The reliance on fossil fuels for electricity production in insular regions creates critical environmental, economic, and logistical challenges, particularly for ecologically fragile islands. Transitioning to renewable energy is essential to mitigate these impacts, enhance energy security, and preserve unique ecosystems. This systematic review addresses key research questions: what practical strategies have proven effective in reducing fossil fuel dependency in island contexts, and what barriers hinder their widespread adoption? By applying the PRISMA methodology, this study examines a decade (2014–2024) of research on renewable energy systems, highlighting successful initiatives such as the integration of solar and wind systems in Hawaii, energy storage advancements in La Graciosa, hybrid renewable grids in the Galápagos Islands, and others. Specific barriers include high upfront costs, regulatory challenges, and technical limitations, such as grid instability due to renewable energy intermittency. This review contributes by synthesizing lessons from diverse case studies and identifying innovative approaches like hydrogen storage, predictive control systems, and community-driven renewable projects. The findings offer actionable insights for policymakers and researchers to accelerate the transition towards sustainable energy systems in island environments.

The rapid integration of photovoltaic (PV) energy into weak low-voltage networks presents significant challenges to grid stability and power quality, highlighting the need for effective power smoothing methods. This paper proposes and evaluates three fuzzy logic-enhanced power smoothing techniques—V2GGlide (low-pass filter), V2GSUN (moving average filter), and V2GSmooth (ramp rate filter)—integrated with a lithium-ion battery energy storage system. The methods were tested under three distinct state-of-charge (SoC) conditions. Results show that V2GGlide achieved the highest variance reduction (22.24%) at high SoC levels, providing superior performance in mitigating power fluctuations and ensuring stable grid output. V2GSUN demonstrated consistent effectiveness, achieving variance reductions of up to 17.82% under low SoC conditions, making it well-suited for systems with intermediate storage availability. V2GSmooth exhibited balanced performance across all SoC levels, combining efficient energy delivery with reduced battery degradation, particularly at lower SoC levels. The proposed methods were validated under real-world conditions in a low-voltage microgrid laboratory connected to a public distribution network, confirming their practical applicability for enhancing PV energy integration and grid stability.

The rapid expansion of electric mobility and distributed renewable generation presents new operational challenges for low-voltage (LV) distribution networks, including increased evening peak demand, higher transformer utilization, and phase unbalance, particularly in emerging regions where options for reinforcement are constrained. This paper describes an open-source Python–OpenDSS framework that combines measured slow-charging profiles with Monte Carlo sampling of electric vehicle (EV) location, vehicle model, initial state of charge, and charging start time, with optional single-phase photovoltaic (PV) sized to offset annual customer demand. Each scenario is evaluated with 100 iterations, and 10-minute time-series power-flow simulations over a one-day (24 h) horizon are summarized using ensemble statistics (e.g., mean and selected percentiles). The framework is applied to a real 50 kVA urban LV feeder in Cuenca, Ecuador (62 residential customers), whose model was built from the utility GIS database and corroborated through an on-site inspection, under EV penetration levels of 5%, 10%, and 15% with EV-only and EV+PV configurations. For the range studied, steady-state voltages and voltage unbalance remain within typical LV compatibility limits, while transformer utilization increases from about 59% in the baseline to around 90% at 15% EV penetration. Co-located PV reduces the net daily energy exchanged with the upstream network but has limited impact on evening transformer peaks. A 7-day extension of the 15% EV scenario is included to illustrate multi-day studies using the same workflow.

Fast charging of electric vehicles can introduce phase-dependent harmonic distortion and voltage unbalance in low-voltage feeders, which may reduce admissible charging capacity even when voltage magnitudes remain within conventional limits. This paper proposes a phase-aware predictive scheduling framework for harmonic hosting management in feeders with a high penetration of electric vehicle charging. The proposed method formulates feeder operation as a predictive decision problem that jointly determines charging power levels, phase allocation, and the selective activation of multifunctional compensation resources under harmonic distortion, voltage unbalance, and neutral-current constraints. Unlike previous studies centered on harmonic characterization, static hosting assessment, or local converter-level mitigation, the proposed approach treats harmonic hosting as an active feeder-level network management problem. The framework is evaluated through time-series harmonic power-flow simulations using charger harmonic emission profiles and realistic feeder parameters. The numerical results indicate that coordinated phase-aware scheduling can increase admissible charging capacity, improve compliance margins for power-quality indices, and reduce mitigation efforts with respect to uncontrolled charging and non-coordinated compensation strategies. Overall, the results support the use of phase-aware scheduling as a feeder-level strategy to improve electric vehicle charging integration under harmonic and unbalanced constraints.

This paper proposes a real-time energy management strategy for low-voltage microgrids that combines short-horizon forecasting with a rule-based supervisory controller to coordinate battery energy storage usage and reactive power support provided by flexible alternating current transmission technologies. The central contribution is the forecast-informed, joint orchestration of active charging and reactive power dispatch to regulate voltage and preserve stability under large photovoltaic variability and uncertain electric vehicle demand. The work also introduces a resilience response index that quantifies performance under external disturbances, forecasting delays, and increasing levels of electric-vehicle integration. Validation is carried out through time-domain numerical simulations in MATLAB/Simulink using realistic solar irradiance and electric vehicle charging profiles. The results show that the coordinated strategy reduces voltage deviation events, maintains stable operation across a wide range of scenarios, and enables electric vehicle charging to be supplied predominantly by renewable generation. Sensitivity analysis further indicates that support from flexible alternating current devices becomes particularly decisive at high charging demand and in the presence of forecasting latency, underscoring the practical value of the proposed approach for distribution-level microgrids.

The availability of reliable digital models of low-voltage (LV) networks is a prerequisite for operation, planning, hosting-capacity, and asset-management studies. In practice, however, utility geodatabases often contain geometric discontinuities, implicit relationships among assets, and incomplete connectivity, which hinders their direct use as topological models for electrical studies. This work proposes a reproducible framework for topological-quality and fitness-for-use screening, applied to a real LV network derived from a utility geographic information system (GIS). The methodology converts geospatial layers into a graph representation, audits load-to-pole and endpoint-to-known-node distances, evaluates structural sensitivity to the endpoint connection tolerance, computes a load-level screening confidence score, characterizes transformer-level coverage under an explicit demand scenario, and evaluates structural sensitivity under synthetic perturbations of the line geometry. The case study comprises 38 urban blocks, 155 poles, 13 distribution transformers, 429 loads, 777 overhead LV segments, and 23 underground LV segments. The mean load-to-pole distance is 10.91 m and the mean endpoint-to-known-node distance is 4.39 m. The fixed 30 m load-to-pole assignment rule retains 416 loads (96.97%) throughout the tolerance sweep, while the number of connected components decreases from 245 to 82 and the number of virtual nodes from 817 to 439 as the endpoint tolerance increases from 0.5 to 5.0 m. The confidence score classifies 32.40% of loads as High, 59.44% as Medium, 5.13% as Low, and 3.03% as Review/Unassigned. Under the stated residential screening assumptions, four transformers exceed the adopted 80% loading threshold. Synthetic perturbations leave the load-to-pole control metrics unchanged by construction, because only line-segment geometry is perturbed, but the number of connected components varies from 78 to 236, with a maximum relative change of 187.80% with respect to the 5 m base graph. The contribution is therefore a transparent GIS quality-screening procedure, not an exact reconstruction or electrical validation of the physical LV topology.