Microgrids have emerged as a promising solution for improving energy access, enhancing local energy independence, and strengthening resilience against environmental, technical, and operational disturbances. Resilience refers to the ability of a system to withstand, adapt to, and recover from disruptive events while maintaining reliable operation. Unlike grid-connected systems, standalone or islanded microgrids must operate autonomously during disturbances, making resilience a critical design and operational consideration. This paper presents an integrative review of the state-of-the-art (SOTA) strategies, indicators, and challenges associated with enhancing the resilience of standalone microgrids. Following a PRISMA-guided review methodology, the study synthesizes 60 primary studies published between 2014 and 2025, supported by 28 contextual references. Coding of the primary studies shows that 95% rely on simulation or analytical modeling, with only three reporting experimental or hardware-in-the-loop validation, which remains the principal limitation of the current evidence base. The discussion is categorized into four major areas: design and planning, control strategies, energy management, and protection systems. In addition, the review examines resilience assessment metrics and emerging cyber-physical resilience approaches for modern standalone microgrids. The paper further identifies major technical, economic, environmental, and regulatory challenges affecting the operation of resilient microgrids, including protection coordination, renewable intermittency, communication dependence, cybersecurity risks, economic limitations, and vulnerability to extreme weather events. Various resilience enhancement approaches—distributed energy resource diversification, adaptive reconfiguration, hierarchical control, predictive energy management, and the interconnection of neighboring standalone microgrids—are critically analyzed to provide a broader understanding of resilient standalone microgrid development. Finally, the review highlights important research gaps and future directions, including the need for standardized resilience metrics, integrated cyber-physical security frameworks, climate-adaptive planning models, and practical implementation strategies. Although significant progress has been achieved in microgrid modeling, control, and optimization, considerable challenges remain in bridging theoretical research with large-scale real-world deployment of resilient standalone microgrids.
Micro-tubular flame-assisted fuel cell (mT-FFC) studies generally focus on specific fuels, individual system components, or a limited range of operating conditions. This study combines system-level modeling with experimental validation of an integrated mT-FFC configuration utilizing a micro-scale thermal partial oxidation reformer. The modeling framework couples partial oxidation reforming, electrochemical conversion in the fuel cell, tail-gas combustion, and heat recovery, and performance is assessed with common fuels such as methane, propane, and n-butane. An experimental setup of the integrated system is operated with n-butane fuel to assess the feasibility of self-sustained operation under non-ideal conditions. A zero-dimensional equilibrium model is developed of the partial oxidation process at equivalence ratios of Φ = 1–5, total fuel/air flow rates of 200–400 mL/min, and fuel utilizations of 0.3–1.0. The model predicts the equilibrium mole fractions of H2, CO, H2O, and CO2, together with the reversible cell potential, electrical efficiency, and electrical power output under adiabatic conditions. Increasing Φ shifts the equilibrium composition from H2O and CO2 toward the electrochemically active species H2 and CO, while the reversible cell potential approaches a plateau under highly fuel-rich conditions. Electrical efficiency increases with fuel utilization and reaches a maximum at intermediate-to-high equivalence ratios before declining under richer conditions. The peak modeled efficiency for methane is approximately 43% at Φ≈4, flow rate = 400 mL/min and fuel utilization = 1.0 compared with approximately 38% for propane and 37% for n-butane. In the experimental portion of the study, the integrated n-butane prototype maintained electrochemical power generation during a 1.4 h constant-current test, although fluctuations in power density were observed. Together, the modeling and experimental results provide a system-level assessment of fuel-dependent performance and the practical feasibility of thermally integrated micro-scale thermal partial oxidation and mT-FFC operation.
Accurate probabilistic forecasting of geographically distributed photovoltaic (PV) generation is challenged by incomplete measurements, weather-dependent cross-site correlations, and quantile crossing. This study proposes a physics-guided framework that integrates a physics-informed Wasserstein generative adversarial network with gradient penalty (PIL-WGAN-GP), a dual-exploration spatiotemporal graph neural network (DEST-GNN), and an arbitrary-quantile recurrent decoder (AQ-RNN). The imputation module combines self-supervised masking, solar geometry, a clear-sky-based power envelope, and deterministic projection. The forecasting module fuses a static spatial prior with a time-varying dependency graph, while the decoder predicts aggregate PV quantiles directly through bounded positive spacings and monotone interpolation. The framework was evaluated in a retrospective case study using hourly PV generation records from 11 Belgian regional PV zones and matched meteorological records in 2025. For 3-, 6-, and 12-h consecutive gaps, PIL-WGAN-GP reduced normalized mean absolute error from 1.450%, 2.473%, and 5.252% for linear interpolation to 0.869%, 1.027%, and 1.300%, respectively. Hard projection eliminated nighttime leakage and upper-envelope violations. Across 1-, 6-, 12-, and 24-h horizons, DEST-GNN-AQ reduced the average daylight nMAE from 3.689% for the no-graph AQ-RNN to 3.509%, with the main gains occurring at 6–24 h. The model generated nested 50%, 70%, 80%, and 90% prediction intervals with a zero quantile-crossing rate. Their empirical coverages were 41.72%, 62.81%, 69.84%, and 85.26%, indicating that structural validity was achieved while calibration remains improvable. Collectively, these findings validate the proposed framework’s ability to bridge physically admissible data reconstruction with reliable, non-crossing probabilistic forecasting for PV clusters.
This study investigates the optimization of biogas combustion in a homogeneous charge compression ignition engine using fuel additives (methanol, ethanol, and acetone) to enhance performance while limiting the peak in-cylinder temperature, which is used as a qualitative indicator of thermal NOx formation tendency. A multi-zone combustion model was developed based on six distinct combustion zones with different initial gas compositions and temperatures. Multi-objective optimization was performed using Genetic Algorithm, Nondominated Sorting Genetic Algorithm II, and Pareto Search to maximize peak pressure, net heat release rate, and accumulated gas heat production, while minimizing in-cylinder temperature to reduce thermal nitrogen oxide formation. The model predicts that the Genetic Algorithm and Nondominated Sorting Genetic Algorithm II solutions increase the power output from 17.5 kW (base case) to 23.1 and 23.5 kW, and the indicated mean effective pressure from 6.8 bar to 10.6 and 10.8 bar, respectively, corresponding to computed efficiencies of 40.1% and 40.8%. The Pareto Search solution gives the highest indicated mean effective pressure (11.3 bar) but a lower power output (21.0 kW) and efficiency (38.7%), because its advanced combustion phasing produces negative work near top dead center. The optimal additive fractions were identified as Genetic Algorithm (0.015 ethanol, 0.015 acetone, 0.021 methanol), Nondominated Sorting Genetic Algorithm II (0.021 ethanol, 0.014 acetone, 0.020 methanol), and Pareto Search (0.075 ethanol, 0.035 acetone, 0.090 methanol). Methanol showed the strongest enhancement effect among the tested additives. These results are simulation-based and have not been experimentally validated; they provide a numerical framework to guide future experimental work.
The growing demand for sustainable energy and the environmental challenges of livestock waste management have intensified interest in biogas as a renewable and circular bioenergy solution. This review critically evaluates biogas production from key livestock wastes such as chicken manure, piggery waste, and cow dung focusing on how feedstock composition, anaerobic digestion pathways, pretreatment methods, and co-digestion strategies influence methane yield and process stability. These substrates differ markedly in biochemical characteristics: chicken manure is nitrogen-rich and prone to ammonia inhibition, piggery waste exhibits high biodegradability and methane potential, and cow dung provides stable digestion but is constrained by lignocellulosic hydrolysis limitations. Comparative analysis of experimental studies reveals wide variability in reported methane yields, driven by differences in operating conditions, reactor design, microbial adaptation, and scale. Persistent gaps between theoretical biomethane potential and experimental performance are discussed, highlighting the roles of ammonia toxicity, hydrolysis-limited kinetics, and scale-dependent losses. Co-digestion and pretreatment consistently enhance methane production by improving nutrient balance and substrate accessibility, yet challenges remain in methodological standardization, process scalability, and long-term operational stability. Key research gaps include limited integration of microbial community analysis, insufficient techno-economic and life-cycle assessments, and minimal validation of digital monitoring systems at full scale. Overall, livestock-based biogas systems represent a viable pathway for renewable energy generation and waste valorization, but their full potential depends on system-level optimization and scalable, economically viable solutions.
