Search Results (1,203)

Ports play a pivotal role in global trade but are also associated with significant environmental and social challenges. Despite growing research on green ports, existing studies remain fragmented, with limited integration between technological, environmental, and governance perspectives within the blue economy framework. This review examines the transition from green port initiatives toward integrated blue-economy-oriented port systems by synthesizing recent advances in sustainable maritime infrastructure, smart port technologies, renewable energy integration, and policy frameworks. The analysis reveals three major findings. First, ports are increasingly evolving into energy-integrated hubs, with leading examples adopting shore power systems, renewable energy microgrids, and hydrogen-based infrastructure, thereby contributing to emissions reductions. Second, digitalization through artificial intelligence, IoT, and data-driven logistics significantly enhances operational efficiency, reduces energy consumption, and improves real-time decision-making. Third, effective governance frameworks that combine regulatory measures and incentive-based instruments are critical to accelerating sustainability transitions while ensuring economic competitiveness. In addition, the review highlights the growing integration of biodiversity conservation, marine pollution mitigation, and community engagement into port management strategies, reflecting a shift toward ecosystem-based approaches. Overall, the findings demonstrate that ports are transitioning from conventional logistics hubs into integrated socio-technical systems that enable low-carbon maritime transport while supporting inclusive and resilient coastal development. Full article

Distributed generation (DG) exhibits inherent volatility and intermittency, and its grid-integration expansion presents formidable challenges to microgrid regulation and control. Conventional control strategies often neglect the uncertainties associated with renewable energy generation and the coordinated management of flexible resources. This paper proposes a multi-time-scale optimal control strategy for microgrids that explicitly accounts for uncertainty. The strategy integrates a collaborative scheduling framework for assets, including electric vehicles (EVs) and energy storage systems, alongside a stochastic optimization model for microgrids that comprehensively incorporates uncertainties from wind and solar power generation, EV operations, and load forecasting errors. The improved Archimedean chaotic adaptive whale optimization algorithm is utilized to solve the optimal scheduling model, while the Latin hypercube sampling (LHS) technique is employed to address uncertainty-related problems in the optimization process. Case study results demonstrate that, in comparison with traditional optimal scheduling strategies, the proposed approach more effectively mitigates uncertainties in real-world operations, reduces microgrid operational risks, achieves a significant reduction in scheduling costs, and concurrently fulfills the dual objectives of microgrid economic efficiency and operational security. Full article

The high penetration of distributed renewable energy in rural microgrids imposes severe physical-layer fluctuations, weak information-layer communication, and limited computing-layer resources. These triple constraints create a fundamental tension: high-precision forecasting and real-time scheduling are required, yet edge devices face severe resource limitations. To resolve this, we present an edge-deployable energy management system (EMS) that achieves forecasting–scheduling co-optimization. We first propose an Adaptive Gated Dual-stream Network (AGDN), which employs a feature-dimension gated fusion mechanism to overcome the limitations of the local dependency strengths of Long Short-Term Memory (LSTM) and the global perception capabilities of Transformer models under volatile rural conditions. This approach achieves a Mean Absolute Percentage Error (MAPE) of 4.2% for load forecasting, outperforming baseline models by a significant margin. Next, we introduce a Prediction Uncertainty-Guided Quantum-Inspired Optimization (PUG-QIO) algorithm that adaptively maps prediction confidence intervals to quantum rotation angles, enabling deep integration of forecasting and scheduling and yielding an energy utilization rate of 93.2%. Finally, a Temporal Sensitivity-Aware Differentiated Pruning (TSADP) strategy is developed to maintain forecasting accuracy under a 63% parameter compression, overcoming the deployment barrier for high-precision models on edge devices. A 30-day field trial confirms that the proposed system meets the stringent rural requirements across four critical dimensions: forecasting accuracy, real-time responsiveness, lightweight architecture, and economic viability. Overall, the proposed system satisfies four key rural requirements: forecasting accuracy (MAPE = 4.2%), real-time response (≤10 s), lightweight deployment (memory < 500 MB), and economic viability (27.3% fuel cost reduction). Full article

Islanded photovoltaic microgrids with limited inertial support can undergo steep frequency excursions after sudden generation loss or abrupt load changes. This paper develops and evaluates a synthetic inertia strategy supported by a supercapacitor energy storage unit for fast frequency containment in this type of system. The proposed approach commands rapid active-power injection or absorption from the measured rate of change of frequency, thereby emulating the immediate inertial contribution usually associated with rotating machines while preserving a simple and physically interpretable control structure. The supercapacitor is represented through a resistance–capacitance model that includes equivalent series resistance and is interfaced through a bidirectional buck–boost power converter subject to practical current, voltage, and power limits. Rather than claiming a fundamentally new storage-support concept, the contribution of this paper lies in providing a transparent and constraint-consistent benchmark that integrates measured operating profiles, explicit supercapacitor limits, hybrid frequency–RoCoF support, and stress-aware comparative assessment under a common set of plant assumptions. The methodology is assessed in time-domain simulations under representative benchmark disturbances, including an approximately ten percent photovoltaic generation loss, a ten percent load increase, and a combined event. Performance is evaluated through the peak rate of change of frequency, frequency nadir, integral error indices, time outside the admissible band, and supercapacitor stress indicators such as current peaks, voltage depletion, and energy throughput. An additional non-ideal assessment is also included to examine the behavior of the RoCoF-based support law under bounded frequency-measurement perturbations and delayed control action. A complementary variability-driven case based on a highly fluctuating measured irradiance window is also used to examine the behavior of the adaptive energy-management mechanism under repeated photovoltaic-power variations. A local small-signal analysis is also included to show that the selected gain region is dynamically plausible in the unsaturated regime. The results show that the proposed adaptive hybrid strategy improves the overall frequency response while maintaining admissible supercapacitor operation, thus providing a stronger methodological basis for rapid frequency support in islanded photovoltaic microgrids. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article

This work uses battery-coupled power electronic converter systems and distributed backstepping controllers to improve the reliability of electrical distribution networks. The motivation is to prevent blackouts such as the 28 April 2025 outage in Spain, Portugal, and the south of France. It is now accepted that a rapid rise in solar power injections caused AC overvoltage above grid code limits, triggering photovoltaic (PV) park disconnections as overvoltage self-protection. This case study considers near-Zero-Energy Buildings (nZEBs) connected to the Madeira Island isolated microgrid, where PV power installation is increasing excessively. The main university facility will be upgraded as an nZEB, using roughly 3000 m² of unshaded rooftops plus coverable parking areas to install PV panels. Optimizing the profits/energy cost ratio, a PV power system of around 560 kW can be planned, and the Battery Storage System (BSS) energy capacity can be estimated. The BSS is connected to the university nZEB via backstepping-controlled multilevel converters to manage PV and BSS, enabling the building to contribute to voltage and frequency regulation. Distributed multilevel converters inject renewable energy into the medium-voltage network, regulating active and reactive power to prevent overvoltages shutting down the PV inverters. This removes sustained overvoltage and maximizes PV penetration while augmenting AC grid reliability and resilience. When there is excess solar power and reactive power is insufficient to reduce voltage, controllers slightly curtail PV active power to eliminate overvoltage, maintaining operation with minimal revenue loss while preventing long interruptions, thereby improving grid reliability and power quality. Full article

Energy management of multi-carrier energy hubs (MCEHs) is a challenging task, particularly when fuel cell electric vehicle (FCEV) stations are included, due to the stochastic nature of FCEV demand, system loads, and integrated renewable energy resources (RERs) such as wind turbines (WTs) and photovoltaic (PV) systems. This paper aims to optimize the energy management of an MCEH-based microgrid to simultaneously minimize total operating costs and emissions. To this end, a novel enhanced quadratic interpolation optimization (EQIO) algorithm is proposed. The proposed EQIO algorithm incorporates two key improvements: a best-to-mean quasi-oppositional-based learning (BMQOBL) strategy and an evaluation mutation (EM) strategy. The performance of EQIO is evaluated using the CEC 2022 benchmark functions, and the obtained results are compared with those of other optimization techniques. Three case studies are investigated: (i) energy management of the MCEH microgrid without RERs, (ii) sustainable operation (with RERs), and (iii) sustainable operation with RERs combined with the application of demand-side response (DSR). Moreover, the proposed framework explicitly supports long-term sustainability goals by enhancing renewable energy utilization, reducing the carbon footprint, and promoting cleaner transportation through efficient integration of FCEV infrastructure. The results demonstrate that integrating RERs reduces operating costs and emissions by 51.47% and 59.69%, respectively, compared to the case without RERs. Furthermore, the combined application of RERs and DSR achieves cost and emission reductions of 55.26% and 53.93%, respectively, compared to the case without RERs. Full article

The paper proposed an adaptive preference-based multi-objective optimization framework of intelligent energy management in smart microgrids that are dynamically adapted to operational priorities with regard to real-time grid conditions, stakeholder preferences, and environmental constraints. The suggested hierarchical algorithm combines an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an advanced dynamic preference weight distribution system that can trade off between minimization of operational cost. Reduction of carbon emission, enhancement of voltage stability, enhancement of power quality and maximization of system reliability and adaptability to different operational conditions, such as renewable energy intermittency, demand response schemes and emergencies. The framework presents a new multi-layered preference-learning module that represents the intricate stakeholder priorities in terms of more sophisticated fuzzy logic-based decision matrices, neural network preference prediction, and adaptive reinforcement learning methods and transforms them into dynamic optimization weights with feedback mechanisms. Large-scale simulations on a modified IEEE 33-bus test system coupled with various renewable energy sources, energy storage facilities, electric vehicle charging points, and smart appliances demonstrate superior improvements in performance: 23.7% operational costs reduction, 31.2% carbon emissions reduction, 18.5% system reliability improvement, 15.3% voltage stability increase and 12.8% reduction of deviations in power quality. The proposed system has an adaptive nature with better performance in a variety of operating conditions such as peak demand times, renewable energy intermittency events, grid-connected and islanded operations, emergency load shedding situations, and cyber–physical security risks. The framework is shown to be highly effective under different conditions of uncertainty and variation in parameters and communication delay through intense sensitivity analysis and robustness testing, thus demonstrating its practical applicability in real-world applications of smart grids. Full article

Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation for prediction, providing a contextualized framework that surpasses the limitations of traditional approaches. Representative trajectories (A–D), derived from entropy and variability metrics, were consolidated from historical user data and used as the basis for modeling. Two complementary approaches were implemented: ARIMA models, which capture endogenous dynamics, and ARX models, which extend this capacity by incorporating exogenous cyclical variables (hour, day of the week, month) and lagged predictors. A systematic grid search was conducted to identify optimal parameter configurations, followed by validation through rolling forecasts with a 24-h horizon, relevant for operators of microgrids, institutional managers, and energy planners. Performance was evaluated using MAE, RMSE, MAPE, and SMAPE, ensuring comparability across trajectories. Results show that ARIMA consistently achieved lower error rates in stable trajectories (A and C), with SMAPE values around 2.0%, while ARX provided substantial improvements in irregular ones (B and C), reducing SMAPE from 3.7–5.9% to approximately 2.2–2.6%. In highly irregular profiles (D), all models converged to similar accuracy (SMAPE ≈ 9.0%). When applied to individual users, predictive errors varied more widely depending on trajectory assignment: stable users showed SMAPE values around 3–4%, while irregular users exhibited much higher errors, exceeding 18–21%. Unlike conventional methods that treat characterization and prediction as separate processes, this study integrates both into a unified framework, enabling forecasts to capture stability, cyclicity, and adaptability. The methodology was further applied to individual users by assigning them to representative trajectories and adjusting predictions through baseline scaling. Overall, the findings demonstrate that embedding forecasts within characterized trajectories transforms prediction into a contextualized analysis of flexibility, enabling accurate short-term forecasts and supporting practical applications in energy planning, demand management, and economic dispatch. The framework has been designed to support electricity demand forecasting across multiple contexts, from microgrids and institutional systems to larger territorial and national scales. Through contextual calibration, the methodology ensures adaptability and broader relevance for energy forecasting and demand-side management. Full article

The large-scale penetration of Distributed Energy Resources (DERs), the proliferation of Energy Communities, and the increasing provision of flexibility services are fundamentally transforming distribution network operation, rendering traditional Distribution Management Systems (DMSs) structurally inadequate. This paper addresses this structural gap by proposing and experimentally validating A-ISolE, a novel hybrid Artificial Intelligence (AI) architecture that natively integrates centralized and distributed intelligence within a unified DMS framework. The core scientific contribution of this work lies in the formulation and deployment of a coordinated, hierarchical AI paradigm in which cloud-level predictive and optimization modules dynamically interact with edge-level autonomous control agents. Specifically, the paper introduces: (1) an integrated forecasting state estimation pipeline with AI-enhanced grid observability; (2) intelligent fault location and optimal feeder reconfiguration algorithms embedded into operational control loops; and (3) distributed edge control strategies enabling autonomous yet coordinated microgrid stabilization. The architecture is validated on a real pilot microgrid in Sanremo (Italy). Experimental results demonstrate quantifiable gains in many parameters, substantiating the feasibility of hybrid centralized/distributed AI as a foundational paradigm for future resilient and decarbonized distribution networks. Full article

Remote off-grid microgrids are often locked into diesel-backed operation because renewable variability creates multi-day and seasonal energy gaps that short-duration batteries cannot economically bridge. This work examines how renewable hydrogen can complement batteries and dispatchable biomass to push an existing hybrid microgrid toward near-autonomous, low-carbon operation, while remaining robust under future electrification demands. The analysis is based on real operational load insights from a remote off-grid system, combined with techno-economic optimization in HOMER Pro. The examined architecture includes PV panels, battery energy storage, a biomass CHP unit, and a diesel generator as backup; the hydrogen pathway additionally incorporates an electrolysis, storage and a PEMFC. Three scenarios are considered: a baseline PV/BAT configuration, an intermediate PV/BAT/BIO configuration that strengthens dispatchable renewable supply and short-term flexibility, and a PV/BAT/BIO/H₂ configuration targeting an increase in renewable energy penetration (REP). Results show that hydrogen integration shifts the system from curtailment-limited, diesel-supported operation to storage-enabled operation: surplus renewable production that would otherwise be curtailed is converted into hydrogen and later dispatched during prolonged deficits, enabling deep diesel displacement without compromising reliability. Hydrogen-enabled configurations achieve 90–99% REP, reduced diesel consumption, and lower CO₂ emissions, primarily by converting curtailed surplus into storable hydrogen. A rule-based EMS highlights technology complementarity across timescales, with batteries providing diurnal balancing and hydrogen covering longer deficits, which also reduces battery cycling stress. Overall, the study clarifies key design trade-offs, especially the need for coordinated PV expansion and storage sizing, and illustrates how a multi-storage portfolio can support high renewable penetration in such systems. Full article

Electricity systems increasingly operate under deep uncertainty driven by geopolitical risk, volatile fuel markets, trade fragmentation, security threats, and technological change. Under such conditions, cost-optimal planning based on assumed trajectories may lead to fragile outcomes, particularly for small and geopolitically exposed systems such as Israel’s. This paper assesses the resilience of alternative electricity transition strategies for Israel using a qualitative robustness framework inspired by Decision Making under Deep Uncertainty and scenario-based energy security analysis. Six policy-relevant strategies are evaluated across structurally distinct stress scenarios. Resilience is assessed along three dimensions: security of supply, dependency exposure, and economic vulnerability, using anchored qualitative scoring and dominance rules. The results indicate that gas-centric strategies exhibit limited robustness, while strategies combining solar deployment with adaptive gas management, smart grids, microgrids, and domestic clean-technology capabilities achieve higher resilience across a wide range of futures. The paper contributes a structured qualitative approach to resilience assessment and offers policy-relevant insights for electricity transitions under deep uncertainty. Full article

In this paper, the dynamic modeling and integrated simulation of a ship microgrid system designed to enhance power quality and energy efficiency in electric propulsion vessels are proposed. The proposed system consists of a photovoltaic (PV) array, a battery energy storage system (BESS), a diesel generator, and a propulsion system, all of which are organically integrated through power conversion devices. To compensate for the intermittent nature of solar power, a control strategy featuring Maximum Power Point Tracking (MPPT) for the PV system and bidirectional DC/DC converter control for the battery was implemented. Specifically, a control logic to stabilize the system output in response to the fluctuating loads of the electric propulsion system was developed using PSCAD (v50) software. The simulation results demonstrate that the proposed control strategy maintains DC-link voltage deviation within ±1.8% and achieves a settling time of less than 0.8 s while optimizing propulsion efficiency (peak-shaving ratio 25–30%) under both constant and variable speed operating conditions. Battery SOC variation is limited to 18–88%, preventing overcharge or discharge. This research provides a foundational framework for the design of energy management systems (EMSs) and grid stability assessments for future eco-friendly electric propulsion ships. Full article

Current peer-to-peer (P2P) energy trading systems face important challenges in decentralised trading environments, particularly in managing participant trustworthiness, preventing dishonest behaviour, and mitigating transaction defaults. These limitations reduce transaction reliability and weaken trust among participants in community-scale energy trading markets. Although P2P energy trading enables communities to exchange locally generated renewable energy in smart environments, existing platforms often lack effective mechanisms to regulate participant behaviour and support reliable transactions. This paper proposes RepuTrade, a blockchain-based P2P energy trading platform tailored for community-scale microgrids. The proposed framework integrates a reputation-based consensus mechanism and a dynamic collateral management scheme that is directly linked to participant reputations such that trading reliability can be strengthened through behavioural incentives. In addition, a reputation-driven matching algorithm preferentially pairs highly reputable participants to improve market stability and trust. Simulation-based evaluation, involving 200 users across 8 trading rounds, shows that the RepuTrade framework consistently achieves higher trade success rates (92–99% compared to 83–95% in the baseline) and reduces defaults by more than 40% (27–44 vs. 55–72 per run). The results further reveal a strong negative correlation between user reputation and default probability, indicating that higher reputation is associated with a lower likelihood of dishonest behaviour. Overall, under the simulated settings considered in this study, the proposed framework improves transaction reliability and execution efficiency by reducing failed trades and lowering consensus validation latency. These findings contribute to the design of trust-aware decentralised energy trading mechanisms and provide simulation-based insights for developing more reliable and transparent community-scale renewable energy markets. Full article

Power fluctuations and scheduling uncertainties caused by large-scale renewable energy grid integration have made the existing homogeneous energy storage solutions struggle in some cases to balance economic efficiency with dynamic response speed. To address the above challenge, this paper proposes a hybrid energy storage system integrating superconducting magnetic energy storage and hydrogen electric storage, and a corresponding dual-layer model predictive control energy management framework is therefore designed. This framework lies on its cross-timescale hierarchical coordination mechanism. Analytic validation in a typical high-fluctuation renewable microgrid scenario demonstrates that compared to conventional single-layer control strategies, the proposed management system reduced total operating costs by 55.5%, extended system stabilization time by 64.2%, decreased hydrogen storage system mode switching frequency by 59.9%, and simultaneously lowered computational burden by over 97%. This effectively enhanced power supply reliability and extended equipment service life. This innovative framework provides a practical solution for coordinated energy storage control in microgrids having a high ratio of renewable penetration. Full article