
allowed to have a participation rate higher than 60.5%, specifically 58.2%, for the years
2025 and 2050 in terms of electricity production through transformation.
The model calculates the maximum level of involvement of thermal power plants by
considering the projected lifespan and process efficiency of these plants. This measure was
implemented to incentivize the generation of energy from sources that emit lower levels of
greenhouse gases (GHGs).
After conducting 143 tests with various parameter settings, the most effective approach
for generating four alternative scenarios in energy production through transformation was
determined. These tests considered factors such as the rule of dispatching energy, process
efficiencies of power plants, the percentage participation of power plants, the maximum
capacity availability, and the merit order effect.
The results for the projection of the energy production through transformation for
four different scenarios were produced using the aforementioned model. To enhance the
clarity of the results, the model emphasizes the future projections that are relevant. These
projections focus on the period starting from the year 2025 and extend beyond, covering a
five-year timeframe. This period is crucial, as it is predicted to witness substantial strategic
shifts in the energy sector.
The assumption made is that there will be no change in energy production from the
transformation of coal types for the REF scenario up until 2030, which is the last year of
the projection from the report on the energy strategy of Serbia. When the framework of
the calculation procedure conducted by LEAP was taken into consideration, the offered
assumption demonstrated the best outcomes through the model testing. Therefore, the
focus in both of the above situations was on correcting the movements of the other types
of transformations with the goal of ensuring the accuracy of the total projection. Further
modifications in the specified subcategory would complicate the calculation process and
hinder an accurate depiction of the energy conversion, hence disrupting the forecast of
coal-based primary energy generation.
For the other types of energy conversion, there is a forecast of slightly slower growth
in production compared to the data until 2030. An example of this is energy production
through the conversion of oil and oil derivatives, where a slower increase of 0.08% is
expected. This prediction considers new trends in the energy sector and the delay in
implementing energy strategies. Furthermore, the reference scenario suggests that there
will be a rise in the value of accessible power because of conversion, with the prevalence of
environmentally harmful fuels still prevailing, which affects the overall economic stabil-
ity [
41
]. The model demonstrates that the official data entered regarding planned capital
investments in the energy sector may potentially improve the relatively low operational
efficiency of thermal power plants and combined heat and power facilities, which typically
have a maximum availability rate of over 80%.
As shown in Table 2, an issue that could hinder the long-term sustainability of the
energy system in the REF scenario is the high electrical consumption needed for the
continuous operation of thermal power plants. This consumption is accounted for in the

Sustainability 2024,16, 7204 12 of 22
model under the self-consumption section of the energy sector. It is important to remember
that approximately 92% of coal consumption is used as a fuel input for energy conversion in
thermal power plants [
36
]. Furthermore, alternative fuels utilized in the conversion process
within thermal power plants, such as sub-bituminous coal, exhibit a significant release
of greenhouse gases. The subsequent section takes into account all emissions and inputs
them into the LEAP model. In the REF scenario, the increase in thermal energy output
through transformation is expected to put extra strain on the energy sector’s import side,
as natural gas is the primary fuel used for this process. The potential danger associated
with the use of fossil fuels is also considered in the LEAP model, where the presence of
toxic compounds is measured to determine the total energy efficiency of the system [42].
Table 2. Energy production by transformation in the REF scenario (in 1000 million tons of oil
equivalent—mtoe).
Year Oil and Oil Derivatives Heat Electricity Coal
2050 5270.1 1428.2 4111.2 474.0
2049 5201.2 1408.0 4072.9 474.0
2048 5132.3 1387.9 4034.7 474.0
2047 5063.5 1366.9 3996.6 474.0
2046 4994.6 1345.7 3958.5 474.0
2045 4925.7 1324.6 3920.5 474.0
2044 4861.4 1303.9 3886.1 474.0
2043 4797.1 1283.2 3851.8 474.0
2042 4732.8 1262.5 3817.5 474.0
2041 4668.5 1241.8 3783.3 474.0
2040 4604.1 1221.1 3749.1 474.0
2039 4544.0 1203.4 3714.1 474.0
2038 4483.8 1185.6 3679.2 474.0
2037 4423.7 1167.2 3644.3 474.0
2036 4363.5 1148.8 3609.4 474.0
2035 4303.3 1130.5 3573.6 474.0
2034 4255.6 1119.9 3542.0 474.0
2033 4207.9 1109.3 3510.4 474.0
2032 4160.3 1098.7 3478.7 474.0
2031 4112.6 1088.1 3446.9 474.0
2030 4064.9 1077.6 3415.1 474.0
2029 4010.9 1057.5 3373.3 474.0
2028 3956.9 1037.5 3331.2 474.0
2027 3902.9 1017.5 3289.0 474.0
2026 3848.9 997.6 3246.5 474.0
2025 3794.9 977.7 3203.9 474.0
Source: authors’ analysis.
Due to the multidisciplinary nature of energy production through transformation, it
is difficult to conduct analyses and projections on this process, particularly regarding the
establishment of a reliable energy supply and distribution system. The SCOIE1 scenario
depicts a progressive decline in the system’s reliance on oil derivatives, as seen in Table 3.
The disparity between this scenario and the REF scenario grows from 372.3 thousand tons
of oil equivalent in 2025 to 1.155 million tons of oil equivalent in 2050.
It should be noted that the percentage changes are referring to the time period be-
ginning in 2025 and to the ratio of total values that occurred within the time range that
was observed. Undoubtedly, the percentage differences would be far larger between these
possibilities and the reference scenario if the full simulation was viewed regarding the base
year of 2011.

Sustainability 2024,16, 7204 13 of 22
Table 3. Energy production by transformation in the SCOIE1 scenario (in 1000 mtoe).
Year Wood Fuels Oil and Oil Derivatives Heat
Electricity
Coal
2050 51.7 4115.1 1278.8 3394.1 407.7
2049 51.7 4088.6 1261.4 3382.1 406.6
2048 51.7 4062.0 1244.0 3370.1 405.5
2047 51.7 4035.5 1226.7 3358.1 404.4
2046 51.6 4008.9 1209.3 3346.1 403.3
2045 51.6 3982.4 1191.3 3334.0 402.2
2044 51.6 3931.7 1171.8 3322.6 401.1
2043 51.5 3881.0 1152.3 3311.2 400.0
2042 51.5 3830.3 1132.8 3299.8 398.9
2041 51.5 3779.6 1113.3 3288.3 397.8
2040 51.4 3729.0 1092.2 3276.8 396.7
2039 51.4 3695.7 1077.1 3267.8 395.6
2038 51.4 3662.5 1061.9 3258.7 394.6
2037 51.3 3629.2 1046.8 3249.5 393.5
2036 51.3 3595.9 1031.6 3240.4 392.4
2035 51.3 3562.7 1016.5 3231.3 391.3
2034 51.2 3540.3 1007.3 3223.5 390.2
2033 51.2 3517.8 998.1 3215.7 389.1
2032 51.2 3495.4 988.9 3207.9 388.0
2031 51.2 3472.9 979.8 3195.4 386.9
2030 51.1 3450.5 970.6 3180.6 385.8
2029 51.1 3444.9 955.3 3151.7 391.1
2028 51.1 3439.4 940.0 3122.8 396.4
2027 51.0 3433.8 924.8 3093.8 401.7
2026 51.0 3428.2 909.5 3064.8 407.1
2025 51.0 3422.6 894.2 224.3 691.0
Source: authors’ analysis.
This presentation covers the time span from 2030 to 2050. The projections indicate a
much greater utilization of wood fuels in the SCOIE2 scenario as compared to the SCOIE1
scenario, particularly for the manufacturing of wood pellets and wood briquettes. The
primary objective is to create a market for wood and agricultural biomass in order to
enhance the competitiveness of producers in this region.
Wood pellets may be effectively used for energy generation, with a capacity utilization
rate of above 90% [
43
]. Transitioning the center of gravity from conventional fuels to
renewable energy sources and targeted biofuels is a gradual and ongoing process. Based
on the model’s results, it is highly unlikely that there would be a total shift towards “clean”
energy in the transformation sector by 2050, as shown over the relatively short period
of time.
If the installation of renewable energy sources is not precise, it could exacerbate
the issue of importing electricity and place a financial burden on the state budget. The
Fiscal Council’s study reveals that the failure to evaluate fiscal risks and quantify expenses
may once again result in using loans to cover costs, as is currently the situation with
specific public businesses in the energy sector [
44
]. The risk also lies in the potential
complementarity of energy sources, which might indirectly harm new production facilities
by abruptly discontinuing the usage of one source.
The SCOIE2 scenario demonstrates a significant decline in energy production from oil,
resulting in a production level of only 2583.6 thousand tons of equivalent oil by the last year
of the simulation. The scenario predicts that possible shortages of refined petroleum energy
would be offset by the establishment of additional biofuel production plants. Equally
significant is the integrated generation of heat and electricity in contemporary CHP (Com-
bined Heat and Power) facilities, which is likewise encompassed in the situation, with the
corresponding incentivized purchasing costs. According to the Ministry of Mining and
Energy of Serbia [
36
], the proportion of co-generation plants in Serbia is now quite small.
As a result, the modeling of the scenario aims to address and improve these deficiencies.

Sustainability 2024,16, 7204 14 of 22
It is evident that, in the SCOIE1 scenario, the development of electricity output will
be slower compared to the REF and SCOIE2 scenarios. Specifically, when comparing the
SCOIE1 scenario to the REF scenario, this forecast assumes an energy efficiency increase,
resulting in lower end energy consumption and electricity consumption in power plants.
Figure 4provides more information about the disparity in energy production patterns
between the REF and SCOIE1 scenarios.
Sustainability 2024, 16, x FOR PEER REVIEW 15 of 23
resulting in lower end energy consumption and electricity consumption in power plants.
Figure 4 provides more information about the disparity in energy production paerns
between the REF and SCOIE1 scenarios.
Figure 4. The difference in the dynamics of the energy transformation between the REF and SCOIE1
scenarios. Source: Heaps [31].
Given that the emphasis will be placed on developing new technologies in the field
of electricity, the graphic presentation reveals disparities that appear to be substantially
bigger than the absolute values that are displayed. This is because the creation of thermal
energy through transformation is the subject of the aention. In the SCOIE2 scenario, the
modeling incorporates the export-driven element, which means that the demand for en-
ergy goods in the domestic market would surpass its needs. According to the SCOIE2
scenario for the year 2050 outlined in Table 4, the anticipated output of heat energy
through transformation is expected to be 51,140 TJ (Terajoules). This is lower than the ex-
pected 53,541 TJ for the SCOIE1 scenario and 59,796 TJ for the REF scenario. Additionally,
it is crucial to investigate the operational effectiveness of power plants in terms of con-
verting energy. Therefore, in the SCOIE2 scenario, there was a reduction of 14,643 TJ in
the use of natural gas as a power plant input in comparison to the REF scenario. The model
also demonstrates the disparity in using the SCOIE1 scenario, with a decrease of 8806 TJ
compared to the REF scenario. In the SCOIE1 and SCOIE2 scenarios, significant reductions
in natural gas consumption for heat energy production through transformation are ex-
pected in 2036 and 2041, respectively. These savings are estimated to surpass 5000 TJ com-
pared to the REF scenario.
Additionally, the model predicts that, by the year 2050, there will be a decrease in the
disparity between the amount of money spent on energy production and the total energy
generated through transformation. Specifically, the estimated energy expenditure for the
REF scenario is 286,803 TJ, while, for the SCOIE2 scenario, it is 158,999 TJ. The SCOIE2
scenario exhibits a significant level of rapid changes, particularly throughout the time
span from 2033 to 2044.
According to the SCOIE2 scenario, the simulation assumes that creating a favorable
investment environment and establishing a stable, long-term relationship between privi-
leged producers and public companies would reduce the amount of thermal energy pro-
duction, which is a significant source of environmental pollution. Considering the eco-
nomic contributions of the analysis, it is crucial to note that this category also incurs sig-
nificant variable costs in energy generation. The primary objective of incorporating the
full potential of renewable energy sources in the SCOIE2 scenario is to prioritize the ad-
vancement of electricity as a catalyst for economic growth. While time series analyses have
Figure 4. The difference in the dynamics of the energy transformation between the REF and SCOIE1
scenarios. Source: Heaps [31].
Given that the emphasis will be placed on developing new technologies in the field of
electricity, the graphic presentation reveals disparities that appear to be substantially bigger
than the absolute values that are displayed. This is because the creation of thermal energy
through transformation is the subject of the attention. In the SCOIE2 scenario, the modeling
incorporates the export-driven element, which means that the demand for energy goods in
the domestic market would surpass its needs. According to the SCOIE2 scenario for the
year 2050 outlined in Table 4, the anticipated output of heat energy through transformation
is expected to be 51,140 TJ (Terajoules). This is lower than the expected 53,541 TJ for the
SCOIE1 scenario and 59,796 TJ for the REF scenario. Additionally, it is crucial to investigate
the operational effectiveness of power plants in terms of converting energy. Therefore, in
the SCOIE2 scenario, there was a reduction of 14,643 TJ in the use of natural gas as a power
plant input in comparison to the REF scenario. The model also demonstrates the disparity
in using the SCOIE1 scenario, with a decrease of 8806 TJ compared to the REF scenario. In
the SCOIE1 and SCOIE2 scenarios, significant reductions in natural gas consumption for
heat energy production through transformation are expected in 2036 and 2041, respectively.
These savings are estimated to surpass 5000 TJ compared to the REF scenario.
Additionally, the model predicts that, by the year 2050, there will be a decrease in the
disparity between the amount of money spent on energy production and the total energy
generated through transformation. Specifically, the estimated energy expenditure for the
REF scenario is 286,803 TJ, while, for the SCOIE2 scenario, it is 158,999 TJ. The SCOIE2
scenario exhibits a significant level of rapid changes, particularly throughout the time span
from 2033 to 2044.

Sustainability 2024,16, 7204 15 of 22
Table 4. Energy production by transformation for the SCOIE2 scenario (in 1000 mtoe).
Year Wood Fuel Oil and Oil
Derivatives Heat
Electricity
Coal Biofuel
Production
2050 25.7 2583.6 1221.5 5418.2 258.7 369.4
2049 26.6 2587.6 1208.8 5366.6 258.9 363.3
2048 27.4 2591.6 1196.0 5315.0 259.1 357.3
2047 28.2 2595.6 1183.3 5263.3 259.3 351.3
2046 29.1 2599.6 1170.6 5211.5 259.6 345.3
2045 29.9 2603.6 1157.9 5159.7 259.8 339.5
2044 30.7 2607.6 1140.1 5137.1 258.5 333.6
2043 31.6 2611.6 1122.3 5114.5 257.2 327.9
2042 32.4 2615.6 1104.6 5091.9 255.9 322.1
2041 33.2 2619.6 1086.8 5569.3 254.5 316.5
2040 34.1 2623.6 1069.0 5546.6 253.2 310.8
2039 34.9 2627.6 1052.6 5524.8 251.9 305.3
2038 35.8 2631.6 1036.3 5503.0 250.6 299.7
2037 36.6 2635.6 1019.9 5481.2 249.3 294.3
2036 37.4 2639.6 1003.6 5759.3 248.0 288.8
2035 38.3 2643.6 987.2 5737.3 246.7 283.5
2034 39.1 2649.0 986.0 5703.3 245.3 278.1
2033 39.9 2654.3 984.8 5669.2 244.0 272.9
2032 40.8 2659.7 983.6 5631.3 242.7 267.6
2031 41.6 2665.0 982.4 7541.2 241.4 262.5
2030 42.4 2670.4 981.3 7496.6 240.1 257.4
2029 43.3 2603.4 964.7 7470.4 245.1 252.3
2028 44.1 2536.4 948.2 7444.0 250.1 247.3
2027 45.0 2469.3 931.7 7417.4 255.1 242.3
2026 45.8 2402.3 915.2 7390.5 260.1 237.4
2025 46.6 2335.3 898.7 7363.5 265.1 232.5
Source: authors’ analysis.
According to the SCOIE2 scenario, the simulation assumes that creating a favor-
able investment environment and establishing a stable, long-term relationship between
privileged producers and public companies would reduce the amount of thermal energy
production, which is a significant source of environmental pollution. Considering the
economic contributions of the analysis, it is crucial to note that this category also incurs
significant variable costs in energy generation. The primary objective of incorporating
the full potential of renewable energy sources in the SCOIE2 scenario is to prioritize the
advancement of electricity as a catalyst for economic growth. While time series analyses
have not thoroughly investigated the role of electricity in economic development and its
cause-and-effect relationship, it is evident that increased energy usage is strongly correlated
with a positive trend in GDP [
3
]. Figure 5provides a visual depiction of the changes in
movement between the REF and SCOIE2 scenarios, highlighting the notable variations that
can impact the stability of energy systems.
The risk of system imbalance arises when there is a sudden and poorly planned re-
placement of available capabilities. This can lead to unexpected power supply interruptions
that have the potential to entirely damage the power system. In addition, it should be noted
that the proposed increase in energy reserves, as determined by the Energy Agency of the
Republic of Serbia [
39
], would be approximately twice the current level for the SCOIE1
scenario, and up to five times greater for the SCOIE2 scenario. An issue arises about the
export of power generated from these sources, specifically the concern of overloading the
transmission infrastructure, which negatively impacts end-customers.

Sustainability 2024,16, 7204 16 of 22
Sustainability 2024, 16, x FOR PEER REVIEW 16 of 23
not thoroughly investigated the role of electricity in economic development and its cause-
and-effect relationship, it is evident that increased energy usage is strongly correlated
with a positive trend in GDP [3]. Figure 5 provides a visual depiction of the changes in
movement between the REF and SCOIE2 scenarios, highlighting the notable variations
that can impact the stability of energy systems.
Figure 5. The difference in the dynamics of energy transformation between the REF and SCOIE2
scenarios. Source: Heaps [31].
The risk of system imbalance arises when there is a sudden and poorly planned re-
placement of available capabilities. This can lead to unexpected power supply interrup-
tions that have the potential to entirely damage the power system. In addition, it should
be noted that the proposed increase in energy reserves, as determined by the Energy
Agency of the Republic of Serbia [39], would be approximately twice the current level for
the SCOIE1 scenario, and up to five times greater for the SCOIE2 scenario. An issue arises
about the export of power generated from these sources, specifically the concern of over-
loading the transmission infrastructure, which negatively impacts end-customers.
The obligatory RES reserves criterion is a significant indicator of the strategic link to
the transmission system. Put simply, RES producers would assume accountability in the
case of a decrease in production and unforeseen interruptions in the functioning of power
facilities. Considering that these sources often have poor capacity utilization due to the
seasonal nature of the job, the request for energy storage was deemed legitimate during
the model’s development. Furthermore, the recommendations about the maer of balanc-
ing responsibility are acknowledged as vital to the strategic advancement of the energy
industry.
The LEAP tool enables the evaluation of the practical achievement of the objectives
of privileged energy producers by determining the average annual usage of energy
sources. For instance, if a criterion is established stating that a wind power plant can only
operate at 25% of its full capacity per year (with variations depending on the season),
LEAP will calculate the total capacity of the power plant based on the criterion for the
availability of renewable energy sources. Hence, it is crucial to incorporate the internal
capacity for energy production through conversion when integrating new technologies so
as to facilitate energy generation in situations of unforeseen increases in energy require-
ments. The model will utilize the inherent capability of the power plants to meet the nec-
essary margin level of the anticipated energy reserves. The electricity generated from re-
newable energy sources in this scenario constitutes an average of 30% (subject to variation
depending on the specific kind of energy).
Figure 5. The difference in the dynamics of energy transformation between the REF and SCOIE2
scenarios. Source: Heaps [31].
The obligatory RES reserves criterion is a significant indicator of the strategic link
to the transmission system. Put simply, RES producers would assume accountability in
the case of a decrease in production and unforeseen interruptions in the functioning of
power facilities. Considering that these sources often have poor capacity utilization due
to the seasonal nature of the job, the request for energy storage was deemed legitimate
during the model’s development. Furthermore, the recommendations about the matter
of balancing responsibility are acknowledged as vital to the strategic advancement of the
energy industry.
The LEAP tool enables the evaluation of the practical achievement of the objectives of
privileged energy producers by determining the average annual usage of energy sources.
For instance, if a criterion is established stating that a wind power plant can only operate
at 25% of its full capacity per year (with variations depending on the season), LEAP will
calculate the total capacity of the power plant based on the criterion for the availability
of renewable energy sources. Hence, it is crucial to incorporate the internal capacity for
energy production through conversion when integrating new technologies so as to facilitate
energy generation in situations of unforeseen increases in energy requirements. The model
will utilize the inherent capability of the power plants to meet the necessary margin level of
the anticipated energy reserves. The electricity generated from renewable energy sources in
this scenario constitutes an average of 30% (subject to variation depending on the specific
kind of energy).
Increased system load and greater demand for electricity from internal capacity can
also occur as a result of increased losses from other upstream modules. Therefore, for
instance, higher levels of thermal energy losses would place a heavy load on the reserves
of the electrical module. The margin of planned reserves is exclusively determined for the
SCOIE2 scenario to assess its potential influence on energy production through transforma-
tion in comparison to other scenarios. The assigned values for wind energy, solar energy
on buildings, solar energy on the ground, and geothermal energy are 100,000 tons/year,
10,000 tons/year
, 30,000 tons/year, and 5000 tons/year, respectively. However, it should
be noted that the mentioned figures are not necessarily anticipated to be achieved based
on practical forecasts, nor are they projected to be fully utilized for the energy industry’s
requirements. Conversely, endogenous capacities were implemented to assess the long-
term viability of using exogenous capacity in the context of energy transformation with
the objective of substituting conventional energy sources. Table 4provides comprehensive
data for the energy transformation forecasts in the SCOIE2 scenario.
From an economic perspective, stabilizing the system and fully utilizing renewable
energy sources (RESs) in line with the SCOIE2 scenario’s energy requirements would

Sustainability 2024,16, 7204 17 of 22
undoubtedly provide significant benefits for the competitiveness of energy entities, for
stakeholders, and the overall economic growth. Table 5presents the predicted cumulative
value of the total energy import and export for different sources of energy, including coal,
electricity, oil, and oil derivatives, based on the reported price changes from 2012 to 2023.
Table 5. Projection of cumulative benefits from the import and export of energy of Serbia for the
period until 2050 (in euros).
Scenario REF SCOIE1 SCOIE2
Import 27,385,482,226 26,189,170,246 25,519,658,714
Export 12,821,423,796 15,896,245,419 23,391,927,037
Source: authors’ analysis.
While the SCOIE2 scenario shows favorable outcomes in terms of the ratio of energy
imports and exports, the main concern is whether the ideal circumstances necessary for
promoting renewable energy sources at the national level by 2050 can be realized. Given
the specific criteria, it may be inferred that the decision to stop importing crude oil caused
significant changes to the SCOIE2 scenario. There is a clear and evident shift towards
reducing the dependence on crude oil for energy conversion. This can be demonstrated by
contrasting it with the REF scenario, in which the projected energy consumption for the
year 2050 is 211,839 TJ, a figure that is considerably greater than the 88,120 TJ derived from
the whole capacity of the RES SCOIE2 scenario. Furthermore, there will be a substantial
increase in power production until 2031 as long as the maximum availability of these energy
sources remains unchanged. Starting in 2032, the model predicts a gradual shift in energy
sources because the system is unable to completely replace the use of fossil fuels within a
specific period. The problem of not meeting the specified annual requirement of 61,379 TJ
of crude oil will not be resolved in the SCOIE2 scenario. According to the findings, Serbia
will not be capable of completely replacing the use of fossil fuels with renewable energy
sources (RESs) by 2050 due to the constraints in the energy production capacity.
In this instance, it is possible to demonstrate that the REF scenario will result in an
extraordinarily significant rise in total expenses, particularly in the area of energy generation
through transformation. In the event that privileged energy producers did not shoulder
some of the obligation, the strain that is placed on the system would be exponentially
increased. With the REF scenario, the potential for the instability of the energy system is
demonstrated once more in the context of the occurrence of a total stoppage in the supply
of power and a significant negative impact that might develop in terms of environmental
protection. This possibility is presented in the framework of the hypothetical situation. It is
possible to claim that this scenario poses the biggest threat to the energy security of Serbia
when evaluated from the perspective of energy flows at the national level based on these
results, which are examined through the framework of the prior results of assessing the
cost efficiency of the REF scenario.
The study also analyzed the whole costs of electricity generation through transfor-
mation by considering the fixed incentive purchase prices for energy producers in Serbia.
The model included the official data from the Energy Agency of the Republic of Serbia to
determine the unique incentive purchase prices for RES technologies. By analyzing the
data, we can assess the impact of incentive purchase prices on the overall costs of power
production in the energy industry. The projection of the cumulative total costs to produce
electricity by transformation in Serbia for the period up to 2050 is given in Table 6.
Incorporating both macro- and microeconomic analyses strengthened the findings
by providing a more thorough framework for evaluating the efficacy of contemporary
technology in this area. Thus, the results show that the SCOIE2 scenario is the most
environmentally beneficial, but it is highly unlikely to be achievable given the energy
sector’s existing limitations.

Sustainability 2024,16, 7204 18 of 22
Table 6. Projection of cumulative total costs to produce electricity by transformation in Serbia for the
period up to 2050 (in euros).
Scenario REF SCOIE1 SCOIE2
Cumulative total energy
transformation costs 60,429,898,300 57,875,858,706 80,698,521,392
Source: authors’ analysis.
The findings suggest that the incremental integration of renewable energy sources
(RESs) would result in a decrease in overall expenses, even though the loads from the
incentive purchase price side would have a greater absolute value owing to the instal-
lation of additional production capabilities. Improvements in energy efficiency, such as
energy savings, new energy storage systems, reduced electricity losses in distribution and
transmission, and the increased process efficiency of power plants, would result in both
enhanced energy efficiency and lower overall costs in terms of subsidized electricity sales
prices. When comparing the REF scenario to the SCOIE1 scenario, there is a noticeable
reduction in expenses of a little more than EUR 2.5 billion.
To further verify the trustworthiness of the empirical data, the case of the SCOIE2
scenario is also examined. The previous research concluded that implementing the SCOIE2
scenario might lead to a decrease in energy security, making it more challenging to take
into account the economic considerations of the specified energy mix. In the context of a
significant focus on exporting energy, enhancing the time forecasts and flexibility of the
energy system could potentially yield more accurate insights into the economic rationale
for supporting renewable energy sources within the SCOIE2 scenario. If there were data
available on future electricity costs, a thorough study could be performed. The absence of
certain components in the specified evaluation indicates that the financial viability of RES
investments, as per the SCOIE2 scenario, cannot be verified.
The objective of obtaining these data was to assess the long-term sustainability of
energy development and the financial viability of the scenarios offered for stakeholders.
An investigation was conducted, among other objectives, to see if the electricity grid
could remain stable for the specified timeframe. The collected results provide valuable
insights for the future advancement of the energy sector and validate the assertions made
by representatives of the public company “Elektromreža Srbije” (National company for
energy transmission) about the balance of energy systems.
6. Conclusions
The selection of the LEAP tool for bottom-up energy modeling was found to be
suitable for the unique requirements of analyzing relationships within the sector and for
providing significant estimates on the future utilization of energy sources. The program’s
sophistication in the chosen methodological framework allowed for the prediction of the
utilization of renewable energy sources in terms of the increase in energy efficiency. The
assessment of how the usage of renewable energy sources affects the cost effectiveness
of the system further validates the rationale for implementing the recommended energy
development strategies. An exposition of the many types of energy models and their
comparison elucidates the rationale behind selecting the bottom-up approach. The act
of constructing the model significantly influenced the uniqueness of the research. The
chosen approach was demonstrated to have contributed to the attainment of the objective of
energy mix predictions that are feasible at the national level, in alignment with the present
condition of the energy sector.
The energy system is designed to prioritize the dispatch of energy forms with the
lowest net present value of social costs using an optimization framework based on linear
programming. This ensures that current energy needs are met efficiently. The research
aimed to emphasize the significance of utilizing renewable energy sources and the incen-
tive mechanism for their utilization. The LEAP instrument successfully fulfilled all of
the requirements of the conducted energy modeling. To address the issue of the seasonal

Sustainability 2024,16, 7204 19 of 22
variability in renewable energy production, one might augment the inherent capability of
power plants that generate energy from sustainable sources. This feature offers assistance
to the model in the event of unforeseen fluctuations in energy demand and the circum-
stances surrounding an energy crisis. Furthermore, the LEAP instrument incorporates
the utilization of internal capabilities to ensure that the energy reserves remain at the
intended level during the energy production process. This is crucial for optimizing the
energy development projection model.
The decision to use the LEAP instrument for predicting and optimizing the evolution
of the energy sector of Serbia until 2050 was justified, as it successfully incorporated both
macroeconomic and microeconomic analyses. The specified software tool allowed for the
input of all crucial assumptions, as well as endogenous and exogenous variables of the
model. LEAP was chosen for the investigation due of its versatility in analyzing various
aspects of the energy system. The successful utilization of the LEAP software instrument
may be inferred from its effective implementation in accordance with the predetermined
strategy for autonomously developing a distinctive model of future energy flow projections.
By utilizing this tool, all pertinent information regarding the operations of the energy
industry were incorporated into the empirical study.
Upon performing a thorough analysis of the energy balances, it became evident that
both RES utilization alternatives would result in a substantial enhancement to the energy
sector’s efficiency. However, considering the energy transformation perspective, incorpo-
rating the proportional RES capacity in the SCOIE1 scenario would yield more favorable
outcomes for the overall sustainability of the system development. The specified option
suggests that, during the observed simulation time, there is a requirement to import fossil
fuels for their utilization in the conversion process. However, this consumption is signifi-
cantly reduced because of the increased utilization of RESs. The integration of ideal features
would enable the indicated module to be created in compliance with the green energy
regulatory policy, facilitating the gradual substitution of outdated technology drives.
It was found that the projection of energy production via transformation yielded some
interesting results. This was a category that, within the LEAP instrument, offered the largest
degree of flexibility for selecting parameters, with the intention of introducing a number
of significant model criteria. As a result, for instance, mandated reserves of renewable
energy sources were incorporated into the model. These reserves were considered to be
an important measure of strategic link to the transmission system. It turned out that the
reserves that were discussed before are especially significant for the assumption of respon-
sibility by privileged electricity producers if there is a decline in production or unforeseen
disruptions in the functioning of the power plants that they run. It has been determined,
based on the findings of the model, that the capacities that are now available within the
national electricity grid transmission of Serbia would not be capable of independently
taking on the task of balancing. Two functions were introduced into the model to take
into consideration the answer to the problem that was discussed. These functions are the
margin of projected energy reserves and the subcategory of energy storage from emerging
technologies. From the perspective of the economic analysis of the growth of the energy
sector, it was established that the expenses of balancing responsibility should be carried by
producers from the field of renewable energy sources. This was proven. All of the other
possibilities could result in significant disruptions within the electricity system by the year
2050, as it is currently witnessed.
The aforementioned results indicate once again the importance of this research for key
stakeholders from the energy sector in Serbia. A comparative examination of alternative
scenarios can produce the following conclusion: if the SCOIE1 scenario is realized, the best
degree of energy security and sustainable development of energy from the perspective
of energy production by transformation would be attained. This conclusion is based
on the results that were presented above. The model demonstrates the significant level
of interconnectedness among energy organizations and the potential for a substantial

Sustainability 2024,16, 7204 20 of 22
imbalance in the system if business operations do not adhere to the regulations of energy
security and value generation for all stakeholders.
In conclusion, the prospects for effective renewable energy generation during the
energy transformation are significantly enhanced through the principle of value co-creation.
This collaborative framework fosters innovation, inclusivity, and sustainability, ensuring
that renewable energy projects deliver maximum environmental, social, and economic
benefits. By engaging multiple stakeholders in the co-creation process, renewable energy
initiatives can achieve greater acceptance, efficiency, and long-term viability [
45
,
46
]. As the
world continues to navigate the complexities of the energy transition, embracing value co-
creation will be crucial for developing a sustainable project society that not only addresses
immediate energy needs but also builds a resilient and equitable future for all [47].
Author Contributions: Conceptualization, N.B., B.I. and N.P.; Methodology, N.B.; Software, N.B.,
D.M. and Z.R.; Validation, N.M., D.M., Z.R., M. ´
C. and J.A.R.; Formal Analysis, N.B.; Investigation,
N.B.; Data Collection, all authors; Data Curation, D.M., J.A.R. and N.M.; Writing—original draft
preparation, N.B., B.I., N.P. and J.A.R.; Writing—review and editing, B.I. and N.P.; Visualization, N.B.,
D.M., Z.R. and N.M.; Supervision N.P. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Conflicts of Interest: The authors declare no conflicts of interest.
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