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Review

Renewable-Based Isolated Power Systems: A Review of Scalability, Reliability, and Uncertainty Modeling

by
Mehrdad Ghahramani
,
Daryoush Habibi
*,
Seyyedmorteza Ghamari
,
Hamid Soleimani
and
Asma Aziz
School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
*
Author to whom correspondence should be addressed.
Clean Technol. 2025, 7(3), 80; https://doi.org/10.3390/cleantechnol7030080
Submission received: 29 June 2025 / Revised: 22 August 2025 / Accepted: 3 September 2025 / Published: 8 September 2025
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Abstract

Electric power systems are increasingly becoming more decentralized. Many communities depend on isolated power systems that operate independently of the main grid. Remote, islanded, and isolated systems face challenges due to the intermittency and unpredictability of renewable energy sources. This paper reviews the current status of renewable integration and control in stand-alone power systems. It examines techniques to enhance system reliability through energy storage, hybrid systems, and advanced predictive models. Additionally, the issues related to connecting stand-alone systems, focusing on reliability and renewable penetration, are discussed. The scalability of stand-alone power systems is analyzed based on classifications of small-, medium-, and large-scale systems, highlighting their differences and specific challenges. The South West Interconnected System of Western Australia is used as a case study at a large scale to illustrate the complexities of operating a power system with high levels of rooftop solar and wind units. This paper also reviews various methodologies for modeling the uncertainty associated with these systems, which are categorized into stochastic, fuzzy, hybrid, Information Gap Decision Theory, robust, interval, and data-driven approaches. The advantages and limitations of each method in uncertainty modeling are discussed.

1. Introduction

In recent years, electricity grids have progressively transitioned toward greater decentralization [1]. The first and most important objective of these networks is to ensure the uninterrupted supply of energy and to achieve the maximum possible cost-effectiveness using the integration of renewable energy sources (RESs) [2]. Sustainable energy networks have been presented to increase the security and flexibility of integrated distributed energy systems consisting of conventional fossil-based sources and RESs [3]. Meanwhile, the increase in energy demand and global concerns for ecological problems have provided a shift towards carbon-neutral technologies [4]. Photovoltaic (PV) systems and wind turbines (WT) have been capturing more and more attention due to their abundant resources, scalability, and low environmental impact. Global capabilities of solar photovoltaic and wind energy are expanding as alternatives to conventional centralized fossil fuel power generation [5].
As shown in Figure 1, isolated power systems are electrical networks that operate independently from the main grid due to reasons such as geographical isolation, remote areas, and difficult-to-access terrains [6]. They serve to deliver reliable and continuous power in regions where connecting to a central grid is not feasible or practical [7]. Their goal is to support energy equipment, promote regional economic growth, and decrease reliance on foreign resources [8]. However, these systems also have notable disadvantages, including high operational costs, limited infrastructure, and increased vulnerability to supply disruptions, which reduce the systems’ reliability and resilience [9]. Moreover, the intermittent and unpredictable nature of RESs, such as solar and wind, exacerbates these challenges. Based on these points, it can be concluded that although shifting to decentralized RESs offers many benefits, like reducing greenhouse gas emissions and increasing energy independence, it also poses risks for isolated renewable-based power systems [10].
It is well known that the predictability of solar and wind power is lower than that of traditional power plants, mainly due to uncertainties related to weather conditions and the time of day. This fluctuation configuration causes variations in electricity generation, which may lead to instability, quality issues, and reliability problems in the power grid [11,12]. With the increasing number of WTs and rooftop PVs, focus is shifting toward the lack of inertia in islanded power systems. Grid stability depends on the vital inertia from traditional spinning sources, such as diesel generators (DGs) [13]. Without developed cooperation and coordinated management strategies for RESs, various challenges may arise for islanded power systems. These include fluctuations in frequency and voltage, which disrupt the stability of the power grid and may cause damage to electrical devices [14]. Finally, reducing system inertia at high renewable shares makes the electricity grid more vulnerable to disturbances and raises the risk of blackouts, outages, and reliability issues [15].
Given the widespread use of wind and distributed rooftop PV generation, this review paper focuses on examining the considerations for consistent methods to analyze, manage, and address uncertainty in the planning and operation of isolated power systems. Its value lies in its ability to examine obstacles and issues in isolated power systems of different sizes using various uncertainty modeling methods. The contributions of this review paper are summarized as follows:
  • It provides insights into available techniques and methods for enhancing the reliability and performance of isolated power systems.
  • It conducts a comprehensive analysis of both connected and isolated power systems for renewable energy expansion and overall system stability.
  • It categorizes isolated systems into small-scale, medium-scale, and large-scale systems and analyzes their unique features and challenges.
  • It reviews and compares optimization and uncertainty modeling approaches, highlighting their strengths and the problems they can address.
  • It emphasizes critical technical and economic issues and explores how isolated power systems can become sustainable and maintain resilience over the long term.
This paper introduces a novel scalability-based classification (small-, medium-, and large-scale systems) with associated advantages, disadvantages, and case-specific strategies, enabling targeted recommendations for diverse isolated contexts. Additionally, this paper synthesizes a comprehensive comparison of uncertainty modeling methods, incorporating emerging hybrid and robust approaches, and provides an in-depth analysis of interconnection benefits not holistically addressed in prior syntheses. This contributes a forward-looking framework for optimizing reliability and cost-effectiveness in renewable-dominant isolated systems.
The remainder of the article is organized as follows: Section 2 reviews the integration of renewable energy sources into isolated power systems, synthesizes reliability studies, and discusses options for interconnection. Section 3 classifies isolated systems by scale, small, medium, and large, and summarizes their characteristic features and challenges. Section 4 presents optimization and uncertainty handling approaches for planning and operation. Detailed mathematical formulations are provided in Appendix A. Section 5 identifies research gaps and outlines future directions. Section 6 presents the main findings and conclusions of this paper.

2. Integration of Energy Resources in Stand-Alone Power Systems

Numerous researchers have discussed the complexity and challenges of integrating distributed energy sources into remote, islanded, and isolated power systems [16,17,18,19]. They have also provided insight into the complex dynamics and possible solutions of these systems. The existing literature on distributed energy systems commonly used in isolated power systems is shown in Table 1. Such studies show that only a few stand-alone power systems can operate entirely on renewable energy, while most depend on renewable sources for most of their electricity generation. The usual energy sources for isolated power systems are hydro, wind, PV, and energy storage systems (ESSs), while geothermal and ocean are applied at a reduced range [20].

2.1. Reliability Studies in Isolated Power Systems

The inherently uncontrollable and instantaneous nature of distributed resources and RES is a major challenge for the stability of isolated power systems, which is discussed, for example, in [57], along with various strategies to improve the reliability of these systems, such as ESSs, demand-side management, distributed generation, microgrids, and smart grids. In [58], the study of isolated power systems highlights reliability and sustainability issues in renewable-integrated power systems. The authors propose advanced methods for addressing the issue of RES deployment. These include ESSs, hybrid energy systems, microgrids, demand-side management, distributed generation, and smart grids. In [59], a modest-sized battery was shown to improve renewable resource use and off-grid resource reliability while providing economic benefits through lower operational costs and increased dependability in a PV–DG–ESS setup.
The work in [55] optimized the compositions of RESs for six stand-alone power systems. The energy sources include solar, wind, and diesel, while energy storage options consist of pumped hydro storage (PHS) and batteries. The results show significant cost reductions in generation as renewable penetration increases, with optimal points between 40% and 80%, primarily from wind. Battery storage is preferred at low power levels, while PHS seems more suitable at renewable shares above 70%, although decreasing battery costs could challenge this balance. The work in [60] shows how reliability influences the optimal design of renewable energy systems for stand-alone power systems. It examines four cases with different WT capacity factors and renewable penetration levels in South Korea and analyzes the optimal capacity of PV panels, WTs, and batteries in each case. The work in [61] examines the performance of an MW-scale wind-based isolated energy project. The wind fluctuation problem is addressed by coupling an ESS to wind power generation through a water storage system with a pump (pumping system) between two artificially created lakes. A six MW (3.2 MWh) battery ESS on Graciosa’s isolated power system began providing wind and solar energy as a base-load source [62]. This economically benefited the system, achieving a 100% instantaneous renewable penetration level, a 65% annual renewable share, and enhanced system reliability. Various architectures and control techniques explored to improve the reliability of renewable-based remote power systems are shown in Table 2.
This review identifies numerous techniques used to enhance the reliability of remote renewable power systems. The key findings are as follows:
  • An ESS is crucial for utilizing the intermittent and variable characteristics of RESs. Its main role is survivability, as most research focuses on system reliability.
  • Hybrid ESSs (battery and hydrogen) and hybrid renewable sources (wind and solar) are popular and effective for increasing reliability.
  • Modeling and optimizing system designs using predictive simulation based on machine learning models has become more common.
  • The trade-off between costs and reliability is a primary decision-making factor for finding economically feasible solutions with high reliability.
  • Broader sustainability issues, along with system-scale requirements, from island microgrids to rural distribution systems, are also discussed.
A common feature of these studies is their emphasis on energy storage solutions and hybrid system configurations to address renewable energy variability. Optimizing such systems for cost, performance, and reliability requires the development of advanced predictive models and simulations.

2.2. Interconnection of Isolated Power Systems

The interconnection of isolated power systems has been extensively studied in various papers due to their ability to improve the integration of RESs, increase system reliability, and provide economic benefits. The work in [69] examines the impact of interconnection on renewable energy integration and highlights economic and environmental advantages. In [70], the authors examined the economic and technical viability of large-scale wind power for an islanded power system connected to the mainland grid and found significant economic advantages compared to isolated systems. A subsequent study in [71] introduces energy storage into the analysis, highlighting the benefits of PHS in capturing excess wind energy. In [72], the authors analyze the complete interconnection of all islands with isolated power systems of the Canarias Archipelago, outlining a roadmap to achieve a 100% renewable energy system. They emphasize that the effective integration of energy across sectors, water pumping, traditional energy storage, and demand-side management is crucial for the success of intermittent RES integration. The paper [73] advocates that connecting autonomous systems can be beneficial for enhancing the use of local renewable energy sources. The findings suggest that the share of RESs in final energy consumption could be increased, RES power curtailment could be reduced, and energy security could be improved by using a distributed storage system instead of centralized storage. In [74], the authors examined the technical and economic benefits that can be obtained from current and future isolated power systems. The studies reviewed, shown in Table 3, include techniques such as comparative analysis, optimization models, cost–benefit analysis, simulation studies, and system modeling.
The papers on interconnection solutions highlight the very attractive benefits of connecting isolated power systems. Key insights include the following:
  • Linking isolated power systems can greatly improve system reliability by reducing bottlenecks in shedable load and decreasing uncertainties associated with RESs.
  • Integration enables deeper penetration and more effective utilization of RESs.
  • Interconnecting systems offers significant economic benefits, including quicker payback periods and increased efficiency. Environmental benefits include reduced water use and lower greenhouse gas emissions.
  • Interconnected grids lessen the impact on ESSs during charging, optimize the use of ESS, enhance RES penetration, and curtail power.
  • Interconnection boosts the security and reliability of power networks.

3. Multi-Scope Assessment of Isolated Power Systems

Isolated power systems vary significantly in size and complexity; small [9], medium-sized [79], and large systems [80] all have distinct characteristics. This classification aids in understanding their properties, issues, and limitations. Most differences among these power systems relate to their power capacity, complexity, and infrastructure. The design and operation of isolated power systems rely heavily on the system’s scale and the technology employed.

3.1. Small-Scale Isolated Power Systems

Small, isolated power systems are commonly used in remote areas, rural populations, and households. Their low cost is advantageous for decentralized applications that provide electrical power in remote locations without a grid, where simple and inexpensive photovoltaic systems [81] or small wind turbines [82] can generate low levels of electricity at minimal cost if the system is installed, operated, and maintained properly [83]. However, they have limited capacity and cannot meet peak demand. As a result, they may need to rely on temporary alternatives like backup generators [84] or energy storage [85] due to the intermittency of the energy source [86]. When it comes to rural electrification, grid extension is identified as the primary solution [87]. However, this option is highly challenging for most remote villages due to high costs, limited grid availability, long construction times [88], and logistical issues [89]. In such cases, stand-alone energy systems are often one of the better options for remote rural areas [90]. Several studies have calculated the electrification rates in Asia–Pacific countries [91] and South Asian countries [92]. For example, remote areas of Singapore and Thailand are already fully electrified, while remote regions of Bhutan and Cambodia have less than 20% electrification because they are not priorities for private companies or the government. From a technical perspective, according to the study in [93], the Canary Islands (Spain–EU) were used to analyze annual electrical energy demand, onshore RES, and conventional energy generation. The authors aimed to identify the prevalence, causes, and implications of offshore wind energy curtailments caused by weather and technical constraints and to provide an indication of the costs to the overall energy system and infrastructure investment. The advantages and disadvantages of small-scale isolated power systems are listed in Table 4.

3.2. Medium-Sized Isolated Power Systems

Medium-sized isolated power systems are used for generation in large villages and communities, islands, and industrial complexes [94]. Wind farms, solar arrays, and small hydro plants can also be connected to these systems [95]. They are more reliable and powerful than smaller ones, although they might need backup power or energy storage to handle fluctuating demand or intermittent energy generation [96]. Globally, there are over 100,000 inhabited islands. In some of these islands, electricity is generated entirely from renewable sources, but in most, it accounts for less than 10% [97]. The reliance on external funding, technology, an uncertain policy environment, and limited and inconsistent institutional support for RESs exposes isolated power systems to significant risks [98]. Additionally, a lack of skills, knowledge of the proposed RES technology, and energy planning further complicates these challenges [99]. About 70% of island economies depend heavily on tourism, with a major energy demand driver being the tourist industry in European islands [100]. This sector increases energy consumption and economic growth by creating jobs and enhancing the municipality’s attractiveness [101]. The authors have demonstrated that developing a 100% RES system on Andros Island is feasible and advantageous, considering tourism’s positive economic impact [102]. A summary of the strengths and weaknesses of medium-sized, isolated power systems is presented in Table 5 [103].

3.3. Large-Scale Isolated Power Systems

Large-scale, isolated power systems are designed to supply electricity to an entire region or country without relying on larger grids. These networks include extensive infrastructure such as wind [104] and solar farms [105], traditional power plants [22], and dependable transmission and distribution systems [106]. Additionally, optimizing large-scale isolated systems to reduce their environmental impact and maximize economic viability can be challenging [107]. The study in [108] investigates the impact of batteries on the power system of Northern Ireland, which is isolated and has a high renewable energy share, in terms of generation costs, emissions, ramping time, and extra energy. In [109], the author discusses the methods used to assess system strength, strategies to address system weaknesses, and potential future challenges. Table 6 provides a complete overview of the strengths and weaknesses of large-scale isolated power systems. Table 7 reviews the challenges and strategies for integrating some real-world widespread isolated power systems.
To clarify the issues and challenges in decentralized isolated power systems, the Western Australia power system is examined as an example of a typical large-scale isolated power system. The South West Interconnected System (SWIS) of Western Australia is experiencing increased integration of RESs [120]. This serves as a good example of a stand-alone power system that operates independently from the electrical networks of Eastern Australia. Western Australia is also quickly establishing a foundation of sustainable energy by installing more than 350,000 domestic rooftop PV systems across the SWIS [121]. Since the energy transition project started in March 2019 [122], the SWIS’s utility-scale renewable capacity has grown significantly [123]. The rising penetration of RESs, such as solar energy, which lacks inertia, greatly reduces the inertia of isolated power systems [124]. Reduced inertia challenges the stability and reliability of the grid. Furthermore, a more widespread PV supply has caused noticeable midday demand drops in system operations. This pattern, often called the “duck curve,” shows how net system load varies throughout the day, which usually peaks in the morning and evening as residential load increases, while midday demand falls due to high solar generation. As the sun sets and solar output quickly decreases, other power sources must ramp up rapidly to meet the remaining demand, posing risks to grid stability [125].
As shown in Figure 2, heavy cloud cover over Perth until 1:00 PM on 13 December 2022 led to a rapid drop in demand and a spread in PV output. This created difficult situations where generators had to quickly reduce their output to maintain system security.
Quick, smaller demand swings can also occur due to cloud behavior. This is illustrated in Figure 3, which references a real PV generation dataset recorded on 18 November 2022. Patchy clouds caused highly volatile fluctuations in distributed PV generation, which affected the demand profile. The light hanging in the mist absorbs sunlight instead of reflecting it into the camera’s photodetectors.
The example of Western Australia demonstrates the risks and potential of wind and solar energy in isolated power systems. These challenges become more significant during a typical spring and fall day when solar energy production is high and energy demand is low [127]. On such days, electricity generated from residential and commercial PV systems is fed into the grid, satisfying the energy needs. The increasing penetration of distributed photovoltaic (DPV) systems in Western Australia’s isolated power system amplifies the challenges caused by weather-related fluctuations in power supply. Without proper protection schemes, the Australian Energy Market Operator (AEMO) has raised concerns about possible rolling and cascading blackouts, highlighting the importance of effective energy system management and secure system operation given the intermittent nature of renewable energy sources.

4. Isolated Power System Optimization

Optimization studies of isolated power systems examine the methods, models, and approaches used to ensure a reliable, cost-effective, and environmentally sustainable energy supply in remote areas. Figure 4 schematically illustrates the main optimization and decision-making methodologies employed in these systems. Each family of methods offers distinct advantages, and the choice of approach depends on the problem’s characteristics, data availability, and the desired balance between reliability, performance, and computational efficiency. In summary, optimization tools for isolated power systems can be broadly categorized into four main families, each addressing different modeling needs and system challenges. Deterministic methods, such as linear programming, nonlinear programming, and mixed-integer linear programming, are best suited for well-defined models where system parameters are known with certainty. They are computationally efficient and provide reliable solutions for structured problems. Stochastic methods, including scenario-based models and Monte Carlo simulations, are designed to explicitly capture uncertainty by incorporating probability distributions of renewable generation and load demand, making them suitable for analyzing variability and quantifying risk. Robust optimization methods, such as classic robust optimization, adjustable robust optimization, and distributionally robust optimization, ensure feasible solutions under worst-case or uncertain conditions, which is particularly valuable for risk-averse decision-making in power system planning. Finally, artificial intelligence and metaheuristic methods, encompassing techniques like genetic algorithms, particle swarm optimization, neural networks, and reinforcement learning, are powerful for tackling complex and nonlinear problems that are difficult to model analytically. These approaches are often hybridized with deterministic or stochastic methods to improve accuracy and provide innovative solutions for managing the complexity of isolated power systems.
Recent studies highlight the application of these approaches in diverse contexts. In [128], the design and optimization of hybrid isolated microgrids are explored, incorporating renewable and conventional components in northern Bangladesh. In [129], an attempt is made to address the challenge of providing stable power to remote villages in northwest China by examining a stand-alone hybrid energy system. It employs mixed-integer linear programming to identify an optimal design and then conducts a techno-economic analysis of various system configurations. The study in [130] introduces a new optimization model for the operation of multi-owner microgrids, contrasting it with single-owner microgrids. It offers methods for both connected and isolated operation modes. In [131], the authors also discuss a distributed solar–biogas isolated energy system for remote areas. It scales the system using a two-stage mixed integer linear programming (MILP) problem solved by Benders decomposition. The work in [132] develops a hybrid PV–wind energy system with biomass and storage capabilities to meet the power demands of a remote region. It uses the artificial bee colony algorithm to determine the optimal size of components, and its efficiency is validated against other methods. In [133], the optimal design of an islanded hybrid microgrid for various dispatch controls is examined, focusing on component sizing, system performance, and reliability analysis.
Efficiency, reliability, and cost remain central considerations in the optimization of stand-alone systems, which frequently involve multiple objectives. Table 8 summarizes common optimization targets across these studies, emphasizing the diverse goals of energy system design and operation.

4.1. Techniques for Optimizing Isolated Power Systems

Optimization methods for isolated power systems can be grouped into three related families, which depend on the available information about uncertainty and the level of risk the planner is willing to accept. Deterministic models use fixed parameters and a fully defined setup, while risk-based models handle uncertainty with probability distributions and scenarios, and uncertainty-dominant models rely on set-based robustness when distributions are either unavailable or unreliable. Each approach addresses different decision-making situations in isolated systems with high renewable energy penetration.
Deterministic optimization is suitable when parameters are well defined over the decision horizon and the model structure is trusted. Inputs are regarded as known values, and outputs come directly from these values without uncertainty propagation [136]. These models are often used for design and integration analyses with defined operating ranges. For instance, a deterministic approach has been used to connect a commercial-scale biogas plant with photovoltaic generation and battery storage, offering a manageable basis for component sizing and operational coordination [137].
Risk-based optimization tackles forecast errors and variability when statistical data is available. These methods, often known as stochastic optimization, use probability distributions and scenarios to reduce expected costs or meet chance constraints at specific confidence levels [138]. In the context of mini-grids with wind turbines, photovoltaic units, and diesel generators, risk-based modeling has been utilized to assess reliability impacts and operating behavior under changing resources and demands, enhancing scheduling decisions amid quantified uncertainty [139].
When uncertainty is intense and probability distributions are unreliable, uncertainty-dominant methods are preferred. Robust optimization ensures feasibility and performance against all possible outcomes within defined uncertainty sets, providing decisions that remain dependable under worst-case scenarios [140]. In practice, planners often combine stochastic and robust strategies to find a balance between efficiency and resilience, a method proven effective for isolated systems where some variables can be described probabilistically, while others are only bounded or have limited scenario data [141].
These three families should be seen as a toolkit rather than mutually exclusive options. Deterministic models are suitable for problems with precise data and short timeframes. Risk-based models work well when reliable probabilistic information is available, and robust models are useful when only limits or limited samples exist. Combining these approaches can be helpful, with robust constraints protecting critical boundaries while stochastic parts reflect expected performance.

4.2. The Concept of Uncertainty in Isolated Power Systems

One of the definitions of intermittency in independent power systems is their decrepitude, and the amount of information needed for their operation explains the difference between the available data and the information required [142]. There is always some imprecision in the decisions made by operators of isolated power systems [143]. Additionally, the increasing integration of renewable energy sources, such as wind and solar power, into remote power systems introduces new uncertainties in performance and investment decision-making. To clarify some of the key uncertainty areas within each group, Figure 5 illustrates the main uncertainty groups that emerge in isolated power systems.
According to Figure 5, in isolated power systems, planners and operators must manage seven interconnected areas of uncertainty that create a complex landscape of risk. At the system’s core, daily operations are directly affected by the inherent instability of renewable generation, which fluctuates with solar irradiance and wind speed. At the same time, load demand is always changing due to unpredictable consumer behavior, long-term population growth, and seasonal patterns. Furthermore, the physical reliability of components introduces risk through potential equipment failures, gradual battery degradation, and declines in generator efficiency. Beyond these internal workings, several external factors create significant unpredictability. The system is exposed to volatile energy prices, which are influenced by factors like diesel supply chains, transport disruptions, and general market fluctuations. Broader market and economic conditions, including electricity prices, investment costs for new infrastructure, and overall macroeconomic stability, also introduce risk. A clear understanding of these different types of uncertainty is crucial for selecting the right analytical tools. For challenges with abundant historical data, such as the short-term variability of weather and load, stochastic models are highly effective. In contrast, for risks like sudden policy changes or price shocks where reliable data is scarce, methods such as robust optimization or IGDT are more suitable because they do not require precise probability distributions. To solve the most complex system design and control problems, advanced computational techniques like metaheuristics are often employed, frequently in combination with other models.
A key feature of future power networks is the short-term and long-term planning of these networks amid high levels of uncertain data [144]. Several concepts have been developed in energy system planning to address uncertainties. For example, in [145], a stochastic mixed-integer programming formulation was proposed for optimal power system configurations in isolated regions. A stochastic simulation model was used to analyze future electricity supply in 2025 in Fiji, with a high penetration of RESs [146]. Reference [147] developed a MILP model for long-term energy planning in mainland Greece and non-interconnected islands. Additionally, a MILP model was introduced for the optimal design of wind–PV hybrid systems in off-grid Peruvian microgrids, minimizing initial investment costs by determining the location and size of generators and the overall microgrid configuration [148]. In real-world problems, many constraints can be violated by altering one data point, making the solution infeasible or even impossible [149].

4.3. Uncertainty Modeling Approaches

Uncertainty modeling is critical for optimizing isolated power systems given the variability of renewable sources, loads, and other parameters. This section overviews key approaches, organized into subsections for clarity: stochastic (probabilistic), fuzzy, hybrid, IGDT, robust, interval, and deterministic methods. Each subsection discusses the conceptual basis, strengths, weaknesses, and relevant applications in isolated systems, with detailed mathematical equations provided in Appendix A for reference.
The main goal of these methods is to address the impact of uncertain input parameters on a system’s output parameters [150]. A summary of different strategies for managing uncertainties in isolated power systems is shown in Table 9, each with its own advantages and areas of application. Reducing uncertainties in remote power systems requires a comprehensive approach that combines various modeling techniques. These techniques help improve the reliability, stability, and cost-effectiveness of power systems by providing robust solutions to manage the inherent variability and uncertainty of RESs and load demand. Using these approaches together ensures that isolated power systems operate effectively and remain resilient to uncertainty.
Table 10 comprehensively compares various uncertainty modeling methods used in different studies related to isolated power systems. Each method has its advantages and disadvantages, making them suitable for different applications and various uncertainty modeling scenarios. Selecting the appropriate uncertainty modeling method depends on the requirements of the specific isolated power system under study. This includes the type of uncertainty, data availability, computational resources, and scalability needs in simulation. Readers may employ this table as a decision-making framework for selecting an uncertainty modeling technique tailored to the attributes of their isolated power system. For example, stochastic methods are apt for systems with substantial historical data and probabilistic distributions (e.g., renewable output variability), offering precise statistical outputs despite higher computational demands. In contrast, robust or IGDT approaches suit environments with severe, non-probabilistic uncertainties and limited data, prioritizing worst-case resilience at the expense of potential conservatism. Fuzzy or hybrid methods are preferable for qualitative ambiguities, such as vague load forecasts, while interval analysis provides bounds for bounded uncertainties with moderate complexity. Ultimately, the choice should balance factors like scalability (e.g., high for distributed robust methods in large-scale systems), data requirements, and desired output granularity to ensure optimal reliability and operational efficiency.

4.3.1. Stochastic Method

The probabilistic approach uses various techniques to model uncertainty, including MCS, Latin hypercube sampling (LHS), and others. MCS is one of the most common and accurate numerical methods [141]. It is any technique that provides approximate answers to quantitative problems through statistical sampling. In [151], a multi-stage stochastic optimization model was proposed for a stand-alone energy system using a new version of the nested L-shaped method for precise solutions. This algorithm also assesses the microgrid’s reliability under different weather conditions throughout the entire decision-making horizon. The uncertainty of solar energy and heat load in islanded microgrid operations was examined in [152], considering different dispatch strategies. MCS is utilized in [153] to study the reliability implications of probabilistic distributed generation. The study of [154] examined the uncertainty in energy demand and wind power output by generating random samples for uncertain inputs using MCS and applied a multi-linear method to address them. Increasing the uncertainty parameters in MCS requires many simulations, leading to a large amount of computational effort. Point estimation calculations are more efficient due to their easier implementation and higher accuracy [155,185,186]. Additionally, ref. [156] used a new point estimation method to model uncertainties related to load demand and solar generation in an islanded DC microgrid. The stochastic optimization approach is adopted in [157] to evaluate the uncertainty of an island-capable power system and minimize the gap between scheduling results and real-world operation needs at a specific confidence level. The study of [158] offers extensive innovation in stochastic–heuristic approaches for electrical supply optimization in hybrid energy systems, which combine PV, WT, DG, and ESSs.

4.3.2. Fuzzy Method

The idea of using fuzzy method modeling was first introduced by [181]. To handle the uncertainty related to WT, PV power generation, and thermal power, the fuzzy constraint planning model [159] is developed. In [160], fuzzy logic is applied for a smooth switch between grid-connected and stand-alone modes in distributed generation systems. The success of the fuzzy method depends on how fuzzy MFs are chosen. The calculation is relatively simple and examines how uncertain parameters affect the system. To create a strong management system, in [161] the fuzzy predictive range model is used as a predictive tool, considering the nonlinear dynamic behavior and uncertainty of RESs.

4.3.3. Hybrid Methods

Hybrid optimization approaches have the capability to handle multiple uncertainties, enabling the energy system optimization strategy to meet real-time scheduling needs. In [162], the authors addressed the impact of intermittent renewable power generation on the active power loss of the distribution system using fuzzy and MCS techniques. The work in [163] proposed a fuzzy-scenario hybrid framework, addressing uncertainty from both the intermittent RES and the time-varying load demand. A related concept was presented in [164], where a random-enabled procedure uses fuzzy set theory to minimize uncertainties in reliability data inputs, such as the approach rate, service time, and protection system activity.

4.3.4. IGDT

IGDT is especially suitable for making robust decisions when input parameters are uncertain, such as RESs in isolated power systems. It can address challenges related to fluctuating market prices, power generation, and uncertain loads in the optimized operation of isolated energy systems. A new energy management system is proposed for resilient stand-alone microgrids in [165]. This paper uses a stochastic–IGDT approach with pseudo-values to represent uncertain factors in different states and to robustly handle component failures. Additionally, ref. [166] introduced a chance-constrained IGDT model for multi-period microgrid expansion planning that accounts for both stochastic and non-stochastic uncertainties. A scheduling policy for energy systems is presented in [167], which employs a data-driven IGDT optimization technique. Energy system operators develop cautious, profit-oriented policies to protect against and respond to risks associated with uncertain prices.

4.3.5. Robust Method

The robust optimization approach is specifically designed to handle optimization problems under uncertainty. This method is especially effective when a complete probabilistic model of the uncertain data is unavailable or unreliable [187,188]. The study in [170] developed a two-stage generalized robust scheduling model that accounts for uncertainty in the heat load of the heating pipe, ambient temperature, and heat dissipation coefficient using fuzzy constraints. In [171], multi-objective robust optimization is applied to minimize the total cost of power sources and energy storage maintenance and operation in isolated hybrid systems. In [172], three objectives are presented: reducing the levelized cost, minimizing CO2 emissions, and limiting grid voltage variation. Additionally, a design oriented toward extreme weather conditions has been established. In [173], a student article proposes a method to control the capacity of wind power, PV, ESS, and diesel generators, formulating a powerful optimization model for a system with islands comprising wind, light, diesel, and storage.

4.3.6. Interval Analysis

This method assumes that uncertain parameters vary within a known range. The interval analysis approach is used to manage uncertainties by defining a set of possible values for each uncertain input parameter. The study of [174] proposes an energy system coordination strategy based on interval-optimized methods, which consider the demand response and wind power uncertainties. The study of [175] examines source–load synergies and presents a novel multi-target interval optimization framework for energy system planning problems, accounting for supply and demand uncertainties.

4.3.7. Distributed Robust Optimization Method

This approach is more generalizable because it uses a set of possible PDFs for vague parameters, rather than depending on a single specific PDF. This enables decision-makers to handle uncertainty or the lack of concrete information about the basic PDFs. In [176], a two-stage distributionally robust model is introduced to optimize the design and operation of an isolated microgrid. It simultaneously optimizes the design and operating strategies under renewable generation uncertainty, aiming to minimize both investment and operational costs. In [177], a probability-constrained distributionally robust energy management model is developed for a remote microgrid. This microgrid includes dispersed generators, storage systems, and renewable sources like wind power. The model’s objective function considers generation costs, emissions, and ESS degradation. A new regret analysis method, stochastic p-robust optimization, is introduced in [178]. It is a hybrid approach that combines features of both stochastic optimization and robust optimization.

4.3.8. Data-Driven Optimization Method

Data-driven optimization is a new approach that uses machine learning algorithms to enhance decision-making under uncertainty [182,183,184]. The core idea of this method is to approximate, using observed data, both the distribution and the pattern of uncertain parameters. Since it incorporates information directly into decision-making, this approach is more robust than traditional uncertainty modeling techniques. This technique is a powerful tool for solving complex optimization problems in isolated power systems and finding efficient solutions while managing uncertainties.
Figure 6 illustrates a decision-support framework that connects two key system descriptors, data availability and system scale, to appropriate classes of optimization and uncertainty modeling methods for isolated power systems. The framework identifies four data availability categories: extensive historical data, which favors stochastic methods and data-driven optimization; limited qualitative data, where fuzzy methods, IGDT, and robust optimization are most suitable; interval data, which is ideally addressed by interval analysis and robust methods; and mixed data, which requires hybrid approaches to integrate quantitative and qualitative inputs. The system scale is categorized as small-scale, where stochastic, fuzzy, or robust methods are typically sufficient, and large-scale, where robust and data-driven approaches are essential to handle complexity and distributed architectures.

5. Future Research Directions

This review paper examined various aspects of isolated power systems, focusing on strategies to improve reliability, interconnection methods, scalability considerations, and approaches to managing uncertainties, as identified through a review of different studies. Despite valuable and substantial works and studies in isolated power system concepts, some gaps remain in integrating RESs into power systems.

5.1. Gaps in Integration of High-Penetration Renewables

Although substantial studies have addressed the integration of RESs in power systems, net-zero isolated power systems with high penetration of wind turbines and rooftop PVs require further investigation. Future research should prioritize the development of efficient optimization models for such stand-alone systems, incorporating modern mathematical programming tools to minimize costs while ensuring stability.

5.2. Gaps in Scale-Specific Studies

Extensive research has focused on islanded and remote power systems, often emphasizing distributed, small-scale, or densely populated isolated systems. However, there is a need for a deeper exploration of operational and control challenges in large-scale isolated power systems, such as Western Australia’s SWIS. This includes examining electricity generation operations, distribution, and consumption scales, as well as overall system robustness and reliability.

5.3. Gaps in Uncertainty Modeling Techniques

Existing algorithms for uncertainty modeling in the literature are often outdated and insufficient for handling real-world uncertainties. Advanced methods beyond classical approaches, such as artificial intelligence and deep learning algorithms, should be explored to better simulate RES uncertainties. These could aid in modeling uncertainty, optimizing resource allocation, reducing operating costs, and enhancing grid stability through improved analysis and estimation of wind and solar generation.

6. Conclusions

This paper comprehensively reviews the key aspects of isolated power systems, emphasizing strategies to improve their reliability, interconnection methods, scalability, and uncertainty modeling. A review of reliability strategies shows that energy storage systems are vital in managing the variability of renewable energy sources in isolated power systems. Additionally, the investigations reveal that hybrid systems, which combine multiple storage options and renewable sources with advanced predictive models, are essential for optimizing cost-effectiveness and reliability. The research highlights that interconnection strategies offer significant benefits, such as increased reliability, higher renewable energy penetration, economic savings, and environmental advantages. This review categorizes isolated power systems by scale, identifying unique benefits and challenges for small-, medium-, and large-scale systems. The findings indicate that small-scale systems are affordable but face scalability issues, while medium-sized systems serve larger communities but require substantial investments. Large-scale systems face notable challenges related to extreme weather, infrastructure, and renewable energy intermittency. However, they can improve reliability through hybrid systems, advanced control models, and infrastructure upgrades. Western Australia’s SWIS has been examined as a large-scale isolated power system to provide insights into the challenges and issues faced by such systems. It is concluded that without proper strategies, systems with high renewable energy penetration are vulnerable to power outages and widespread blackouts. This review also covers approaches to address power generation and demand uncertainties, including stochastic, robust, interval, IGDT, and data-driven methods. These studies emphasize the importance of robust uncertainty modeling techniques to improve system reliability, stability, and cost-effectiveness under uncertain conditions.

Author Contributions

Conceptualization, M.G., D.H. and A.A.; methodology and systematic search strategy, M.G.; literature screening and data extraction, M.G.; critical appraisal and quality assessment, M.G. and H.S.; data synthesis and formal analysis, M.G.; visualization, M.G.; writing—original draft preparation, M.G.; writing—critical review and editing, D.H., S.G., H.S., and A.A.; supervision, D.H. and A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This study received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
RESsRenewable Energy Sources
PVPhotovoltaic
WTWind Turbine
ESSsEnergy Storage Systems
DGDiesel Generator
SWISSouthwest Interconnected System
IGDTInformation Gap Decision Theory
PHSpumped hydro storage
DRDemand Response
LHSLatin Hypercube Sampling
DPVDistributed Photovoltaic
MSCMonte Carlo Simulation
MFMembership Function
AEMOAustralian Energy Market Operator
WAWestern Australia
MILPMixed-Integer Linear Programing
PDFsProbability Distribution Functions
MCSMonte Carlo Simulation

Appendix A. Mathematical Formulations for Uncertainty Modeling

Appendix A provides detailed supporting information related to the methods and analyses presented in this work. It includes the mathematical formulations of the optimization models, extended parameter definitions, and supplementary explanations that enhance the understanding of the approaches discussed in the main text. This additional material serves as a reference for readers seeking deeper insight into the technical aspects and ensures the reproducibility of the study.

Appendix A.1. Stochastic Method

The stochastic approach modifies the relationship between a multivariate function y and the random vector Z as y = f Z . Z is a vector of the form Z = z 1 , z 2 , … , z m , where z 1 , z 2 , … , z m are random parameters with known PDFs. The objective is to identify the PDF of the output variable y. In an isolated power system, f is a function that describes a system model (e.g., a set of load flow equations). The system comprises input uncertain parameters (e.g., power injections by renewable energy resources and electric loads), and y is the output variable (e.g., total active losses and total operating costs). MCS involves generating a large number of random samples from the PDFs of Z and evaluating y for each sample. In the first step, N M C samples { z i 1 , z i 2 , … , z i N M C } are generated for each random variable z i according to their PDF, as provided in (A1), and then the output y for each sample is calculated according to (A2).
Z ( k ) = [ z 1 k , z 2 k , … , z m ( k ) ] for   k = 1 , 2 , … , N M C
y ( k ) = f ( Z ( k ) )
The last step is analyzing the outcomes { y ( 1 ) , y ( 2 ) ,   … ,   y ( N M C ) } using statistical measures such as the mean (A3), variance (A4), histograms, and confidence intervals.
E [ y ] = 1 N M C ∑ k = 1 N M C   y ( k )
V a r ( y ) = 1 N M C − 1 ∑ k = 1 N M C   y ( k ) − E [ y ] 2

Appendix A.2. Fuzzy Method

In an isolated power system where the total generation cost y = f x 1 , x 2 , … , x n is a function of uncertain input parameters x 1 , x 2 , … , x n such as renewable energy generation, and load demand, where X is the vector of input uncertain parameters, each represented by a fuzzy set A ~ i with an associated MF μ A ~ i ( x ) , A MF μ A ~ i ( x ) defines the degree of membership of x i in the fuzzy set A ~ i . This function maps each element x i to a value between 0 and 1, indicating its degree of belonging to the set. An α-cut of a fuzzy set A ~ i is a crisp set that contains all elements of the universe of discourse U with MF values greater than or equal to α. The α-cut of A ~ i is denoted as A i α according to (A5), where
A i α = x ∈ U ∣ μ A ~ i ( x ) ≥ α
The α-cut can also be expressed as an interval A i α = A _ i α , A ‾ i α , where A _ i α and A ‾ i α are the lower and upper bounds, respectively. The α-cuts for the output y are determined by applying the function f to the α-cuts of the input parameters. For each α-cut, y α is obtained as shown in (A6).
y α = m i n X α   f X α , m a x X α   f X α
where X α represents the α-cuts of the input parameters.

Appendix A.3. Hybrid Method

In an isolated power system, the total cost y is influenced by both stochastic and fuzzy uncertainties. The objective function is y = f X , Z , where Z is a vector of stochastic uncertain parameters including renewable generation and load consumption with known density PDFs and X is a vector of fuzzy uncertain parameters (electricity prices) described by fuzzy sets with MFs. In the hybrid MCS–fuzzy method, for each k th Monte Carlo sample Z ( k ) = [ z 1 k , z 2 k , … , z m ( k ) ] and each α-cut of fuzzy parameter x i α = A _ i α , A ‾ i α , the output is according to (A7).
y ( k , α ) = m i n X α   f X α , Z ( k ) , m a x X α   f X α , Z ( k )

Appendix A.4. IGDT Method

In a stand-alone power system, the overall operational cost is affected by uncertainties such as renewable power generation. These uncertainties are characterized using IGDT in order to make robust decisions. The set of Equations (A8) and (A9) can be represented as the optimization problem.
y = m i n d   f ( X , d )
H ( X , d ) = 0 ;   G ( X , d ) ≥ 0
where X is the vector of input parameters (subject to severe uncertainty) and d is the vector of decision variables. H and G represent the equality and inequality constraints, respectively. The function f ( X , d ) describes the relationship between the decision variables d and the uncertain input parameters X . In IGDT, the uncertainty in input parameters X is expressed as deviations from their predicted values X ¯ . The objective is to make decisions d that remain robust against these uncertainties. The optimization constraints can be formulated as shown in (A10) to (A13).
f ( X , d ) ≤ l c
l c = ( 1 + ξ ) × y ¯
X ~ ∈ U ( α , X ¯ )
U ( α , X ¯ ) = | X − X ¯ X ¯ | ≤ α
where ξ is the degree to which the decision-maker tolerates the deterioration of the objective function and y ¯ is the predicted value of y. In addition α is the uncertainty level, while X ¯ is the forecasted value of X, and U ( α , X ¯ ) is the set of all values of X whose deviation from X ¯ will not exceed α ⋅ X ¯ . The robustness of a decision d based on the requirement l c is defined as the maximum value of α where the operator is sure that the constraints are always satisfied.

Appendix A.5. Robust Method

In robust optimization, the function z = f X , y is defined, where X represents uncertain parameters and y represents decision variables. The objective is to maximize z while ensuring that the solution is robust against uncertainty in X .
m a x y   z = f ( X , y )
X ∈ U ( X )
where U ( X ) is the uncertainty set for X . Given that z is linear concerning X , it can be reformulated as shown in (A16) to (A19).
m a x y   z
z ≤ f ( X ‾ , y )
f ( X ‾ , y ) = A ( y ) ⋅ X ‾ + g ( y )
X ‾ ∈ U ( X ) = { X ∣ ∥ X − X ‾ ∥ ≤ X ^ }
where X ‾ is the predicted value and X ^ is the maximum possible deviation of X from X ‾ . The robust counterpart of the optimization problem ensures that the objective function remains optimal even when the predicted values of X deviate within the uncertainty set. The robust optimization problem is defined as shown in (A20) to (A24).
m a x y   z
z ≤ f X ‾ , y
f ( X ‾ , y ) = A ( y ) ⋅ X ‾ + g ( y ) − m a x w i   ∑ i     a i ( y ) ⋅ X ^ i ⋅ w i
∑ i     w i ≤ Γ
0 ≤ w i ≤ 1
where Γ denotes the degree of conservativeness and w i represents the uncertainty of auxiliary variables. The robust counterpart is solved by converting it into a dual form, as shown in (A25).
m i n ξ i , β   Γ β + ∑ i   ξ i β + ξ i ≥ a i ( y ) ⋅ X ^ i
Inserting the dual form, the robust optimization can be reformulated as shown in (A26) to (A29).
m a x y , ξ i , β   z
z ≤ f X , y
f ( X , y ) = A ( y ) ⋅ X ‾ + g ( y ) − Γ β − ∑ i     ξ i
β + ξ i ≥ a i ( y ) ⋅ X ^ i

Appendix A.6. Interval Analysis

Consider a multivariate function f = f ( x 1 , x 2 , … , x n ) , where l b i ≤ x i ≤ u b i with l b i and u b i being the lower and upper bounds of the uncertain parameter x i , respectively. The objective is to determine the range of possible values for the output f given these intervals. P ( x ) represents the probability distribution of the uncertain parameter x within the interval, as formulated in (A30).
P ( x ) = ∫ a d A 1 1 σ 2 π e − ( x − μ ) 2 / 2 σ 2
where μ is the mean and σ is the standard deviation of the uncertain parameter.

Appendix A.7. Distributed Robust Optimization Method

In an isolated power system, the total cost of operation is a function of cost, represented by y, and is influenced by the uncertain variability of RESs. The optimization problem is to find the optimal decision, represented by d, to minimize the total operation cost, based on the uncertainty in RESs. The optimal problem can be written as shown in (A31).
y = f ( X , d )
where X = x 1 , x 2 , … , x m includes uncertain parameters like renewable energy generation and demand and d = d 1 , d 2 , … , d N includes decisions on energy generation, storage, and dispatch. The uncertainty set U ( X ) is defined as shown in (A32).
U ( X ) = { X ∣ ∥ X − X ‾ ∥ ≤ Δ }
where X ‾ is the nominal value of X and Δ is the maximum deviation. The decision variables d are distributed across N subsystems, each responsible for a part of the system’s operation:
  • d 1 : Decisions for the first renewable source;
  • d 2 : Decisions for the second renewable source;
  • …;
  • d N : Decisions for the N th unit;
The objective function is to minimize the total operation cost y, as shown in (A33).
m i n d 1 , d 2 , … , d N m a x X ∈ U ( X )   f ( X , d )

Appendix A.8. Data-Driven Optimization Method

The objective function can be expressed as shown in (A34), and the constraints are described as shown in (A35)–(A37).
min x , y   ( c T x + e T y )
A x ⩽ b
T x + W y ⩾ d
x ∈ X ;   y ∈ Y ,
where c and e are cost vectors and x and y are decision variables. In addition, A, T, and W are matrices representing system constraints, and b and d are vectors representing limits and demands. X and Y define the feasible regions for x and y. Utilizing machine learning methodologies in robust uncertainty optimization offers a powerful solution for addressing the uncertainty of the optimization problem. This subset is subsequently employed in constructing the ambiguity set, reducing the computational burden of distributed robust models. The formulation can be written as shown in (A38).
m i n x ∈ X ( c T x + m a x P ∈ D E P [ Q ( x , ξ ) ] )
This objective function within this framework seeks to optimize the highest possible anticipated value of the recourse function Q ( x , ξ ) over the encompassing ambiguity set D. This set consists of a complete assortment of potential distributions P for the stochastic variable ξ (inherent certainty features) and the set X (presents the feasibility region for the decision variable x).

References

  1. Rezkallah, M.; Chandra, A.; Ibrahim, H. Wind–PV–battery hybrid off-grid system: Control design and real-time testing. Clean Technol. 2024, 6, 471–493. [Google Scholar] [CrossRef]
  2. Soleimani, H.; Habibi, D.; Ghahramani, M.; Aziz, A. Strengthening Power Systems for Net Zero: A Review of the Role of Synchronous Condensers and Emerging Challenges. Energies 2024, 17, 3291. [Google Scholar] [CrossRef]
  3. Bade, S.O.; Tomomewo, O.S.; Meenakshisundaram, A.; Dey, M.; Alamooti, M.; Halwany, N. Multi-Criteria optimization of a hybrid renewable energy system using particle swarm optimization for optimal sizing and performance evaluation. Clean Technol. 2025, 7, 23. [Google Scholar] [CrossRef]
  4. Geng, Y.; Liu, Q.; Zheng, H.; Yan, S. Two-stage collaborative power optimization for off-grid wind–solar hydrogen production systems considering reserved energy of storage. Energies 2025, 18, 2970. [Google Scholar] [CrossRef]
  5. Yasmin, R.; Nabi, M.N.; Rashid, F.; Hossain, M.A. Solar, wind, hydrogen, and bioenergy-based hybrid system for off-grid remote locations: Techno-economic and environmental analysis. Clean Technol. 2025, 7, 36. [Google Scholar] [CrossRef]
  6. Ghahramani, M.; Habibi, D.; Ghamari, S.; Aziz, A. Addressing uncertainty in renewable energy integration for Western Australia’s mining sector: A robust optimization approach. Energies 2024, 17, 5679. [Google Scholar] [CrossRef]
  7. Maliat, A.; Kotian, S.; Ghahremanlou, D. Assessment of a hybrid renewable energy system incorpo-rating wind, solar, and storage technologies in Makkovik, Newfoundland and Labrador. J. Sustain. Energy 2024, 3, 87–104. [Google Scholar] [CrossRef]
  8. Pengtem, C.; Deeum, S.; Amirullah; Ohgaki, H.; Romphochai, S.; Bhumkittipich, P.; Bhumkittipich, K. Design of multi-objective energy management for remote communities connected with an optimal hybrid integrated photovoltaic–hydropower–battery energy storage system (PV-HP-BESS) using improved particle swarm optimization. Energies 2025, 18, 2250. [Google Scholar]
  9. Ahmed, M.M.; Das, B.K.; Das, P.; Hossain, M.S.; Kibria, M.G. Energy management and sizing of a stand-alone hybrid renewable energy system for community electricity, fresh water, and cooking gas demands of a remote island. Energy Convers. Manag. 2024, 299, 117865. [Google Scholar]
  10. REN21. Renewables 2023 Global Status Report Collection, Renewables in Energy Supply; REN21 Secretariat: Paris, France, 2023. [Google Scholar]
  11. Ghahramani, M.; Sadat-Mohammadi, M.; Nazari-Heris, M.; Asadi, S.; Mohammadi-Ivatloo, B. Introduction and literature review of the operation of multi-carrier energy networks. In Planning and Operation of Multi-Carrier Energy Networks; Nazari-Heris, M., Asadi, S., Mohammadi-Ivatloo, B., Eds.; Springer: Cham, Switzerland, 2021; pp. 39–57. [Google Scholar]
  12. Ghahramani, M.; Zare, K.; Mohammadi-Ivatloo, B. Incorporating parking lot and demand response in energy procurement of a commercial complex as a smart large consumer. In Proceedings of the 27th Iranian Conference on Electrical Engineering (ICEE), Yazd, Iran, 30 April–2 May 2019. [Google Scholar]
  13. Rangarajan, S.S.; Collins, E.R.; Senjyu, T. Grid forming inverter as an advanced smart inverter for augmented ancillary services in a low inertia and a weak grid system towards grid modernization. Clean Technol. 2024, 6, 1011. [Google Scholar] [CrossRef]
  14. Sánchez, A.S.; Junior, E.P.; Gontijo, B.M.; de Jong, P.; dos Reis Nogueira, I.B. Replacing fossil fuels with renewable energy in islands of high ecological value: The cases of Galápagos, Fernando de Noronha, and Príncipe. Renew. Sustain. Energy Rev. 2023, 183, 113527. [Google Scholar] [CrossRef]
  15. Aljarrah, R.; Fawaz, B.B.; Salem, Q.; Karimi, M.; Marzooghi, H.; Azizipanah-Abarghooee, R. Issues and challenges of grid-following converters interfacing renewable energy sources in low inertia systems: A review. IEEE Access 2024, 12, 5534–5561. [Google Scholar] [CrossRef]
  16. Wang, J.; Song, C.; Huang, Z.; Campbell, A.; Konarova, M. Remote island renewable transition potential: Affordable, reliable and sustainable generation optimisation for Mornington island. Renew. Sustain. Energy Transit. 2024, 5, 100084. [Google Scholar] [CrossRef]
  17. Moosavian, S.F.; Noorollahi, Y.; Shoaei, M. Renewable energy resources utilization planning for sustainable energy system development on a stand-alone island. J. Clean. Prod. 2024, 439, 140892. [Google Scholar] [CrossRef]
  18. Rettig, E.; Fischhendler, I.; Schlecht, F. The meaning of energy islands: Towards a theoretical framework. Renew. Sustain. Energy Rev. 2023, 187, 113732. [Google Scholar] [CrossRef]
  19. Pombo, D.V.; Martinez-Rico, J.; Spataru, S.V.; Bindner, H.W.; Sørensen, P.E. Decarbonizing energy islands with flexibility-enabling planning: The case of Santiago, Cape Verde. Renew. Sustain. Energy Rev. 2023, 176, 113151. [Google Scholar] [CrossRef]
  20. Manoo, M.U.; Shaikh, F.; Kumar, L.; Arıcı, M. Comparative techno-economic analysis of various stand-alone and grid connected (solar/wind/fuel cell) renewable energy systems. Int. J. Hydrogen Energy 2024, 52, 397–414. [Google Scholar] [CrossRef]
  21. Akram, U.; Shah, R.; Amjady, N.; Mithulananthan, N.; Islam, S. Active and reactive power injection of energy storage for short-term frequency stability in islanded power systems. J. Energy Storage 2024, 90, 111914. [Google Scholar] [CrossRef]
  22. Ahmadipour, M.; Othman, M.M.; Salam, Z.; Alrifaey, M.; Ridha, H.M.; Veerasamy, V. Optimal load shedding scheme using grasshopper optimization algorithm for islanded power system with distributed energy resources. Ain Shams Eng. J. 2023, 14, 101835. [Google Scholar] [CrossRef]
  23. Willenberg, D.; Winkens, A.; Linnartz, P. Impact of wind turbine generator technologies and frequency controls on the stable operation of medium voltage islanded microgrids. Electr. Power Syst. Res. 2020, 189, 106760. [Google Scholar] [CrossRef]
  24. Lorenzi, G.; da Silva Vieira, R.; Silva, C.A.S.; Martin, A. Techno-economic analysis of utility-scale energy storage in island settings. J. Energy Storage 2019, 21, 691–705. [Google Scholar] [CrossRef]
  25. Hamilton, J.; Negnevitsky, M.; Wang, X.; Lyden, S. High penetration renewable generation within Australian isolated and remote power systems. Energy 2019, 168, 684–692. [Google Scholar] [CrossRef]
  26. Rodriguez, M.; Arcos-Aviles, D.; Guinjoan, F. Simple fuzzy logic-based energy management for power exchange in isolated multi-microgrid systems: A case study in a remote community in the Amazon region of Ecuador. Appl. Energy 2024, 357, 122522. [Google Scholar] [CrossRef]
  27. Sheng, L.; Zhou, Z.; Charpentier, J.-F.; Benbouzid, M. Stand-alone island daily power management using a tidal turbine farm and an ocean compressed air energy storage system. Renew. Energy 2017, 103, 286–294. [Google Scholar] [CrossRef]
  28. Suomalainen, K.; Silva, C.; Ferrão, P.; Connors, S. Wind power design in isolated energy systems: Impacts of daily wind patterns. Appl. Energy 2013, 101, 533–540. [Google Scholar] [CrossRef]
  29. Elkholy, M.; Said, T.; Elymany, M.; Senjyu, T.; Gamil, M.M.; Song, D.; Ueda, S.; Lotfy, M.E. Techno-economic configuration of a hybrid backup system within a microgrid considering vehicle-to-grid technology: A case study of a remote area. Energy Convers. Manag. 2024, 301, 118032. [Google Scholar] [CrossRef]
  30. Islam, A.S.; Rahman, M.M.; Mondal, M.A.H.; Alam, F. Hybrid energy system for St. Martin Island, Bangladesh: An optimized model. Procedia Eng. 2012, 49, 179–188. [Google Scholar] [CrossRef]
  31. Bernardino, M.; Rusu, L.; Soares, C.G. Evaluation of the wave energy resources in the Cape Verde Islands. Renew. Energy 2017, 101, 316–326. [Google Scholar] [CrossRef]
  32. Camus, C.; Farias, T. The electric vehicles as a mean to reduce CO2 emissions and energy costs in isolated regions. The São Miguel (Azores) case study. Energy Policy 2012, 43, 153–165. [Google Scholar] [CrossRef]
  33. Khan, M.R.B.; Jidin, R.; Pasupuleti, J.; Shaaya, S.A. Optimal combination of solar, wind, micro-hydro and diesel systems based on actual seasonal load profiles for a resort island in the South China Sea. Energy 2015, 82, 80–97. [Google Scholar] [CrossRef]
  34. Ma, T.; Yang, H.; Lu, L. Study on stand-alone power supply options for an isolated community. Int. J. Electr. Power Energy Syst. 2015, 65, 1–11. [Google Scholar] [CrossRef]
  35. Pikra, G.; Salim, A.; Prawara, B.; Purwanto, A.; Admono, T.; Eddy, Z. Development of small scale concentrated solar power plant using organic Rankine cycle for isolated region in Indonesia. Energy Procedia 2013, 32, 122–128. [Google Scholar] [CrossRef]
  36. Yu, Z.; Lin, J.; Liu, F.; Li, J.; Zhao, Y.; Song, Y. Optimal Sizing of Isolated Renewable Power Systems with Ammonia Synthesis: Model and Solution Approach. IEEE Trans. Power Syst. 2024, 39, 6372–6385. [Google Scholar] [CrossRef]
  37. Demiroren, A.; Yilmaz, U. Analysis of change in electric energy cost with using renewable energy sources in Gökceada, Turkey: An island example. Renew. Sustain. Energy Rev. 2010, 14, 323–333. [Google Scholar] [CrossRef]
  38. Kumar, S.R.; Gafaro, F.; Daka, A.; Raturi, A. Modelling and analysis of grid integration for high shares of solar PV in small isolated systems–A case of Kiribati. Renew. Energy 2017, 108, 589–597. [Google Scholar] [CrossRef]
  39. Feio, A.D.; da Silva, F.C.; Teixeira, M.A.; Maria, A.C.L.; da Silva, G.B.S. Viability of renewable energy integration in isolated systems in Brazil–A case study at Trindade Island (Espírito Santo, Brazil). Renew. Energy 2024, 222, 119805. [Google Scholar] [CrossRef]
  40. Bekele, G.; Boneya, G. Design of a photovoltaic-wind hybrid power generation system for Ethiopian remote area. Energy Procedia 2012, 14, 1760–1765. [Google Scholar] [CrossRef]
  41. Contestabile, P.; Di Lauro, E.; Galli, P.; Corselli, C.; Vicinanza, D. Offshore wind and wave energy assessment around Malè and Magoodhoo Island (Maldives). Sustainability 2017, 9, 613. [Google Scholar] [CrossRef]
  42. Ma, T.; Yang, H.; Lu, L. A feasibility study of a stand-alone hybrid solar–wind–battery system for a remote island. Appl. Energy 2014, 121, 149–158. [Google Scholar] [CrossRef]
  43. Al-Badi, A. Hybrid (solar and wind) energy system for Al Hallaniyat Island electrification. Int. J. Sustain. Energy 2011, 30, 212–222. [Google Scholar] [CrossRef]
  44. Zhao, C.; Wang, F.; Xu, J.; Tan, C.; Østergaard, P.A. Optimal planning and operation for a grid-connected solar-wind-hydro energy system in wastewater treatment plants. Renew. Energy 2024, 230, 120791. [Google Scholar] [CrossRef]
  45. Amin, M.R.; Roy, R.B.; Hasan, M.M. Modeling and optimization of decentralized Microgrid system for st. Martin’s island in Bangladesh. Int. J. Energy Inf. Commun. 2014, 5, 1–12. [Google Scholar] [CrossRef]
  46. Hossain, M.; Mekhilef, S.; Olatomiwa, L. Performance evaluation of a stand-alone PV-wind-diesel-battery hybrid system feasible for a large resort center in South China Sea, Malaysia. Sustain. Cities Soc. 2017, 28, 358–366. [Google Scholar] [CrossRef]
  47. Abos, H.; Ave, M.; Martínez-Ortiz, H. A case study of a procedure to optimize the renewable energy coverage in isolated systems: An astronomical center in the North of Chile. Energy Sustain. Soc. 2017, 7, 7. [Google Scholar]
  48. Peerapong, P.; Limmeechokchai, B. Optimal electricity development by increasing solar resources in diesel-based micro grid of island society in Thailand. Energy Rep. 2017, 3, 1–13. [Google Scholar] [CrossRef]
  49. Wu, Y.-K.; Lee, C.-Y.; Jan, B.-S.; Huang, Y.-Q. A Wind-Solar-Energy Storage System Leading to High Renewable Penetration in the Island System of Kinmen. Engineering 2013, 5, 81. [Google Scholar]
  50. Ye, B.; Zhang, K.; Jiang, J.; Miao, L.; Li, J. Towards a 90% renewable energy future: A case study of an island in the South China Sea. Energy Convers. Manag. 2017, 142, 28–41. [Google Scholar] [CrossRef]
  51. Panapakidis, I.P.; Sarafianos, D.N.; Alexiadis, M.C. Comparative analysis of different grid-independent hybrid power generation systems for a residential load. Renew. Sustain. Energy Rev. 2012, 16, 551–563. [Google Scholar] [CrossRef]
  52. Enevoldsen, P.; Sovacool, B.K. Integrating power systems for remote island energy supply: Lessons from Mykines, Faroe Islands. Renew. Energy 2016, 85, 642–648. [Google Scholar] [CrossRef]
  53. Chade, D.; Miklis, T.; Dvorak, D. Feasibility study of wind-to-hydrogen system for Arctic remote locations–Grimsey island case study. Renew. Energy 2015, 76, 204–211. [Google Scholar]
  54. Gherbi, A.D.; Arab, A.H.; Salhi, H. Improvement and validation of PV motor-pump model for PV pumping system performance analysis. Sol. Energy 2017, 144, 310–320. [Google Scholar] [CrossRef]
  55. Almaktoof, A.M.; Raji, A.K.; Kahn, M.T.; Ekhlat, M.A. Batteryless PV desalination system for rural areas: A case study. J. Energy S. Afr. 2015, 26, 29–37. [Google Scholar] [CrossRef]
  56. Bhakta, S.; Mukherjee, V. Performance indices evaluation and techno economic analysis of photovoltaic power plant for the application of isolated India’s island. Sustain. Energy Technol. Assess. 2017, 20, 9–24. [Google Scholar] [CrossRef]
  57. Kuang, Y.; Zhang, Y.; Zhou, B.; Li, C.; Cao, Y.; Li, L.; Zeng, L. A review of renewable energy utilization in islands. Renew. Sustain. Energy Rev. 2016, 59, 504–513. [Google Scholar] [CrossRef]
  58. Cross, S.; Padfield, D.; Ant-Wuorinen, R.; King, P.; Syri, S. Benchmarking island power systems: Results, challenges, and solutions for long term sustainability. Renew. Sustain. Energy Rev. 2017, 80, 1269–1291. [Google Scholar] [CrossRef]
  59. Caminiti, C.M.; Ragaini, E.; Barbieri, J.; Dimovski, A.; Merlo, M. Limited-Size BESS for Mitigating the Impact of RES Volatility in Grid-Tied Microgrids in Developing Countries: Focus on Lacor Hospital. In Proceedings of the 2024 IEEE PES/IAS PowerAfrica, Johannesburg, South Africa, 5–9 August 2024; pp. 1–5. [Google Scholar]
  60. Song, J.; Oh, S.-D.; Yoo, Y.; Seo, S.-H.; Paek, I.; Song, Y.; Song, S.J. System design and policy suggestion for reducing electricity curtailment in renewable power systems for remote islands. Appl. Energy 2018, 225, 195–208. [Google Scholar] [CrossRef]
  61. Godina, R.; Rodrigues, E.; Matias, J.; Catalão, J. Sustainable energy system of El Hierro island. In Proceedings of the International Conference on Renewable Energies and Power Quality (ICREPQ’15), La Coruña, Spain, 25–27 March 2015; pp. 46–51. [Google Scholar]
  62. Stenzel, P.; Schreiber, A.; Marx, J.; Wulf, C.; Schreieder, M.; Stephan, L. Environmental impacts of electricity generation for Graciosa Island, Azores. J. Energy Storage 2018, 15, 292–303. [Google Scholar] [CrossRef]
  63. Trapani, D.; Marocco, P.; Ferrero, D.; Lindberg, K.B.; Sundseth, K.; Santarelli, M. The potential of hydrogen-battery storage systems for a sustainable renewable-based electrification of remote islands in Norway. J. Energy Storage 2024, 75, 109482. [Google Scholar] [CrossRef]
  64. Roy, D. Modelling an off-grid hybrid renewable energy system to deliver electricity to a remote Indian island. Energy Convers. Manag. 2023, 281, 116839. [Google Scholar] [CrossRef]
  65. Huo, D.; Santos, M.; Sarantakos, I.; Resch, M.; Wade, N.; Greenwood, D. A reliability-aware chance-constrained battery sizing method for island microgrid. Energy 2022, 251, 123978. [Google Scholar]
  66. López-Prado, J.L.; González-Sánchez, J.W.; Vélez, J.I.; Garcia-Llinas, G.A. Reliability assessment in rural distribution systems with microgrids: A computational-based approach. IEEE Access 2022, 10, 43327–43340. [Google Scholar] [CrossRef]
  67. Gioutsos, D.M.; Blok, K.; van Velzen, L.; Moorman, S. Cost-optimal electricity systems with increasing renewable energy penetration for islands across the globe. Appl. Energy 2018, 226, 437–449. [Google Scholar] [CrossRef]
  68. Xiao, L.; Muttaqi, K.M.; Agalgaonkar, A.P. Reliability assessment of modern distribution networks embedded with renewable and distributed resources. Electr. Power Syst. Res. 2022, 212, 108374. [Google Scholar] [CrossRef]
  69. Georgiou, P.N.; Mavrotas, G.; Diakoulaki, D. The effect of islands’ interconnection to the mainland system on the development of renewable energy sources in the Greek power sector. Renew. Sustain. Energy Rev. 2011, 15, 2607–2620. [Google Scholar] [CrossRef]
  70. Kapsali, M.; Kaldellis, J.; Anagnostopoulos, J. Investigating the techno-economic perspectives of high wind energy production in remote vs interconnected island networks. Appl. Energy 2016, 173, 238–254. [Google Scholar] [CrossRef]
  71. Kapsali, M.; Anagnostopoulos, J. Investigating the role of local pumped-hydro energy storage in interconnected island grids with high wind power generation. Renew. Energy 2017, 114, 614–628. [Google Scholar] [CrossRef]
  72. Gils, H.C.; Simon, S. Carbon neutral archipelago–100% renewable energy supply for the Canary Islands. Appl. Energy 2017, 188, 342–355. [Google Scholar] [CrossRef]
  73. Pfeifer, A.; Dobravec, V.; Pavlinek, L.; Krajačić, G.; Duić, N. Integration of renewable energy and demand response technologies in interconnected energy systems. Energy 2018, 161, 447–455. [Google Scholar] [CrossRef]
  74. Lobato, E.; Sigrist, L.; Rouco, L. Value of electric interconnection links in remote island power systems: The Spanish Canary and Balearic archipelago cases. Int. J. Electr. Power Energy Syst. 2017, 91, 192–200. [Google Scholar] [CrossRef]
  75. Mora-Jacobo, E.G.; Tovar-Facio, J.; Ponce-Ortega, J.M. Optimal planning for power systems considering the interconnections between isolated regions. Environ. Dev. Sustain. 2024, 26, 8859–8891. [Google Scholar] [CrossRef]
  76. Badr, A.A.; Safari, A.; Ravadanegh, S.N. Segmentation of interconnected power systems considering microgrids and the uncertainty of renewable energy sources. IET Gener. Transm. Distrib. 2023, 17, 3814–3827. [Google Scholar] [CrossRef]
  77. Josia, H.R. Off-Grid Solar PV System Design and Analysis in Isolated Island for Sustainable Energy Access: A Case Study in Sukun Island, Indonesia. J. Sol. Energy Res. 2023, 8, 1609–1621. [Google Scholar]
  78. Alves, M.; Segurado, R.; Costa, M. Increasing the penetration of renewable energy sources in isolated islands through the interconnection of their power systems. The case of Pico and Faial islands, Azores. Energy 2019, 182, 502–510. [Google Scholar] [CrossRef]
  79. Tsiaras, E.; Andreosatou, Z.; Kouveli, A.; Tampekis, S.; Coutelieris, F.A. Off-grid methodology for sustainable electricity in medium-sized settlements: The case of Nisyros Island. Clean Technol. 2025, 7, 16. [Google Scholar] [CrossRef]
  80. Jabbar, W.A.; Annathurai, S.; Rahim, T.A.A.; Fauzi, M.F.M. Smart energy meter based on a long-range wide-area network for a stand-alone photovoltaic system. Expert Syst. Appl. 2022, 197, 116703. [Google Scholar] [CrossRef]
  81. Abusaq, M.; Zohdy, M.A. Optimizing renewable energy integration through innovative hybrid microgrid design: A case study of Najran Secondary Industrial Institute in Saudi Arabia. Clean Technol. 2024, 6, 397–417. [Google Scholar] [CrossRef]
  82. Nirbheram, J.S.; Mahesh, A.; Bhimaraju, A. Techno-economic optimization of standalone photovoltaic-wind turbine-battery energy storage system hybrid energy system considering the degradation of the components. Renew. Energy 2024, 222, 119918. [Google Scholar] [CrossRef]
  83. Aftab, M.A.; Byrne, J.; Hazendonk, P.; Johnson, D.; Spencer, L.; Weis, T. Implementing tri-brid energy systems for renewable integration in Southern Alberta, Canada. Clean Technol. 2024, 6, 1038–1056. [Google Scholar] [CrossRef]
  84. Abdulkarim, A.; Faruk, N.; Alozie, E.; Olagunju, H.; Aliyu, R.Y.; Imoize, A.L.; Adewole, K.S.; Imam-Fulani, Y.O.; Garba, S.; Baba, B.A.; et al. Advances in the design of renewable energy power supply for rural health clinics, case studies, and future directions. Clean Technol. 2024, 6, 921–953. [Google Scholar] [CrossRef]
  85. Mainali, B.; Pachauri, S.; Rao, N.D.; Silveira, S. Assessing rural energy sustainability in developing countries. Energy Sustain. Dev. 2014, 19, 15–28. [Google Scholar] [CrossRef]
  86. Javadi, F.; Rismanchi, B.; Sarraf, M.; Afshar, O.; Saidur, R.; Ping, H.; Rahim, N. Global policy of rural electrification. Renew. Sustain. Energy Rev. 2013, 19, 402–416. [Google Scholar] [CrossRef]
  87. Mainali, B.; Silveira, S. Alternative pathways for providing access to electricity in developing countries. Renew. Energy 2013, 57, 299–310. [Google Scholar] [CrossRef]
  88. Katti, P. Rural electrification through solar and wind hybrid system: A self sustained grid free electric power source. Energy Procedia 2012, 14, 2081–2087. [Google Scholar] [CrossRef]
  89. Liu, S.; Yan, J.; Yan, Y.; Zhang, H.; Zhang, J.; Liu, Y.; Han, S. Joint operation of mobile battery, power system, and transportation system for improving the renewable energy penetration rate. Appl. Energy 2024, 357, 122455. [Google Scholar] [CrossRef]
  90. Mandelli, S.; Barbieri, J.; Mereu, R.; Colombo, E. Off-grid systems for rural electrification in developing countries: Definitions, classification and a comprehensive literature review. Renew. Sustain. Energy Rev. 2016, 58, 1621–1646. [Google Scholar] [CrossRef]
  91. Brown, D.; Dahlan, N.; Richardson-Barlow, C. Evaluating microgrid business models for rural electrification: A novel framework and three cases in Southeast Asia. Energy Sustain. Dev. 2024, 80, 101443. [Google Scholar] [CrossRef]
  92. Oanh, N.T.K.; Huy, L.N. Atmospheric Emissions from Electricity Generation in Southeast Asia: Development Trend and Policy Responses. Curr. Pollut. Rep. 2024, 10, 54–69. [Google Scholar] [CrossRef]
  93. Martín-Betancor, M.; Osorio, J.; Ruíz-García, A.; Nuez, I. Technical-economic limitations of floating offshore wind energy generation in small isolated island power systems without energy storage: Case study in the Canary Islands. Energy Policy 2024, 188, 114056. [Google Scholar] [CrossRef]
  94. Hussain, M.N.; Zaman, M.R.; Halim, M.A.; Ali, M.S.; Khan, M.Y.A. A Comprehensive Review of Renewable and Sustainable Energy Sources with Solar Photovoltaic Electricity Advancement in Bangladesh. Control Syst. Optim. Lett. 2024, 2, 1–7. [Google Scholar]
  95. Tran, Q.T.; Davies, K.; Sepasi, S. Isolation microgrid design for remote areas with the integration of renewable energy: A case study of Con Dao Island in Vietnam. Clean Technol. 2021, 3, 804–820. [Google Scholar] [CrossRef]
  96. Awan, M.M.A.; Asghar, A.B.; Javed, M.Y.; Conka, Z. Ordering technique for the maximum power point tracking of an islanded solar photovoltaic system. Sustainability 2023, 15, 3332. [Google Scholar] [CrossRef]
  97. Choi, Y.-J.; Oh, B.-C.; Acquah, M.A.; Kim, D.-M.; Kim, S.-Y. Optimal operation of a hybrid power system as an island microgrid in south-korea. Sustainability 2021, 13, 5022. [Google Scholar] [CrossRef]
  98. Abdelghany, M.B.; Mariani, V.; Liuzza, D.; Glielmo, L. Hierarchical model predictive control for islanded and grid-connected microgrids with wind generation and hydrogen energy storage systems. Int. J. Hydrogen Energy 2024, 51, 595–610. [Google Scholar] [CrossRef]
  99. Güven, A.F.; Yörükeren, N.; Tag-Eldin, E.; Samy, M.M. Multi-objective optimization of an islanded green energy system utilizing sophisticated hybrid metaheuristic approach. IEEE Access 2023, 11, 103044–103068. [Google Scholar] [CrossRef]
  100. Robinson, S.-A. Climate change adaptation limits in small island developing states. In Limits to Climate Change Adaptation; Springer: Berlin/Heidelberg, Germany, 2018; pp. 263–281. [Google Scholar]
  101. Dornan, M. Renewable energy development in small island developing states of the Pacific. Resources 2015, 4, 490–506. [Google Scholar] [CrossRef]
  102. Erdinc, O.; Paterakis, N.G.; Catalão, J.P. Overview of insular power systems under increasing penetration of renewable energy sources: Opportunities and challenges. Renew. Sustain. Energy Rev. 2015, 52, 333–346. [Google Scholar] [CrossRef]
  103. Castro, M.T.; Delina, L.L.; Ocon, J.D. Transition pathways to 100% renewable energy in 208 island mini-grids in the Philippines. Energy Strategy Rev. 2024, 52, 101315. [Google Scholar] [CrossRef]
  104. Ali, Y.A.; Ouassaid, M.; Cabrane, Z.; Lee, S.-H. Enhanced Primary Frequency Control Using Model Predictive Control in Large-Islanded Power Grids with High Penetration of DFIG-Based Wind Farm. Energies 2023, 16, 4389. [Google Scholar] [CrossRef]
  105. Liu, L.; Senjyu, T.; Kato, T.; Howlader, A.M.; Mandal, P.; Lotfy, M.E. Load frequency control for renewable energy sources for isolated power system by introducing large scale PV and storage battery. Energy Rep. 2020, 6, 1597–1603. [Google Scholar] [CrossRef]
  106. Lu, B.; Blakers, A.; Stocks, M. 90–100% renewable electricity for the south west interconnected system of western Australia. Energy 2017, 122, 663–674. [Google Scholar] [CrossRef]
  107. Tayal, D. Achieving high renewable energy penetration in Western Australia using data digitisation and machine learning. Renew. Sustain. Energy Rev. 2017, 80, 1537–1543. [Google Scholar] [CrossRef]
  108. McIlwaine, N.; Foley, A.M.; Best, R.; Morrow, D.J.; Al Kez, D. Modelling the effect of distributed battery energy storage in an isolated power system. Energy 2023, 263, 125789. [Google Scholar] [CrossRef]
  109. Qays, M.O.; Ahmad, I.; Habibi, D.; Aziz, A.; Mahmoud, T. System strength shortfall challenges for renewable energy-based power systems: A review. Renew. Sustain. Energy Rev. 2023, 183, 113447. [Google Scholar] [CrossRef]
  110. Holdmann, G.P.; Wies, R.W.; Vandermeer, J.B. Renewable energy integration in Alaska’s remote islanded microgrids: Economic drivers, technical strategies, technological niche development, and policy implications. Proc. IEEE 2019, 107, 1820–1837. [Google Scholar] [CrossRef]
  111. Manoharan, Y.; Olson, K.; Headley, A.J. Optimization of Energy Storage Systems and Demand Side Management to Maximize Water Utility Savings: A Hawaii Case Study. J. Eng. Sustain. Build. Cities 2023, 4, 011001. [Google Scholar] [CrossRef]
  112. Miller, N.; Manz, D.; Johal, H.; Achilles, S.; Roose, L.; Griffin, J.P. Integrating high levels of wind in island systems: Lessons from Hawaii. In Proceedings of the 2010 IEEE International Conference on Sustainable Energy Technologies (ICSET), Kandy, Sri Lanka, 6–9 December 2010; pp. 1–8. [Google Scholar]
  113. Cole, P.; Banks, G. Renewable energy programmes in the South Pacific–Are these a solution to dependency? Energy Policy 2017, 110, 500–508. [Google Scholar] [CrossRef]
  114. Gutman, P.F.; Makolo, P.; Zamora, R. Modelling and analysis of load frequency control in small power systems: A case study of New Zealand network. In Proceedings of the 2020 Australasian Universities Power Engineering Conference (AUPEC), Hobart, Australia, 29 November–3 December 2020; pp. 1–6. [Google Scholar]
  115. Mehta, S. Electricity Security in a Hydro-Based Electric Power System: The Particular Case of Iceland. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2016. [Google Scholar]
  116. Andrianesis, P.; Liberopoulos, G.; Varnavas, C. The impact of wind generation on isolated power systems: The case of Cyprus. In Proceedings of the 2013 IEEE Grenoble Conference, Grenoble, France, 16–20 June 2013; pp. 1–6. [Google Scholar]
  117. Michael, M.A.; Petoussis, A.G.; Stavrinos, S.M.; Polycarpou, A. The impact of load modeling on power flow studies for the cyprus power system. In Proceedings of the 2014 49th International Universities Power Engineering Conference (UPEC), Cluj-Napoca, Romania, 2–5 September 2014; pp. 1–6. [Google Scholar]
  118. Kaluthanthrige, R.; Rajapakse, A.D.; Lamothe, C.; Mosallat, F. Optimal sizing and performance evaluation of a hybrid renewable energy system for an off-grid power system in northern Canada. Technol. Econ. Smart Grids Sustain. Energy 2019, 4, 4. [Google Scholar] [CrossRef]
  119. Longo, M.; Yaïci, W.; Foiadelli, F. Hybrid renewable energy system with storage for electrification–Case study of remote northern community in Canada. Int. J. Smart Grid 2019, 3, 63–72. [Google Scholar]
  120. Leading Western Australia’s Brighter Energy Future; Energy Policy Western Australian. Available online: https://www.wa.gov.au/system/files/2021-07/Energy-Transformation-Strategy-Stage2-July2021.pdf (accessed on 5 April 2025).
  121. Hadingham, W.; Rayney, K.; Blaver, A.; Smart, B.; Thomas, J. Distributed energy resources roadmap: How the state of Western Australia is leading in integration. IEEE Power Energy Mag. 2021, 19, 76–88. [Google Scholar] [CrossRef]
  122. Government of Western Australia. Energy Transformation Strategy 2019–2021; Department of Treasury: Perth, Australia, 2019. Available online: https://www.wa.gov.au/system/files/2019-08/Energy-Transformation-Strategy.pdf (accessed on 5 April 2025).
  123. The Energy Transformation Taskforce. Distributed Energy Resources Roadmap. 2020. Available online: https://www.wa.gov.au/government/distributed-energy-resources-roadmap (accessed on 5 April 2025).
  124. Ghahramani, M.; Heris, M.N.; Zare, K.; Ivatloo, B.M. Incorporation of demand response programs and wind turbines in optimal scheduling of smart distribution networks: A case study. In Proceedings of the 2017 Conference on Electrical Power Distribution Networks Conference (EPDC), Semnan, Iran, 19–20 April 2017; pp. 25–32. [Google Scholar]
  125. Everything You Need to Know About the Duck Curve; Synergy. (n.d.) Available online: https://www.synergy.net.au/Blog/2021/10/Everything-you-need-to-know-about-the-Duck-Curve (accessed on 12 May 2025).
  126. AEMO. Quarterly Energy Dynamics: Q4 2022; Australian Energy Market Operator: Melbourne, Australia, 2023; Available online: https://aemo.com.au/-/media/files/major-publications/qed/2022/qed-q4-2022.pdf (accessed on 11 August 2025).
  127. Ebeed, M.; Aleem, S.H.A. Overview of uncertainties in modern power systems: Uncertainty models and methods. In Uncertainties in Modern Power Systems; Elsevier: Amsterdam, The Netherlands, 2021; pp. 1–34. [Google Scholar]
  128. Ishraque, M.F.; Shezan, S.A.; Ali, M.; Rashid, M. Optimization of load dispatch strategies for an islanded microgrid connected with renewable energy sources. Appl. Energy 2021, 292, 116879. [Google Scholar] [CrossRef]
  129. Ji, L.; Liang, X.; Xie, Y.; Huang, G.; Wang, B. Optimal design and sensitivity analysis of the stand-alone hybrid energy system with PV and biomass-CHP for remote villages. Energy 2021, 225, 120323. [Google Scholar] [CrossRef]
  130. Li, J.; Liu, Y.; Wu, L. Optimal operation for community-based multi-party microgrid in grid-connected and islanded modes. IEEE Trans. Smart Grid 2016, 9, 756–765. [Google Scholar] [CrossRef]
  131. Li, C.; Yang, H.; Shahidehpour, M.; Xu, Z.; Zhou, B.; Cao, Y.; Zeng, L. Optimal planning of islanded integrated energy system with solar-biogas energy supply. IEEE Trans. Sustain. Energy 2019, 11, 2437–2448. [Google Scholar] [CrossRef]
  132. Singh, S.; Singh, M.; Kaushik, S.C. Feasibility study of an islanded microgrid in rural area consisting of PV, wind, biomass and battery energy storage system. Energy Convers. Manag. 2016, 128, 178–190. [Google Scholar] [CrossRef]
  133. Shezan, S.; Ishraque, F.; Muyeen, S.; Arifuzzaman, S.; Paul, L.C.; Das, S.K.; Sarker, S.K. Effective dispatch strategies assortment according to the effect of the operation for an islanded hybrid microgrid. Energy Convers. Manag. X 2022, 14, 100192. [Google Scholar] [CrossRef]
  134. Bhandari, B.; Lee, K.-T.; Lee, G.-Y.; Cho, Y.-M.; Ahn, S.-H. Optimization of hybrid renewable energy power systems: A review. Int. J. Precis. Eng. Manuf. Technol. 2015, 2, 99–112. [Google Scholar] [CrossRef]
  135. Deng, X.; Lv, T. Power system planning with increasing variable renewable energy: A review of optimization models. J. Clean. Prod. 2020, 246, 118962. [Google Scholar] [CrossRef]
  136. Selim, A.; El-Shimy, M.; Amer, G.; Ihoume, I.; Masrur, H.; Guerrero, J.M. Hybrid off-grid energy systems optimal sizing with integrated hydrogen storage based on deterministic balance approach. Sci. Rep. 2024, 14, 6888. [Google Scholar] [CrossRef]
  137. Lai, C.S.; McCulloch, M.D. Sizing of stand-alone solar PV and storage system with anaerobic digestion biogas power plants. IEEE Trans. Ind. Electron. 2016, 64, 2112–2121. [Google Scholar] [CrossRef]
  138. Er, G.; Soykan, G.; Canakoglu, E. Stochastic optimal design of a rural microgrid with hybrid storage system including hydrogen and electric cars using vehicle-to-grid technology. J. Energy Storage 2024, 75, 109747. [Google Scholar] [CrossRef]
  139. Kumar, K.P.; Saravanan, B. Recent techniques to model uncertainties in power generation from renewable energy sources and loads in microgrids–A review. Renew. Sustain. Energy Rev. 2017, 71, 348–358. [Google Scholar]
  140. Keyvandarian, A.; Saif, A.; Pelot, R. Robust optimal sizing of a stand-alone hybrid renewable energy system using machine learning-based uncertainty sets. Energies 2025, 18, 1130. [Google Scholar]
  141. Giannelos, S.; Pudjianto, D.; Zhang, T.; Strbac, G. Energy hub operation under uncertainty: Monte Carlo risk assessment using Gaussian and KDE-based data. Energies 2025, 18, 1712. [Google Scholar] [CrossRef]
  142. Tafula, J.E.; Justo, C.D.; Moura, P.; Mendes, J.; Soares, A. Multicriteria decision-making approach for optimum site selection for off-grid solar photovoltaic microgrids in Mozambique. Energies 2023, 16, 2894. [Google Scholar] [CrossRef]
  143. Lv, H.; Wu, Q.; Ren, H.; Li, Q.; Zhou, W. A Two-stage Decision-making Approach for Optimal Design and Operation of Integrated Energy Systems Considering Multiple Uncertainties and Diverse Resilience Needs. Energy 2024, 305, 132375. [Google Scholar] [CrossRef]
  144. Zhang, B.; Liu, B. An adaptive scheduling method for standalone microgrids based on deep Q-network and particle swarm optimization. Energies 2025, 18, 2133. [Google Scholar] [CrossRef]
  145. Liang, D.; Ma, L.; Wang, P.; Li, Y.; Luo, Y. Adaptive design of solar-powered energy systems based on daily clearness state evolution. Energies 2024, 17, 2328. [Google Scholar] [CrossRef]
  146. Dornan, M.; Jotzo, F. Renewable technologies and risk mitigation in small island developing states: Fiji’s electricity sector. Renew. Sustain. Energy Rev. 2015, 48, 35–48. [Google Scholar] [CrossRef]
  147. Georgiou, P.N. A bottom-up optimization model for the long-term energy planning of the Greek power supply sector integrating mainland and insular electric systems. Comput. Oper. Res. 2016, 66, 292–312. [Google Scholar]
  148. Ferrer-Martí, L.; Domenech, B.; García-Villoria, A.; Pastor, R. A MILP model to design hybrid wind–photovoltaic isolated rural electrification projects in developing countries. Eur. J. Oper. Res. 2013, 226, 293–300. [Google Scholar] [CrossRef]
  149. Mavromatidis, G.; Orehounig, K.; Carmeliet, J. A review of uncertainty characterisation approaches for the optimal design of distributed energy systems. Renew. Sustain. Energy Rev. 2018, 88, 258–277. [Google Scholar] [CrossRef]
  150. Soroudi, A.; Amraee, T. Decision making under uncertainty in energy systems: State of the art. Renew. Sustain. Energy Rev. 2013, 28, 376–384. [Google Scholar] [CrossRef]
  151. Kizito, R.; Liu, Z.; Li, X.; Sun, K. Multi-stage stochastic optimization of islanded utility-microgrids design after natural disasters. Oper. Res. Perspect. 2022, 9, 100235. [Google Scholar] [CrossRef]
  152. Ishraque, M.F.; Shezan, S.A.; Rashid, M.M.; Bhadra, A.B.; Hossain, M.A.; Chakrabortty, R.K.; Ryan, M.J.; Fahim, S.R.; Sarker, S.K.; Das, S.K. Techno-economic and power system optimization of a renewable rich islanded microgrid considering different dispatch strategies. IEEE Access 2021, 9, 77325–77340. [Google Scholar] [CrossRef]
  153. Abul’Wafa, A.R.; Taha, A. Reliability evaluation of distribution systems under μ grid-tied and islanded μ grid modes using Monte Carlo simulation. Smart Grid Renew. Energy 2014, 2014, 52–62. [Google Scholar] [CrossRef]
  154. Oh, U.; Lee, Y.; Choi, J.; Karki, R. Reliability evaluation of power system considering wind generators coordinated with multi-energy storage systems. IET Gener. Transm. Distrib. 2020, 14, 786–796. [Google Scholar] [CrossRef]
  155. Arefin, S.S. Optimization techniques of islanded hybrid microgrid system. In Renewable Energy-Resources, Challenges and Applications; Intech Open: London, UK, 2020. [Google Scholar]
  156. Zhao, B.; Conejo, A.J.; Sioshansi, R. Unit commitment under gas-supply uncertainty and gas-price variability. IEEE Trans. Power Syst. 2016, 32, 2394–2405. [Google Scholar] [CrossRef]
  157. Liu, G.; Starke, M.; Xiao, B.; Zhang, X.; Tomsovic, K. Microgrid optimal scheduling with chance-constrained islanding capability. Electr. Power Syst. Res. 2017, 145, 197–206. [Google Scholar] [CrossRef]
  158. Prior, J.; Drees, T.; Miro, M.; Kuhlenkötter, B. Systematic literature review of heuristic-optimized microgrids and energy-flexible factories. Clean Technol. 2024, 6, 1114–1141. [Google Scholar]
  159. Chen, F.; Huang, G.; Fan, Y.; Chen, J. A copula-based fuzzy chance-constrained programming model and its application to electric power generation systems planning. Appl. Energy 2017, 187, 291–309. [Google Scholar] [CrossRef]
  160. Hmad, J.; Houari, A.; Trabelsi, H.; Machmoum, M. Fuzzy logic approach for smooth transition between grid-connected and stand-alone modes of three-phase DG-inverter. Electr. Power Syst. Res. 2019, 175, 105892. [Google Scholar] [CrossRef]
  161. Valencia, F.; Collado, J.; Sáez, D.; Marín, L.G. Robust energy management system for a microgrid based on a fuzzy prediction interval model. IEEE Trans. Smart Grid 2015, 7, 1486–1494. [Google Scholar] [CrossRef]
  162. Soroudi, A. Possibilistic-scenario model for DG impact assessment on distribution networks in an uncertain environment. IEEE Trans. Power Syst. 2012, 27, 1283–1293. [Google Scholar] [CrossRef]
  163. Dong, Q.; Sun, Q.; Huang, Y.; Li, Z.; Cheng, C. Hybrid possibilistic-probabilistic energy flow assessment for multi-energy carrier systems. IEEE Access 2019, 7, 176115–176126. [Google Scholar] [CrossRef]
  164. Baghaee, H.R.; Mirsalim, M.; Gharehpetian, G.B.; Talebi, H.A. Fuzzy unscented transform for uncertainty quantification of correlated wind/PV microgrids: Possibilistic–probabilistic power flow based on RBFNNs. ET Renew. Power Gener. 2017, 11, 867–877. [Google Scholar] [CrossRef]
  165. Jiang, Y.; Wan, C.; Chen, C.; Shahidehpour, M.; Song, Y. A hybrid stochastic-interval operation strategy for multi-energy microgrids. IEEE Trans. Smart Grid 2019, 11, 440–456. [Google Scholar] [CrossRef]
  166. Wu, C.; Sui, Q.; Lin, X.; Wang, Z.; Li, Z. Scheduling of energy management based on battery logistics in pelagic islanded microgrid clusters. Int. J. Electr. Power Energy Syst. 2021, 127, 106573. [Google Scholar] [CrossRef]
  167. Liao, Z.; Wang, B.; Tao, W.; Liu, Y.; Hu, Q. Research on decision optimization and the risk measurement of the power generation side based on quantile data-driven IGDT. Energies 2024, 17, 1585. [Google Scholar] [CrossRef]
  168. Ben-Tal, A.; Nemirovski, A. Robust convex optimization. Math. Oper. Res. 1998, 23, 769–805. [Google Scholar] [CrossRef]
  169. Zugno, M.; Morales, J.M.; Madsen, H. Commitment and dispatch of heat and power units via affinely adjustable robust optimization. Comput. Oper. Res. 2016, 75, 191–201. [Google Scholar] [CrossRef]
  170. Zhou, H.; Li, Z.; Zheng, J.; Wu, Q.; Zhang, H. Robust scheduling of integrated electricity and heating system hedging heating network uncertainties. IEEE Trans. Smart Grid 2019, 11, 1543–1555. [Google Scholar] [CrossRef]
  171. Yang, Z.; Ghadamyari, M.; Khorramdel, H.; Alizadeh, S.M.S.; Pirouzi, S.; Milani, M.; Banihashemi, F.; Ghadimi, N. Robust multi-objective optimal design of islanded hybrid system with renewable and diesel sources/stationary and mobile energy storage systems. Renew. Sustain. Energy Rev. 2021, 148, 111295. [Google Scholar] [CrossRef]
  172. Rodríguez-Gallegos, C.D.; Yang, D.; Gandhi, O.; Bieri, M.; Reindl, T.; Panda, S. A multi-objective and robust optimization approach for sizing and placement of PV and batteries in off-grid systems fully operated by diesel generators: An Indonesian case study. Energy 2018, 160, 410–429. [Google Scholar] [CrossRef]
  173. Gu, Z.; Qi, H.; Liu, Z.; Zhang, R. Robust Optimization Model of Island Energy System Based on Uncertainty of Wind and Photovoltaic. In Application of Intelligent Systems in Multi-Modal Information Analytics: Proceedings of the 2020 International Conference on Multi-model Information Analytics (MMIA2020), Changzhou, China, 5–6 March 2020; Springer: Berlin/Heidelberg, Germany, 2021; Volume 2, pp. 171–179. [Google Scholar]
  174. Su, Y.; Nie, W.; Zhou, Y.; Tan, M.; Qiao, H. An interval based cost-emissions optimization strategy for gas-electricity integrated energy systems under uncertainty and demand response. In Proceedings of the 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, 26–28 November 2017; pp. 1–6. [Google Scholar]
  175. Liu, H.; Tian, S.; Wang, X.; Cao, Y.; Zeng, M.; Li, Y. Optimal planning design of a district-level integrated energy system considering the impacts of multi-dimensional uncertainties: A multi-objective interval optimization method. IEEE Access 2021, 9, 26278–26289. [Google Scholar] [CrossRef]
  176. Shi, Z.; Zhang, T.; Liu, Y.; Feng, Y.; Wang, R.; Huang, S. Optimal design and operation of islanded multi-microgrid system with distributionally robust optimization. Electr. Power Syst. Res. 2023, 221, 109437. [Google Scholar] [CrossRef]
  177. Shi, Z.; Liang, H.; Huang, S.; Dinavahi, V. Distributionally robust chance-constrained energy management for islanded microgrids. IEEE Trans. Smart Grid 2018, 10, 2234–2244. [Google Scholar] [CrossRef]
  178. Yu, D.; Wu, J.; Wang, W.; Gu, B. Optimal performance of hybrid energy system in the presence of electrical and heat storage systems under uncertainties using stochastic p-robust optimization technique. Sustain. Cities Soc. 2022, 83, 103935. [Google Scholar] [CrossRef]
  179. Dantzig, G.B. Linear programming under uncertainty. Manag. Sci. 1955, 1, 197–206. [Google Scholar] [CrossRef]
  180. Beale, E.; Dantzig, G.B.; Watson, R. A first order approach to a class of multi-time-period stochastic programming problems. Stoch. Program. 1986, 84 Pt I, 103–117. [Google Scholar]
  181. Zadeh, L.A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1978, 1, 3–28. [Google Scholar] [CrossRef]
  182. Chapaloglou, S.; Varagnolo, D.; Marra, F.; Tedeschi, E. Data-driven energy management of isolated power systems under rapidly varying operating conditions. Appl. Energy 2022, 314, 118906. [Google Scholar] [CrossRef]
  183. Christopoulos, K.; Zafirakis, D.; Kavadias, K.A.; Kaldellis, J.K. Data-driven energy analysis for a set of rooftop photovoltaic systems featuring different installation characteristics: A pairwise, early-life performance comparison. Energies 2025, 18, 1677. [Google Scholar] [CrossRef]
  184. Buckley, N.; Bo, C.; Delkhah, F.; Byrne, N.; Ní Shearcaigh, A.; Brennan, S.; Correa, D.P. Evaluation of a peer-to-peer smart grid using digital twins: A case study of a remote European island. Energies 2024, 17, 5541. [Google Scholar] [CrossRef]
  185. Ghahramani, M.; Nazari-Heris, M.; Zare, K.; Mohammadi-Ivatloo, B. Energy and reserve management of a smart distribution system by incorporating responsive-loads/battery/wind turbines considering uncertain parameters. Energy 2019, 183, 205–219. [Google Scholar] [CrossRef]
  186. Ghahramani, M.; Nazari-Heris, M.; Zare, K.; Mohammadi-Ivatloo, B. A two-point estimate approach for energy management of multi-carrier energy systems incorporating demand response programs. Energy 2022, 249, 123671. [Google Scholar] [CrossRef]
  187. Ghahramani, M.; Nazari-Heris, M.; Zare, K.; Mohammadi-ivatloo, B. Robust short-term scheduling of smart distribution systems considering renewable sources and demand response programs. In Robust Optimal Planning and Operation of Electrical Energy Systems; Springer International Publishing: Cham, Switzerland, 2019; pp. 253–270. [Google Scholar]
  188. Ghahramani, M.; Heris, M.N.; Zare, K.; Ivatloo, B.M. Robust optimization of renewable energy based distribution networks considering electrical energy storage and fuel cell. In Proceedings of the Electrical Engineering (ICEE), Iranian Conference on, Mashhad, Iran, 8–10 May 2018; pp. 1343–1349. [Google Scholar]
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Figure 1. Isolated power systems and high-inertia main grids.
Figure 1. Isolated power systems and high-inertia main grids.
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Figure 2. Market load profile on 13 December 2022 in the SWIS. Adapted from: Australian Energy Market Operator (AEMO), Quarterly Energy Dynamics—Q4 2022, Figure 80, © 2023 AEMO [126].
Figure 2. Market load profile on 13 December 2022 in the SWIS. Adapted from: Australian Energy Market Operator (AEMO), Quarterly Energy Dynamics—Q4 2022, Figure 80, © 2023 AEMO [126].
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Figure 3. Market load profile on 18 November 2022 showing rapid demand swings in the SWIS. Adapted from: Australian Energy Market Operator (AEMO), Quarterly Energy Dynamics—Q4 2022, Figure 81, © 2023 AEMO [126].
Figure 3. Market load profile on 18 November 2022 showing rapid demand swings in the SWIS. Adapted from: Australian Energy Market Operator (AEMO), Quarterly Energy Dynamics—Q4 2022, Figure 81, © 2023 AEMO [126].
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Figure 4. Optimization and decision-making methods for isolated power systems.
Figure 4. Optimization and decision-making methods for isolated power systems.
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Figure 5. Key uncertainties in isolated power systems.
Figure 5. Key uncertainties in isolated power systems.
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Figure 6. Decision-support framework for selecting uncertainty modeling methods in isolated power systems.
Figure 6. Decision-support framework for selecting uncertainty modeling methods in isolated power systems.
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Table 1. Energy source integration in remote, islanded, and isolated power systems.
Table 1. Energy source integration in remote, islanded, and isolated power systems.
Case Study as Isolated Power SystemContributionDGHydroWindPVHydrogenESSGeo
Thermal		Ref.
IEEE 9-bus islanded systemProposing an active and reactive power-based fast frequency response.××✓✓×✓×[21]
Islanded power systemReduce the amount of load curtailed.✓×✓××××[22]
Islanded microgridFrequency control of wind turbines results in stable islanded microgrid operation.✓×✓××××[23]
Island of Terceira (Azores)Renewable-based electricity production grows by up to 46%.✓×✓××✓✓[24]
King Island, Flinders Island, and Rottnest IslandFacilitate high renewable penetrations and a reduction in capital cost.✓×✓✓×✓×[25]
Remote community, Amazon region, EcuadorReducing energy waste and decreasing fossil fuel costs. ✓××✓×✓×[26]
A stand-alone islandReduced fossil fuel consumption and CO2 emissions×××✓×××[27]
Azores archipelago, PortugalAligning wind turbine placement with wind patterns.××✓××××[28]
Hybrid backup systemTechno-economic analyses of different hybrid isolated power systems.××✓××✓×[29]
St. Martin Island, BangladeshReducing carbon dioxide (CO2) by fourteen tons/year.✓×✓✓×✓×[30]
Cape Verde IslandsUsing wave energy resources, surpassing 7 kW/m in the island’s waters.×✓×××××[31]
The São Miguel IslandReduced electricity costs and CO2 emissions by geothermal energy.××××××✓[32]
Tioman Island, South China SeaThe studied hybrid system was identified as an optimal solution.✓××✓✓✓×[33]
Three off-grid islands, Hong KongFound PV–wind–diesel–ESS as the best solution.✓×✓✓×✓×[34]
Isolated region, IndonesiaExplored the feasibility of concentrated solar power.×××✓×××[35]
Inner Mongolia, ChinaReducing the computational burden for solving large-scale optimization problems.××✓×✓✓×[36]
Gökceada Island, TurkeyThe advantages of wind energy in Gökceada include lower energy costs.××✓××××[37]
KiribatiSystem stability with controlled PV curtailment under varying conditions.×××✓×××[38]
Trindade Island, BrazilOffer a 100% renewable energy solution.✓✓✓✓×✓×[39]
A remote area, in EthiopianConfirmed renewable energy availability at 30–40 Cents/kWh.✓×✓✓×✓×[40]
Malè and Magoodhoo IslandFound significant wave and wind power density.×✓✓××××[41]
A remote islandReplaced the existing diesel generator with a 100% renewable××✓✓×✓×[42]
Al Hallaniyat IslandCalculated a USD 0.222/kWh cost for a hybrid system.✓×✓✓×✓×[43]
Jeju IslandLower environmental impacts and more significant social benefits.✓×✓××××[44]
St. Martin Island, BangladeshMinimized greenhouse gas emissions.✓×✓✓×✓×[45]
Tioman Island, MalaysiaReduced costs and CO2 emissions.✓×✓✓×✓×[46]
An astronomical center in the Atacama Desert, ChileStorage requirements for 64% coverage of renewables.××✓✓✓××[47]
An isolated island in ThailandUsing PV to lower costs and reduce emissions.×××✓×××[48]
Kinmen IslandA new unit commitment scheduling to manage renewable energy.××✓✓×✓×[49]
Island in the South China SeaCarbon reduction rates of 87% to 95%.××✓✓×✓×[50]
Off-grid residence, GreeceIdentified the wind–diesel system as an optimal solution.✓×✓××××[51]
Faroe Islands, MykinesStudied technical feasibility and the development potential of the system.××✓×✓××[52]
Grimsey Island, IcelandAchieved the lowest operational cost among the investigated configurations.✓×✓×✓××[53]
Remote and scattered regions, AlgeriaImprovement in PV pumping systems for water supply in remote regions.×××✓×××[54]
Mrair-Gabis village, LibyaCapability of small-scale PV-based desalination systems for rural areas.×××✓×××[55]
Andaman and Nicobar, IndiaIdentified an optimal PV configuration with a 2.5 kW PV.×××✓×××[56]
Note: ✓ indicates the energy source is integrated in the study; × indicates the energy source is not considered.
Table 2. Reliability strategies used for renewable-based isolated power systems.
Table 2. Reliability strategies used for renewable-based isolated power systems.
Ref.Objective of the StudyStrategies ExploredContribution of the Study
[57]Explored strategies to enhance reliabilityEnergy storage systems, demand-side management, microgrids, and smart gridsAddressed the intermittent and seasonal nature of renewable energy
[63]Explored strategies to enhance reliabilityRenewable-based energy system with hybrid storage (battery and hydrogen)Determining the optimal configuration minimizes the cost of a 100% renewable supply
[64]Determine the optimal design for delivering electricity to a remote island settlementBattery storage facilities and machine learning models to predict system performanceFinds an economically competitive system with a lower levelized cost of energy
[58]Focused on sustainability issuesIncluding ESS, hybrid energy sources, microgrids, smart grids, demand response (DR), and DGsEmphasized the simulation of renewable energy sources
[65]Develop a planning and sizing method to maximize storage benefits in island microgridsOptimally size energy storageOptimal trade-off between reliability and operating cost
[59]Enhance reliability and RES use for critical infrastructureLimited-size BESS with PV–diesel; rule-based control; optimal sizing via PVShowed small BESS can cut curtailment, improve off-grid adequacy, and lower costs
[66]Reliability of rural distribution systems considering different penetration levels of RESBattery energy storage systems in parallel with the DG
hybrid microgrid		Monte Carlo simulation (MCS) to calculate reliability indices
[67]Improvement in cost and reliabilityDiesel generator-pumped hydro storageEvaluated renewable penetration levels and storage needs
[60]Improving design and addressing reliability issuesEnergy storage systemsAssessed different renewable penetration scenarios
[61]Addressing reliability issues in the presence of large-scale RESLinking a water-pumped storage system with wind power generation.Projected over 75% renewable contribution in the early decades
[62]Reducing costs and enhancing reliability6 MW battery energy storage system High renewable penetration up to 100% and 65% annual shares
[68]Investigate the influence of RES and distributed resources on the reliabilityBattery energy storage and electric vehiclesCalculate customer-side reliability indices
Table 3. Interconnection strategies for enhanced renewable integration.
Table 3. Interconnection strategies for enhanced renewable integration.
StudyHighlights and ContributionsMethodResults
[69]Assessing the impact of interconnection on isolated power systemsComparative analysisIdentified economic and environmental benefits
[75]Sustainable energy transition in the Mexican electricity systemOptimization modelDecreased water consumption and greenhouse gas emissions by up to 45%
[70]Techno-economic feasibility of wind energy production Cost–benefit analysis Revealed substantial cost savings compared to isolated scenarios
[71]Assessing pumped hydro storage impact on interconnection analysisSimulation studyHighlighted advantages of storage for harnessing excess wind power
[76]Segmentation of power systems to prevent uncontrolled islanding Simulation studyReduced load shedding, considering RES uncertainties
[72]Interconnection of all islands in the Canarias Archipelago case studyScenario analysisProposed pathway to 100% renewable energy through smart integration
[77]Design of an off-grid solar PV for an isolated island in IndonesiaSizing and simulationValidated stability, maintaining voltage levels, and meeting safety
[73]Interconnection of a group of isolated power systems leveraging local renewable resourcesSystem
modeling		Increased RES shares, reduced RES curtailment, and enhanced energy security through vehicle-to-grid systems and ESSs.
[74]Techno-economic analyses of existing and projected isolated systemsEvaluation studyEnhancing the efficiency and cost-effectiveness of thermal units
[78]Implementation of RES in the Pico and Faial islands System
modeling		Increased RES penetration by 50 percentage points by 2030
Table 4. Advantages and disadvantages of small-scale isolated power systems.
Table 4. Advantages and disadvantages of small-scale isolated power systems.
StrengthsResultWeaknessesResult
Affordable to install and maintainEnables accessibility for remote communities with constrained budgetsLimited power outputPower shortages during high demand or adverse weather
Reliant on a single renewable sourceEasy to manageDependence on a single sourceVulnerable to intermittency, requiring backup power
Ideal for basic needsSuitable for essential electricity services such as lightingChallenges in scaling upExpansion requires significant modifications and investment
Table 5. Advantages and disadvantages of medium-sized isolated power systems.
Table 5. Advantages and disadvantages of medium-sized isolated power systems.
StrengthsResultWeaknessesResult
Serve larger communities and industriesMore stable for large populations and industrial applicationsSubstantial upfront investmentsMore cost-effective than large systems but still requires significant initial funding
Integration of multiple energy sources and storage solutionsReduces vulnerability to intermittent generationAdvanced maintenance and monitoringRequires more sophisticated maintenance and monitoring
Scalability and complexity balanceSuitable for diverse applicationsBackup power solutionsNecessary during low-generation periods
Table 6. Advantages and disadvantages of large-scale isolated power systems.
Table 6. Advantages and disadvantages of large-scale isolated power systems.
StrengthsResultWeaknessesResult
Serve larger communities and industriesConsistent electricity supplySubstantial upfront investmentsRequires financial investments in plants, transmission, and distribution
Diversified energy sourcesEnhances energy security and reliabilityEnvironmental impact managementChallenges in land use and wildlife conservation
Stimulates local economiesBrings economic growth to areasRegulatory processesComplex regulatory environment
High penetration of renewable sourcesReduces carbon emissions and dependence on imported fuelsVariability and energy storage challengesRequires advanced storage and grid stability solutions
Table 7. Large-scale isolated power systems.
Table 7. Large-scale isolated power systems.
Ref.LocationEnergy SourcesKey ChallengesIntegration StrategiesContributions/Highlights
[110]AlaskaNatural gas, hydroelectric, and relying heavily on dieselExtreme cold, higher energy costs, management of RES, resilient operation, intermittency of RES.Implementation of renewable–diesel hybrid systems.Explores technical challenges in RES integration; reviews the socio-political and economic landscape for microgrid adoption
[111]HawaiiElectricity from the grid, solar powerHigh electricity usage for water pumping and intermittent renewable resources.Using an energy storage system and demand-side management.Optimization framework for pump dispatch and analyzing the impact of increasing solar power on electricity purchased
[112]HawaiiHigh levels of wind power Isolation from mainland networks and uncertainty of wind power.Modifications to thermal plants and advanced wind turbine features.Identifies wind integration challenges and strategies for improving system economics and reliability
[113]Maldives, Fiji, and Seychelles100% renewable energy, mainly via solar PVDispersed islands are vulnerable to climate change and sea-level rise. Overreliance on solar PV and a lack of a diverse energy mix contribute to this.Broadening energy sources to include biofuels.Reviews renewable electricity generation policy and programs
[114]New Zealand Hydropower, geothermal, and natural gasMountainous terrain and frequency stability in low-inertia conditions.Load frequency control models with virtual inertia and energy storage systems.Discusses a load frequency control model for frequency stability in low-inertia conditions.
[115]IcelandNearly 100% renewable (hydro and geothermal)Aging infrastructure and severe weather vulnerabilities.Ongoing infrastructure upgrades and resilience planning.Focuses on the long-term security of the electricity supply, considering environmental goals
[116]CyprusFossil fuels, solar, and wind Managing day-ahead unit commitment and economic dispatch.Simulation models to predict and manage wind integration.Explores the impact of wind generation on isolated systems through various scenarios.
[117]CyprusFossil fuels, solar, and wind generationSemi-arid conditions, isolated in the eastern Mediterranean, and uncertainties in load representation. A ZIP load model for voltage-dependent load analysis.Establishes a method for estimating parameter values for voltage-dependent load models
[118]Northern CanadaTransitioning from diesel to hybrid systemsAccessibility to energy and reducing petroleum dependency.HOMER software-based grid optimization for retrofitting.An optimization study for retrofitting remote off-grid systems to hybrid RESs.
[119]Ontario, CanadaDiesel generators, hydroelectric, wind, solarRemote areas, extreme cold, challenging, and expansive terrainHybrid optimization model to find the best renewable mix.Examines the use of microgrids to address energy accessibility and reduce dependency on petroleum
Table 8. Objectives for optimization in isolated power systems [134,135].
Table 8. Objectives for optimization in isolated power systems [134,135].
Objective Specific ObjectivesDescription
Cost MinimizationCapital CostsMinimize initial costs for system setup, equipment, and installation.
Operational CostsReduce ongoing costs of operation, maintenance, and fuel.
Energy CostsLower the cost per unit of energy produced.
Net Present Value Maximize NPV by considering all discounted future cash flows.
Environmental ImpactCarbon EmissionsReduce greenhouse gas emissions from power generation.
PollutionMinimize environmental contamination (air, water, and soil).
Resource UtilizationOptimize the use of natural resources to ensure sustainability.
Reliability and ResilienceSystem ReliabilityEnhance the reliability of the power supply using reliability indices.
Energy SecurityImprove the self-sufficiency and security of the energy supply.
Resilience to DisruptionsIncrease the system’ s ability to recover from disruptions.
Efficiency MaximizationEnergy EfficiencyImprove the efficiency of energy conversion.
Resource EfficiencyMaximize output per unit of resource consumed.
Quality of Energy ServicePower QualityEnhance voltage and frequency stability and reduce harmonics.
Service CoverageExpand energy services to more areas and underserved populations.
Energy Storage OptimizationStorage CapacityOptimize the capacity of storage to balance supply and demand.
Storage EfficiencyImprove charge and discharge efficiencies, minimizing losses.
Energy Mix Energy diversification Optimize the sources mix to balance reliability and the environmental impact.
Renewable IntegrationIncrease the proportion of renewable energy in the energy mix.
Table 9. Uncertainty modeling methods in isolated power systems.
Table 9. Uncertainty modeling methods in isolated power systems.
Ref.Main ContributionMCSStochasticFuzzyHybridIGDTRobust IntervalDeterministic
[128]Design and optimization of isolated microgrids in northern Bangladesh.×××××××✓
[129]Optimal design of stand-alone hybrid systems in northwest China.×✓××××××
[130]A novel optimization model for multi-owner microgrid operations.×✓××××××
[131]Optimization using a two-stage MILP for a distributed solar–biogas system.×✓××××××
[133]Optimized design of a stand-alone hybrid microgrid on Melville Island.×✓××××××
[137]Deterministic integration of a biogas power plant with PV systems and ESSs.×××××××✓
[139]Stochastic optimization for reliability assessments in isolated power systems.✓✓××××××
[140]Stochastic and robust optimization for isolated systems under uncertainty.✓✓×××✓××
[141]Addressing uncertainty in isolated systems using various optimization methods.×××××✓××
[145]Stochastic mixed-integer programming for power systems in isolated areas.×✓××××××
[146]Stochastic simulation to assess future electricity supply in Fiji.✓×××××××
[147]A MILP model for long-term energy planning in Greece, including isolated areas.×××××××✓
[148]MILP for optimizing hybrid wind–PV systems in isolated Peruvian communities.×××××××✓
[149]Solution feasibility under data variability in isolated power systems.×××××✓××
[144]Planning of power networks under high amounts of uncertain data.×××××✓××
[151]A multi-stage model developed an advanced nested L-shaped algorithm.×✓××××××
[152]Uncertainty modeling in rich islanded microgrid operation.×✓××××××
[153]MC simulations to analyze distributed generation’s impact on reliability.✓×××××××
[154]MC simulations and a multi-linear method for energy demand and wind power output.✓×××××××
[155]A point estimation method for higher accuracy and efficiency.×××××××✓
[156]A point estimation method for modeling uncertainties in load demand and solar generation.×××××××✓
[157]A stochastic optimization approach addressing uncertainty in power systems with islanding capability.×✓×××✓××
[158]A stochastic–heuristic approach for optimizing electrical supply in off-grid hybrid systems.×✓×✓××××
[159]A fuzzy constraint planning model for power generation uncertainties.××✓×××××
[160]Fuzzy logic for the transition between grid-connected and stand-alone modes.××✓×××××
[161]A fuzzy predictive range model for robust energy management.××✓×××××
[162]Combining fuzzy and Monte Carlo techniques for power loss analysis.✓×✓✓××××
[163]A fuzzy–scenario hybrid approach for uncertain renewable power generation.××✓✓××××
[164]A random-possibility technique using fuzzy set theory for reliability data uncertainties.××✓✓××××
[165]A stochastic–IGDT approach for resilient isolated microgrids.×✓××✓×××
[166]A chance-constrained IGDT model for microgrid expansion planning.×✓××✓×××
[167]A hybrid data-driven IGDT optimization approach for energy systems scheduling.×✓××✓×××
[168]Improved robust optimization models to minimize deviation from optimal solutions.×××××✓××
[169]An affine adjustable robust optimization model for energy systems.×××××✓××
[170]A two-stage robust scheduling model using fuzzy constraints.××✓××✓××
[171]Multi-objective robust optimization for isolated hybrid systems.×××××✓××
[172]Robust optimization for extreme weather conditions and grid management.×××××✓××
[173]A robust model for an island energy system with renewable sources.×××××✓××
[174]Interval optimization for energy system coordination under uncertainty.××××××✓×
[175]Multi-target interval optimization for addressing energy system planning issues.××××××✓×
[176]A two-stage robust model for isolated microgrid design and operation.××××××✓×
[177]A chance-constrained robust energy management model for an isolated microgrid.××××××✓×
[178]Stochastic p-robust optimization combining stochastic and robust approaches.×✓×××✓××
Note: ✓ indicates that the method is applied in the corresponding study; × indicates that the method is not considered.
Table 10. Uncertainty modeling methods.
Table 10. Uncertainty modeling methods.
MethodInputOutput AdvantagesDisadvantagesComputational ComplexityScalabilityData RequirementsRefs.
StochasticProbability distribution functions (PDFs)Statistical measures like expectation, variance, etc.Easy to implement and provides clear statistical interpretationsRequires extensive historical data, computationally intensive, and approximate resultsHighModerateLarge amounts of historical data[151,152,153,154,155,156,157,158,179,180]
FuzzyMembership Function (MF)Membership function valuesConverts linguistic and qualitative knowledge into numerical dataComplex implementation and subjective interpretationsModerateLowExpert knowledge or subjective data[159,160,161,181]
HybridMF and PDFCombined membership and probabilistic valuesSimultaneous handling of both types of uncertaintiesHighly computationally demandingVery HighLowBoth qualitative and quantitative data[162,163,164]
IGDTForecasted valuesDecision variables that meet requirementsEffective for severe uncertaintiesCan be overly conservativeHighLowForecasted values and minimal data[165,166,167]
Robust
Method		IntervalsControlled conservativenessSuitable when only interval data is availableChallenging to apply in nonlinear modelsHighModerateInterval data[169,170,171,172,173]
Interval
Analysis		IntervalsBounds of output valuesStraightforward implementation when intervals are knownNeglects correlations between intervals and potentially too conservativeModerateModerateInterval data[174,175]
Distributed Robust
Method		IntervalsDistributed decision variablesEnhances scalability, reduces computational burden and localized optimizationRequires efficient coordination mechanisms and potential for communication overheadHighHighInterval data and coordination mechanisms[176,177,178]
Data-Driven OptimizationEmpirical DataData-informed ambiguity set and optimized objective valueLeverages real-world data for robust solutions and captures complex patternsDependent on data quality and requires significant data processing and machine learning expertiseHighHighLarge amounts of empirical data[182,183,184]
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Ghahramani, M.; Habibi, D.; Ghamari, S.; Soleimani, H.; Aziz, A. Renewable-Based Isolated Power Systems: A Review of Scalability, Reliability, and Uncertainty Modeling. Clean Technol. 2025, 7, 80. https://doi.org/10.3390/cleantechnol7030080

AMA Style

Ghahramani M, Habibi D, Ghamari S, Soleimani H, Aziz A. Renewable-Based Isolated Power Systems: A Review of Scalability, Reliability, and Uncertainty Modeling. Clean Technologies. 2025; 7(3):80. https://doi.org/10.3390/cleantechnol7030080

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Ghahramani, Mehrdad, Daryoush Habibi, Seyyedmorteza Ghamari, Hamid Soleimani, and Asma Aziz. 2025. "Renewable-Based Isolated Power Systems: A Review of Scalability, Reliability, and Uncertainty Modeling" Clean Technologies 7, no. 3: 80. https://doi.org/10.3390/cleantechnol7030080

APA Style

Ghahramani, M., Habibi, D., Ghamari, S., Soleimani, H., & Aziz, A. (2025). Renewable-Based Isolated Power Systems: A Review of Scalability, Reliability, and Uncertainty Modeling. Clean Technologies, 7(3), 80. https://doi.org/10.3390/cleantechnol7030080

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