Modeling and Simulation Tools for Smart Local Energy Systems: A Review with a Focus on Emerging Closed Ecological Systems’ Application

Abstract

The growing importance of microgrids—linking buildings with distributed energy resources and storage—is driving the evolution of Smart Local Energy Systems (SLESs). These systems require advanced modeling and simulations to address growing complexity, decentralization, and interoperability. This review presents an analysis of commonly used environments and methods applied in the design and operation of SLESs. Particular emphasis is placed on their capabilities for multi-domain integration, predictive control, and smart automation. A novel contribution is the identification of Closed Ecological Systems (CES) and Life Support Systems (LSSs)—fully or semi-isolated environments designed to sustain human life through autonomous recycling of air, water, and other resources—as promising new application domains for SLES technologies. This review explores how concepts developed for building and energy systems, such as demand-side management, IoT-based monitoring, and edge computing, can be adapted to CES/LSS contexts, which demand isolation, autonomy, and high reliability. Challenges related to model integration, simulation scalability, and the bidirectional transfer of technologies and modeling between Earth-based and space systems are discussed. This paper concludes with a SWOT analysis and a roadmap for future research. This work lays the foundation for developing sustainable, intelligent, and autonomous energy infrastructures—both terrestrial and extraterrestrial.

1. Introduction

The shift toward a sustainable and decentralized energy paradigm has led to the emergence of smart local energy systems (SLESs). These are localized and flexible infrastructures that combine renewable energy sources (RESs), advanced automation, and active user engagement. Although no universally accepted definition exists—due to the emerging and context-specific nature of the concept—SLESs are typically characterized by three interrelated components: local energy generation and balancing, digitalization and control systems, and social innovation with participatory governance models [1,2,3]. This diversity reflects the fact that local systems can take various forms depending on regional needs, technological maturity, and policy frameworks. From a technical perspective, SLESs increasingly integrate distributed energy resources (DERs), storage systems, and flexible demand-side strategies. These components are typically organized across several energy vectors, including electricity, heating, and transport [4,5]. The deployment of information and communication technologies (ICT) and building automation and control systems (BACSs) is pivotal to this coordination. These technologies facilitate real-time monitoring, dynamic control, and predictive optimization across both residential and district scales. In particular, home and building energy management systems (HEMS/BEMS) have become critical enablers of local energy intelligence, thereby allowing users and the systems to which they are connected to respond autonomously to changing conditions and external signals [3,6,7]. Recent studies have emphasized the relevance of occupancy-based control and human-in-the-loop optimization approaches in grid-connected microgrids that couple energy and comfort management, as demonstrated by Korkas et al. [8]. Current research in this area focuses on the application of artificial intelligence (AI)-driven algorithms, including deep reinforcement learning, for adaptive control. Another important direction involves the development of smartness indicators, such as the Smart Readiness Indicator (SRI), which support the evaluation of functional performance and responsiveness in smart buildings. These aspects have been explored in recent studies [9,10], with further methodological contributions discussed in [11,12]. These developments are increasingly converging into unified frameworks that combine energy efficiency, flexibility, and user comfort within intelligent local infrastructures [9,10,11,12].

In addition to technological advances, SLESs are also grounded in evolving social and economic models that support local ownership, shared governance, and participatory innovation. Recent studies have explored the potential of emerging configurations, such as energy cooperatives, peer-to-peer trading schemes and community energy platforms, as mechanisms to democratize access to clean energy and enhance local resilience [13,14]. These structures are increasingly supported by digital platforms that facilitate transparent communication, collective decision-making, and dynamic tariff management. The latest investigations have indicated a need for integrated planning approaches that combine technical simulation with social engagement strategies, thereby enabling the co-design of energy systems that reflect local preferences and capacities [15]. Furthermore, the development of tailored financial instruments, including local tariffs, shared investment schemes, and municipal guarantees, is recognized as a key enabler of long-term viability and equitable participation in SLESs. Research in this area is moving toward more holistic models of energy citizenship, in which users are viewed not just as consumers or data points, but as active participants in shaping the function and direction of local energy systems. Foundational discussions can be found in [16,17], with further developments presented in [3,18].

A critical component in the development and operation of SLESs is the utilization of modeling and simulation techniques, which facilitate system design, scenario analysis, real-time control, and long-term performance evaluation. Simulation tools such as EnergyPLAN facilitate cross-sectoral and national-to-local assessments of energy configurations [3,16,17,18]. In contrast, platforms such as OPEN offer detailed simulation of DER dynamics, control strategies, and optimization processes [5,19]. In recent years, the role of modeling has evolved beyond physical and economic dimensions, encompassing behavior prediction, uncertainty analysis, and digital twin (DT)-based system supervision. Integrated simulation environments have been shown to facilitate the testing of dynamic control mechanisms, such as model-predictive control (MPC). Their application includes the evaluation of demand-side management (DSM) and demand-side response (DSR) strategies, as shown in studies [7,9]. Additional analyses on these topics are also discussed in [20,21]. Furthermore, these environments can be used to assess the resilience of SLESs under fluctuating environmental and consumption conditions. Hybrid modeling approaches, which link physical system behavior with AI-based forecasting or control, have also gained prominence. These approaches offer scalable tools for decision-making and system adaptation in real time. Co-modeling techniques, which facilitate the integration of multiple domain-specific models (e.g., electrical, thermal, behavioral) into a unified simulation framework, are increasingly employed to capture the complexity and interdependencies inherent in SLESs. It is becoming increasingly evident that these capabilities are of critical importance as local systems become more autonomous, data-driven, and responsive to user and grid-side requirements [1,3,15], a trend that is not limited to terrestrial applications. Indeed, the recent surge of interest in a sustained human presence beyond Earth—driven by both public space agencies and private initiatives—has created new logistical, energy, and operational challenges for long-duration crewed missions [22]. This trend is also evident in academia, where technical universities worldwide have established dedicated degree programs and research tracks in space engineering, life support systems, and extraterrestrial habitat design. This further reinforces the relevance of SLES-derived methodologies for space-based applications.

Many of the methodologies developed for local systems, such as hierarchical control, demand-side optimization, and integrated multi-domain modeling, are directly applicable to the autonomous infrastructures needed in such missions. This perspective naturally leads to the concept of closed ecological systems (CESs), where autonomous and fully enclosed systems integrate biological, physical, and control subsystems into a cyber–physical life support infrastructure capable of sustaining human life in extreme environments such as the Moon or Mars [23,24]. Closed systems go beyond traditional microgrids by combining multi-vector energy flows with matter loops and bioregenerative processes, all within the framework of strict long-term controllability. The architectural design of these systems necessitates the integration of photosynthetic bioreactors, waste management subsystems, and dynamic environmental control mechanisms. Recent studies emphasize that the parallels between CESs and islanded microgrids—including the need for distributed generation, hierarchical control systems, and multi-scale optimization—enable the transfer of proven microgrid control architectures, such as ISA-95-based frameworks, into CES operation [23], while also highlighting critical factors for controlled closed ecosystems [25] and advanced stoichiometric modeling approaches [26].

In this context, modeling and simulation become indispensable not only for control design and performance prediction but also for evaluating long-term system viability, detecting anomalies, and enabling predictive maintenance. It is imperative that models incorporate highly heterogeneous domains (electrical, thermal, biological), operate across multiple timescales, and maintain coherence between physical measurements and simulated states. The concept of CESs further expands the principles of DSM and DSR by incorporating the regulation of biological rhythms, the prioritization of life-critical subsystems, and the closed-loop optimization of environmental parameters such as oxygen, carbon dioxide, and humidity [27,28]. Consequently, SLESs have emerged as a unifying framework for intelligent, decentralized energy infrastructure, thereby bridging the domains of automation, environmental sustainability, and community empowerment. The extension of this work towards the extreme autonomy of CESs highlights the transformative potential of modeling and simulation in enabling complex, adaptive, and resilient infrastructures, both on Earth and beyond.

Bearing in mind all these aspects, the following review makes an original contribution by identifying and framing CESs and life support systems (LSSs) as emerging domains of application for SLES modeling and control approaches. The extension of local energy system principles to life support environments not only opens up a novel interdisciplinary research frontier but also sets new expectations and challenges for existing technologies. The building automation systems with energy management functions, which were originally developed for the purpose of enhancing user comfort and energy flexibility in smart buildings, must evolve to support autonomous regulation of closed habitats, including critical life-sustaining parameters. In a similar fashion, modeling and simulation tools must be capable of accommodating coupled multi-domain dynamics (biological, chemical, thermal, electrical), operating across a broad spectrum of time scales, and integrating co-modeling frameworks. This ensures predictive, adaptive, and fail-safe operation under extreme autonomy conditions. These challenges necessitate hybrid, scalable, and semantically integrated solutions that span the domains of energy informatics, automation, and bio-regenerative system control. By integrating smart buildings, local energy systems, and closed-loop life support infrastructures into one analytical framework, this review establishes a foundation for future interdisciplinary research and innovation in modeling, simulation, and intelligent control of sustainable, resilient, and autonomous environments.

The rest of the review is organized as follows. Section 2 outlines the review methodology and literature selection criteria. Section 3 provides an analytical overview of modeling and simulation tools applied to SLESs, microgrids, and buildings. Section 4 discusses the challenges and research trends related to extending these tools toward CESs and LSSs. Section 5 presents a thematic discussion on key areas of tool adaptation, integration strategies, and Earth–space technology transfer, culminating in a SWOT analysis. Finally, Section 6 concludes this paper and outlines future research directions.

2. Materials and Methods

This review was conceptually inspired by the one of the latest publications “New Horizons for Control and Energy Management of Closed Ecological Systems” [27], which outlines future directions in energy management, control, and predictive technologies within the context of closed systems with life support functions. Drawing on this foundation, the objective of this review is to investigate how methods, tools, and systems developed for smart buildings and local energy infrastructures can contribute to the development and operation of CES platforms for long-duration space missions. In particular, the review explores the adaptation of BACS, Internet of Things (IoT), energy management, and simulation technologies to autonomous, human-centered, closed-loop environments.

The methodology applied in this review was structured in four main phases, which are presented in the following subsections.

2.1. Scoping and Framing

The review was positioned at the intersection of three interrelated domains:

• Smart building and infrastructure technologies, including building and home automation with energy management;
• SLESs, including microgrids, DSM, and DT;
• CESs and LSSs relevant to long-duration human spaceflight and off-Earth habitats.

The main research focus was to determine whether and how existing knowledge and technologies from the building and energy domains could be effectively transferred and adapted to the needs of CES and LSS architectures. In particular, this review investigates the modeling and simulation methods and tools employed across these domains in research and practical implementation, in order to assess their applicability to highly autonomous, human-centered, closed-loop environments. This framing was also motivated by the growing convergence of research trends in energy informatics, embedded control, and space systems engineering, which increasingly highlight the need for integrated, self-regulating platforms capable of operating in isolated, resource-limited conditions. The review, therefore, adopts a systems-level perspective to analyze the design, optimization, and long-term operation of energy and control subsystems. This includes demand-side strategies that incorporate user feedback and distributed control, as explored in recent work on smart zoning and adaptive comfort control by Prof. Kosmatopoulos’ group [29]. Particular emphasis is placed on cross-domain comparison and the transferability of modeling strategies, simulation environments, and analytical frameworks in both terrestrial and space-based contexts.

2.2. Literature Search Strategy

A multi-stage search strategy was adopted, combining systematic database querying with targeted selection from publisher-specific resources. The primary bibliographic databases used in the initial search phase were Web of Science, Scopus, and, to a supplementary extent, Google Scholar. The keywords used to search for relevant papers, combined using various Boolean operators, included the following: local energy systems, microgrids, modeling, simulation, building automation and control systems, life support system, closed energy system, digital twin, habitat, and demand-side management.

Based on the initial filtering of abstracts and titles, full-text sources were retrieved from the following publisher platforms: ScienceDirect (Elsevier), IEEE Xplore, SpringerLink, and Wiley Online Library. The selection of papers was based on the following inclusion criteria: firstly, the research articles and conference papers had to be peer-reviewed; secondly, the primary period of publication was between 2010 and 2025; thirdly, particular emphasis was given to papers published from 2015 onwards.

2.3. Literature Categorization

The finalized literature set corresponds to the complete set of references in this review, representing a curated selection of peer-reviewed journal articles and conference papers spanning SLESs, microgrids, building automation, and CESs/LSSs. The size of this set reflects the novelty of the topic, particularly for CES and LSS integration with microgrid and SLES tools, where the body of relevant research remains limited. The sources were then grouped into two main categories:

• The initial group encompasses works centered on the modeling, simulation, and control of smart buildings and energy systems. The aforementioned studies frequently utilize building-level instruments and investigate subjects including, but not limited to, energy performance, automation, and control strategies in smart environments;
• The second group focuses on the design and operation of SLESs and microgrids, often highlighting simulation platforms, co-simulation architectures, and decision-support frameworks relevant to integrated local energy systems.

This categorization formed the foundation for the analytical overview presented in Section 3, which employs a domain-oriented approach across SLESs, microgrids, and buildings.

2.4. Identified Gap and Contribution Rationale

The review process revealed a clear imbalance in the literature: while numerous studies address smart buildings and local energy systems in terrestrial settings, significantly fewer works extend their methods to closed-loop systems applicable in space habitats or closed environments. This discrepancy is particularly evident in the integration of automation, energy management, and simulation tools for highly autonomous, life-supporting systems. Existing contributions frequently exhibit a lack of a unified perspective that integrates the domains of energy, environmental, and biological sciences.

This observation forms the rationale behind this review’s contribution: to systematically explore the transferability of modeling and simulation approaches from buildings and local energy infrastructures to CES and LSS scenarios. By synthesizing relevant tools, methods, and application contexts, the review supports future work on intelligent, resilient, and self-regulating systems.

3. Modeling and Simulations—Analytical Overview

In the context of SLESs, buildings, and district-level infrastructures, modeling and simulation are foundational but distinct concepts that are frequently discussed in the reviewed literature. For the clarity of the subject, this section provides the primary and most detailed overview of tools, approaches, and domain-specific applications. Subsequent Section 4 and Section 5 will refer to these descriptions when discussing transferability to CESs and LSSs, without repeating full technical characteristics. Modeling can be defined as the process of abstracting real-world energy systems into mathematical, logical, or agent-based representations. As discussed in [30,31], these models can capture the structure, behavior and interactions of physical components, control systems, and user behaviors. By way of contrast, simulation can be defined as the process of executing these models to analyze system behavior over time under different scenarios, operational conditions, or control strategies [32,33]. This distinction is discussed and emphasized in several research studies and papers. For instance, Schiera et al. [34] discuss the co-simulation frameworks and describe how domain-specific models (e.g., thermal dynamics, PV generation, user behavior) are created independently and then simulated together in a synchronized environment using platforms like Mosaik or functional mock-up interface (FMI)-based orchestration. The utilization of a modular structure facilitates the concept of plug-and-play flexibility, in addition to supporting distributed computation. This phenomenon demonstrates the separation of modeling and simulation in both logic and implementation. Furthermore, the application of simulation is conventionally linked to the evaluation of system performance, the optimization of configurations, and the testing of control strategies with hardware-in-the-loop (HIL) techniques [35,36]. In contrast, modeling predominantly concentrates on conceptual design, system abstraction, or multi-domain integration [37,38].

This analytical section, therefore, builds upon these distinctions to classify tools according to their modeling abstraction capabilities and simulation functionality, as well as their scope, interoperability, and use in decision-making processes across various smart energy, building, and local microgrid applications.

3.1. Modeling—Tools, Approaches, and Applications

As briefly mentioned above, modeling is the backbone of system abstraction and conceptual representation in smart energy applications. It allows researchers and engineers to formalize physical, cyber, and socio-technical elements, such as energy flows, building behavior, and district-level infrastructure, into logical structures that can be analyzed, optimized, and simulated. Analysis of the publications selected for this review shows that the following modeling tools and methods are used most extensively. These are presented in

Table 1. Cumulative analysis of modeling tools in the literature.

Modeling Tool	Occurrences	Papers/Articles
EnergyPlus	7	[32,36,39,40,41,42,43]
Modelica	6	[30,32,34,44,45,46]
TRNSYS	4	[33,35,47,48]
Matlab/Simulink	4	[33,47,49,50]
GridLAB-D	4	[51,52,53]

.

These tools form the technological basis for modeling energy systems, ranging from detailed building simulations (e.g., EnergyPlus and TRNSYS) to modular, multi-domain representations (e.g., Modelica and MATLAB/Simulink), as well as agent-based, grid-oriented platforms (e.g., GridLAB-D). Each of these environments has its own specific strengths. For instance, EnergyPlus is ideal for thermal comfort and building envelope modeling, Modelica is perfect for component-based cyber–physical simulation, and MATLAB is excellent for integration with predictive control logic.

However, modeling approaches in energy systems research differ in more than just the tools applied; they also differ in their underlying paradigms. Five major categories emerge from the reviewed literature, as presented in

Table 2. Cumulative analysis of modeling methods in the literature.

Modeling Method/ Approach	Occurrences	Papers/Articles
Physical modeling	5	[30,35,39,44,48]
Optimization-based modeling	5	[36,40,41,54,55]
Geospatial/GIS-based modeling	4	[41,43,56,57]
Data-driven modeling	3	[37,58,59]
Hybrid modeling	3	[32,34,60]

.

Taken together, these modeling approaches reflect the diversity of challenges addressed in the design and evaluation of smart building and energy systems. Physical modeling remains dominant in building and district-level studies due to its ability to accurately represent thermal, electrical, and mechanical behaviors. Optimization-based models are essential for system planning and control strategy assessment. Geographic information system (GIS)-based methods also support spatially resolved urban and district energy planning. Data-driven and hybrid approaches are increasingly used to capture dynamic behaviors and enable integration with predictive control or AI systems.

This literature review presents a wide range of modeling practices used in various energy domains. For this review, particular attention is paid to modeling applications in the context of SLESs, microgrids, and buildings. These three domains represent critical interfaces between energy generation, consumption, and management. The author reviewed the nature of the models developed for each domain, focusing on their structure, purpose, and innovative contributions.

3.1.1. SLES Modeling Domain

In SLES modeling, the emphasis is placed on capturing the multi-vector structure of local energy systems, often integrating electricity, heat, and mobility components. The models employed in this domain are designed to represent distributed infrastructures, their control hierarchies, and actor-specific behaviors within local communities.

It is a common practice among researchers and engineers to adopt modular physical modeling frameworks for the purpose of representing components and interactions. To exemplify this, one may consider Modelica-based models, which, as Qiu et al. [30] have demonstrated, facilitate the abstraction of thermal and electrical subsystems with both high temporal and structural resolutions. An additional focus in SLESs is on spatial and infrastructural representation, where tools such as DesignBuilder and EnergyPlus are employed to develop energy demand models at the district scale [58,61]. Rezaei et al. [55] present a district-level low-carbon planning method that combines building-level simulation and data-driven post-processing using MATLAB. Optimization-based models are also prominent, especially those incorporating investment and operational trade-offs under uncertainty. For instance, Zwicki-Bemhard et al. [36] have developed a district energy planning framework that integrates probabilistic load models and techno-economic variables. In a similar vein, Ascione et al. [41] propose the integration of GIS-based modeling for retrofitting scenarios, by establishing a linkage between geospatial data and demand and envelope models.

3.1.2. Microgrid Modeling Domain

The focus of microgrid modeling lies in the representation of autonomous system behavior, local energy balances, and resource complementarities. In this context, the primary objective of models is to delineate the way diverse generation and storage units interact with one another under the constraints of operational logistics at the local level.

Parejo et al. [49] employ multi-energy modeling in MATLAB to characterize the interplay between thermal and electric subsystems within a smart building microgrid. The model is designed to capture user preferences, climate conditions, and system feedback mechanisms. In this domain, physical modeling with Modelica is also employed. Wu et al. [45] construct detailed thermal and electric subsystem models of a building energy system, highlighting the capability of Modelica to handle multi-domain representations within microgrids. Furthermore, Cai et al. [34] employ the use of Modelica to represent physical models of energy components, including but not limited to heat pumps, thermal storage, and electrical loads, within the context of a smart building microgrid. The modeling approach that has been developed enables the scalable integration of thermal and electrical subsystems as part of a distributed simulation architecture. Another, GridLAB-D, an agent-based modeling tool, is utilized to abstract microgrid components and their control rules. In the work by Chassin et al. [51], this tool is adapted to model distribution system dynamics and load control under local resource scenarios. Moreover, Wang et al. [52] have developed a testbed that integrates GridLAB-D and MATLAB to model a distribution-level microgrid that incorporates renewable energy sources and demand response mechanisms. GridLAB-D is utilized to construct detailed agent-based models of loads, DERs, and control logic, thereby enabling the exploration of self-regulating microgrid behavior in response to dynamic conditions.

3.1.3. Building Modeling Domain

In the field of building-focused modeling, engineers and scientists primarily seek to represent thermal dynamics, HVAC system behavior, and user-driven energy use patterns. High-resolution physical modeling is the prevailing paradigm in this field, with tools such as EnergyPlus, TRNSYS, and Modelica being utilized.

In their work, Harish et al. [39] explore a range of modeling strategies for thermal zones, airflows, and control systems in building environments. They underscore the necessity of precise envelope and occupancy modeling. In a similar manner, Dogkas et al. [48] constructed detailed TRNSYS models to evaluate the performance of hybrid heating systems in multi-family housing. In the field of structural engineering, there has been a recent focus on hybrid modeling approaches that integrate physical models with behavioral or statistical layers. This integration of physical and numerical methods has proven to be a fruitful avenue for exploring the complex interplay between infrastructural elements and their statistical properties. Duerr et al. [58], for instance, extend traditional physical models with machine learning-based demand profiles to support responsive control. Further discussion of this topic can be found in [37]. In their seminal study, Liu et al. identify and categorize hybrid modeling approaches that combine physical models (e.g., thermodynamic equations, equipment-level logic) with data-driven layers (e.g., deep learning, graph-based models) for capturing complex energy interactions in urban buildings. These hybrid models are particularly well-suited to multi-energy systems, where electricity, heat, and cooling interact dynamically with building operations. The spatial dimension of building modeling is also a salient feature. The utilization of GIS-based models facilitates the conversion of building stock and urban morphology into zonal demand profiles and retrofit impact models, as evidenced by Hoffner et al. [56].

To summarize, the reviewed modeling efforts for various domains are characterized by three features. Firstly, there is growing integration across domains (thermal–electrical–spatial). Secondly, there is the use of optimization and control-oriented abstractions. Thirdly, there is the development of modular, scalable modeling platforms. Whether applied to buildings, microgrids, or community-level systems, models increasingly aim to bridge the gap between technological complexity and practical decision-making needs.

3.2. Simulation—Tools, Approaches, and Applications

Simulation is a pivotal component of energy systems research, functioning as a proving ground for evaluating system behavior, validating control strategies, and comparing alternative scenarios under diverse technical and operational conditions. In contrast to modeling, which is concerned with abstracting the system structure, simulation is employed to study system behavior over time, frequently with dynamic, uncertain, or complex interactions. It enables researchers and practitioners to reproduce realistic operating conditions, assess system stability, and optimize performance prior to implementation.

A review of the literature reveals the application of simulation in a variety of domains, ranging from building-level thermal efficiency to district-level load balancing and smart grid dynamics. The subsequent

Table 3. Cumulative analysis of simulation tools in the literature.

Simulation Tool	Occurrences	Papers/Articles
EnergyPlus	6	[32,39,40,41,43,58]
TRNSYS	4	[33,35,47,48]
Modelica	3	[34,44,45]
MATLAB/Simulink	3	[47,49,50]
GridLAB-D	3	[51,52,53]
IDA ICE	3	[40,42,62]
OpenDSS	3	[5,57,63]
HOMER	2	[54,64]

and

Table 4. Cumulative analysis of simulation methods in the literature.

Simulation Method /Approach	Occurrences	Papers/Articles
Dynamic simulation	5	[44,45,48,49,50]
Building energy simulation	5	[39,40,41,43,58]
Thermal energy simulation	4	[35,39,44,48]
Electric grid simulation	4	[50,53,54,57]
HIL (hardware-in-the-loop)	4	[32,46,52,53]

present a compendium of the most frequently employed simulation instruments and methodologies, accompanied by a selection of pertinent publications.

The analysis identifies EnergyPlus, TRNSYS, and IDA ICE as the most frequently used simulation tools, with each supporting comprehensive and multi-layered simulations across energy domains. In such cases, the utilization of tools such as Modelica and MATLAB/Simulink is also widespread, particularly in instances where dynamic behaviors and control mechanisms represent a central focus of the study. In contrast, platforms such as GridLAB-D, OpenDSS, and HOMER are designed to fulfil more specialized roles, typically focusing on specific aspects including distribution networks, power flow, or techno-economic analysis. These results underscore the heterogeneity of simulation environments in energy research.

The following Table 4 provides a more detailed exploration of the dominant simulation methods and approaches that are employed across the same body of literature.

The analysis reveals that dynamic simulation and building energy simulation are the most prevalent approaches, each appearing in five distinct publications. These methods are critical for the capture of time-dependent behavior and energy use patterns, particularly in systems with control dynamics or occupant interaction. The simulations of thermal energy and electric grids are closely related, reflecting the significance of simulating heat flows and electrical power in local energy systems. It is noteworthy that HIL approaches have been documented in four publications, suggesting a mounting interest in real-time validation of control strategies and cyber–physical integration. This development underscores the mounting convergence of simulation with experimental and hardware-based environments in the context of smart grids and microgrid applications.

A comprehensive overview of the extant literature indicates a wide range of applications of simulation in the energy sector, including studies of grid-scale stability and detailed performance evaluations of buildings. The objective of this review is to analyze simulation approaches in the context of SLESs, microgrids, and buildings. These domains illustrate how simulation is used not only for scenario testing and optimization but also increasingly for real-time validation and cyber–physical integration. The subsections that follow provide a concise overview of the observed applications and motivations for simulation across these three domains.

3.2.1. SLES Simulation Domain

In the context of SLESs, simulation is utilized to evaluate multi-vector energy flows, distributed control, and interoperability of subsystems. The aforementioned studies typically utilize modular and flexible tools capable of capturing the complex interactions between electrical, thermal, and control domains.

For instance, Barbierato et al. [32] utilize EnergyPlus and FMI-based interfaces to simulate SLES interactions within a hybrid co-simulation framework, thereby enabling a detailed analysis of control strategies across domains. In a similar manner, the OPEN platform, which has been validated using OpenDSS, supports the simulation of decentralized smart energy scenarios and real-time energy management [5]. In other studies, district-scale simulations have been utilized to analyze energy distribution and optimization strategies. Rezaei et al. [55] employ DesignBuilder and MATLAB to model low-carbon district scenarios, emphasizing the significance of dynamic simulations in supporting planning and decarbonization objectives. In a related application, Zwicki-Bemhard et al. integrate probabilistic elements into their simulation of district energy under uncertainty using URBANopt and optimization algorithms [36]. In addition to technical integration, numerous studies underscore the significance of simulating scalability and flexibility in SLESs. For instance, Oluah et al. present a structured overview of simulation-based modeling approaches for scaling up SLESs, identifying technical, economic, and social indicators used in energy system transformation scenarios. The analysis incorporates both established tools, such as EnergyPLAN, and novel modular platforms, including PyLESA [31]. Another contribution is derived from a study that focused on simulating local flexibility and performance metrics in decentralized systems. Utilizing a component-level modeling approach, the authors assess the flexibility potential, emissions, and load-shifting capabilities in smart neighborhoods incorporating electric vehicles (EVs), thermal storage, and demand-responsive appliances [65]. The presented examples illustrate that simulation in SLES contexts functions as both a design instrument and an analytical framework for policy, optimization, and system architecture evaluation at local scales.

3.2.2. Microgrid Simulation Domain

The focus of microgrid simulation lies in real-time control, islanding behavior, and multi-energy integration. It is well established that tools such as MATLAB/Simulink, Modelica, and GridLAB-D are commonly utilized for the purpose of capturing dynamic responses and interactions between DERs, storage systems, and control algorithms.

In a biologically inspired application, Parejo et al. [49] simulate a homeostatic energy management system for thermal and electrical balance in a building-based microgrid using MATLAB/Simulink. Ntomalis et al. simulate grid-forming and frequency stability behaviors on Madeira Island under high renewable penetration, emphasizing the importance of dynamic simulation in non-interconnected systems [50]. It is also worthy of note that a significant number of studies have incorporated HIL approaches for the purpose of validating microgrid control in real-time. For instance, in [53], real-time simulators are coupled with GridLAB-D and EnergyPlus to test smart grid components under realistic operating conditions. In another case, the authors utilize GridMat (a MATLAB–GridLAB-D interface) for HIL validation of microgrid controllers in a cyber–physical setting [46].

3.2.3. Building Simulation Domain

The application of simulation tools is most extensive at the building level, where they support performance evaluation, retrofit analysis, and integration of renewable energy and control systems. The aforementioned software, namely EnergyPlus, TRNSYS, and IDA ICE, are dominant in this field, offering high-resolution models of HVAC systems, occupant behavior, and thermal dynamics.

Harish et al. [66] present an overview of simulation techniques for building energy systems, comparing the strengths and limitations of different tools. Dogkaz et al. [48] utilize TRNSYS to evaluate hybrid heating configurations in multi-family buildings under cold-climate conditions, with a particular focus on seasonal efficiency. IDA ICE is distinguished by its meticulous modeling of indoor comfort and HVAC systems. In a comparative study by Meiers et al., IDA ICE was found to outperform TRNSYS and EnergyPlus in replicating measured indoor temperatures in cold climates [42]. Furthermore, Ascione et al. [41] combine GIS, EnergyPlus, and MATLAB to simulate the impacts of building retrofitting across neighborhoods, emphasizing the spatial and temporal resolution of simulation in planning scenarios. Hybrid approaches have also emerged, as evidenced by [58], where Duerr et al. integrate machine learning forecasts with physical simulations for real-time building control and energy scheduling. Furthermore, certain studies employ HIL simulation to evaluate the real-time interaction between building energy systems and control hardware. In [53], the authors implement a Smart Home HIL configuration that combines EnergyPlus with physical HVAC devices (e.g., thermostats, air conditioners) and GridLAB-D for electric system simulation. This configuration facilitates the evaluation of control strategies within a range of realistic weather profiles, electricity tariffs, and dynamic thermal responses. These examples emphasize the importance of simulation in connecting building performance, occupant needs, and smart control systems through detailed and often hybrid simulation environments.

Considering these analyses, it is concluded that simulation plays a pivotal role in the analysis and design of smart energy systems, in addition to supporting performance evaluation, the development of control strategies, and the exploration of future scenarios across SLESs, microgrids, and buildings. The selection of tools and methods is informed by the unique demands of each domain, encompassing temporal precision and the extent of system integration. The increased utilization of real-time and HIL methodologies underscores the transition towards more interactive, validated, and cyber-physically integrated simulation environments.

3.3. Co-Modeling and Co-Simulation Approaches

Bearing in mind the progressively intricate nature of contemporary energy systems, co-simulation has emerged as a pivotal methodology for integrating multi-domain models—encompassing thermal, electrical, control, and ICT domains—within a cohesive simulation workflow. It facilitates the interoperability of tools and solvers with different time steps and numerical methods in real-time or batch simulation environments. The adoption of the FMI standard has been instrumental in facilitating cross-platform compatibility and the reusability of models.

As demonstrated in studies such as Schiera et al., the advantages of distributed multi-model co-simulation using Mosaik and FMI to link smart building subsystems in a scalable and flexible environment are apparent [34]. In a similar vein, Barbierato et al. [32] have employed a hybrid simulation approach to integrate the control, communication, and physical layers within a cohesive architecture for smart grids. At the interface of energy and ICT, Aslam et al. integrate OMNeT++ with MATLAB/Simulink using the CSMO framework to capture time delays and network dynamics in smart grid control [67]. A broader review by Palensky et al. outlines architectures and technical challenges in co-simulation of cyber–physical energy systems, including synchronization, data exchange, and real-time constraints [68].

While the term co-modeling is occasionally encountered in the extant literature, it is notable that a standardized definition is absent. The term is typically used in a conceptual sense, for example, to describe collaborative or interdisciplinary model development. Conversely, co-simulation is a well-established and increasingly necessary approach to address the modular, heterogeneous, and real-time nature of smart energy systems. Its increasing utilization is indicative of the need for interoperability and cross-domain integration in both design and operational analysis. The relevance of co-simulation is predicated on its ability to link models from different engineering domains and software environments, even when these rely on incompatible solvers or time resolutions. This facilitates detailed and system-level analysis without compromising domain-specific accuracy. Examples of this phenomenon include

• Coupling MATLAB/Simulink with OMNeT++ for integrated control and communication analysis;
• Integrating EnergyPlus with GridLAB-D to combine building dynamics with grid-side behaviors;
• Using the FMI standard to interface models from tools such as Modelica, Simulink, and TRNSYS.

It is noteworthy that such configurations preserve modeling independence while supporting advanced scenario analysis, real-time testing, and holistic system validation. Consequently, this renders co-simulation a cornerstone of modern smart energy management system research. Given the central role of co-simulation in both SLES and CES contexts, the technical descriptions presented here will be referenced in later sections without full repetition, focusing instead on unique constraints and adaptations for closed-loop systems.

4. Challenges of SLESs and CESs: Emerging Research, Engineering, and Application Trends

As was briefly mentioned in Section 1, the evolution of energy systems towards decentralization, intelligence, and flexibility has given rise to advanced concepts such as SLESs, microgrids, and smart buildings. These frameworks integrate local generation, storage, flexible loads, and user-aware control, forming a mature yet continuously evolving class of solutions. In recent years, there has been an increasing tendency to utilize digital tools, predictive modeling, and multi-domain coordination with a view to enhancing energy autonomy, resilience, and sustainability [58,65].

Despite their technical sophistication, these systems are undergoing continuous development to address emergent demands pertaining to interoperability, user engagement, and integration across energy, water, mobility, and communication layers. This expanding scope creates a conceptual and technological bridge to a new class of systems like CESs [32]. It is evident that this kind of system represents a context that is fundamentally more constrained and interdependent. The development of closed systems can be traced back to their inception as experimental ecosystems, with subsequent adaptation for utilization in space LSSs. The fundamental design objective of CESs is to facilitate the sustenance of human life within fully or semi-isolated environments, achieved through the implementation of closed material and energy loops. In contradistinction to local energy systems or conventional microgrids, CESs are required to guarantee long-term autonomy within water, air, food, and energy cycles, frequently with minimal or no resupply. Recent years have seen a resurgence of interest in the fields of extraterrestrial bases, long-duration space missions, and lunar or Martian habitats. This has led to a renewed focus on research into these systems, which are now regarded not only as space engineering challenges but also as extreme testbeds for sustainable system integration [69,70]. However, research and development in CESs and LSSs remains in its infancy, particularly regarding space-based applications, which explains the limited body of scientific literature available in this domain.

The ongoing development of smart energy systems is particularly relevant to CESs, due to the convergence of technologies and control philosophies. It is evident that a significant proportion of the tools and strategies that have been developed in the context of SLESs, including but not limited to hierarchical control architectures, co-simulation environments, model-predictive control, and DTs, are currently being explored or adapted for utilization in closed systems [27]. Furthermore, space-based CESs and LSSs act as paradigm models for circular economy practices and closed-loop system thinking, thereby providing feedback to Earth-bound applications in remote or resource-scarce regions [70,71].

4.1. SLESs—Integration Potential and Toolchains

As outlined in Section 3, SLESs rely on a variety of modeling and simulation environments that support multi-domain and modular configurations. Recent developments demonstrate a growing convergence of simulation engines, control platforms, and data-driven components, thereby enabling integrated design, operation, and optimization of decentralized energy systems. Core platforms such as EnergyPlus, TRNSYS, and Modelica remain central for building and district-scale energy modeling, often extended through co-simulation with MATLAB/Simulink, GenOpt, or Python-based optimization engines to broaden analytical capabilities and control integration [40,42].

The use of co-simulation frameworks is of particular pertinence in the context of the interconnection of heterogeneous subsystems, including but not limited to thermal storage, photovoltaics, EV charging, HVAC, and user interfaces. Solutions such as MOSAIK, BCVTB, and FMI-based workflows have been shown to maintain solver independence while enabling synchronized data exchange across domains. This integration of fast-response and slow-varying dynamics within a unified framework has been demonstrated to mark a significant advancement in the field [32,34]. Platforms such as SmartBuilds and the OPEN framework further enhance real-time simulation, data exchange, and prototyping of control logic by linking physical models with live or synthetic data streams, supporting scenario testing and decision-making for operators and planners [5,58].

A common emphasis across these toolchains is modularity, scalability, and interoperability. Standardized interfaces such as FMI facilitate flexible reconfiguration and integration of new technologies or operational objectives. The remaining challenges include the alignment of temporal and spatial resolutions across models, the incorporation of stochastic user behavior, and the balancing of multi-objective performance criteria [65,72]. It is evident that the sophistication of SLES toolchains, particularly in the domains of co-simulation, hierarchical control, and the integration of heterogeneous subsystems, establishes a robust foundation for their adaptation to more constrained and closed-loop environments. These environments encompass CESs and space-based life support infrastructures

4.2. CESs—Specific Environment and Conditions for Simulation and Modeling

In contrast, CESs exemplify a pronounced and highly integrated paradigm of autonomous operation, wherein the complete cycle of all resources is internally managed with minimal or no reliance on external input. This imposes a set of stringent conditions that profoundly affect the modeling and simulation of such systems. Conventional SLESs may still interact with the larger grid or external infrastructure; however, CESs operate in isolated, self-contained configurations, frequently under extreme or extraterrestrial conditions. Consequently, these systems must be characterized by precision, reliability, and systemic integration across energy, environmental, and biological subsystems.

The first key feature of closed systems is strict closure of material loops. High levels of recycling must be achieved for water, oxygen, carbon dioxide, and nutrients. This requires detailed modeling of tightly interconnected biochemical and physical processes. A prominent example is the MELiSSA (Micro-Ecological Life Support System Alternative) developed by the European Space Agency (ESA), which includes several bioreactors. Each one performs a specific ecological task, such as carbon dioxide absorption, oxygen regeneration, or waste recycling [23,24]. Modeling in such systems must represent microbial activity, nutrient transport, and gas–liquid exchanges—often across different spatial and temporal scales.

A second major requirement is full autonomy, due to the lack of connection to external energy or data networks. CESs, therefore, demand simulation and control systems that are energy-efficient, fault-tolerant, and capable of operating under uncertainty. As shown by Ciurans et al. [23], hierarchical control architectures help integrate metabolic loads, environmental regulation, and human activity using modular and layered designs. In a broader sense, Jones [28] describes decentralized sensor and control systems that offer redundancy and allow reconfiguration without human input—crucial for long-term missions. Similarly, the review of analog habitats by Heinicke et al. [69] highlights the need for automation routines and autonomous supervision beyond traditional control, including health monitoring and local decision-making.

The third critical feature is the need for extreme reliability and precise life support performance. To meet this, hybrid modeling approaches are increasingly used. These combine deterministic system equations with probabilistic or adaptive methods. Bazmohammadi et al. [27] point to key challenges, such as unpredictable human behavior, fluctuating resource availability, and non-linear system dynamics. These demand the use of probabilistic modeling, Markov-based control, and digital twins for predictive planning and improved simulation accuracy. As Chebbo et al. observe [73], these methods are especially useful for anomaly detection and scenario planning. They help analyze how failures propagate in systems with built-in redundancy, such as CES-integrated microgrids. Additionally, modeling of human variability and metabolic shifts has driven the development of agent-based and hybrid stochastic–deterministic frameworks.

Finally, CESs require integrated modeling of energy, environment, and human factors—viewed not as separate domains but as interdependent subsystems. Environmental models should be able to track thermal comfort, humidity, air composition (e.g., oxygen, carbon dioxide, volatile organic compounds), and the performance of life support elements such as plant chambers or water recycling systems. Ellery et al. [24] show that closed-loop flows—such as carbon, nitrogen, phosphorus, and water cycles—form the ecological backbone of CESs and LSSs. These flows must be modeled with cross-domain interactions in mind. In the same spirit, holistic modeling frameworks have been proposed to support the development of self-sustaining environments for long-duration space missions [74]. These approaches promote efficient resource use and enable continuous regeneration.

In conclusion, it is rather clear that the application of simulation and modeling in closed systems with life support functions extends far beyond the scope of traditional energy system analysis. The integration of biological, behavioral, and psychological factors into energy and environmental control loops introduces new layers of complexity that demand interdisciplinary approaches. The advent of emergent concepts, such as digital twins and AI-based predictive control, facilitates real-time monitoring, fault detection, and adaptive system management under conditions of elevated uncertainty and autonomy requirements. Recent studies have demonstrated the implementation of CES digital twins that incorporate sensor feedback, probabilistic crew behavior modeling, and AI-driven decision support [27]. These developments indicate new research and technological innovation directions, both in the context of future space missions and in the advancement of next-generation, human-centered SLES applications on Earth.

4.3. Transferability of SLES Modeling and Simulation Approaches to CESs

As highlighted in Section 4.1 and Section 4.2, a significant proportion of SLES tools and methods can be adapted to CESs, despite the differences in scope, autonomy, and operational constraints. It is evident that platforms which facilitate multi-domain coupling, modular architectures, and co-simulation—which are pivotal to the system-of-systems nature of CESs—are of particular pertinence. TRNSYS can be extended with CES-specific components (e.g., waste-heat recovery, plant lighting), while Modelica’s object-oriented structure and FMI compatibility support the scalable integration of biological and energy subsystems [32,42]. The extensive utilization of FMI across SLES and co-simulation research facilitates the establishment of cross-platform simulation chains, which are imperative for CES design [67].

The utilization of complementary tools has been demonstrated to serve as a means of augmenting the capabilities of CES modeling. GridLAB-D offers agent-based simulation of microgrid dynamics, while OMNeT++, when coupled with MATLAB, supports the evaluation of communication network performance and control layer robustness [28,51,52]. Real-time optimization environments such as SmartBuilds and the OPEN platform show promise for supervisory control, but further development is required to incorporate biological and human-factor modeling relevant to LSSs [58].

The subsequent

Table 5. Comprehensive summary of tools’ transferability to CESs.

Tool/Framework	Description/Role in SLES	Transferability to CES	Co-Simulation Capability
EnergyPlus	Widely used for building energy modeling; useful for thermal and ventilation modeling in CES	Moderate—limited biological/environmental coupling	Moderate—supported via BCVTB, FMI
TRNSYS	Flexible multi-domain simulation; supports custom loops and thermal subsystems	High—extensible with new CES modules	High—native support for co-simulation and FMI integration
Modelica	Object-oriented, multi-domain modeling; supports FMI and custom component libraries	Very High—strong for integrated CES models	Very High—extensive FMI and tool coupling capabilities
MATLAB/Simulink	Widely used for control systems and component modeling	High—suitable for subsystem-level modeling and integration	High—FMI, Simulink co-simulation, real-time HIL
FMI (Functional Mock-up Interface)	Standard for model exchange and co-simulation	Very High—supports modular, cross-platform integration	Core co-simulation model
Co-simulation (MOSAIK, BCVTB, etc.)	Couples models across tools and domains	High—needed for integration of energy, life support, and control	Core co-simulation model
SmartBuilds	Real-time simulation with EnergyPlus + control/data layers	Moderate—lacks direct support for biological loops	Moderate—integrated co-simulation architecture
OPEN platform	Open-source framework for SLES coordination and simulation	Moderate—promising structure but needs CES-specific modules	Moderate—supports modular agent-based coordination
GridLAB-D	Used for modeling distributed power systems and control logic in smart grids	Moderate—suitable for electrical layers in CES microgrids	Moderate—supports coupling with EnergyPlus and real-time testbeds
OMNeT++/CSMO	Simulates communication networks and integrates with energy models via MATLAB	Moderate—enables ICT performance evaluation in CES scenarios	High—supports energy–ICT co-simulation with MATLAB/Simulink

provides a comprehensive overview of the aforementioned tools, summarizing their current roles in SLESs, their potential for CES adaptation, and their readiness for co-simulation.

While all of the discussed platforms offer valuable capabilities, their readiness levels and suitability for closed-systems applications vary. Tools such as Modelica, TRNSYS, and MATLAB/Simulink, due to their modularity and support for FMI, are currently among the most transferable and mature environments for CES-oriented modeling. In contrast, platforms such as SmartBuilds, OPEN, and OMNeT++, while promising, still require extensions or custom modules to fully address the unique constraints of life support scenarios. This assessment highlights the need for strategic prioritization in future research—focusing first on the adaptation of high-readiness tools for CES simulation, while gradually advancing support for biological, behavioral, and cyber–physical layers in emerging platforms.

It is important to emphasize that dedicated CES simulation tools are still in their development phase. Existing SLES platforms, particularly those supporting FMI, co-simulation, and a modular system architecture, provide a robust starting point. The incorporation of digital twins, AI-driven planning, machine learning, and biological behavior layers will be instrumental in addressing the distinct requirements of long-duration, life-sustaining systems.

5. Discussion

It has been demonstrated in Section 3 and Section 4 that the subjects related to the modeling and simulation of modern energy systems are multi-faceted and intricate. This is especially evident in the growing prevalence of multi-system configurations, which is indicative of an integration-oriented trend. Section 5 offers a synopsis of the salient discussion points and delineates the potential evolution of modeling and simulation tools and methods within the emergent domain of applications in closed systems with life support functions. While this review focuses on the technical and methodological dimensions of SLES modeling and simulation, it is important to note that the practical implementation of such systems can be significantly influenced by local economic, regulatory, and institutional conditions. These factors, although outside the scope of this paper, represent critical enablers or barriers that should be considered in future interdisciplinary research on SLES deployment.

5.1. Extension of SLES Modeling and Simulation Tools Toward CES and LSS Contexts

The complexity of CES and LSS requires modeling approaches that go beyond conventional energy system simulations. Such approaches should integrate physical, environmental, and biological domains into a unified framework. It is important to note that a number of tools have been developed for use in SLESs, including TRNSYS, Modelica, EnergyPlus, and Simulink. These tools have been shown to provide a strong foundation for this purpose when extended to address the tightly coupled energy, environmental, and life support processes in CESs. In such contexts, it is imperative to model physical flows (e.g., electricity, heat, fluids) in conjunction with environmental quality (e.g., air composition, humidity) and bioregenerative subsystems (e.g., plant growth, waste recycling, human activity).

It is evident that hybrid modeling and cross-domain coupling are imperative for the development of closed systems. The utilization of co-simulation frameworks and standardized interfaces, such as FMI, is very important. This enables the integration of thermal simulation engines, control logic, environmental dynamics, and biological processes within a unified architecture. It is evident that Modelica and Simulink are particularly well suited to such integration due to their modular, equation-based structures.

It is predicted that future tool development for closed systems with life support will advance towards enhanced multi-physics and multi-scale capabilities, supporting human-in-the-loop evaluation, real-time system behavior analysis, and resilience testing under diverse scenarios. In order to achieve adaptability and operational autonomy in long-duration missions and other extreme environments, it is imperative that there is integration with AI-driven decision support and digital twin platforms.

5.2. Smart Building Technologies in Space Systems: From BACS/HEMS/BEMS to CES Smart Control Architectures

The development of advanced control architectures in the domains of CESs and LSSs has the potential to build directly on the technologies that are fundamental to smart buildings with energy management systems. They have been designed to coordinate complex interactions between HVAC, lighting, generation, storage, and indoor environmental quality. Modular, standardized, and hierarchical structures are offered by these systems, which are well suited for autonomous habitats. Core functions such as dynamic scheduling, fault detection, occupancy-based control, and demand-side energy management can be adapted to the closed-loop, resource-limited conditions of CESs and LSSs.

The integration of IoT technologies and edge computing has become increasingly imperative in environments characterized by constrained communication latency, energy availability, and computational capacity [75,76,77]. Localized processing facilitates real-time responsiveness, augmented autonomy, and enhanced resilience—characteristics that are indispensable for long-duration missions or isolated off-grid systems. Dense sensor networks have been developed for the continuous monitoring of environmental parameters (e.g., CO₂ concentration, temperature, humidity), air and water quality, and resource use. The resulting time-sensitive data requires lightweight, distributed processing and on-device intelligence to ensure timely, reliable control decisions.

Recent advancements in ultra-low-power microcontroller-based edge platforms have enabled analytics, anomaly detection, and control logic to be executed directly at the point of measurement. This approach has been shown to reduce reliance on centralized computing, minimize data transmission, and enhance robustness in mission-critical scenarios. It is anticipated that future development will concentrate on IoT-driven, edge-enabled control systems with unified data protocols and common models, with a view to ensuring interoperability. The convergence of automation, sensing, and environmental control will underpin compact, scalable platforms for autonomous habitat management, supporting digital twins and adaptive decision-making in CESs and LSSs

5.3. Energy Strategies in CESs and LSSs: Renewable Energy, Storage, and DSM/DSR

The management of energy in the context of CESs and LSSs requires architectures that are fully autonomous, resilient, and resource efficient. Renewable sources, including photovoltaics, fuel cells, and bioenergy, in conjunction with local storage solutions such as batteries and hydrogen, constitute the fundamental framework of sustainable, mission-critical supply, operating within stringent spatial, maintenance, and operational constraints. Transactive energy models from terrestrial smart grids offer concepts for distributed control and load coordination; however, their direct use in CESs is limited by the absence of external markets and economic signals. Nevertheless, the underlying principles—distributed decision-making, priority-based scheduling, and adaptive control—can be applied to optimize efficiency, autonomy, and resilience in closed-loop systems.

In settings where there is an absence of interaction with the market and where the primary objective is to fulfil a mission-critical function, the strategies employed by DSM and DSR transition from being market-driven to being state-driven. In such environments, load classification, scenario-based forecasting, and adaptive shedding schemes are employed to maintain stability and to prevent cascading failures. Edge computing is of pivotal importance in this context, given its capacity to facilitate real-time processing, local decision-making, and coordination of energy assets without the need for continuous communication with a central controller. The incorporation of low-power, embedded units in close proximity to sensors and actuators has been demonstrated to enhance responsiveness and robustness, thereby ensuring the long-term operability of the system under conditions of limited bandwidth or remote operation.

The findings from long-duration space missions, including the implementation of redundancy planning, fail-safe design, and high-reliability architectures, offer valuable insights that are directly applicable to CESs and terrestrial microgrids in off-grid or high-resilience applications. In the future, the integration of generation, storage, and intelligent control will be increasingly dependent on AI-driven optimization, multi-agent coordination, and DT-based predictive management. In such contexts, it is imperative to meticulously manage trade-offs between flexibility and stability, as well as efficiency and redundancy. This is due to the fact that margins for error in CESs are negligible, and resource recovery is critical.

5.4. Bidirectional Earth–Space Technology Transfer

The relationship between terrestrial and space-based technologies is increasingly bidirectional, with mutual exchange of concepts, tools, and design principles. In this context, Earth–space technology transfer refers to the adaptation and application of technical solutions, system architectures, and modeling approaches developed for space missions to Earth-based infrastructures, as well as the transfer of terrestrial innovations to the design of extraterrestrial systems. It is evident that space missions have been instrumental in pioneering innovations in resource circularity, system autonomy, and off-grid infrastructure. These innovations have subsequently influenced the development of resilient urban systems and smart buildings on Earth. Concepts such as water and air recycling, ultra-efficient energy management, and compact, integrated system design—originally developed for life support in space—are now finding application in eco-districts, zero-emission buildings, and remote settlements.

Conversely, the rapid advancement of building automation, smart grid technologies, and IoT-based control on Earth is informing the next generation of extraterrestrial habitats. Modular BACSs and HEMSs, local energy management strategies, and edge-enabled monitoring frameworks are increasingly regarded as adaptable building blocks for LSS architectures, offering flexibility, scalability, and operational robustness under extreme conditions.

Modeling and simulation tools have been identified as playing a central role in this exchange, serving as a common language between space engineering and urban infrastructural design. The utilization of shared platforms and methodologies, including co-simulation, digital twins, and scenario-based system testing, facilitates cross-domain collaboration, virtual prototyping, and the transfer of validated control strategies between terrestrial and extraterrestrial contexts.

Emerging trends indicate an increasing interest in cross-sector standardization, interoperability frameworks, and open simulation ecosystems. These allow for the efficient adaptation of Earth-tested solutions to the constraints of space missions, and vice versa. It is hypothesized that, in the long term, this synergy may accelerate the development of resilient, self-sufficient systems. These systems would benefit both planetary sustainability and deep space exploration.

5.5. Summary SWOT Analysis—Smart Energy and Building Solutions in CESs and LSSs

To synthesize the preceding discussion, a SWOT analysis is presented in

Table 6. SWOT matrix: Applicability of smart energy systems and modeling tools in CESs and LSSs.

Strengths	Weaknesses
Mature simulation tools (e.g., TRNSYS, Modelica, Simulink) adaptable to multi-domain CES models Proven BACS/BEMS/HEMS architectures with modular, scalable design IoT and edge computing enable real-time, decentralized control Renewable and storage technologies validated in microgrid applications Cross-domain modeling enables co-simulation and digital twins	Limited readiness of existing tools for biological/environmental integration High complexity of integrated system control in CES Constrained computing power and energy in isolated LSS environments Lack of market-driven mechanisms (e.g., tariffs) limits DSM/DSR adaptation Limited standardization across tools and protocols for CES-specific use
Opportunities	Threats
Transfer of space-based resource efficiency and circularity to Earth-based systems Application of Earth-derived smart grid control in space habitats Development of interoperable, AI-supported control and monitoring platforms Integration of edge AI and microcontroller-based edge analytics Expansion of simulation environments for virtual testing of extreme scenarios	Environmental and operational extremes challenge system robustness Failures in automation can compromise critical life support functions (redundancy) Data overload and communication bottlenecks in remote/off-grid settings Cybersecurity risks in autonomous, interconnected systems Cost and complexity of adapting existing tech to mission-specific needs

to assess the applicability of smart energy and building technologies, including modeling and simulation tools, in the context of CESs and LSSs. The analysis under discussion highlights key technical, operational, and strategic aspects relevant to both terrestrial and space applications.

The analysis confirms the high potential for cross-domain technology transfer, particularly in areas such as modular automation, decentralized control, and multi-domain simulation. Notwithstanding the challenges associated with integration complexity, resource limitations, and environmental constraints, the strategic development of interoperable, adaptive systems appears both feasible and necessary. It is vital to acknowledge the necessity of continued research and innovation in edge computing, AI-driven optimization, and co-simulation platforms if the full benefits of smart energy and building solutions are to be realized in future CES and LSS deployments.

6. Conclusions

This review provides a comprehensive analysis of the modeling and simulation tools used in the development and operation of SLESs, microgrids, and intelligent buildings. A particular focus has been placed on the examination of their applicability within the emerging domains of CESs and LSSs. This study confirms that established tools such as TRNSYS, Modelica, EnergyPlus, MATLAB/Simulink, and GridLAB-D already possess features—such as modularity, co-simulation support, and multi-domain modeling—that align well with the needs of these highly autonomous and resource-constrained environments.

A central contribution of this work is the identification and framing of closed systems with life support functions as high-potential application domains for smart energy and building technologies. Modeling and simulation are positioned not only as technical instruments for optimization but also as enabling frameworks for system-level integration across physical, environmental, and biological domains.

To realize this potential, future research should be guided by a set of prioritized directions structured by near-term feasibility and anticipated system-level impact:

• Autonomous building and energy automation systems—building automation has advanced significantly, with BACSs, BEMSs, and HEMSs evolving toward integrated, multi-domain control architectures. These systems already demonstrate high readiness levels for managing energy, indoor environments, and selected biological processes in closed-loop configurations;
• Edge computing and AI with lightweight computational intelligence—the combination of computer science and AI research offers promising tools such as edge computing platforms, real-time coordination frameworks, and lightweight learning algorithms. These technologies are particularly suited for constrained, mission-critical environments, where robustness and autonomy are essential;
• Robust multi-modal energy infrastructure engineering—the integration of electrical, thermal, environmental, and biological energy sub-systems into unified, adaptive architectures remains a critical engineering challenge. These systems must be designed for long-duration operation, predictive diagnostics, and resilience under dynamic loads;
• Cross-domain simulation and digital twin environments—Interdisciplinary simulation research should focus on the development of shared digital twin platforms and co-simulation environments. These tools are vital for validating hybrid models, supporting virtual commissioning, and ensuring interoperability between heterogeneous domains.

These directions will guide the advancement of next-generation cyber–physical–bio infrastructures—intelligent, resilient, and self-regulating by design—capable of supporting both sustainable terrestrial systems and future off-Earth habitats.