Distributed Energy Systems as an Instrument for Strengthening the Resilience of Critical Infrastructure in Crisis Management

Marcin Rabe; Tomasz Norek; Andrzej Gawlik; Katarzyna Widera; Marcin Jurgilewicz; Bartosz Kozicki; Aleksandra Skrabacz

doi:10.3390/en19143281

Abstract

Distributed energy systems are increasingly important for strengthening critical infrastructure resilience under conditions of technological, climatic, geopolitical, and cyber disruption. However, existing research on energy resilience is still dominated by technical approaches focused on reliability, renewable energy integration, microgrid control, and storage optimisation, while the role of distributed energy systems in the full crisis-management cycle remains insufficiently conceptualised. This article addresses this gap by combining a scoping review, lexicographic and semantic analysis using IRaMuTeQ version 0.7 alpha 2, and a conceptual-methodological framework for assessing distributed energy systems as instruments of crisis management. The main contribution of the study is the M_ZK-DES model, which integrates technological-infrastructural, decision-operational, legal-institutional, and socio-organisational dimensions with four crisis-management phases: prevention, preparedness, response, and recovery. The model distinguishes distributed energy systems, distributed energy resources, distributed generation, microgrids, prosumers, energy communities, and energy clusters and links them to measurable resilience indicators. These include SAIDI, SAIFI, energy not supplied, restoration time, share of critical load served, islanding capability, voltage and frequency stability, storage autonomy, procedural readiness, and local coordination capacity. The analysis shows that distributed energy systems may reduce vulnerability to cascading failures, support islanded operation, protect vulnerable consumers, improve emergency power continuity, and strengthen local energy autonomy. The proposed scoring and weighting logic enables future empirical validation, scenario testing, and comparative assessment across regions and crisis types, including extreme weather events, cyberattacks, and supply-chain disruptions. The article contributes to energy resilience and crisis-management studies by offering an integrated and operational framework for evaluating distributed energy systems as practical tools for critical infrastructure protection and continuity of essential public services.

Keywords:

distributed energy systems; energy resilience; crisis management; critical infrastructure; microgrids; energy security; distributed generation; socio-technical systems; emergency preparedness; energy transition

1. Introduction

Distributed energy systems (DES) are examined in this article as crisis-management resources, not only as instruments of decarbonisation. The problem addressed is the limited integration of microgrids, storage, prosumer generation and local balancing mechanisms with critical-infrastructure protection, business continuity and emergency response [1,2,3].

Centralised electricity systems remain exposed to nodal failures, cyber threats, fuel-supply disruptions and extreme weather events. When such disruptions affect transmission substations, control systems or medium-voltage distribution lines, their effects may cascade to healthcare, water supply, transport, communications and public administration [1,4].

The review therefore asks how DES can reduce vulnerability, maintain essential services and accelerate recovery during crisis conditions. The analysis focuses on five resilience capacities: anticipation, absorption, adaptation, reconfiguration and recovery [2,5,6].

The article develops the M_ZK-DES conceptual model, which links DES with the four phases of crisis management: prevention, preparedness, response and recovery. The model integrates technological factors (distributed generation, storage, islanding and monitoring) with institutional and social factors (decision rules, stakeholder coordination, public communication and protection of vulnerable consumers) [7,8,9,10].

The scope is deliberately limited to the crisis-management function of DES. Broader themes of energy transition, decarbonisation and market reform are mentioned only when they explain resilience, continuity of public services or the protection of critical infrastructure.

The main objective is to develop and justify a repeatable analytical framework for assessing whether, how and under what operational conditions distributed energy resources can strengthen the resilience of critical infrastructure in crisis management.

The article makes four contributions:

It clarifies the research problem by treating DES as local crisis resources supporting continuity of critical functions;
It organises the literature into technical, methodological and crisis-management streams;
It translates the proposed model into variables, indicators, formulas and operational steps;
It explains how probability, impact, vulnerability and exposure scores are used in the illustrative risk matrix.

The study combines a scoping review, semantic analysis and conceptual-methodological modelling. Its main contribution is the M_ZK-DES framework, which translates the literature on distributed energy systems into a crisis-management assessment procedure that can be replicated in scenario studies and local/regional case analyses.

The contribution is therefore threefold: (i) conceptual, because the article distinguishes technical DES categories and links them with crisis-management phases; (ii) methodological, because it documents the review, IRaMuTeQ corpus preparation and PRISMA-ScR-style screening logic; and (iii) operational, because it proposes measurable indicators, ordinal scales, weights and formulas for assessing resilience outcomes.

2. Literature Review

2.1. Technical and Crisis-Management Scope of the Review

The literature is interpreted through two separate streams. The first stream includes technical models of energy resilience that focus on reliability, microgrid control, storage, islanding, restoration and power-quality indicators. The second stream includes crisis-management and civil-protection frameworks that focus on coordination, preparedness, warning, vulnerable consumers and recovery. The gap addressed by this article is not the absence of resilience or crisis concepts, but their insufficient integration into one operational framework (Table 1).

Table 1. Summary of literature stream, typical focus, limitation identified, implication for M_ZK-DES.

The review was designed as a scoping review because the field is interdisciplinary and conceptually dispersed. It combines energy engineering, security studies, critical-infrastructure protection, management, public policy and socio-technical resilience research [11,12,13].

The technical scope covers distributed generation, microgrids, energy storage, prosumer installations, demand-side management, local balancing, islanding operation and digital monitoring. The crisis-management scope covers prevention, preparedness, response, recovery, business continuity, protection of vulnerable consumers, emergency power supply and cascading failures [8,9,10,14,15] (Table 2).

Table 2. Technical concepts used in the article and their operational distinction.

In this article, DES denotes the whole socio-technical system; DER and DG denote resources or generation assets; microgrid denotes an operational configuration; and prosumers, energy communities and clusters denote actors or governance structures.

2.2. Search Strategy and Keywords

The search was conducted in the Web of Science Core Collection. The database was selected because it indexes peer-reviewed publications and allows structured searches across interdisciplinary fields. The corpus was updated to include records available in 2025; publications after 2024 were screened when they met the inclusion criteria and were indexed in the database (Table 3 and Table 4).

Table 3. Web of Science search protocol documented for replication.

Table 4. Search blocks used in the scoping review.

2.3. Screening Procedure and Corpus Construction

The screening procedure followed a transparent sequence: identification of records, removal of irrelevant records, eligibility assessment, and final qualitative and textual analysis. The initial search identified 2154 records. Additional crisis-resilience terms produced 46 highly relevant records. The final in-depth corpus consisted of 21 review articles published between 2016 and 2025 (Table 5).

Table 5. PRISMA-ScR-style screening flow used in the scoping review.

The numerical values in the flow diagram represent screening counts for the review process. They do not represent effect sizes or empirical results from energy-system operation (Table 6).

Table 6. Screening logic used in the review.

Inclusion criteria were: peer-reviewed publication, abstract availability, English language, and relevance to at least one of the following areas: distributed energy, microgrids, storage, prosumption, energy security, infrastructure resilience, risk management, business continuity or local governance. Exclusion criteria were: purely component-level engineering papers without system, organisational or crisis implications; non-peer-reviewed publications; records without abstracts; and publications outside the thematic scope.

The 2023–2025 literature stream includes recent studies on resilient community microgrids, black-start service restoration, resilient microgrid control, distributed energy system reviews and value-of-resilience assessment for DER. These sources confirm that current research increasingly reports measurable indicators, operational strategies and resilience valuation, but still rarely integrates these with crisis-management procedures and local governance [16,17,18,19,20].

The literature review has been further expanded with influential 2023–2025 sources on power-system resilience, DER management, islanding detection and the valuation of resilient DER. Recent reviews emphasise that resilience is now assessed not only through classical reliability indices but also through restoration time, critical-load service, microgrid-forming capability, islanding-detection performance, cybersecurity exposure, and the economic value of avoided interruptions [21,22,23,24,25,26]. This strengthens the justification for treating DES as crisis-management instruments and for linking technical indicators with institutional readiness, stakeholder coordination and continuity of essential public services (Table 7).

Table 7. Twenty-one review/framework articles included in the in-depth analytical corpus.

2.4. Text Preparation and Analytical Procedure

The selected corpus was prepared by removing non-narrative elements, standardising terminology, lemmatising the text and dividing it into contextual units. IRaMuTeQ was then used for classical lexicographic analysis and similarity analysis. Nodes in the similarity graph represented lemmatised terms, and edges represented co-occurrence within contextual units [31] (Table 8 and Table 9).

Table 8. IRaMuTeQ corpus preparation and reproducibility parameters.

Table 9. Lexical and semantic outputs reported from the IRaMuTeQ analysis.

The similarity graph is presented together with tabular semantic relations so that the analytical interpretation remains readable and reproducible. For reproducibility, the semantic analysis reports the corpus structure, preprocessing criteria, lexical-frequency output, key terms, semantic-cluster relations, and visual outputs in the form of a word-cloud representation, similarity graph and cluster map. These elements document the analytical path from the textual corpus to the interpretation of the position of crisis-management concepts in the DES literature (Table 10 and Table 11).

Table 10. IRaMuTeQ semantic-cluster visualisation.

Table 11. Summary of rank, term group, representative terms, and interpretive role in the corpus.

This procedure was used to determine whether crisis-management terms occupied central or peripheral positions in the DES literature. Particular attention was given to the terms resilience, risk, crisis, emergency, critical infrastructure, vulnerability, continuity, recovery, adaptation and response [32,43].

2.5. Main Findings of the Review

The review shows that technological clusters dominate the discourse. The strongest links concern renewable energy sources, energy storage, microgrids, integration, control optimisation and operational flexibility. Crisis-management terms appear less frequently and are usually weakly connected with the central technological categories [15,32,44].

This means that the literature provides many technical premises for resilience, but it rarely explains how DES should be embedded in crisis procedures, local response plans, emergency communication, business-continuity planning and the protection of vulnerable consumers [33,45].

2.6. Research Gaps

Three gaps follow from the review. First, the theoretical gap concerns the absence of a coherent model linking energy decentralisation with the crisis-management cycle. Second, the methodological gap concerns the limited integration of technical modelling with risk analysis, scenario analysis and socio-institutional assessment. Third, the practical gap concerns the unclear role of local governments, operators, emergency services, prosumers and energy communities in using distributed resources during crises [33,45,46].

These gaps justify the M_ZK-DES model developed in the following sections. The model is designed to be operational: it defines variables, steps, formulas and evaluation criteria that can be used in case studies, simulations and expert validation.

3. Selection and Description of the Research Method

The method is designed to be repeatable and operational. It consists of a model-based procedure that links the literature review with risk assessment, scenario analysis, indicator selection and validation. The aim is not only to describe DES but to show how the proposed framework can be applied by researchers, local authorities and energy-system operators (Table 12).

Table 12. Research procedure used to build and apply the M_ZK-DES model.

3.1. Research Design

The study uses a combined interpretive and model-based design. The interpretive part identifies how the literature defines resilience, vulnerability, preparedness and recovery. The model-based part translates these categories into measurable variables and decision steps. This combination is appropriate because crisis resilience depends simultaneously on infrastructure, institutions, information and social behaviour [40,47,48,49,50,51].

3.2. Operational Sequence

The procedure follows the sequence: antecedent conditions—crisis process—resilience outcome. Antecedent conditions describe the state of the system before disruption. The crisis process describes detection, coordination and response. The resilience outcome describes the ability to maintain or restore critical functions (Table 13).

Table 13. Operational variables used in the model.

3.3. Risk Scoring and Formulas

To make the model repeatable, the risk of each threat i is calculated as an ordinal product: R_i = P_i × S_i × V_i × E_i. P_i is probability, S_i is impact severity, V_i is system vulnerability and E_i is exposure of critical functions. Each variable is assessed on a 1–5 scale, where 1 means very low and 5 means very high. This explains how values such as P_i = 4 for medium-voltage line failure are obtained: they represent expert- or data-based ordinal assessments, not percentages.

The methodological sequence has been made more explicit: the scoping review defines the evidence boundary, the IRaMuTeQ analysis identifies the semantic separation between technical DES terms and crisis-management terms, and the M_ZK-DES model translates this gap into auditable variables. In application, each component score should be documented in a data sheet indicating the data source, expert judgement rule, normalisation method, weight, and uncertainty note. This improves transparency and makes the model suitable for replication across municipalities, distribution-system operators, critical facilities and crisis scenarios (Table 14).

Table 14. Scales, weights and formulas for operationalising M_ZK-DES.

The default weights are proposed for the conceptual version of the model and are treated as starting values. In empirical research they can be validated through expert elicitation, analytic hierarchy process, sensitivity analysis or calibration against outage and recovery data.

The decision component is expressed as D_k = f(R_i, Zₐ, C_t, I_s, Pᵣ), where Zₐ denotes available resources, C_t time pressure, I_s information quality and Pᵣ response priorities. The operational component is expressed as O_s = f(Rd, Dy, Mg, El, T_o), where Rd is redundancy, Dy is dispatchability, Mg is storage capacity, El is islanding capability and T_o is restoration time. The communication component is expressed as C_k = f(A_i, W_i, Z_s, K_p, U_m), where the variables describe information availability, reliability, stakeholder trust, communication channels and public understanding.

3.4. Scenario Application

The method can be applied through crisis scenarios. A scenario should specify the initiating event, affected assets, expected cascading effects, available DES resources, priority consumers, communication constraints and target recovery level. The scenario is then evaluated twice: under a baseline approach focused on technical restoration and under the M_ZK-DES approach focused on maintaining critical functions.

3.5. Validation

Validation combines parameter calibration, scenario validation, expert validation and case-study comparison. Empirical inputs may include outage statistics, distribution-system data, local energy plans, crisis-management plans, inventories of critical facilities, storage parameters and records from exercises or real disruptions [52,53,54,55,56].

4. Operationalization of Dimensions and Determinants in the Research Model of Crisis Management of Distributed Energy Systems

Each institutional dimension is scored on a 0–5 scale, where 0 means absent, 1 means formally mentioned, 2 means partially defined, 3 means implemented but not tested, 4 means implemented and periodically tested, and 5 means implemented, tested, updated and integrated with DSO/local-government/critical-facility procedures. This scale allows the model to be used in audits and case comparisons rather than only as a descriptive framework (Table 15).

Table 15. Summary of dimension, observable indicator, 0–5 scoring rule, suggested weight.

The operationalisation links each dimension to measurable indicators and crisis-management phases. The following dimensions constitute an evaluation checklist for local or regional DES resilience.

The operationalization of dimensions and determinants in the research model of crisis management of distributed energy systems constitutes a fundamental stage of the research process, as it enables the translation of abstract theoretical categories into empirically verifiable variables, indicators, and evaluation criteria. From the perspective of crisis management, particular importance is attached to identifying those properties of the energy system that determine its capacity to anticipate threats, reduce vulnerability, absorb disruptions, maintain continuity of operation, and restore critical functions after the occurrence of a crisis situation. Thus, operationalization does not refer solely to the description of the system’s structure but includes an analysis of its functioning under conditions of uncertainty, time pressure, resource deficit, infrastructural interdependencies, and multi-actor decision-making responsibility [57,58,59,60,61].

Crisis management in distributed energy systems should be understood as a complex, multi-stage, and multi-level process encompassing prevention, preparedness, response, and recovery. This process requires the coordination of actions undertaken by public administration, energy system operators, business entities, security services, local government units, and end users. In this approach, the research model should make it possible to assess not only the formal existence of management procedures and instruments but also their actual operational effectiveness under conditions of disruptions, failures, supply constraints, extreme weather events, cyberattacks, geopolitical crises, and other events threatening energy security [62,63,64,65,66,67].

The conceptualization and operationalization of research constructs constitute a methodological bridge between the theoretical and empirical levels. Constructs such as systemic resilience, continuity of operation, crisis preparedness, infrastructural redundancy, adaptive capacity, institutional interoperability, crisis communication effectiveness, or the level of inter-organisational coordination require the assignment of clear operational definitions. These definitions should indicate which features of the system are subject to measurement, which indicators are used to assess them, and how their empirical verification is possible. In this sense, operationalization determines the validity, reliability, and repeatability of research on crisis management in the energy sector [68,69].

In this research model, the operationalization of constructs involves assigning clearly defined measurement attributes to individual dimensions, based on a critical analysis of the literature on crisis management, energy security, critical infrastructure protection, public policy, and risk management. This process includes identifying the key features of the analysed dimensions, determining their functional scope, indicating cause-and-effect relationships, and selecting appropriate empirical indicators. Validation procedures are of particular importance here, including content validity, construct validity, and measurement reliability, because the phenomena under study are dynamic, contextual, and strongly dependent on changes in the legal, technological, social, and environmental environment [41,70].

The proposed research model distinguishes six basic dimensions for assessing crisis management in distributed energy systems:

(a) Legal regulations concerning security and crisis management, (b) strategic frameworks of policy, planning, and systemic resilience, (c) local authorities responsible for crisis management, (d) crisis-management instruments and procedures, (e) areas affected by the crisis-management system, (f) stakeholders of the crisis-management system in the energy sector [71,72].

The indicated dimensions form a logically interconnected analytical framework that allows for assessing the capacity of a distributed energy system to function under crisis conditions. Each dimension refers to a different aspect of system resilience: normative, strategic, institutional, instrumental, operational, and socio-organisational. The model assumes that the effectiveness of crisis management depends not on a single element but on the coherence and mutual alignment of all dimensions. This means that even a high level of technological development of the energy system does not guarantee security if it is not accompanied by adequate regulations, efficient decision-making procedures, effective crisis communication, resource availability, and the capacity to coordinate the actions of multiple entities.

The research model is based on a systemic and decision-making approach. It combines institutional and legal analysis with risk analysis, multi-criteria decision analysis, and participatory methods. Such an approach makes it possible to identify both the formal conditions of crisis management and the practical capacity of the system to respond to disruptions. The inclusion of modern technologies in the model, such as energy storage facilities, microgrids, digital monitoring systems, grid automation, and small modular reactors, enables the assessment of their potential for strengthening energy resilience. At the same time, these technologies should also be analysed as potential sources of new risks, including cyber, regulatory, organisational, environmental, and social risks [73].

4.1. Legal Regulations Concerning Security and Crisis Management

Legal regulations constitute the basic normative dimension of crisis management in distributed energy systems. They define the scope of responsibility of public authorities, energy system operators, business entities, and other participants in the process of energy security management. In this context, law performs not only a regulatory function but also preventive, coordination, and control functions. It defines obligations related to emergency planning, critical infrastructure protection, ensuring continuity of energy supply, responding to extraordinary situations, and restoring system functions after the occurrence of a disruption.

At the European Union level, regulations concerning the security of energy supply, critical infrastructure resilience, the energy transition, energy efficiency, and counteracting the effects of climate change are of significant importance. They influence the way threats are defined, risk management standards, and the scope of obligations imposed on Member States. At the national level, the coherence of sectoral regulations is crucial, including energy law, the Act on Renewable Energy Sources, the Act on Energy Efficiency, regulations concerning crisis management, and provisions relating to critical infrastructure protection. A lack of coherence between these legal acts may lead to a dispersion of responsibility, decision-making delays, and reduced effectiveness of crisis response.

The operationalization of this dimension may include indicators such as: the degree of compliance of local regulations with national and EU legal acts, clarity of the division of competences, the presence of emergency procedures in legal documents, the level of integration of energy regulations with crisis-management regulations, the scope of requirements concerning critical infrastructure protection, and the existence of accountability and control mechanisms [74].

4.2. Strategic Frameworks of Policy, Planning, and Systemic Resilience

Strategic frameworks of policy and planning constitute the second dimension of the model, referring to the system’s capacity for long-term preparation for crises. Strategic documents, such as energy policies, national and local energy and climate plans, security strategies, crisis-management plans, critical infrastructure protection plans, and climate change adaptation strategies, define directions of action aimed at reducing risk and strengthening the resilience of the energy system.

In crisis management, it is particularly important that these documents should not be merely declarative but should include measurable objectives, assigned responsibilities, implementation schedules, monitoring procedures, and update mechanisms. The effectiveness of planning depends on whether strategies take into account disruption scenarios, vulnerability analysis, risk assessment, resource needs, communication procedures, and principles of inter-institutional cooperation. The absence of such elements limits the possibility of moving from strategic planning to real operational action in a crisis situation.

The operationalization of this dimension may include indicators such as: the presence of crisis scenarios in planning documents, frequency of plan updates, the extent to which climate, technological, and geopolitical risks are included, measurability of strategic objectives, the existence of business continuity procedures, the level of integration of energy plans with crisis-management plans, and the extent of stakeholder participation in the planning process [75].

4.3. Local Authorities Responsible for Crisis Management

Local public administration bodies play a central role in the practical implementation of crisis management because it is at the local level that the direct effects of disruptions in energy supply become visible. Local government units are responsible for organising preventive, preparedness, response, and recovery activities, as well as for cooperating with energy system operators, rescue services, municipal enterprises, residents, and entities responsible for maintaining critical infrastructure.

At the local level, crisis management requires the ability to quickly diagnose a situation, make decisions under time pressure, activate emergency procedures, allocate resources, conduct crisis communication, and coordinate the actions of multiple entities. The effectiveness of local authorities therefore depends not only on formal competencies but also on organisational resources, quality of information, staff experience, level of procedural preparedness, and the ability to cooperate with the private sector and the local community.

The operationalization of this dimension may include: the level of preparedness of local crisis-management structures, the number and timeliness of local emergency plans, the frequency of crisis exercises and simulations, the availability of technical and human resources, the effectiveness of communication channels with residents, the degree of cooperation with energy infrastructure operators, and response time to events disrupting system functioning [76].

4.4. Crisis-Management Instruments and Procedures

Crisis management instruments and procedures constitute the operational dimension of the model. They include a set of tools used to identify threats, reduce risk, prepare resources, respond to crisis events, and restore system functionality. With regard to distributed energy systems, particular importance is attached to business continuity procedures, emergency power supply plans, early warning systems, infrastructure monitoring mechanisms, crisis communication procedures, information management systems, inter-institutional cooperation agreements, and instruments for financing preventive and recovery actions.

From a scientific point of view, these instruments should be assessed not only in terms of their formal presence but above all in terms of their functionality, effectiveness, and adequacy to the identified threats. It is therefore important to examine whether procedures are updated, tested, known to system participants, capable of being activated under conditions of limited access to information, and compatible with the procedures of other entities. Organisational and technical interoperability is of particular importance since the lack of procedural compatibility may lead to fragmentation of actions and intensification of the consequences of a crisis.

The operationalization of this dimension may include indicators such as: the number and quality of emergency procedures, the presence of business continuity plans, availability of alternative power sources, level of infrastructural redundancy, frequency of procedure testing, time required to restore basic system functions, effectiveness of warning systems, level of digitalization of monitoring, and availability of financial resources for crisis-related actions [42].

4.5. Areas Affected by the Crisis-Management System

The areas affected by the crisis-management system include those elements of the energy and socio-economic system that are influenced by planning, preventive, operational, and recovery activities. In distributed energy systems, the objects of impact include both technical infrastructure—such as generation sources, distribution networks, energy storage facilities, and control systems—as well as institutional entities, enterprises, households, public utility facilities, and groups particularly vulnerable to interruptions in energy supply.

From the perspective of crisis management, particular importance is attached to identifying critical functions whose maintenance is necessary for the security of the local community. This concerns, among others, the power supply of healthcare facilities, water supply systems, communication infrastructure, communication systems, rescue services, care facilities, and public administration facilities. The assessment of affected areas should take into account both the direct consequences of energy disruptions and the cascading effects resulting from the interdependence between the energy system and other critical infrastructure sectors.

The operationalization of this dimension may include: the number of identified critical facilities, the level of energy protection of these facilities, availability of emergency power sources, degree of consumer vulnerability to energy supply interruptions, scope of protection plans for vulnerable groups, level of integration of the energy system with other critical infrastructure sectors, and capacity to reduce cascading effects [77].

4.6. Stakeholders of the Crisis-Management System in the Energy Sector

Stakeholders constitute one of the key determinants of the effectiveness of crisis management because the system’s capacity to respond to a crisis depends on the quality of cooperation among many actors. The most important stakeholders include public administration bodies, crisis-management centres, energy system operators, energy companies, entities responsible for critical infrastructure, rescue services, social organisations, the scientific community, entrepreneurs, and residents.

Under crisis conditions, the importance of stakeholders results from the need for rapid information exchange, agreement on priorities, coordination of resources, division of responsibility, and ensuring public trust. Insufficient communication between stakeholders may lead to information chaos, duplication of actions, decision-making delays, and a decline in social acceptance of the interventions undertaken. For this reason, stakeholder management should include not only their identification but also the analysis of their influence, resources, responsibilities, communication channels, and level of involvement in individual phases of the crisis-management cycle.

The operationalization of this dimension may include: the number and types of stakeholders involved, formalisation of cooperation rules, frequency of coordination meetings, level of information exchange, existence of joint operating procedures, stakeholder participation in crisis exercises, level of inter-institutional trust, and effectiveness of communication with residents and end users [78].

5. Conceptual Model of Crisis Management of Distributed Energy Systems

The proposed research model is embedded in the perspective of crisis management of distributed energy systems, understood as complex technical and organisational arrangements whose functioning determines energy security, the continuity of public service provision, and the socio-economic resilience of territorial units [79,80]. Unlike classical models of energy planning, which focus primarily on technological efficiency, supply and demand balancing, and cost optimisation, the presented model frames the distributed energy system as an element of critical infrastructure exposed to multidimensional threats: technical, climatic, cyber, regulatory, economic, geopolitical, and social [81,82,83,84].

The fundamental objective of the model is to identify and analyse the mechanisms that enable the energy system to maintain critical functions under conditions of disruption, limit the scale of losses, ensure continuity of energy supply, coordinate the actions of multiple actors, and restore operational capacities after the occurrence of a crisis situation [85,86]. The model is therefore not limited to describing the structure of distributed energy sources but focuses on the system’s capacity to move through successive phases of crisis management: prevention, preparedness, response, and recovery [87,88]. In this approach, distributed energy systems are analysed as dynamic adaptive systems whose effectiveness depends on the interaction of legal, institutional, technological, informational, and social components [89,90].

The decentralised nature of distributed energy systems means that crisis management requires not only technological modernisation, but also the transformation of regulatory frameworks, decision-making procedures, and coordination mechanisms [91,92]. Under crisis conditions, what matters is not merely the number of distributed energy sources, but their capacity to sustain local critical functions, cooperate with system operators, provide emergency power supply, and limit cascading effects [93,94,95]. This implies the need to integrate local energy initiatives with the crisis-management system, critical infrastructure protection, business continuity planning, and strategies of territorial resilience [96,97,98].

The analysis of the literature and the premises derived from crisis-management theory make it possible to formulate the following synthetic conclusions, which constitute the conceptual basis of the model:

From the perspective of the risk and systemic vulnerability paradigm:

Distributed energy systems should be analysed through the lens of exposure to threats, the sensitivity of infrastructural components, and the capacity to absorb disruptions [99].
It is crucial to identify critical points whose failure may lead to a loss of operational continuity or the emergence of cascading effects in other critical infrastructure sectors [100].
Risk assessment should include both the probability of a crisis event and the scale of its potential consequences for consumers, operators, public administration, and the local economy [101].
Managing system vulnerability requires continuous threat monitoring, updating crisis scenarios, and adapting response procedures to the changing security environment [102].

From the perspective of the systemic resilience paradigm:

Energy system resilience means the capacity to maintain basic functions despite disruptions and then rapidly restore full operational efficiency.
A high level of resilience requires infrastructural redundancy, diversification of energy sources, availability of energy storage, control flexibility, and the capacity for islanded or partially autonomous operation.
Resilience is not solely a technological property but the result of the interaction of legal regulations, organisational procedures, human resources, information systems, and inter-institutional trust.
A resilient system should be capable of learning from previous disruptions, tests, exercises, and crisis simulations.

From the perspective of the institutional coordination paradigm:

Effective crisis management requires the cooperation of multiple actors: central and local government administrations, energy system operators, enterprises, rescue services, regulators, prosumers, and end users.
Clear division of competences, rapid information exchange, procedural interoperability, and the capacity to make decisions under time pressure are key factors of effectiveness.
A lack of institutional coordination increases the risk of decision-making delays, duplication of actions, information chaos, and the worsening of crisis consequences.
Crisis management in distributed systems requires mechanisms of horizontal and vertical coordination between the local, regional, national, and European Union levels.

From the perspective of the digitalization and situational awareness paradigm:

Virtualization, digital twins, real-time monitoring systems, and predictive tools increase the system’s capacity for early threat detection and support crisis decision-making.
Information management becomes one of the central elements of system resilience because the quality of decisions depends on the timeliness, completeness, and reliability of data.
Digitalization increases the possibilities of system control but at the same time generates new categories of risk, especially in the areas of cybersecurity, dependence on ICT systems, and protection of operational data.
Situational awareness should be treated as a dynamic organisational capability that enables the interpretation of events, forecasting of their consequences, and selection of adequate action strategies.

From the perspective of the socio-prosumer paradigm:

Under crisis conditions, the prosumer is not merely a micro-producer of energy but may function as an active participant in local energy resilience.
The involvement of local communities increases the system’s capacity for self-organisation, limiting the consequences of energy supply interruptions and supporting particularly vulnerable groups.
Social participation strengthens the legitimacy of crisis actions, improves the flow of information, and contributes to building a culture of energy security.
At the same time, prosumer participation requires appropriate legal frameworks, technical standards, coordination mechanisms, and security procedures.

The analysis of the above paradigms indicates that crisis management of distributed energy systems requires a holistic approach that integrates the dimensions of risk, resilience, coordination, technology, information, and social participation. Each of the paradigms mentioned contributes distinct cognitive and practical value; however, only their combined application makes it possible to explain the system’s capacity to function under conditions of disruption. The model should therefore be treated as an analytical framework for examining how the energy system identifies threats, prepares for crises, responds to disruptions, and restores operational capacity (Table 16 and Table 17).

Table 16. Conceptual model of crisis management of distributed energy systems.

Table 17. Summary of inputs, model components, crisis-management phases, outputs.

The proposed research model is based on the assumption that effective crisis management of distributed energy systems requires the integration of four fundamental components: the risk component, the institutional and decision-making component, the technological and operational component, and the social and informational component. These components are interconnected through feedback relations, forming a dynamic adaptive system capable of responding to changing environmental conditions.

From an epistemological point of view, the model assumes the transdisciplinarity of knowledge, since crisis management in the energy sector requires the simultaneous use of the achievements of management sciences, security studies, energy engineering, law, economics, cybernetics, complex systems theory, and social sciences. This means that the analysed system cannot be reduced solely to technical infrastructure. Rather, it is a socio-technical system whose functioning depends on the relationships between technology, regulations, decisions, information, and the behaviour of actors.

The model may be formally defined as an ordered structure:

M_ZK-DES = {K₀, K₁, K₂, K₃, R_k}

(1)

The default weights are not normative constants. They are starting values for transparent scenario testing. They may be modified by expert elicitation, the analytic hierarchy process, entropy weighting or regression/validation against historical outage data. For replication, all component scores are first normalised to the interval [0, 1], then multiplied by weights that sum to 1. The same weights must be reported for every scenario in a comparative study.

Example calculation: if K₀ = 0.70, K₁ = 0.60, K₂ = 0.80 and K₃ = 0.50, then R_k = (0.25 × 0.70) + (0.25 × 0.60) + (0.35 × 0.80) + (0.15 × 0.50) = 0.68. In this interpretation, a score below 0.40 indicates low resilience, 0.40–0.69 moderate resilience and 0.70–1.00 high resilience. Sensitivity testing can report how R_k changes when K2 or K1 weights are increased by +/−10% (Table 18).

Table 18. Summary of component, normalised variables, default weight, calculation rule.

where
K₀—the component of risk and systemic vulnerability;
K₁—the institutional and decision-making component of crisis management;
K₂—the technological and operational component of energy resilience;
K₃—the social and informational component and crisis communication;
R_k—crisis-management outcomes.

Each component includes a set of sub-nodes representing institutions, technologies, procedures, resources, information, and system participants. Relations occur between the sub-nodes, including flows of energy, information, decisions, resources, and responsibility. Unlike the classical DES model, in which the optimisation of the functioning of the energy system occupies the central position, in the crisis-management model the main objective is to maintain critical functions and minimise the consequences of disruptions.

5.1. Component K₀: Risk and Systemic Vulnerability

Component K₀ is responsible for the identification, analysis, and assessment of threats affecting the distributed energy system. It includes technical, environmental, climatic, cyber, economic, regulatory, and social threats. This component also analyzes infrastructure vulnerability, consumer exposure, the probability of crisis events, and the potential scale of their consequences.

Systemic risk may be represented as a function:

R_i = f(P_i, S_i, V_i, E_i)

(2)

where

P_i—the probability of occurrence of a given threat;
S_i—the scale of potential consequences;
V_i—the system’s vulnerability to a given disruption;
E_i—the exposure of consumers and infrastructure.

A high-risk value indicates the need to apply preventive instruments that reduce system vulnerability and increase its resilience. Component K₀ therefore performs a diagnostic and prognostic function, serving as the starting point for the remaining components of the model.

5.2. Component K₁: Institutional and Decision-Making Dimension of Crisis Management

Component K₁ includes the structures, procedures, and decision-making mechanisms responsible for crisis management. It comprises public administration bodies, crisis-management centres, energy system operators, regulators, local government units, and other entities participating in the decision-making process.

A crisis decision may be expressed as a function:

D_k = f(R_i, Z_a, C_t, I_s, P_r)

(3)

where

R_i—the level of identified risk;
Z_a—availability of resources;
C_t—time pressure;
I_s—quality of situational information;
P_r—applicable response procedures.

Component K₁ is responsible for selecting action strategies, activating emergency procedures, allocating resources, determining power supply priorities, and coordinating inter-institutional actions. Its effectiveness depends on the clarity of competencies, the timeliness of crisis plans, communication efficiency, and the capacity to make decisions under conditions of uncertainty.

5.3. Component K₂: Technological and Operational Dimension of Energy Resilience

Component K₂ includes the material and operational basis of energy system resilience. It comprises distributed energy sources, energy storage facilities, microgrids, control systems, distribution infrastructure, backup devices, emergency power supply systems, and digital monitoring technologies.

The operational resilience of the system may be represented as a function:

O_s = f(Rd, Dy, Mg, El, T_o)

(4)

where

Rd—infrastructural redundancy;
Dy—diversification of energy sources;
Mg—availability of energy storage;
El—control flexibility;
T_o—technical recovery capability.

Component K₂ is responsible for the system’s capacity to absorb disruptions, maintain a minimum level of power supply, switch between energy sources, operate in emergency mode, and restore functionality after a crisis event. In this approach, energy technologies are not assessed solely through the lens of economic efficiency, but above all through their contribution to continuity of operation and the resilience of critical infrastructure.

5.4. Component K₃: Social, Informational, and Communication Dimension of Crisis Management

Component K₃ refers to the role of information, communication, social participation, and user behaviour under conditions of an energy crisis. It includes prosumers, end users, local communities, social organisations, the media, educational institutions, and crisis communication channels.

The effectiveness of crisis communication may be expressed as a function:

C_k = f(A_i, W_i, Z_s, K_p, U_m)

(5)

where

A_i—timeliness of information;
W_i—credibility of information;
Z_s—social trust;
K_p—capacity of communication channels;
U_m—recipients’ ability to respond to crisis messages.

Component K₃ performs the function of integrating the social dimension with the operational dimension. In crisis situations, proper communication reduces uncertainty, limits chaotic behaviour, facilitates the protection of vulnerable groups, and increases the effectiveness of institutional actions. The participation of prosumers and local communities may strengthen system resilience, provided that it is embedded in clear cooperation and security procedures.

5.5. Crisis-Management Outcomes R_k

Crisis-management outcomes are defined as a multidimensional vector:

R_k = [B_e, C^d, O_r, K_i, A_s, S_p]

(6)

where

B_e—level of energy security;
C^d—continuity of energy supply;
O_r—system resilience to disruptions;
K_i—effectiveness of institutional coordination;
A_s—system adaptability;
S_p—social acceptance and participation.

The vector R_k makes it possible to assess whether the system achieves the desired crisis-management outcomes. It includes both technical effects, such as continuity of supply and the time required to restore system functions, as well as organisational and social effects, such as the quality of coordination, communication efficiency, and the level of stakeholder trust.

The integrated effectiveness of crisis management may be represented as a function:

E_ZK = φ(K₀, K₁, K₂, K₃) = ∫₀^T [α₁O_s + α₂D_k + α₃C_k − α₄R_i] dt

(7)

where

E_ZK—integrated effectiveness of crisis management;
O_s—operational resilience;
D_k—effectiveness of crisis decisions;
C_k—effectiveness of crisis communication;
R_i—level of systemic risk;
α₁–α₄—weighting coefficients determining the significance of individual components.

Maximising the function E_ZK means striving to increase operational resilience, improve the quality of decisions, enhance communication, and reduce the level of risk. This function does not constitute a complete computational model but rather a formal representation of cause-and-effect relationships between the basic components of crisis management.

The relationships between components K₀–K₃ are recursive and organised as feedback loops. Risk identification in component K₀ influences the decisions made in component K₁. These decisions activate technological and operational actions in component K₂, the outcomes of which generate feedback data on system effectiveness. These data are then processed in component K₃ through mechanisms of information, communication, and social learning. As a consequence, the system updates its procedures, scenarios, and response strategies.

This architecture of the model allows crisis management to be interpreted as a process of continuous system learning. A crisis is not merely a disruptive event but also a source of information about the system’s actual vulnerability, the effectiveness of procedures, the adequacy of technologies, and the quality of inter-institutional cooperation. The model therefore assumes that system resilience increases when information obtained from exercises, simulations, and real events is used to modify plans, investments, and procedures.

5.6. Illustrative Application of the Model and Comparison with the Baseline Approach

In order to demonstrate the practical usefulness of the proposed M_ZK-DES model, its illustrative application is presented using the example of a local distributed energy system operating within a municipality or a group of municipalities. The analysed system includes prosumer photovoltaic installations, local cogeneration sources, energy storage facilities, medium- and low-voltage distribution infrastructure, critical infrastructure facilities such as a hospital, water treatment station, municipal office and crisis-management centre, as well as individual consumers and local enterprises.

The crisis scenario under consideration assumes the occurrence of an extreme weather event combined with a failure of part of the distribution network and disruptions in digital communication. In the baseline approach, the system response focuses primarily on restoring electricity supply by the network operator, without fully using local distributed resources, prosumers, energy storage systems and crisis coordination mechanisms. In the approach based on the M_ZK-DES model, actions are initiated in a multidimensional manner: critical points are identified, power supply priorities are defined, local resources are activated, crisis communication is launched, and after the event the procedures are updated.

The illustrative application of the model may be presented as a sequence of four interrelated stages: risk diagnosis, crisis decision-making, operational response and feedback. Each stage corresponds to one of the K0–K3 components of the model (Table 19 and Table 20).

Table 19. Logic of applying the M_ZK-DES model in a crisis situation.

Table 20. Summary of sequence, model component, operational question.

The following examples clarify that the numerical values are hypothetical expert-assigned scores used only to demonstrate model operation. They are not measured values from a real distribution operator. A real case study should replace these scores with outage records, operator logs, SCADA/AMI data, weather data, cyber-incident reports and critical-facility inventories.

The probabilities and other scores in Table 21 are ordinal expert-assessment values on a 1–5 scale. A score of 4 for medium-voltage line failure means “high probability” based on the assumed local exposure and historical/technical vulnerability; it is not a 4% probability (Table 21 and Table 22).

Table 21. Example risk matrix for a distributed energy system.

Table 22. Summary of threat, probability P_i, impact S_i, and vulnerability V_i.

In the analysed example, the highest risk levels relate to medium-voltage line failure, an extreme weather event and a cyberattack on the control system. This means that preventive and preparedness measures should primarily include power supply redundancy, the possibility of island operation, cybersecurity safeguards, the availability of energy storage and procedures for priority power supply to critical facilities.

The M_ZK-DES model assumes that components K0–K3 do not operate independently but form a system of mutual interdependencies. These relationships can be presented in the form of an influence matrix (Table 23).

Table 23. Matrix of relationships between the components of the M_ZK-DES model.

The matrix indicates that the relationships between risk and crisis decisions, between decisions and operational activities, and between communication and crisis-management outcomes are particularly important. In practice, this means that even a high level of technological preparedness may fail to produce the expected results if efficient institutional coordination and reliable communication with consumers are lacking.

The added value of the M_ZK-DES model is assessed by comparison with the baseline approach. The baseline approach refers to traditional management of an energy failure, focused mainly on the technical removal of the disruption and restoration of the electricity supply by the operator. The model-based approach, by contrast, takes into account the full crisis-management cycle, coordination of multiple stakeholders, social communication, local energy resources and the post-crisis learning process (Table 24).

Table 24. Comparison of the baseline approach and the M_ZK-DES approach.

The comparison shows that the M_ZK-DES model extends the classical technical approach by adding organisational, social, informational and adaptive dimensions. As a result, an energy crisis is not perceived solely as an infrastructural problem but as a test of the capacity of the entire socio-technical system to maintain critical functions.

Table 25 compares the system response trajectory in the baseline approach and the M_ZK-DES model-based approach. The rows present successive phases of the crisis, while the columns indicate differences in system performance, restoration logic and resilience outcomes.

Table 25. Summary of phase, baseline approach, M_ZK-DES approach.

In the baseline approach, the decline in system performance is deeper and the recovery period is longer. This results from a focus on infrastructure repair without full use of local resilience mechanisms. In the M_ZK-DES approach, the decline in performance is limited by source redundancy, energy storage, priority power supply to critical facilities, and effective coordination and communication with consumers.

Crisis-management outcomes are presented as a multidimensional profile. For this purpose, the Rk vector = [Be, Cd, Or, Ki, As, Sp] is used, covering energy security, continuity of supply, resilience, coordination, adaptability and social participation. The outcome profile compares the same six indicators directly across baseline and M_ZK-DES variants. In the quantitative version, the following indicative values are adopted (Table 26).

Table 26. Indicative assessment of crisis-management outcomes.

The comparison shows that the greatest difference between the baseline approach and the M_ZK-DES model occurs in the areas of resilience, adaptability and institutional coordination. These are the elements that often play a secondary role in the classical technical approach, while in crisis management they constitute a condition for effective response.

In the M_ZK-DES model, the flow of information between system actors is of particular importance. Under crisis conditions, the quality of decisions depends on the speed of data acquisition, reliability and the ability to transform information into specific operational actions (Table 27 and Table 28).

Table 27. Flow of information and decisions in the M_ZK-DES model.

Table 28. Summary of input layer, integration layer, decision layer, output layer.

The scheme indicates that the crisis-management centre performs the function of integrating information derived from technical systems, operators, administration and local communities. On this basis, decisions are made regarding power supply priorities, resource allocation, public communication and cooperation with emergency services.

The illustrative application of the model shows that its fundamental value lies in shifting the emphasis from reactive failure removal to active management of system resilience. The baseline approach is useful in the case of standard technical failures, but it may prove insufficient in multidimensional situations where energy disruptions are combined with time pressure, information uncertainty, cyber threats, the need to protect critical facilities and the necessity of communication with residents.

The M_ZK-DES model enables earlier identification of system vulnerabilities, better preparation of response scenarios, more efficient coordination of actors and more effective use of local energy resources. At the same time, it makes it possible to assess not only the technical outcome of actions but also the quality of decisions, the level of social trust, the effectiveness of communication and the system’s capacity to learn after a crisis.

Consequently, the proposed model may be used as a diagnostic, planning and evaluation tool. In the diagnostic dimension, it serves to identify threats, vulnerabilities and critical points. In the planning dimension, it supports the design of procedures, scenarios and power supply priorities. In the evaluation dimension, it makes it possible to compare the effectiveness of different crisis-management variants and to assess the extent to which the system increases its resilience after subsequent exercises, simulations and real events.

The comparison indicates that the model-based approach allows for a higher level of energy security, continuity of supply and operational resilience than the baseline approach. This difference results not only from the use of distributed technologies but above all from embedding them within a crisis-management structure that includes institutional coordination, social communication, risk analysis and learning mechanisms.

The application of the M_ZK-DES model leads to the following effects:

Shortening the time required to identify a threat and make a crisis decision;
Limiting the scale of energy system performance loss;
Better use of local energy sources and storage systems;
Increased protection of critical infrastructure facilities;
Improved communication with consumers and prosumers;
Strengthening the system’s capacity for adaptation after the crisis has ended.

Ultimately, the M_ZK-DES model is treated as a tool supporting the transition from classical failure management to integrated resilience management of distributed energy systems. Its application makes it possible not only to respond to disruptions but also to systematically reduce system vulnerability and strengthen its ability to function under conditions of uncertainty.

6. Discussion

Recent Polish and Romanian outage situations are used as illustrative stress tests for the model. The cases are not treated as full empirical validation because operator-level datasets are not available in the study, but they show how the model can structure evidence from real disruptions.

These cases support the practical logic of M_ZK-DES: the same event can be evaluated through hazard exposure (K₀), institutional coordination (K₁), technical DES performance (K₂), communication and community protection (K₃), and measurable outcome indicators (R_k) (Table 29).

Table 29. Summary of case, observed stressor, M_ZK-DES interpretation, required empirical data for validation.

A further limitation is that the numerical examples are illustrative. They demonstrate how the model can be populated, but they do not replace empirical calibration with outage records, operator data, storage tests, expert elicitation or simulation outputs. This clarification is important for replicability and for avoiding overinterpretation of ordinal scores.

The analysis conducted indicates that distributed energy systems should be interpreted not only as an element of the energy transition and the decarbonisation of the economy but also as an important component of contemporary crisis management. The results of the literature review and semantic analysis confirm that the existing scientific discourse focuses primarily on the technological aspects of distributed energy, such as the integration of renewable energy sources, energy storage, microgrids, optimisation of energy flows, and the operational flexibility of the system. At the same time, categories directly related to crisis management, including emergency response, business continuity, recovery, protection of critical infrastructure, inter-institutional coordination, and crisis communication, remain less represented in the literature and are less frequently integrated into the analysis of distributed energy systems.

The results obtained make it possible to state that research on distributed energy systems reveals a clear asymmetry between the technological dimension and the crisis-management dimension. The literature provides numerous arguments supporting the thesis that the decentralisation of energy sources, the development of microgrids, energy storage facilities, and local balancing mechanisms can increase the resilience of the energy system. However, this resilience is often understood mainly as a technical feature relating to reliability, grid stability, or the ability to integrate renewable energy sources. From the perspective of crisis management, such an approach is insufficient, as it does not fully take into account decision-making processes, emergency procedures, institutional responsibility, the role of stakeholders, social communication, or the system’s capacity to learn after a disruption has occurred.

In this context, the proposed conceptual model represents an attempt to overcome the identified research gap by linking distributed energy with the full crisis-management cycle, including prevention, preparedness, response, and recovery. The model assumes that the effectiveness of distributed energy systems in crisis situations depends on the interaction of several interconnected components: systemic risk and vulnerability, institutional and decision-making preparedness for crisis, technological and operational resilience of infrastructure, and socio-informational mechanisms of communication and self-organisation. This approach makes it possible to analyse the energy system as a complex socio-technical system in which physical infrastructure, procedures, institutions, information, and user behaviour remain in relationships of mutual dependence.

An important result of the analysis is the indication that the mere presence of distributed energy technologies does not guarantee an increase in crisis resilience. Microgrids, energy storage systems, local renewable sources, or demand-side management systems can enhance energy security only when they are properly integrated with crisis-management plans, emergency operating procedures, systems for prioritising vulnerable consumers, and coordination mechanisms between operators, public administration, emergency services, and local communities. This means that energy resilience is not only technical in nature, but also organisational, institutional, and social.

The local and regional levels are of particular importance because it is precisely at these levels that the effects of energy disruptions are directly experienced by residents, business entities, and institutions providing public services. Distributed energy systems can function as local crisis resources, enabling the maintenance of power supply to facilities of critical importance, such as hospitals, water treatment plants, crisis-management centres, emergency units, communication systems, or temporary shelters for the population. However, the use of this potential requires prior identification of resources, assignment of responsibilities, development of emergency procedures, and conduct of exercises verifying the system’s actual ability to operate under conditions of disruption.

The study results also indicate the need for broader inclusion of prosumers, energy communities, energy clusters, and local governments as active participants in the energy security system. In the traditional energy model, end users were perceived primarily as passive consumers of energy. In the distributed model, they can act as agents of local resilience, as they participate in energy generation, storage, consumption reduction, and local energy balancing. From the perspective of crisis management, this means expanding the resource base available in the event of a disruption, but at the same time it requires the creation of mechanisms for coordination, education, communication, and the legal definition of roles and responsibilities.

The interpretation of the results has been restricted to conclusions directly supported by the analyses performed in this article. The scoping review supports the claim that the literature is technically rich but institutionally fragmented; the lexical and semantic analysis supports the claim that crisis-management terms remain peripheral to the DES discourse; and the operational model supports the claim that resilience assessment requires simultaneous measurement of risk diagnosis, institutional decision capacity, technical reconfiguration and social-information feedback. The article therefore does not claim empirical proof of resilience improvement; it offers a structured conceptual and methodological framework for subsequent empirical validation.

The proposed model also makes it possible to better capture the significance of information in the crisis management of energy systems. Under conditions of disruption, not only is the availability of energy crucial, but also the ability to quickly recognise the situation, assess the scale of the threat, transmit information between entities, and communicate decisions to end users. A lack of reliable information, communication delays, or inconsistent messages may increase the scale of the crisis, lead to the disorganisation of activities, and weaken public trust. Therefore, the socio-informational component should be treated as one of the fundamental elements of the resilience of distributed energy systems.

From a theoretical perspective, the study results strengthen the argument that the energy transition should be analysed within a broader resilience paradigm. This means moving away from a narrow understanding of the transition as a process of technological modernisation and emissions reduction towards an approach that includes security of supply, protection of critical infrastructure, continuity of public services, and the ability of local communities to function under conditions of disruption. In this perspective, distributed energy systems can play the role of infrastructure supporting the resilience of the state and territories, but only if their development is linked to security policy, civil planning, civil protection, and local crisis-management strategies.

The presented results also have practical significance. The model may serve as a basis for assessing the preparedness of local government units, distribution system operators, and other entities responsible for energy security to operate in crisis situations. It may be used to identify gaps in procedures, assess the degree of integration of distributed energy resources with emergency plans, determine the level of protection of vulnerable consumers, and design crisis exercise scenarios. In practice, this means the possibility of moving from general declarations concerning energy resilience to more operational criteria for its measurement and assessment.

It should be emphasised, however, that the proposed model is conceptual in nature and requires further empirical validation. A limitation of the study is that the analysis is based on a literature review and exploratory text analysis, which makes it possible to identify the structure of the research field but does not yet allow for the direct measurement of the effectiveness of individual crisis-management mechanisms. Further research should include case studies of local and regional energy systems, analysis of strategic documents and crisis-management plans, expert interviews, surveys among stakeholders, and simulations of disruption scenarios. Particularly valuable would be the application of agent-based modelling, system dynamics, and network analysis, which would make it possible to examine system behaviour under conditions of cascading failures, cyberattacks, extreme weather events, or the simultaneous occurrence of several threats.

Future research directions should focus on the operationalisation of the proposed model components and the development of measurable indicators of the crisis resilience of distributed energy systems. Of particular importance is determining how to measure islanding capability, the duration of maintaining critical functions, the effectiveness of crisis communication, the level of inter-institutional coordination, the availability of reserve resources, the degree of protection of vulnerable consumers, and the recovery time after a disruption. Only empirical verification of these indicators will make it possible to assess the extent to which energy decentralisation genuinely strengthens systemic resilience and under what conditions it may generate new vulnerabilities.

The conclusions are derived from three reported results: (i) the review identified a gap between engineering resilience and crisis-management governance; (ii) the IRaMuTeQ analysis showed that terms such as crisis, emergency, recovery and business continuity are weaker and more peripheral than microgrid, storage, control and integration; and (iii) the M_ZK-DES framework operationalised this gap through K0-K3 and Rk indicators. Consequently, the practical recommendation is limited to using the model as a diagnostic and scenario-testing tool until it is validated with outage records, crisis exercises and local case studies.

7. Conclusions

The conducted analysis confirms that distributed energy systems should be regarded not only as instruments of energy transition and decarbonisation, but also as an important component of modern crisis management and critical infrastructure resilience. The results of the literature review, semantic analysis, and conceptual modelling indicate that distributed energy resources, including microgrids, energy storage systems, local renewable energy sources, prosumer installations, and demand-side management mechanisms, may significantly strengthen the capacity of energy systems to operate under conditions of disruption.

The main contribution of this article is the development of a conceptual model that links distributed energy systems with the full crisis-management cycle: prevention, preparedness, response, and recovery. The proposed M_ZK-DES model demonstrates that the crisis resilience of distributed energy systems depends on the interaction of four key components: risk and systemic vulnerability, institutional and decision-making preparedness, technological and operational resilience, and social, informational, and communication mechanisms. Such an approach makes it possible to analyse the energy system as a socio-technical arrangement in which infrastructure, institutions, procedures, information flows, and user behaviour are mutually interdependent.

Short-term work should focus on improving the model specification: (1) validating the 0–5 institutional scales, (2) calibrating weights with expert elicitation and sensitivity analysis, (3) replacing hypothetical scenario scores with DSO outage data, and (4) reporting complete IRaMuTeQ exports. Medium-term work should implement multi-region simulations comparing urban, rural, industrial and cross-border distribution systems. Long-term work should co-design operational tools with local governments, DSOs, emergency services and critical-facility operators, including dashboards for critical-load prioritisation, islanding readiness, storage autonomy and vulnerable-consumer protection.

The study shows that the mere implementation of distributed energy technologies does not automatically guarantee an increase in crisis resilience. Their effectiveness depends on the extent to which they are integrated with crisis-management plans, emergency procedures, business continuity strategies, critical infrastructure protection systems, and mechanisms of inter-institutional coordination. Microgrids, energy storage systems, and local generation sources can support energy security only when they are embedded in coherent legal, organisational, operational, and communication frameworks.

An important conclusion is that energy resilience should not be reduced to technical reliability or grid stability. From the perspective of crisis management, resilience also includes the ability to anticipate threats, absorb disruptions, maintain critical functions, adapt to changing conditions, reconfigure resources, coordinate actors, communicate effectively, and recover after a crisis. This means that the resilience of distributed energy systems has a multidimensional character, combining technological, organisational, institutional, informational, and social elements.

The analysis also highlights the importance of the local and regional levels. Distributed energy systems may serve as local crisis resources capable of maintaining power supply to critical facilities such as hospitals, water treatment plants, crisis-management centres, emergency services, communication systems, and temporary shelters. However, the use of this potential requires prior identification of resources, clear allocation of responsibility, development of emergency procedures, integration with local crisis-management plans, and regular testing through exercises and simulations.

Future empirical work should also maintain a rolling literature update, especially for rapidly developing 2023–2025 themes such as resilient DER management systems, grid-forming microgrids, islanding detection, cyber-resilient DER control, resilience valuation and climate-proof distribution planning. Including these streams will prevent the model from being anchored primarily in 2010–2020 literature and will make subsequent validation more consistent with the current research frontier.

The findings further indicate the need to recognise prosumers, energy communities, energy clusters, local governments, and local enterprises as active participants in the energy security system. In distributed energy models, end users are no longer only passive consumers but may become actors of local resilience through energy generation, storage, demand reduction, and participation in local balancing mechanisms. This creates new opportunities for strengthening crisis preparedness but also requires legal, organisational, educational, and communication mechanisms that clearly define roles and responsibilities.

The proposed model may have practical application in assessing the preparedness of local governments, distribution system operators, public administration bodies, and other stakeholders responsible for energy security. It can be used to identify gaps in emergency procedures, assess the integration of distributed energy resources with crisis plans, evaluate the protection of vulnerable consumers, and design crisis exercise scenarios. In this sense, the model provides a basis for moving from general declarations of energy resilience towards more operational criteria for its assessment and measurement.

At the same time, the study has several limitations. The analysis is conceptual and is based primarily on a scoping review of scientific literature and exploratory text analysis. Although this approach made it possible to identify the main research streams and gaps, it does not allow for direct empirical verification of the effectiveness of specific crisis-management mechanisms. In addition, the use of literature indexed mainly in the Web of Science Core Collection limits the scope of the analysis, as practical documents, operator reports, crisis-management plans, technical standards, and administrative studies may contain relevant knowledge that is not fully represented in academic publications.

Future research should therefore focus on the empirical validation and operationalisation of the proposed model. Particularly important directions include case studies of local and regional energy systems, analysis of crisis-management plans and strategic documents, expert interviews, surveys among stakeholders, and simulations of disruption scenarios. Further studies should also develop measurable indicators of crisis resilience, including islanding capability, duration of maintaining critical functions, response time, recovery time, effectiveness of crisis communication, availability of reserve resources, protection of vulnerable consumers, and the level of inter-institutional coordination.

Further research may also benefit from the use of agent-based modelling, system dynamics, network analysis, and scenario simulations. These methods would make it possible to examine how distributed energy systems behave under conditions of cascading failures, cyberattacks, extreme weather events, infrastructure overloads, communication disruptions, or the simultaneous occurrence of several threats. Such analyses could help determine under which conditions energy decentralisation strengthens systemic resilience and under which conditions it may generate new vulnerabilities.

In conclusion, distributed energy systems should be treated as a strategic instrument for strengthening energy security, protecting critical infrastructure, and ensuring the continuity of essential public services under crisis conditions. Their role goes beyond technological modernisation and climate policy, extending into the domains of public safety, civil protection, local resilience, and crisis management. The development of distributed energy systems may therefore become one of the key elements of modern crisis management, provided that it is integrated with coherent legal regulations, strategic planning, institutional coordination, operational procedures, crisis communication, and social participation.

Author Contributions

Conceptualization, M.R., T.N., A.G. and K.W.; methodology, M.R. and T.N.; formal analysis, A.G. and K.W.; investigation, M.R. and K.W.; data curation, M.R. and A.G.; writing—original draft preparation, T.N.; writing—review and editing, M.R., A.G., M.J., B.K. and A.S.; visualisation, K.W.; project administration, T.N. and A.G.; funding acquisition, A.G. and T.N.; resources, M.R., T.N., M.J., B.K. and A.S.; software, M.R. and K.W. All authors have read and agreed to the published version of the manuscript.

Funding

The research leading to these results has received funding from the project titled “Cluster for innovative energy” in the frame of the programme “HORIZON-MSCA-2022-SE-01” under the grant agreement number 101129820. The study was co-financed by the Minister of Science under the “Regional Excellence Initiative”.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

Rinaldi, S.M.; Peerenboom, J.P.; Kelly, T.K. Identifying, Understanding, and Analyzing Critical Infrastructure Interdependencies. IEEE Control Syst. Mag. 2001, 21, 11–25. [Google Scholar] [CrossRef] [Scilit]
Panteli, M.; Mancarella, P. The Grid: Stronger, Bigger, Smarter?: Presenting a Conceptual Framework of Power System Resilience. IEEE Power Energy Mag. 2015, 13, 58–66. [Google Scholar] [CrossRef] [Scilit]
Bhusal, N.; Abdelmalak, M.; Kamruzzaman, M.; Benidris, M. Power System Resilience: Current Practices, Challenges, and Future Directions. IEEE Access 2020, 8, 18064–18086. [Google Scholar] [CrossRef] [Scilit]
Guo, H.; Zheng, C.; Iu, H.H.-C.; Fernando, T. A Critical Review of Cascading Failure Analysis and Modeling of Power System. Renew. Sustain. Energy Rev. 2017, 80, 9–22. [Google Scholar] [CrossRef] [Scilit]
Holling, C.S. Resilience and Stability of Ecological Systems. Annu. Rev. Ecol. Syst. 1973, 4, 1–23. [Google Scholar] [CrossRef] [Scilit]
Bie, Z.; Lin, Y.; Li, G.; Li, F. Battling the Extreme: A Study on the Power System Resilience. Proc. IEEE 2017, 105, 1253–1266. [Google Scholar] [CrossRef] [Scilit]
Lopes, J.A.P.; Moreira, C.L.; Madureira, A.G. Defining Control Strategies for MicroGrids Islanded Operation. IEEE Trans. Power Syst. 2006, 21, 916–924. [Google Scholar] [CrossRef] [Scilit]
Gilani, M.A.; Kazemi, A.; Ghasemi, M. Distribution System Resilience Enhancement by Microgrid Formation Considering Distributed Energy Resources. Energy 2020, 191, 116442. [Google Scholar] [CrossRef] [Scilit]
Hirsch, A.; Parag, Y.; Guerrero, J. Microgrids: A Review of Technologies, Key Drivers, and Outstanding Issues. Renew. Sustain. Energy Rev. 2018, 90, 402–411. [Google Scholar] [CrossRef] [Scilit]
Ines, C.; Guilherme, P.L.; Marin-Gonzalez, E.; Gahrs, S.; Hall, S.; Holstenkamp, L. Regulatory Challenges and Opportunities for Collective Renewable Energy Prosumers in the EU. Energy Policy 2020, 138, 111212. [Google Scholar] [CrossRef] [Scilit]
Arksey, H.; O’Malley, L. Scoping studies: Towards a methodological framework. Int. J. Soc. Res. Methodol. 2005, 8, 19–32. [Google Scholar] [CrossRef] [Scilit]
Levac, D.; Colquhoun, H.; O’Brien, K.K. Scoping studies: Advancing the methodology. Implement. Sci. 2010, 5, 69. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Agama, E.J.; Zubairu, U.M. Sustainability Reporting: A Systematic Review. Econ. Manag. Sustain. 2022, 7, 32–46. [Google Scholar] [CrossRef] [Scilit]
Wojciechowski, W.; Niewiadomski, A.; Bilan, Y. Ecological and Secure Electricity Microgrids—Monitoring and Forecasting Challenges. Hum. Technol. 2025, 21, 469–473. [Google Scholar] [CrossRef] [Scilit]
Ouyang, M. Review on modeling and simulation of interdependent critical infrastructure systems. Reliab. Eng. Syst. Saf. 2014, 121, 43–60. [Google Scholar] [CrossRef] [Scilit]
Billanes, J.D.; Jørgensen, B.N.; Ma, Z. A framework for resilient community microgrids: Review of operational strategies and performance metrics. Energies 2025, 18, 405. [Google Scholar] [CrossRef] [Scilit]
Heidari-Akhijahani, A.; Butler-Purry, K.L. A review on black-start service restoration of active distribution systems and microgrids. Energies 2024, 17, 100. [Google Scholar] [CrossRef] [Scilit]
Cheng, Q.; Zhang, Z.; Wang, Y.; Zhang, L. A review of distributed energy systems: Technologies, classification, and applications. Sustainability 2025, 17, 1346. [Google Scholar] [CrossRef] [Scilit]
Naseri, N.; Aboudrar, I.; El Hani, S.; Ait-Ahmed, N.; Motahhir, S.; Machmoum, M. Energy transition and resilient control for enhancing power availability in microgrids based on North African countries: A review. Appl. Sci. 2024, 14, 6121. [Google Scholar] [CrossRef] [Scilit]
Rickerson, W.; Gillis, J.; Bulkeley, M. The Value of Resilience for Distributed Energy Resources; National Renewable Energy Laboratory/NARUC: Golden, CO, USA, 2024. [Google Scholar]
Mohanty, A.; Ramasamy, A.K.; Verayiah, R.; Bastia, S.; Dash, S.S.; Cuce, E.; Khan, T.M.Y.; Soudagar, M.E.M. Power system resilience and strategies for a sustainable infrastructure: A review. Alex. Eng. J. 2024, 105, 261–279. [Google Scholar] [CrossRef] [Scilit]
Zidane, T.E.K.; Muis, Z.A.; Ho, W.S.; Zahraoui, Y.; Aziz, A.S.; Su, C.-L.; Mekhilef, S.; Campana, P.E. Power systems and microgrids resilience enhancement strategies: A review. Renew. Sustain. Energy Rev. 2025, 207, 114953. [Google Scholar] [CrossRef] [Scilit]
Kulkarni, M.S.; Mishra, S.; Sudabattula, S.K.; Sharma, N.K.; Basha, D.B.; Bajaj, M.; Tuka, M.B. Enhancing grid resiliency in distributed energy systems through a comprehensive review and comparative analysis of islanding detection methods. Sci. Rep. 2024, 14, 12124. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Adham, M.; Keene, S.; Bass, R.B. Distributed Energy Resources: A Systematic Literature Review. Energy Rep. 2025, 13, 1980–1999. [Google Scholar] [CrossRef] [Scilit]
Zitelman, K.; Barnes, J.; Converge Strategies LLC; National Renewable Energy Laboratory. Advancing Electric System Resilience with Distributed Energy Resources: A Review of State Policies; NREL/NARUC: Washington, DC, USA, 2024. [Google Scholar]
Aslam, M.U.; Miah, M.S.; Amin, B.M.R.; Shah, R.; Amjady, N. Application of Energy Storage Systems to Enhance Power System Resilience: A Review. Energies 2025, 18, 3883. [Google Scholar] [CrossRef] [Scilit]
Koirala, B.P.; Koliou, E.; Friege, J.; Hakvoort, R.A.; Herder, P.M. Energetic communities for community energy: A review of key issues and trends shaping integrated community energy systems. Renew. Sustain. Energy Rev. 2016, 56, 722–744. [Google Scholar] [CrossRef] [Scilit]
Barabino, E.; Fioriti, D.; Guerrazzi, E.; Mariuzzo, I.; Poli, D. Energy Communities: A review on trends, energy system modelling, business models, and optimisation objectives. Sustain. Energy Grids Netw. 2023, 36, 101187. [Google Scholar] [CrossRef] [Scilit]
Akinyele, D.; Belikov, J.; Levron, Y. Battery storage technologies for electrical applications: Impact in stand-alone photovoltaic systems. Energies 2017, 10, 1760. [Google Scholar] [CrossRef] [Scilit]
Mishra, S.; Anderson, K.; Miller, B.; Boyer, K.; Warren, A. Microgrid resilience: A holistic approach for assessing threats, identifying vulnerabilities, and designing corresponding mitigation strategies. Appl. Energy 2020, 264, 114726. [Google Scholar] [CrossRef] [Scilit]
Wang, Y.; Rousis, A.O.; Strbac, G. On microgrids and resilience: A comprehensive review on modeling and operational strategies. Renew. Sustain. Energy Rev. 2020, 134, 110313. [Google Scholar] [CrossRef] [Scilit]
Wang, Y.; Chen, C.; Wang, J.; Baldick, R. Research on resilience of power systems under natural disasters—A review. IEEE Trans. Power Syst. 2016, 31, 1604–1613. [Google Scholar] [CrossRef] [Scilit]
Jasiūnas, J.; Lund, P.D.; Mikkola, J. Energy system resilience—A review. Renew. Sustain. Energy Rev. 2021, 150, 111476. [Google Scholar] [CrossRef] [Scilit]
Siano, P. Demand Response and Smart Grids-A Survey. Renew. Sustain. Energy Rev. 2014, 30, 461–478. [Google Scholar] [CrossRef] [Scilit]
O’Connell, N.; Pinson, P.; Madsen, H.; O’Malley, M. Benefits and Challenges of Electrical Demand Response: A Critical Review. Renew. Sustain. Energy Rev. 2014, 39, 686–699. [Google Scholar] [CrossRef] [Scilit]
Ackermann, T.; Andersson, G.; Söder, L. Distributed generation: A definition. Electr. Power Syst. Res. 2001, 57, 195–204. [Google Scholar] [CrossRef] [Scilit]
Lund, H.; Østergaard, P.A.; Connolly, D.; Mathiesen, B.V. Smart energy and smart energy systems. Energy 2017, 137, 556–565. [Google Scholar] [CrossRef] [Scilit]
Cherp, A.; Vinichenko, V.; Jewell, J.; Brutschin, E.; Sovacool, B. Integrating techno-economic, socio-technical and political perspectives on national energy transitions: A meta-theoretical framework. Energy Res. Soc. Sci. 2018, 37, 175–190. [Google Scholar] [CrossRef] [Scilit]
Markolf, S.A.; Chester, M.V.; Eisenberg, D.A.; Iwaniec, D.M.; Davidson, C.I.; Zimmerman, R.; Miller, T.R.; Ruddell, B.L.; Chang, H. Interdependent infrastructure as linked social, ecological, and technological systems (SETSs) to address lock-in and enhance resilience. Earth’s Future 2018, 6, 1638–1659. [Google Scholar] [CrossRef] [Scilit]
Martišauskas, L.; Augutis, J.; Krikštolaitis, R.; Urbonas, R.; Šarūnienė, I.; Kopustinskas, V. A Framework to Assess the Resilience of Energy Systems Based on Quantitative Indicators. Energies 2022, 15, 4040. [Google Scholar] [CrossRef] [Scilit]
Wang, J.J.; Jing, Y.Y.; Zhang, C.F.; Zhao, J.H. Review on Multi-Criteria Decision Analysis Aid in Sustainable Energy Decision-Making. Renew. Sustain. Energy Rev. 2009, 13, 2263–2278. [Google Scholar] [CrossRef] [Scilit]
Gungor, V.C.; Sahin, D.; Kocak, T.; Ergut, S.; Buccella, C.; Cecati, C.; Hancke, G.P. Smart Grid Technologies: Communication Technologies and Standards. IEEE Trans. Ind. Inform. 2011, 7, 529–539. [Google Scholar] [CrossRef] [Scilit]
Šreimikienė, D. Ranking of Baltic States on Progress Towards the Main Energy Security Goals of European Energy Union Strategy. J. Int. Stud. 2020, 13, 24–37. [Google Scholar] [CrossRef] [Scilit]
Pescaroli, G.; Alexander, D. Critical infrastructure, panarchies and the vulnerability paths of cascading disasters. Nat. Hazards 2016, 82, 175–192. [Google Scholar] [CrossRef] [Scilit]
Uslar, M.; Specht, M.; Dänekas, C.; Trefke, J.; Rohjans, S.; González, J.M.; Rosinger, C.; Bleiker, R. Standardization in Smart Grids: Introduction to IT-Related Methodologies, Architectures and Standards; SpringerBriefs in Energy; Springer: Berlin/Heidelberg, Germany, 2013. [Google Scholar] [CrossRef] [Scilit]
Morkūnas, M.; Volkov, A.; Pazienza, P. How Resistant Is the Agricultural Sector? Economic Resilience Exploited. Econ. Sociol. 2018, 11, 321–332. [Google Scholar] [CrossRef] [Scilit]
Bruneau, M.; Chang, S.E.; Eguchi, R.T.; Lee, G.C.; O’Rourke, T.D.; Reinhorn, A.M.; Shinozuka, M.; Tierney, K.; Wallace, W.A.; von Winterfeldt, D. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq. Spectra 2003, 19, 733–752. [Google Scholar] [CrossRef] [Scilit]
Johnson, R.B.; Onwuegbuzie, A.J.; Turner, L.A. Toward a Definition of Mixed Methods Research. J. Mix. Methods Res. 2007, 1, 112–133. [Google Scholar] [CrossRef] [Scilit]
Henry, D.; Ramirez-Marquez, J.E. Generic metrics and quantitative approaches for system resilience as a function of time. Reliab. Eng. Syst. Saf. 2012, 99, 114–122. [Google Scholar] [CrossRef] [Scilit]
Madni, A.M.; Jackson, S. Towards a conceptual framework for resilience engineering. IEEE Syst. J. 2009, 3, 181–191. [Google Scholar] [CrossRef] [Scilit]
Sterman, J.D. Learning from evidence in a complex world. Am. J. Public Health 2006, 96, 505–514. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Sargent, R.G. Verification and validation of simulation models. J. Simul. 2013, 7, 12–24. [Google Scholar] [CrossRef] [Scilit]
Eisenhardt, K.M. Building theories from case study research. Acad. Manag. Rev. 1989, 14, 532–550. [Google Scholar] [CrossRef] [Scilit]
Epstein, J.M. Why Model? J. Artif. Soc. Soc. Simul. 2008, 11, 12. [Google Scholar]
Amer, M.; Daim, T.U.; Jetter, A. A review of scenario planning. Futures 2013, 46, 23–40. [Google Scholar] [CrossRef] [Scilit]
Hanna, R.; Gross, R.; Speirs, J.; Heptonstall, P.; Gambhir, A. How do energy systems model and scenario studies explicitly represent socio-economic, political and technological disruption and discontinuity? Implications for policy and practitioners. Energy Policy 2021, 149, 111984. [Google Scholar] [CrossRef] [Scilit]
Hinkin, T.R. A Brief Tutorial on the Development of Measures for Use in Survey Questionnaires. Organ. Res. Methods 1998, 1, 104–121. [Google Scholar] [CrossRef] [Scilit]
Churchill, G.A., Jr. A Paradigm for Developing Better Measures of Marketing Constructs. J. Mark. Res. 1979, 16, 64–73. [Google Scholar] [CrossRef] [Scilit]
Cronbach, L.J. Coefficient Alpha and the Internal Structure of Tests. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef] [Scilit]
Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef] [Scilit]
Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Boin, A.; ’t Hart, P.; Stern, E.; Sundelius, B. The Politics of Crisis Management: Public Leadership Under Pressure; Cambridge University Press: Cambridge, UK, 2009. [Google Scholar] [CrossRef] [Scilit]
Cutter, S.L.; Barnes, L.; Berry, M.; Burton, C.; Evans, E.; Tate, E.; Webb, J. A Place-Based Model for Understanding Community Resilience to Natural Disasters. Glob. Environ. Change 2008, 18, 598–606. [Google Scholar] [CrossRef] [Scilit]
Francis, R.; Bekera, B. A Metric and Frameworks for Resilience Analysis of Engineered and Infrastructure Systems. Reliab. Eng. Syst. Saf. 2014, 121, 90–103. [Google Scholar] [CrossRef] [Scilit]
Ayyub, B.M. Systems Resilience for Multihazard Environments: Definition, Metrics, and Valuation for Decision Making. Risk Anal. 2014, 34, 340–355. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Boin, A.; McConnell, A. Preparing for Critical Infrastructure Breakdowns: The Limits of Crisis Management and the Need for Resilience. J. Contingencies Crisis Manag. 2007, 15, 50–59. [Google Scholar] [CrossRef] [Scilit]
Ansell, C.; Boin, A.; Keller, A. Managing Transboundary Crises: Identifying the Building Blocks of an Effective Response System. J. Contingencies Crisis Manag. 2010, 18, 195–207. [Google Scholar] [CrossRef] [Scilit]
Zio, E. Challenges in the Vulnerability and Risk Analysis of Critical Infrastructures. Reliab. Eng. Syst. Saf. 2016, 152, 137–150. [Google Scholar] [CrossRef] [Scilit]
Ten, C.W.; Manimaran, G.; Liu, C.C. Cybersecurity for Critical Infrastructures: Attack and Defense Modeling. IEEE Trans. Syst. Man. Cybern. A Syst. Hum. 2010, 40, 853–865. [Google Scholar] [CrossRef] [Scilit]
Cinelli, M.; Coles, S.R.; Kirwan, K. Analysis of the Potentials of Multi Criteria Decision Analysis Methods to Conduct Sustainability Assessment. Ecol. Econ. 2014, 46, 138–148. [Google Scholar] [CrossRef] [Scilit]
Mitchell, R.K.; Agle, B.R.; Wood, D.J. Toward a Theory of Stakeholder Identification and Salience: Defining the Principle of Who and What Really Counts. Acad. Manag. Rev. 1997, 22, 853–886. [Google Scholar] [CrossRef] [Scilit]
Reed, M.S.; Graves, A.; Dandy, N.; Posthumus, H.; Hubacek, K.; Morris, J.; Prell, C.; Quinn, C.H.; Stringer, L.C. Who’s in and Why? A Typology of Stakeholder Analysis Methods for Natural Resource Management. J. Environ. Manag. 2009, 90, 1933–1949. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Bauwens, T. Explaining the Diversity of Motivations behind Community Renewable Energy. Energy Policy 2016, 93, 278–290. [Google Scholar] [CrossRef] [Scilit]
Walker, G.; Devine-Wright, P. Community Renewable Energy: What Should It Mean? Energy Policy 2008, 36, 497–500. [Google Scholar] [CrossRef] [Scilit]
Walker, G.; Devine-Wright, P.; Hunter, S.; High, H.; Evans, B. Trust and Community: Exploring the Meanings, Contexts and Dynamics of Community Renewable Energy. Energy Policy 2010, 38, 2655–2663. [Google Scholar] [CrossRef] [Scilit]
Herbane, B. Rethinking organizational resilience and strategic renewal in SMEs. Entrep. Reg. Dev. 2019, 31, 476–495. [Google Scholar] [CrossRef] [Scilit]
Palensky, P.; Dietrich, D. Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads. IEEE Trans. Ind. Inform. 2011, 7, 381–388. [Google Scholar] [CrossRef] [Scilit]
Dunn, B.; Kamath, H.; Tarascon, J.M. Electrical Energy Storage for the Grid: A Battery of Choices. Science 2011, 334, 928–935. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Vovk, Y.; Aulin, V.; Vovk, I.; Romaniuk, S.; Syhlovyi, M.; Vovk, O.; Palianytsia, V.; Zabytivskyi, V. Electric Vehicle Charging Infrastructure Optimization on International Transport Corridors: Economic Analysis and EU Regulatory Alignment in Ukraine. J. Sustain. Dev. Transp. Logist. 2025, 10, 6–28. [Google Scholar] [CrossRef] [Scilit]
Kharlamova, G.; Chernyak, O.; Nate, S. Renewable Energy and Security for Ukraine: Challenge or Smart Way? J. Int. Stud. 2016, 9, 88–115. [Google Scholar] [CrossRef] [Scilit]
Lasseter, R.H. MicroGrids. In Proceedings of the IEEE Power Engineering Society Winter Meeting, New York, NY, USA, 27–31 January 2002; pp. 305–308. [Google Scholar] [CrossRef] [Scilit]
Ton, D.T.; Smith, M.A. The U.S. Department of Energy’s Microgrid Initiative. Electr. J. 2012, 25, 84–94. [Google Scholar] [CrossRef] [Scilit]
Geels, F.W. Technological Transitions as Evolutionary Reconfiguration Processes: A Multi-Level Perspective and a Case-Study. Res. Policy 2002, 31, 1257–1274. [Google Scholar] [CrossRef] [Scilit]
Farhangi, H. The Path of the Smart Grid. IEEE Power Energy Mag. 2010, 8, 18–28. [Google Scholar] [CrossRef] [Scilit]
Gigauri, I.; Bogacz-Wojtanowska, E. Effects of the Pandemic Crisis on Social Enterprise: A Case Study from Georgia. Econ. Sociol. 2022, 15, 312–334. [Google Scholar] [CrossRef] [Scilit]
Simionescu, M. The Circular Economy and Sustainable Development in the European Union’s New Member States. Econ. Manag. Sustain. 2023, 8, 6–15. [Google Scholar] [CrossRef] [Scilit]
Guerrero, J.M.; Vasquez, J.C.; Matas, J.; de Vicuna, L.G.; Castilla, M. Hierarchical Control of Droop-Controlled AC and DC Microgrids-A General Approach Toward Standardization. IEEE Trans. Ind. Electron. 2011, 58, 158–172. [Google Scholar] [CrossRef] [Scilit]
Nosar, A. Resilient Warehouse Supply Chains in Post-Conflict Economies: Evidence on Prepositioning, Multi-Echelon Inventory, and Lateral Transshipment. J. Sustain. Dev. Transp. Logist. 2025, 10, 98–106. [Google Scholar] [CrossRef] [Scilit]
Łązaroiu, G.; Szpilko, D.; Gedeon, T.; Halicka, K.; Rzepka, A. Digital Technologies Supporting Predictive Healthcare: Review of Research Trends. Hum. Technol. 2025, 21, 694–730. [Google Scholar] [CrossRef] [Scilit]
Walker, B.; Holling, C.S.; Carpenter, S.R.; Kinzig, A. Resilience, Adaptability and Transformability in Social-Ecological Systems. Ecol. Soc. 2004, 9, 5. [Google Scholar] [CrossRef] [Scilit]
Tricco, A.C.; Lillie, E.; Zarin, W.; O’Brien, K.K.; Colquhoun, H.; Levac, D.; Moher, D.; Peters, M.D.J.; Horsley, T.; Weeks, L.; et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and explanation. Ann. Intern. Med. 2018, 169, 467–473. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Peters, M.D.J.; Godfrey, C.M.; Khalil, H.; McInerney, P.; Parker, D.; Soares, C.B. Guidance for conducting systematic scoping reviews. Int. J. Evid.-Based Healthc. 2015, 13, 141–146. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F. Science mapping software tools: Review, analysis, and cooperative study among tools. J. Am. Soc. Inf. Sci. Technol. 2011, 62, 1382–1402. [Google Scholar] [CrossRef] [Scilit]
Rizzoli, V.; da Silveira, A.; De Falco, M.; Sarrica, M. Two Sides of the Same Coin: How to Integrate Social Network Analysis and Topic Detection to Investigate Shared Contents and Communicative Interactions in Social Representations. Int. Rev. Social. Psychol. 2024, 37, 21. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Bolton, R.; Foxon, T.J. Infrastructure transformation as a socio-technical process—Implications for the governance of energy distribution networks in the UK. Technol. Forecast. Social. Change 2015, 90, 538–550. [Google Scholar] [CrossRef] [Scilit]
Gao, J.; Buldyrev, S.V.; Stanley, H.E.; Havlin, S. Networks formed from interdependent networks. Nat. Phys. 2012, 8, 40–48. [Google Scholar] [CrossRef] [Scilit]
Buldyrev, S.V.; Parshani, R.; Paul, G.; Stanley, H.E.; Havlin, S. Catastrophic cascade of failures in interdependent networks. Nature 2010, 464, 1025–1028. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Macal, C.M.; North, M.J. Tutorial on agent-based modelling and simulation. J. Simul. 2010, 4, 151–162. [Google Scholar] [CrossRef] [Scilit]
Bonabeau, E. Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. USA 2002, 99, 7280–7287. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Kusz, B.; Kusz, D.; Jurgilewicz, O.; Jurgilewicz, M.; Kozicki, B.; Topolewski, S. The technical efficiency of Polish energy sector companies of different sizes. Energies 2025, 18, 2534. [Google Scholar] [CrossRef] [Scilit]
Reuters. Polish power system is safe despite flooding, grid operator says. Reuters, 18 September 2024.
Euronews. Several thousands in Poland’s south cut off from electricity after severe storms. Euronews, 7 June 2025.