METHOD FOR EVALUATING THE RESILIENCE CRITERION DURING ITS OPTIMIZATION IN A LOCAL ENERGY SYSTEM WITH CHP
This article develops a quantitative methodology for evaluating and integrating a dynamic resilience criterion of local energy systems with Combined Heat and Power (CHP). Traditional optimization models, focused mainly on cost minimization under deterministic conditions, fail to reflect the stochast...
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General Energy Institute of the National Academy of Sciences of Ukraine
2026
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| author | Khodakivskyi, Vitalii |
| author_facet | Khodakivskyi, Vitalii |
| author_institution_txt_mv | [
{
"author": "Vitalii Khodakivskyi",
"institution": null
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| description | This article develops a quantitative methodology for evaluating and integrating a dynamic resilience criterion of local energy systems with Combined Heat and Power (CHP). Traditional optimization models, focused mainly on cost minimization under deterministic conditions, fail to reflect the stochastic and adaptive nature of resilience during crises such as warfare or infrastructure disruption. The proposed method introduces a multi-stage adaptive algorithm that calculates an effective resilience factor for each generation unit and integrates it directly into the objective function. The model employs non-linear and stochastic functions to simulate real-world effects-such as saturation, thresholds, and sudden shocks stemming from both technical failures and direct physical damages (e.g., from military strikes on CHP utilities or upstream infrastructure), and establishes an economic feedback loop linking technical resilience with operational efficiency. It also accounts for the influence of external support from international organizations and location-based security factors, such as CHP placement within Eco-Industrial Parks (EIPs). By formalizing resilience as a dynamic, state-dependent parameter, this approach enables proactive planning and resource allocation to prevent system collapse rather than merely respond to it. The methodology offers policymakers and system operators a decision-support tool for prioritizing modernization investments that balance cost efficiency and resilience under high uncertainty. The study concludes that embedding resilience metrics into optimization models significantly enhances the sustainability and security of Ukraine’s energy infrastructure during reconstruction and future crisis scenarios, and these models can be replicated to other countries. |
| doi_str_mv | 10.15407/srenergy2026.01.107 |
| first_indexed | 2026-03-24T02:03:42Z |
| format | Article |
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© Khodakivskyi V., 2026
This is an Open Access article under the CC0 1.0 Universal license
https://creativecommons.org/publicdomain/zero/1.0
ISSN 2786-7633. Системні дослідження в енергетиці. 2026. 1(85) 107
https://doi.org/10.15407/srenergy2026.01.107
УДК 620.92
Vitalii Khodakivskyi, https://orcid.org/0009-0007-3237-3476
General Energy Institute of NAS of Ukraine, 172, Antonovycha St., Kyiv, 03150, Ukraine;
District Heating Strategic Working Group of the WASH Cluster in Ukraine
e-mail: etzasu@gmail.com
_______________________________________________________________________________________
METHOD FOR EVALUATING THE RESILIENCE CRITERION DURING
ITS OPTIMIZATION IN A LOCAL ENERGY SYSTEM WITH CHP
Abstract. This article develops a quantitative methodology for evaluating and integrating a dynamic
resilience criterion of local energy systems with Combined Heat and Power (CHP). Traditional
optimization models, focused mainly on cost minimization under deterministic conditions, fail to reflect the
stochastic and adaptive nature of resilience during crises such as warfare or infrastructure disruption. The
proposed method introduces a multi-stage adaptive algorithm that calculates an effective resilience factor
for each generation unit and integrates it directly into the objective function. The model employs non-linear
and stochastic functions to simulate real-world effects-such as saturation, thresholds, and sudden shocks
stemming from both technical failures and direct physical damages (e.g., from military strikes on CHP
utilities or upstream infrastructure), and establishes an economic feedback loop linking technical resilience
with operational efficiency. It also accounts for the influence of external support from international
organizations and location-based security factors, such as CHP placement within Eco-Industrial Parks
(EIPs). By formalizing resilience as a dynamic, state-dependent parameter, this approach enables
proactive planning and resource allocation to prevent system collapse rather than merely respond to it.
The methodology offers policymakers and system operators a decision-support tool for prioritizing
modernization investments that balance cost efficiency and resilience under high uncertainty. The study
concludes that embedding resilience metrics into optimization models significantly enhances the
sustainability and security of Ukraine’s energy infrastructure during reconstruction and future crisis
scenarios, and these models can be replicated to other countries.
Keywords: resilience, local energy system, combined heat and power (CHP), stochastic modeling, district
heating (DH), eco-industrial parks, energy security.
1. Introduction
Traditional techno-economic optimization models for local power systems (LPS) and district heating
systems (DHS) demonstrate limited applicability under crisis conditions, such as those induced by military
aggression or severe infrastructure failures due to a climatic or other reasons. Their objective functions,
typically centered on cost minimization or profit maximization under deterministic assumptions, fail to
adequately capture the non-linear dynamics and stochastic nature of system resilience. This creates a critical
research gap: the absence of a methodology that quantitatively integrates a dynamic resilience criterion into
the optimization framework. This work proposes such a method, designed to shift the planning paradigm from
reactive recovery to proactive, resilience-oriented investment. [1‒3]
Building upon previous research [1] that established the strategic importance of cogeneration (CHP) in
enhancing the energy security and resilience of Ukraine's DHS, this paper presents a first step for methodology
for assessing its economic feasibility alongside resilience criteria. While the strategic advantages of CHP are
evident, particularly under wartime crisis conditions, its practical implementation requires a thorough
understanding of financial viability, which is constrained by limited resources and heightened uncertainty. The
urgency of this task is starkly illustrated by the scale of destruction: recent assessments indicate that total
financial losses in the energy sector during 2022–2024 exceeded USD 33.8 billion, with the DHS sector alone
suffering extensive damage, including 815 damaged boiler houses [4, 5]. The disruptions considered within
mailto:etzasu@gmail.com
108 ISSN 2786-7633. Системні дослідження в енергетиці. 2026. 1(85)
this framework are therefore not limited to conventional technical failures but explicitly include the high-
impact, sudden shocks characteristic of warfare, such as direct physical damage to generation assets and
cascading failures from upstream infrastructure disruptions. In this context, enhancing the resilience of local
energy systems requires exploring decentralized and synergistic solutions. One promising avenue is the
utilization of waste heat from industrial enterprises, the potential of which in Ukraine is estimated at 11.1–12.4
million Gcal/year, primarily concentrated in metallurgical (58 %) and chemical (28 %) industries [6, 7].
However, the practical implementation of this potential is hindered by significant distances (often exceeding
1-2 km) between industrial zones and residential areas, making direct heat transport economically challenging.
Eco-Industrial Parks (EIPs) offer a structural solution to this barrier by fostering the co-location of energy
producers and consumers, thereby creating localized, resilient energy clusters. Within these clusters or at
existing boiler houses, technologies such as the Organic Rankine Cycle (ORC) can be employed to convert
low-grade waste heat into electrical energy, ensuring the autonomous power supply for the facility's own needs
and thus increasing its operational resilience [8‒12].
Effective implementation of such integrated strategies requires advanced spatial and operational
planning tools. Geographic Information Systems (GIS) are a necessary instrument for the modernization and
decarbonization of existing DHS, allowing for the integration of optimization models that account for the
location of renewable and local energy sources relative to consumers. While several GIS products are available
on the Ukrainian market, their functionalities and suitability for the specific needs of DHS modernization vary,
as illustrated in the comparative analysis presented in Figure 1. The practical implementation of GIS, such as
the DH. GIS software in KP "Kyivteploenergo", marks a critical step towards digital transformation. However,
the increasing digitalization of energy infrastructure introduces new vulnerabilities, particularly cyber
threats, making cyber-physical security an integral and non-negotiable component of overall system resilience
[10 – 15].
Figure 1. Comparison of available GIS products in Ukraine (Radar Chart)
This study addresses these multifaceted challenges by developing a quantitative methodology for
evaluating and integrating a dynamic resilience criterion into the techno-economic optimization framework
for local energy systems with CHP, specifically tailored for robust performance under high-uncertainty and
crisis scenarios in Ukraine, which further can be replicated in similar regions and countries.
The purpose of this research is formulated to establish methodological foundation for integrating a
dynamic resilience criterion into the techno-economic optimization of local energy systems with CHP. To
achieve this, the research pursues the following interconnected tasks: (і) To develop a coherent framework that
links qualitative system states, operational conditions, and external threats to an analytically tractable resilience
indicator. (іі) To construct a decision-making sequence that reflects real operational pathways—initial
assessment, threat detection, resource mobilization, temporary redistribution, and modular reconfiguration.
(ііі) To formulate functions capable of capturing key real-world phenomena: saturation of external support,
threshold-dependent security effects (e.g., location within an EIP), degradation dynamics of ageing
ISSN 2786-7633. Системні дослідження в енергетиці. 2026. 1(85) 109
infrastructure, and the probabilistic nature of sudden disruptions. (iv) To analyze how various resilience-
enhancing measures (CHP clustering, islanding capability, NGO support, UN agencies) affect total system
performance under both normal and crisis conditions. (v) To formalize the concept of dynamic resilience by
developing a multi-stage adaptive algorithm that translates qualitative system states and external threats into a
quantifiable, effective resilience factor (𝑅𝑒𝑓𝑓) [1, 5, 16‒18].
2. Literature Review
The resilience of energy systems has gained increasing attention due to the growing frequency of high-
impact, low-frequency (HILF) events such as extreme weather and geopolitical crises [19]. Resilience is
understood as the system’s ability to prepare for, withstand, adapt to, and recover from disturbances, moving
beyond traditional risk management [17, 20, 21] to increase climate adaptation. In contrast to reliability, which
reflects performance under normal conditions, resilience emphasizes the capacity for recovery and adaptation
in the face of unforeseen events [20]. A key distinction is also drawn between static resilience—the recovery
capability under specific conditions — and dynamic resilience, which highlights the system's capacity for
adaptation over varying temporal scales [22]. Despite its importance, a unified definition and consistent
assessment methodology remain lacking, and the terms resilience and reliability are still often used
interchangeably [23]. This research gap is particularly pronounced for DHS, where resilience studies remain
less explored compared to the extensive focus on electric transmission systems [17].
The increasing complexity of modern cyber-physical energy systems necessitates the development of
sophisticated analytical tools to evaluate resilience [21, 24]. Several studies propose quantitative approaches,
though they are often context-specific. The [18] define a resilience index based on the product of Expected
Energy Not Supplied (EENS) and repair time to assess interconnector disruptions. At [25] suggest a composite
metric integrating topological and service-based parameters, while [22] apply Bifurcation Analysis to identify
critical points and measure absorptive and adaptive capabilities in power infrastructure. For highly coupled
Power and Thermal Cyber-Physical Systems (PTCPS), specific frameworks are being developed to assess the
impact of coordinated cyber-physical attacks, using multi-stage resilience curves and coupling indicators to
trace cascading failures [26]. These methods highlight a trend towards dynamic, data-driven assessment but
also underscore the need for adaptable approaches applicable to different energy system configurations.
The challenge of enhancing energy resilience is compounded by the technical state of existing
infrastructure, particularly in countries with large-scale, outdated DHS [27]. In Ukraine, this is especially acute,
with its extensive DHS infrastructure characterized by high dependence on natural gas, aging equipment, and
a lack of backup fuel sources, making the system highly susceptible to cascading failures originating from
either physical or cyber domains [26, 27]. The transformation of this critical infrastructure requires a
scientifically grounded strategy that considers the deep interdependencies between heating and power
networks [27‒29].
Addressing these vulnerabilities requires a shift towards integrated and flexible energy systems, where
coordinated strategies can enhance fault recovery and service restoration [29‒31]. Traditional resilience
enhancement methods often suffer from rigid rules and lack dynamic adjustment mechanisms, limiting their
effectiveness in rapidly changing operating environments [23]. In response, modern approaches leverage
technologies like Power-to-Heat (PtH) and energy storage to enhance flexibility and absorb surplus renewable
energy [1, 3, 32, 33]. Furthermore, advanced strategies such as adaptive protection and predictive maintenance
are being explored to strengthen cyber-physical resilience in real-time [24]. The rise of Artificial Intelligence
offers a new paradigm, providing tools for dynamic risk perception, rapid fault blocking, and intelligent
generation of recovery strategies, thereby overcoming the limitations of traditional physics-based models [23].
Recent advanced frameworks provide sophisticated tools for quantifying interdependencies and
identifying critical contingencies in integrated power and heating networks using tailored Key Performance
Indicators (KPIs) [27]. However, applying such frameworks requires adaptation to specific regional contexts,
particularly the unique security challenges and infrastructural conditions present in Ukraine. Addressing the
Energy Trilemma — balancing energy security, affordability, and sustainability — requires a holistic approach
that connects technological solutions with national policy [34‒38]. This study contributes by developing an
110 ISSN 2786-7633. Системні дослідження в енергетиці. 2026. 1(85)
applied methodology for evaluating and improving the resilience of Ukraine’s local power and heat systems,
using CHP as a core element to navigate these complex challenges during national reconstruction.
3. Methodology
The proposed methodology is structured as a multi-stage adaptive algorithm designed to quantify and
integrate a dynamic resilience criterion into a non-linear optimization problem. Departing from traditional
static and deterministic approaches, this method through the blockchain treats resilience not as a fixed attribute
but as a state-dependent variable that evolves in response to changing operational conditions and external
threats. The core of the method lies its ability to translate complex, often qualitative, crisis scenarios into a
structured, quantitative framework suitable for techno-economic analysis [1, 5, 16, 38].
The methodology is presented in two interconnected parts. First, the conceptual Algorithmic
Framework (Section I) describes the logical sequence of operations, from initial state assessment to the
activation of crisis protocols and resilience enhancement measures. Second, the Mathematical
Formalization (Section II) provides the set of non-linear and stochastic equations that underpin the algorithm,
giving quantitative form to the concepts of saturation, thresholds, and stochastic shocks.
3.1 Algorithmic Framework
The core of the method is a state-dependent algorithm that operates according to the sequence detailed
in points 1-6 below. This structured process allows for a dynamic assessment of system vulnerabilities and the
proactive selection of optimal response strategies based on real-time conditions. It simulates a logical decision-
making process where the system first identifies the operational context (Normal vs. Crisis), then evaluates
available resources and reconfiguration options to enhance resilience, and finally integrates these technical
adjustments into the economic optimization framework. The subsequent section provides the mathematical
formalization that gives quantitative substance to each step of this conceptual algorithm.
The core of the method is a state-dependent algorithm that operates as follows:
1. Initial evaluation of the system state, including the base resilience factor, 𝑅𝑏𝑎𝑠𝑒(𝑖), operational costs,
𝐴(𝑖), and technical parameters (equipment age, MTBF, fuel availability).
2. A criticality filter classifies the operational environment as 'Normal' or 'Crisis' based on external risk
thresholds (e.g., probability of attack, 𝑃𝑎𝑡𝑡𝑎𝑐𝑘). Here, P_attack represents the assessed likelihood of a
kinetic event, such as a direct strike on a facility or a disruption of its critical supply lines.
3. The algorithm evaluates available response strategies based on binary conditions: resource
mobilization (e.g., access to grants), resource migration (e.g., fuel redirection), and system
reconfiguration (e.g., islanding capability).
4. The resilience factor is dynamically adjusted along the selected decision path using non-linear
functions that model real-world effects such as saturation and thresholds.
5. A stochastic shock, 𝑆, is applied to the effective resilience factor to simulate the impact of sudden
failures or attacks. This shock variable, 𝑆, is designed to quantify the immediate degradation of a unit's
operational capacity, ranging from a partial loss of function due to nearby infrastructure damage to a
complete outage from a direct physical strike. This yields a shocked resilience factor, 𝑅𝑠ℎ𝑜𝑐𝑘𝑒𝑑(𝑖).
6. The final resilience value, 𝑅𝑠ℎ𝑜𝑐𝑘𝑒𝑑(𝑖), is used to adjust economic parameters, primarily operational
costs, 𝐴𝑎𝑑𝑗(𝑖), and the penalty term within the optimization's objective function.
In essence, the algorithmic framework shown in Figure 2 represents a structured decision-making protocol.
It begins with a baseline assessment and continuously monitors for threats. Upon detecting a crisis, it navigates
a cascade of decision nodes, prioritizing proactive resilience enhancement measures such as resource
mobilization and system reconfiguration. If these pathways are unavailable, it defaults to a failsafe "Crisis
mode" to protect critical consumers. The final output of this algorithm—the adjusted resilience factor and its
corresponding economic parameters—then serves as the primary input for the formal optimization model,
ensuring that decisions are not only cost-effective but also robust against disruptions.
ISSN 2786-7633. Системні дослідження в енергетиці. 2026. 1(85) 111
Figure 2. Algorithm for response and resilience integration in the optimization of a local energy system
The proposed method operates through a multi-stage process: (i) Assessment of basic resilience ‒
evaluation of initial parameters for each generation unit, including 𝑅𝑏𝑎𝑠𝑒(𝑖), operational costs 𝐴(𝑖), equipment
age, MTBF, and MTTR; (ii) Threat identification ‒ activation of the emergency protocol when the threat level
𝑃𝑎𝑡𝑡𝑎𝑐𝑘(𝑖) ≥ 𝜏; (iii) Resilience enhancement cascade, comprising three conditional pathways: (a) Mobilization
of resources - preventive maintenance and spare part replenishment improving 𝑅𝑎𝑑𝑗(𝑖) = 𝑚𝑖𝑛(1, 𝑅𝑎𝑑𝑗(𝑖) +
𝛥𝑅𝑚𝑎𝑖𝑛𝑡); (b) Alternative redistribution ‒ reallocation of fuel or personnel to prioritized critical nodes; (c)
Modular reconfiguration - activation of islanding, TES, and BES units, ensuring redundancy according to
𝑅𝑣𝑚𝑜𝑑(𝑖) = 1 − (10 − 𝑅𝑎𝑑𝑗(𝑖))
𝑁𝑚𝑜𝑑.
If no enhancement path is available, the Crisis mode ensures minimal service for critical consumers.
The final stages involve Mathematical integration and optimization ‒ adjusting cost and penalty functions
𝑝𝑒𝑛𝑎𝑑𝑗 ‒ and Monitoring & adaptation, where smart metering data dynamically update model parameters
under changing operational or emergency conditions.
3.2 Mathematical Formalization
The transition from the qualitative decision paths of the algorithm to quantitative metrics is achieved
through a set of non-linear and stochastic equations. Each formula is designed to model a specific aspect of
resilience dynamics and directly corresponds to the logic presented in the algorithmic framework.
Formula (1) quantifies the impact of external support, as identified in the "Are grants available / NGOs?"
decision node. A sigmoid function is employed to realistically model the law of diminishing returns: the initial
tranches of support (e.g., spare parts, training) provide a significant boost to reliability, but the marginal benefit
decreases as the support level (𝜉𝑁𝐺𝑂(𝑖) increases, eventually reaching a saturation point (𝐼𝑅). This prevents the
model from overestimating the effect of unlimited aid.
𝛥𝑅𝑁𝐺𝑂(𝑖) =
𝐼𝑅
1+𝑒−𝑘𝑁𝐺𝑂(𝜉𝑁𝐺𝑂(𝑖)−𝑐𝑁𝐺𝑂)
, (1)
where 𝜉𝑁𝐺𝑂(𝑖) is the support intensity, 𝐼𝑅 is the maximum reliability improvement, and 𝑘𝑁𝐺𝑂, 𝑐𝑁𝐺𝑂 define
the saturation point.
The resilience bonus from locating a CHP in an EIP, as considered in the baseline assessment, is not
uniform. Formula (2) models this as a state-dependent, piecewise function. If the security level of the location
(𝑃𝑠𝑒𝑐) meets or exceeds a predefined threshold (𝜏𝐸𝐼𝑃), the full resilience bonus (𝛽𝐸𝐼𝑃) is applied. If the location
is less secure, a reduced bonus is applied, reflecting a higher residual risk. This allows for a more granular
assessment of location-based security.
112 ISSN 2786-7633. Системні дослідження в енергетиці. 2026. 1(85)
𝛽𝐸𝐼𝑃
𝑒𝑓𝑓
= {
𝛽𝐸𝐼𝑃, 𝑖𝑓𝑃𝑠𝑒𝑐 ≥ 𝜏𝐸𝐼𝑃
𝜆𝐸𝐼𝑃𝛽𝐸𝐼𝑃, 𝑖𝑓𝑃𝑠𝑒𝑐 < 𝜏𝐸𝐼𝑃
. (2)
This function models the baseline technical condition of a unit. Formula (3) captures two competing
effects: the accelerating degradation of equipment as its age surpasses a critical point (𝑎𝑔𝑒𝑐𝑟𝑖𝑡), modeled by
the hyperbolic tangent; and the positive, saturating effect of maintenance funding (𝑓𝑢𝑛𝑑), where initial
investments yield the highest returns.
𝑓𝑚𝑎𝑖𝑛𝑡(𝑀) = −𝑎 ×𝑡𝑎𝑛ℎ 𝑡𝑎𝑛ℎ(𝛾(𝑎𝑔𝑒 − 𝑎𝑔𝑒𝑐𝑟𝑖𝑡)) + 𝛿 ×
𝑓𝑢𝑛𝑑
𝑓𝑢𝑛𝑑+ℎ
. (3)
To reflect the unpredictable nature of crises identified by the "External or internal threat?" filter, Formula
(4) introduces a stochastic shock. The effective resilience factor (𝑅𝑒𝑓𝑓) is multiplied by a factor (1 − 𝑆), where
𝑆 is a random variable. This simulates the immediate degradation of a unit's performance during an event like
a physical attack or a critical component failure, moving the model beyond deterministic analysis.
𝑅𝑠ℎ𝑜𝑐𝑘𝑒𝑑(𝑖) = 𝑅𝑒𝑓𝑓(𝑖) × (1 − 𝑆). (4)
A detailed exploration of the specific probability distributions used for the parameterization of𝑆 and
their application in practical calculations will be the subject of subsequent research.
This formula establishes the crucial economic feedback loop. It dictates that adjusted operational costs
(𝐴𝑎𝑑𝑗) are a non-linear function of the final resilience factor. The exponent 𝜂 > 1ensures a synergistic effect:
at higher levels of resilience, even small additional improvements in 𝑅𝑠ℎ𝑜𝑐𝑘𝑒𝑑can lead to disproportionately
larger cost savings, reflecting the high economic value of preventing catastrophic failures.
𝐴𝑎𝑑𝑗(𝑖) = 𝐴(𝑖) × (1 − 𝑘 × 𝑅𝑠ℎ𝑜𝑐𝑘𝑒𝑑(𝑖)
𝜂), 𝜂 > 1. (5)
Finally, the calculated resilience factor is integrated into the main optimization problem. Formula (6)
uses an exponential function to reduce the base penalty term (𝑝𝑒𝑛𝑏𝑎𝑠𝑒) as the resilience 𝑅𝑠ℎ𝑜𝑐𝑘𝑒𝑑 increases.
This creates a powerful incentive within the model, making more resilient generation units significantly more
competitive and economically attractive during the optimization process.
𝑝𝑒𝑛𝑖 = 𝑝𝑒𝑛𝑏𝑎𝑠𝑒,𝑖 × 𝑒𝑥𝑝(−𝜇 × 𝑅𝑠ℎ𝑜𝑐𝑘𝑒𝑑(𝑖)
𝜂). (6)
Collectively, these mathematical formulations (1‒6) provide the engine for the algorithmic framework
outlined in Section 3.1. Their primary function is to compute a single, integrated metric ‒ the shocked resilience
factor (R_s𝑅𝑠ℎ𝑜𝑐𝑘𝑒𝑑(𝑖)‒ for each generation unit. This factor is not merely a static indicator; it is a dynamic
output that synthesizes a unit's baseline technical condition, the non-linear effects of external support and
location-based security, and the stochastic impact of a potential crisis event. The ultimate contribution of this
mathematical apparatus is the establishment of a direct, quantitative link between the physical and
organizational resilience of an asset and its economic representation within the optimization model [1, 16].
This enables the framework to move beyond simple cost-benefit analysis and perform a true risk-informed,
resilience-oriented optimization, where investing in resilience is treated as a measurable and economically
rational strategy.
4. Discussion
The integration of a dynamic resilience criterion into techno-economic optimization frameworks
presents significant potential to enhance the operational security, overall system resilience, strategic planning,
and investment prioritization for local energy systems, aligning with broader national energy security goals
ISSN 2786-7633. Системні дослідження в енергетиці. 2026. 1(85) 113
[38‒42]. One of the primary methodological advancements outlined is the formalization of resilience as a
dynamic, state-dependent variable. The use of non-linear functions, such as sigmoid and piecewise models,
offers substantial promise in capturing the real-world dynamics of crisis response, where the effects of support
and security measures are rarely linear. However, challenges such as the need for robust data for parameter
calibration (e.g., security thresholds, saturation points of external aid) and the inherent complexity of modelling
stochastic events present barriers that necessitate further empirical validation and refinement. The
incorporation of a stochastic shock model also highlights an innovative avenue for moving beyond
deterministic analysis, albeit with associated computational complexities.
From an economic and strategic standpoint, embedding resilience metrics directly into optimization
promises to shift the paradigm from reactive, cost-based planning to proactive, robust optimization. The
proposed framework fosters a decision-making environment where investments in resilience — such as
reinforcing equipment, securing backup fuel, or choosing safer locations like EIPs — are no longer treated as
unquantifiable overheads but as measurable contributors to operational stability and long-term economic
viability [1]. By capitalizing on technologies like CHP, local systems can extend their roles within energy
networks, providing valuable stability services that enhance grid security amidst increasing uncertainty. This,
in turn, bolsters the economic case for decentralized generation in competitive settings, where ensuring service
continuity is crucial.
The practical applicability of this framework is particularly salient in the context of Ukraine's post-
conflict reconstruction. In an environment of high risk and scarce capital, traditional cost-benefit analysis is
insufficient for guiding investment. The proposed methodology offers a more robust alternative. By
quantitatively assessing the value of external support (e.g., from IFIs and NGOs, UN agencies), the security
benefits of strategic siting in hardened locations like EIPs, and the potential economic fallout from sudden
shocks, the framework provides a data-driven tool for policymakers and international partners. It allows for
the prioritization of investments that enhance not only operational efficiency but, more importantly, long-term
system survivability and security.
Despite the clear benefits, several challenges must be addressed. The complex interplay between
different resilience factors requires careful calibration and management, demanding robust data pipelines and
strategic planning. The financial implications of resilience-oriented investments, particularly concerning
upfront costs versus long-term avoided losses, need in-depth analysis through life-cycle cost assessment
(LCCA), which is beyond the scope of the current annual optimization model.
Looking towards future research directions, the framework laid out can be expanded through various
alternative configurations. Given the diverse potential interactions between technical resilience, economic
incentives, and policy measures, further studies could focus on optimizing these relationships to maximize
national and local security outcomes. Advanced modelling techniques, such as machine learning and agent-
based simulations, could be applied to analyze the impact of different resilience strategies under a wider range
of crisis scenarios, providing critical insights for policymakers and system operators.
Additionally, ongoing advancements in smart grid technologies, real-time data analytics, and GIS will
play a pivotal role in refining this methodology. As the operation of local energy systems becomes more data-
driven, integrating advanced analytics could lead to more accurate, real-time updates of the resilience factor
and better coordination between interconnected systems, thereby enhancing overall performance and adaptive
capacity.
5. Conclusions
The development of a quantitative methodology for integrating dynamic resilience into techno-
economic optimization represents a critical advancement from traditional, deterministic planning models. This
research confirms that resilience can be effectively transitioned from a qualitative concept into a quantifiable,
state-dependent variable, calculated through a multi-stage adaptive algorithm. This approach enables proactive
planning and resource allocation to prevent system collapse rather than merely reacting to it. Through the
application of non-linear and stochastic models, the framework captures real-world phenomena such as support
114 ISSN 2786-7633. Системні дослідження в енергетиці. 2026. 1(85)
saturation and sudden shocks, while establishing a direct, quantitative feedback loop linking a system's
physical and organizational resilience to its economic performance within the optimization model.
Building on this methodological foundation, a subsequent study submitted for publication applies these
principles within a novel techno-economic model to analyze the role of CHP, sustainable funding, and strategic
modernization in enhancing Ukraine's energy security. That analysis confirms that preventing system collapse
is significantly more cost-effective than rebuilding and identifies key operational thresholds for CHP viability,
such as the necessity of maintaining at least a 50 % thermal load over 5,000 operating hours. This progression
from a theoretical framework to an applied analysis, where further quantitative calculations will be performed
in subsequent work, demonstrates the practical utility of integrating resilience into economic modeling.
In summary, this research underscores the transformative capacity of integrating dynamic resilience
analytics into energy system planning, positioning these methods at the forefront of sustainable and secure
infrastructure development. Continued innovation and strategic integration of these dynamic assessment tools
will be vital for catering to evolving energy demands and meeting stringent security and environmental targets
globally.
References
1. Khodakivskyi, V., Karpenko, D., Bilous, I., & Savickas, R. (2025). Ensuring Energy Security in Ukraine: The Role
of Cogeneration and Sustainable Funding for District Heating Systems. Rocznik Ochrona Środowiska, 27, 196‒210.
https://doi.org/10.54740/ros.2025.016
2. Derii, V., Nechaieva, T., & Leshchenko, I. (2023). Assessment of the effect of structural changes in Ukraine’s district
heating on the greenhouse gas emissions. Science and Innovation, 19(4), 57–65.
https://doi.org/10.15407/scine19.04.057
3. Babak, V., & Kulyk, M. (2023). Increasing the efficiency and security of Integrated Power System operation through
heat supply electrification in Ukraine. Science and Innovation, 19(5), 100–116.
https://doi.org/10.15407/scine19.05.100
4. Semenenko, O., Kuravskiyi, V., Kliat, Yu., Cherniavskyi, R., & Chernyshova, I. (2025). Energy security as a key
factor in Ukrainian economic resilience in the time of war. Scientific Bulletin of Mukachevo State University. Series
Economics, 12(1), 118‒133. https://doi.org/10.52566/msu-econ1.2025.118
5. Tavlui, T., Sick, M., & Khodakivskyi, V. (2025). DH Strategic Guidance Note 2025-2026.
https://doi.org/10.13140/RG.2.2.13398.51529
6. Kramar, V., & Oliynyk, Ye. (2022). Analysis of waste heat and geothermal heat potentials for district heating in
Ukraine. NGO "Agency for Renewable Energy". Retrieved October 12, 2025, from https://rea.org.ua/wp-
content/uploads/2022/12/analysis_of_waste_heat_and_geothermal_heat_potentials_for_district_heating_in_ukrain
e_en_2022.pdf
7. Savickas, R., Savickiene, L., & Paulauskas, M. Energy efficiency means according to Energy Efficiency
2012/27/EU directive to decrease energy consumption for a final consumer https://doi.org/10.3846/mla.2015.822
8. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH. (n.d.). ReWarm library. Retrieved October
12, 2025, from https://rewarm.nlu.org.ua/s/rewarm/page/main
9. Global Eco-Industrial Parks Programme in Ukraine. (n.d.). GEIPP-Ukraine. Retrieved October 12, 2025, from
https://geipp-ukraine.org/
10. Danfoss Ukraine. Retrieved October 12, 2025, from https://www.danfoss.com/uk-ua/
11. Nikitin, Ye.Ye., & Komkov, I.S. (2021). Geoinformation aspects of district heating modernization. Energy
Technologies & Resource Saving, 2, 44‒51 [in Ukrainian]. https://doi.org/10.33070/etars.2.2021.04
12. Ma, Z. G., & Lygnerud, K. (2025). Digital Transformation of District Heating: A Scoping Review of Technological
Innovation, Business Model Evolution, and Policy Integration. Energies, 18(22), 5994.
https://doi.org/10.3390/en18225994
13. Bielokha, H., Chupryna, L., Denisyuk, S., Eutukhova, T., & Novoseltsev, O. (2023). Hybrid Energy Systems and
the Logic of Their Service-Dominant Implementation: Screening the Pathway to Improve Results. Energy
Engineering, 120(6), 1307‒1323. https://doi.org/10.32604/ee.2023.025863
14. USAID. (2021). Implementation of software for modeling district heating systems Globema DH.GIS in KP
"Kyivteploenergo".
15. Kyivteploenergo. (n.d.). Kyivteploenergo is implementing a geoinformation system for managing the capital's heat
supply complex. Retrieved October 12, 2025, from https://kte.kmda.gov.ua/kyyivteploenergo-vprovadzhuye-
geoinformatsijnu-systemu-upravlinnya-kompleksom-teplopostachannya-stolytsi/ [in Ukrainian].
16. Karpenko, D., Deshko, V., Eutukhova, T., Novoseltsev, O., & Teslenko, O. (2025). Analysis of the Market Aspects
in the Reform of District Heating Systems Under Wartime and Post-war Recovery Conditions. Systems, Decision
and Control in Energy VII (pp. 229‒274). Springer. http://doi.org/10.1007/978-3-031-90466-0_9
https://doi.org/10.54740/ros.2025.016
https://doi.org/10.52566/msu-econ1.2025.118
https://doi.org/10.13140/RG.2.2.13398.51529
https://doi.org/10.3846/mla.2015.822
https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Frewarm.nlu.org.ua%2Fs%2Frewarm%2Fpage%2Fmain&data=05%7C02%7Cvitalii.khodakivskyi%40giz.de%7Caf2f3a2315f64723c77508de4138621e%7C5bbab28cdef3460488225e707da8dba8%7C0%7C0%7C639019907594400397%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Nz2oaMG7J6hC6444S%2FKgIiG6Xwp638ynuVebLTXCbGM%3D&reserved=0
https://geipp-ukraine.org/
https://www.danfoss.com/uk-ua/
https://doi.org/10.3390/en18225994
https://doi.org/10.32604/ee.2023.025863
https://kte.kmda.gov.ua/kyyivteploenergo-vprovadzhuye-geoinformatsijnu-systemu-upravlinnya-kompleksom-teplopostachannya-stolytsi/
https://kte.kmda.gov.ua/kyyivteploenergo-vprovadzhuye-geoinformatsijnu-systemu-upravlinnya-kompleksom-teplopostachannya-stolytsi/
http://doi.org/10.1007/978-3-031-90466-0_9
ISSN 2786-7633. Системні дослідження в енергетиці. 2026. 1(85) 115
17. Krīgers, G., Pakere, I., & Blumberga, D. (2024). Enhancing the Evaluation of District Heating System Resilience:
A Literature Review. CONECT. International Scientific Conference of Environmental and Climate Technologies,
19. https://doi.org/10.7250/CONECT.2024.019
18. Bermejo, I. A., Foretić, H., Kopustinskas, V., & Fulli, G. (2025). Resilience assessment of a power system due to
disruption of interconnectors. Environment Systems and Decisions, 45(3). https://doi.org/10.1007/s10669-025-
10041-2
19. Bahramirad, S., Pandey, S., & Paaso, A. (2025). Generation, Transmission, and Distribution System Resilience: A
Holistic Approach for Enhancing the Power System Sustainability. IEEE Energy Sustainability Magazine, 1(1),
106‒114. https://doi.org/10.1109/ESM.2025.3559291
20. Eremia, M., Toma, L., Sanduleac, M., & Damian, I.-C. (2024). Aspects regarding the resilience concept in power
system Part I: Challenges and resilience in power systems. Journal of Engineering Sciences and Innovation, 9(1),
69‒92. https://doi.org/10.56958/jesi.2024.9.1.69
21. Otalvaro, R. G., & Watson, B. (2025). Bifurcation Analysis for System Resilience: A Case Study on Power
Infrastructure. INCOSE International Symposium, 35(1), 1845‒1859. https://doi.org/10.1002/iis2.70091
22. Xu, S. (2025). Theoretical Research Review on Artificial Intelligence-Enhanced Power System Dynamic Resilience.
Theoretical and Natural Science, 120(1), 70‒77. https://doi.org/10.54254/2753-8818/2025.AD25815
23. Tiwari, S., Chakrabarty, A., Schelly, C., Sahraei-Ardakani, M., Ou, G., & Chen, J. (2025). How electricity utility
practitioners in the United States approach power system resilience. Future Energy, 4(1), 19‒29.
https://doi.org/10.55670/fpll.fuen.4.1.3
24. Collins, J., Brooks, P., & Qureshi, K. (2025). Cyber-Physical Resilience in Modern Power Systems: Adaptive
Protection and Predictive Maintenance Strategies [Preprint]. Retrieved October 12, 2025, from
https://www.researchgate.net/publication/396680172
25. Babu, K. V. S. M., Dwivedi, D., Chakraborty, P., & Pal, M. (2025). Resilience Enhancement of Electrical
Distribution Systems with Mobile Energy Truck. Journal of Clean Energy and Energy Storage, 02.
https://doi.org/10.1142/S2811034X25500054
26. Zhou, B., Su, R., Zang, T., Li, C., Tong, X., & Gong, V. Y. (2025). Resilience Assessment of Power-Thermal
Systems Considering Coordinated Cyber and Physical Attacks. IEEE Internet of Things Journal, PP(99), 1.
https://doi.org/10.1109/JIOT.2025.3589146
27. Nikitin, Ye.Ye., & Dutka, O.V. (2025). Transformation of outdated district heating systems. Energy Technologies
& Resource Saving, 84(3), 65‒79. http://doi.org/10.33070/etars.3.2025.05
28. Zare Oskouei, M., Gökçek, T., Erenoğlu, A. K., Erdinç, O., & Catalão, J. P. S. (2025). Resilience Analytics for
Integrated Power and District Heating Networks by Identifying Critical Contingencies. IEEE Transactions on
Systems Man and Cybernetics Systems, PP(99), 1. https://doi.org/10.1109/TSMC.2025.3612864
29. Lepiksaar, K., Mašatin, V., Latõšov, E., Siirde, A., & Volkova, A. (2021). Improving CHP flexibility by integrating
thermal energy storage and power-to-heat technologies into the energy system. Smart Energy, 2, 100022.
https://doi.org/10.1016/j.segy.2021.100022
30. Wang, K., Xue, Y., Guo, Q., & Sun, H. (2022). A Coordinated Reconfiguration Strategy for Multi-Stage Resilience
Enhancement in Integrated Power Distribution and Heating Networks. IEEE Transactions on Smart Grid, PP(99),
1. https://doi.org/10.1109/TSG.2022.3231590
31. Wang, K., Ge, H., Yang, Y., Pan, Z., Liu, Y., & Zhao, H. (2023). Collaborative Service Restoration with Network
Reconfiguration for Resilience Enhancement in Integrated Electric and Heating Systems. Electronics, 12(18), 3792.
https://doi.org/10.3390/electronics12183792
32. Derii, O.V., Nechaieva, T., & Zgurovets, O.V. (2024). Technological possibilities of increasing the resilience of the
power and district heating systems of Ukraine. Energy Technologies & Resource Saving, 81(4), 5‒21.
https://doi.org/10.33070/etars.4.2024.01
33. Derii, V., Zgurovets, O., Havrylenko, Y., & Zaporozhets, A. (2025). Conditions and Limitations for Implementing
Power-to-Heat Technology in District Heating Systems of Ukraine. Systems, Decision and Control in Energy VII
(pp. 275‒289). Springer. https://doi.org/10.1007/978-3-031-90466-0_10
34. Rovynska, K. (2025). Improving the conceptual approaches of the state to the national resilience system. State
Formation, 1(37), 68‒83. [in Ukrainian]. https://doi.org/10.26565/1992-2337-2025-1-04
35. Aditi, B., Sitorus, O. T., Bulan, T. R. N., Pentana, S., & Hafas, H. R. (2025). Building a Community-Based
Entrepreneurial Ecosystem for Local Economic Resilience. KREATIF Jurnal Pengabdian Masyarakat Nusantara,
5(4), 192‒203. https://doi.org/10.55606/kreatif.v5i4.8558
36. Björner Brauer, H., Håkansson, M., & Willermark, S. M. J. (2025). Exploring energy resilience: households’
perspectives on a changing power system. Energy, Sustainability and Society, 15(1). https://doi.org/10.1186/s13705-
025-00530-2
37. Deshko,V.I., & Karpenko, D.S. (2018). Analysis of conditions for the creation of the local thermal energy market
in Ukraine. Municipal economy of cities, 7(146), 68–76. https://doi.org/10.33042/2522-1809-2018-7-146-68-76
38. Khodakivskyi, V.O., & Karpenko, D.S. (2025). Assessment of the efficiency level of cogeneration under conditions
of modernization and heat supply system backup. POWER ENGINEERING: economics, technique, ecology, 2(80),
12‒19 [in Ukrainian]. https://doi.org/10.20535/1813-5420.2.2025.327134
https://www.researchgate.net/publication/396680172
https://doi.org/10.1142/S2811034X25500054
https://doi.org/10.20535/1813-5420.2.2025.327134
116 ISSN 2786-7633. Системні дослідження в енергетиці. 2026. 1(85)
39. Deshko, V., Bilous, I., Buyak, N., & Shevchenko, O. (2020). The Impact of Energy-Efficient Heating Modes on
Human Body Exergy Consumption in Public Buildings 2020 IEEE 7th International Conference on Energy Smart
Systems (pp. 201‒205), 9160270. https://doi.org/10.1109/ESS50319.2020.9160270
40. Rafati, A., Tahavori, M., & Shaker, H. R. (2025). Data-Driven Reliability Analysis of District Heating Systems for
Asset Management Applications: A Review. Sustainable Cities and Society, 118, 106052.
http://doi.org/10.1016/j.scs.2024.106052
41. Hostos, H., Goepp, V., & Sondi, P. (2025). A Systematic Literature Review of Resilience Approaches in Production
Systems. IEEE Transactions on Engineering Management, PP(99), 1‒48.
https://doi.org/10.1109/TEM.2025.3606576
42. Denysov, V., Babak, V., Zaporozhets, A., Nechaieva, T., & Kostenko, G. (2024). Energy System Optimization
Potential with Consideration of Technological Limitations. Retrieved October 12, 2025, from
https://ssrn.com/abstract=4936175
МЕТОД ОЦІНЮВАННЯ КРИТЕРІЮ РЕЗИЛЬЄНТНОСТІ ПІД
ЧАС ОПТИМІЗАЦІЇ ЛОКАЛЬНОЇ ЕНЕРГЕТИЧНОЇ СИСТЕМИ
З ТЕЦ
Віталій Ходаківський, https://orcid.org/0009-0007-3237-3476
Інститут загальної енергетики НАН України, вул. Антоновича, 172, Київ, 03150, Україна
e-mail: etzasu@gmail.com
Анотація. У статті запропоновано кількісну методологію для оцінки та інтеграції критерію
динамічної стійкості, гнучкості та відновлення (резильєнтності) локальної (розподіленої)
енергетичної системи з комбінованим виробництвом тепла та електроенергії (ТЕЦ). Традиційні
моделі оптимізації, зосереджені переважно на мінімізації витрат за детермінованих умов, не
відображають стохастичний та адаптивний характер резильєнтності під час криз, таких як
воєнні дії або порушення роботи інфраструктури. Запропонований метод передбачає
багатоступеневий адаптивний алгоритм, який обчислює ефективний коефіцієнт резильєнтності
для кожної генераційної одиниці та інтегрує його безпосередньо в цільову функцію. Модель
використовує нелінійні та стохастичні функції для моделювання реальних ефектів, таких як
насичення, порогові значення, а також раптові потрясіння, спричинені як технічними збоями, так
і прямими фізичними пошкодженнями, і встановлює економічний зворотний зв'язок, що пов'язує
технічну резильєнтність з операційною ефективністю. Вона також враховує вплив зовнішньої
підтримки з боку міжнародних організацій та факторів безпеки, пов'язаних із місцем
розташування, таких як розміщення ТЕЦ в екоіндустріальних парках (ЕІП). Формалізуючи
резильєнтність як динамічний параметр, що залежить від стану, цей підхід дозволяє здійснювати
проактивне планування та розподіл ресурсів для запобігання колапсу системи, а не просто
реагувати на нього. Методологія пропонує політикам та операторам систем інструмент
підтримки прийняття рішень для визначення пріоритетності інвестицій у модернізацію, що
забезпечують баланс між економічною ефективністю, стійкістю та відновленням в умовах
високої невизначеності. У дослідженні зроблено висновок, що включення показників резильєнтності
до моделей оптимізації підвищує надійність енергетичної інфраструктури України під час
реконструкції та в майбутніх кризових сценаріях; цю модель можна відтворити в інших країнах.
Ключові слова: енергетична система, когенерація, ТЕЦ, стохастичне моделювання, централізоване
теплопостачання (ЦТ), екоіндустріальні парки, енергетична безпека.
Дата першого надходження статті до журналу: 05.01.2026
Дата прийняття статті до друку після рецензування: 27.01.2026
Дата публікації (оприлюднення): 09.03.2026
http://doi.org/10.1016/j.scs.2024.106052
https://ssrn.com/abstract=4936175
https://orcid.org/0009-0007-3237-3476
mailto:etzasu@gmail.com
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| spelling | systemreorg-article-9422026-07-18T12:57:50Z METHOD FOR EVALUATING THE RESILIENCE CRITERION DURING ITS OPTIMIZATION IN A LOCAL ENERGY SYSTEM WITH CHP Метод оцінювання критерію резильєнтності під час оптимізації локальної енергетичної системи з ТЕЦ Khodakivskyi, Vitalii resilience, local energy system, combined heat and power (CHP), stochastic modeling, district heating (DH), eco-industrial parks, energy security. енергетична система, когенерація, ТЕЦ, стохастичне моделювання, централізоване теплопостачання (ЦТ), екоіндустріальні парки, енергетична безпека. This article develops a quantitative methodology for evaluating and integrating a dynamic resilience criterion of local energy systems with Combined Heat and Power (CHP). Traditional optimization models, focused mainly on cost minimization under deterministic conditions, fail to reflect the stochastic and adaptive nature of resilience during crises such as warfare or infrastructure disruption. The proposed method introduces a multi-stage adaptive algorithm that calculates an effective resilience factor for each generation unit and integrates it directly into the objective function. The model employs non-linear and stochastic functions to simulate real-world effects-such as saturation, thresholds, and sudden shocks stemming from both technical failures and direct physical damages (e.g., from military strikes on CHP utilities or upstream infrastructure), and establishes an economic feedback loop linking technical resilience with operational efficiency. It also accounts for the influence of external support from international organizations and location-based security factors, such as CHP placement within Eco-Industrial Parks (EIPs). By formalizing resilience as a dynamic, state-dependent parameter, this approach enables proactive planning and resource allocation to prevent system collapse rather than merely respond to it. The methodology offers policymakers and system operators a decision-support tool for prioritizing modernization investments that balance cost efficiency and resilience under high uncertainty. The study concludes that embedding resilience metrics into optimization models significantly enhances the sustainability and security of Ukraine’s energy infrastructure during reconstruction and future crisis scenarios, and these models can be replicated to other countries. У статті запропоновано кількісну методологію для оцінки та інтеграції критерію динамічної стійкості, гнучкості та відновлення (резильєнтності) локальної (розподіленої) енергетичної системи з комбінованим виробництвом тепла та електроенергії (ТЕЦ). Традиційні моделі оптимізації, зосереджені переважно на мінімізації витрат за детермінованих умов, не відображають стохастичний та адаптивний характер резильєнтності під час криз, таких як воєнні дії або порушення роботи інфраструктури. Запропонований метод передбачає багатоступеневий адаптивний алгоритм, який обчислює ефективний коефіцієнт резильєнтності для кожної генераційної одиниці та інтегрує його безпосередньо в цільову функцію. Модель використовує нелінійні та стохастичні функції для моделювання реальних ефектів, таких як насичення, порогові значення, а також раптові потрясіння, спричинені як технічними збоями, так і прямими фізичними пошкодженнями, і встановлює економічний зворотний зв'язок, що пов'язує технічну резильєнтність з операційною ефективністю. Вона також враховує вплив зовнішньої підтримки з боку міжнародних організацій та факторів безпеки, пов'язаних із місцем розташування, таких як розміщення ТЕЦ в екоіндустріальних парках (ЕІП). Формалізуючи резильєнтність як динамічний параметр, що залежить від стану, цей підхід дозволяє здійснювати проактивне планування та розподіл ресурсів для запобігання колапсу системи, а не просто реагувати на нього. Методологія пропонує політикам та операторам систем інструмент підтримки прийняття рішень для визначення пріоритетності інвестицій у модернізацію, що забезпечують баланс між економічною ефективністю, стійкістю та відновленням в умовах високої невизначеності. У дослідженні зроблено висновок, що включення показників резильєнтності до моделей оптимізації підвищує надійність енергетичної інфраструктури України під час реконструкції та в майбутніх кризових сценаріях; цю модель можна відтворити в інших країнах. General Energy Institute of the National Academy of Sciences of Ukraine 2026-03-03 Article Article application/pdf https://systemre.org/index.php/journal/article/view/942 10.15407/srenergy2026.01.107 System Research in Energy; No. 1 (85) (2026): System Research in Energy; 107-116 Системні дослідження в енергетиці; № 1 (85) (2026): Системні дослідження в енергетиці; 107-116 2786-7102 2786-7633 uk https://systemre.org/index.php/journal/article/view/942/835 Copyright (c) 2026 Vitalii Khodakivskyi https://creativecommons.org/publicdomain/zero/1.0 |
| spellingShingle | resilience local energy system combined heat and power (CHP) stochastic modeling district heating (DH) eco-industrial parks energy security. Khodakivskyi, Vitalii METHOD FOR EVALUATING THE RESILIENCE CRITERION DURING ITS OPTIMIZATION IN A LOCAL ENERGY SYSTEM WITH CHP |
| title | METHOD FOR EVALUATING THE RESILIENCE CRITERION DURING ITS OPTIMIZATION IN A LOCAL ENERGY SYSTEM WITH CHP |
| title_alt | Метод оцінювання критерію резильєнтності під час оптимізації локальної енергетичної системи з ТЕЦ |
| title_full | METHOD FOR EVALUATING THE RESILIENCE CRITERION DURING ITS OPTIMIZATION IN A LOCAL ENERGY SYSTEM WITH CHP |
| title_fullStr | METHOD FOR EVALUATING THE RESILIENCE CRITERION DURING ITS OPTIMIZATION IN A LOCAL ENERGY SYSTEM WITH CHP |
| title_full_unstemmed | METHOD FOR EVALUATING THE RESILIENCE CRITERION DURING ITS OPTIMIZATION IN A LOCAL ENERGY SYSTEM WITH CHP |
| title_short | METHOD FOR EVALUATING THE RESILIENCE CRITERION DURING ITS OPTIMIZATION IN A LOCAL ENERGY SYSTEM WITH CHP |
| title_sort | method for evaluating the resilience criterion during its optimization in a local energy system with chp |
| topic | resilience local energy system combined heat and power (CHP) stochastic modeling district heating (DH) eco-industrial parks energy security. |
| topic_facet | resilience local energy system combined heat and power (CHP) stochastic modeling district heating (DH) eco-industrial parks energy security. енергетична система когенерація ТЕЦ стохастичне моделювання централізоване теплопостачання (ЦТ) екоіндустріальні парки енергетична безпека. |
| url | https://systemre.org/index.php/journal/article/view/942 |
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