Search Results (564)

Ports are progressively evolving from traditional logistics nodes into integrated energy ecosystems, characterised by increasing electrification of maritime and land-based operations, the deployment of renewable energy sources, and the emergence of new and highly variable energy demand profiles. In this context, the main challenge is not only the availability of renewable energy but also the capacity of port energy systems to provide sufficient electrical power, flexibility, and resilience under increasing operational constraints. These issues are particularly relevant in Mediterranean ports, where limited grid capacity, infrastructure constraints, load variability, and interactions with surrounding urban areas strongly influence energy planning strategies. This paper proposes an integrated framework for the development of sustainable port energy hubs based on renewable generation, energy storage, green hydrogen systems, port microgrids, and intelligent energy management strategies (EMS). The main novelty lies in the integration of these energy vectors within a unified framework that explicitly accounts for the specific operational and infrastructure constraints of Mediterranean ports. The proposed approach aims to optimise the interaction between energy production, distribution, storage, and consumption, with particular attention to the role of hydrogen as a long-duration energy storage vector and as an energy carrier for selected port logistics applications. Through a data-driven Port Energy Baseline Assessment (PEBA), port operational characteristics are translated into quantified energy demand and power requirements, providing the basis for power adequacy assessment and the evaluation of alternative transition pathways. An illustrative application to a representative Mediterranean port, characterized by a peak electricity demand of 42 MW, illustrates how the framework quantifies power requirements, assesses power adequacy under infrastructure constraints, and compares alternative transition pathways based on renewable generation, battery storage, and hydrogen. Full article

This paper presents an approach for optimal operation of self-healing networked microgrids (NMGs) under both normal operation and emergency conditions using the pufferfish optimization algorithm (POA). The proposed methodology is based on an energy management system (EMS) with two levels and independent functions. The lower-level is designed for normal operation, where the local controller of each microgrid (MG) performs the optimal dispatch of power from the dispatchable sources. During an emergency case in any MG, the higher-level EMS is activated, and the global controller is brought into operation. Physically, the NMGs are connected by tie-lines, while cyber links are established to exchange information and control signals for coordinated operation. Each MG operates to supply its local demand during normal operation conditions, resulting in no electrical power exchange between MGs. İn case of generation deficiency or a fault leading to generation outage, electrical power can be exchanged through the existing interconnections, enabling the affected microgrid to receive support from neighboring MGs. The main objective of POA is to minimize the total operating cost, in which the economic impact of network power losses is incorporated into the single objective function. Simulation studies were conducted using MATLAB and DIgSILENT software over one day. The performance of POA was compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) under the same computational settings. Statistical and convergence analyses show that POA achieves the lowest mean operating cost across all studied cases, with low run-to-run variability and favorable convergence behavior. The results demonstrate the effectiveness of the proposed approach in improving the economic operation of NMGs under both normal and emergency conditions. Full article

The central challenge related to the development of electric vehicles (EVs) involves the effective integration of multiple input sources to create a robust and efficient power system. Recent advancements have focused on optimizing the power distribution within hybrid systems that combine batteries, supercapacitors, and renewable sources like solar PV. Conventionally, energy management systems (EMSs) have relied on rule-based algorithms or deterministic optimization methods. However, these techniques often lack adaptability under real-world driving conditions and face significant challenges regarding their generalizability and computational complexity when applied to dynamic driving cycles. Machine learning approaches are capable of modeling the complex, non-linear interactions between multiple energy sources to ensure intelligent power coordination. This paper proposes a novel Extreme Gradient Boosting (XGBoost)-based intelligent EMS for a BLDC motor-driven electric vehicle (e-bike) utilizing a hybrid battery–solar configuration with a supercapacitor for regenerative braking. The proposed system integrates a super-boost converter for efficient multi-source power delivery. The results show accurate energy source identification, effective multi-source coordination, improved energy utilization, reduced battery stress, and a rapid decision-making capability, which prove the feasibility of the proposed method for real-time electric bicycle energy management. The simulation was executed using the MATLAB/Simulink platform, and the obtained results are outlined. Full article

As Renewable Energy Communities (RECs) drive a shift toward decentralized power systems, innovative solutions to manage the inherent intermittency of distributed energy resources are essential. This paper investigates the potential of electric vehicles (EVs) as dynamic flexibility providers within the REC framework. By leveraging smart charging and Vehicle-to-Everything (V2X) technologies, EV fleets can act as key assets to facilitate the transition toward active distribution networks by providing upward and downward power reserves within local flexibility markets. This study presents a Mixed-Integer Linear Programming (MILP)-based Energy Management System (EMS) to optimally manage a case study REC in Northern Italy, characterized by renewable power plants and V2X-enabled EV charging stations for both electric cars and electric trucks. The proposed EMS model aims to simultaneously maximize the energy virtually shared within the REC and the provision of upward and downward reserves by the EV fleet over the considered time horizon. The EMS optimal results are analyzed for two distinct periods of the year, namely one week in spring and one in autumn, demonstrating that the flexibility guaranteed by the EVs can significantly impact the energy-sharing mechanism of the REC while at the same time providing additional revenues to the REC members. Full article

This paper presents an artificial intelligence (AI)-based energy management and control framework for renewable-powered smart microgrids operating under dynamic conditions. The proposed system integrates photovoltaic (PV) generation, battery energy storage, and grid interaction within a MATLAB/Simulink-R2024B environment to improve operational reliability, energy efficiency, and renewable energy utilization. Seven control scenarios were investigated, including baseline operation, Rule-Based Energy Management System (EMS), Proportional–Integral–Derivative (PID), Model Predictive Control (MPC), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN)-assisted forecasting, and Reinforcement Learning (RL)-based optimization. Comparative results demonstrate progressive performance improvements with increasing controller intelligence. The RL-based EMS achieved the highest operational cost reduction (95%), voltage regulation performance (95%), battery state-of-charge management (95%), renewable energy utilization (92%), grid dependency reduction (92%), overall system efficiency (95%), and an overall performance score of 95.4%. The ANN forecasting model attained a forecasting accuracy of 96.2%, corresponding to a Mean Absolute Percentage Error (MAPE) of 3.8%, while achieving a Mean Absolute Error (MAE) of 1.84 kW, Root Mean Square Error (RMSE) of 2.37 kW, and coefficient of determination ($R^2$) of 0.982. For voltage regulation, the RL controller reduced the RMSE, settling time, and overshoot to 1.50 V, 1.8 s, and 0.5%, respectively, compared with 20.0 V, 15.0 s, and 10.0% for the baseline case. Ultimately, the results demonstrate that AI-driven control strategies substantially enhance microgrid stability, battery utilization, renewable energy penetration, and operational efficiency, providing a practical and scalable solution for next-generation intelligent microgrids. Full article

Magnesium oxychloride (MOA) adhesive plywood represents a novel inorganic matrix panel technology that eliminates organic volatile compounds from the adhesive system and avoids high-temperature hot pressing, potentially offering significant carbon footprint advantages. This study presents a comparative life cycle carbon footprint assessment of conventional urea–formaldehyde (UF) plywood and MOA plywood manufactured in China, using 1 m³ of a finished panel as the functional unit under a cradle-to-grave system boundary, comprising the production stage (Modules A1–A3)—explicitly including forestry operations (silviculture, felling, extraction/forwarding, loading and log haulage) and veneer manufacture within Module A1, now reported as a disaggregated inventory and delimited in a system boundary diagram—and the end-of-life stage (Modules C2–C4), evaluated across three end-of-life (EOL) scenarios: incineration, landfill, and mechanical recycling. Foreground data (process energy, adhesive formulation, transport distances) are metered/primary data collected over a full production year at a single large-scale plywood plant in Suqian, Jiangsu; background data are from ecoinvent v3.9.1 (cut-off), characterised with IPCC AR6 GWP100. Results indicate that MOA plywood generates approximately 253 kg CO₂-e/m³ at the production stage (A1–A3), compared with 301 kg CO₂-e/m³ for UF plywood, a reduction of 15.8% (47.5 kg CO₂-e/m³). Contribution analysis attributes virtually the entire gap to process energy (steam 65.7%, electricity 34.3%), while adhesive raw materials and inbound transport cancel to within rounding, demonstrating that the advantage is a process energy rather than a green chemistry phenomenon. A parameter-specific one-at-a-time analysis and a 200,000-run Monte Carlo simulation with triangular distributions show no reversal of the UF–MOA ranking in any of the 200,000 realisations within the adopted uncertainty ranges, with an approximately 56 kg CO₂-e/m³ median advantage (5th–95th percentile of about 31–85). Under EOL incineration, MOA plywood retains a substantial advantage even after the newly quantified burden of flue gas HCl neutralisation (13.3 kg CO₂-e/m³) and inorganic residue management (0.9 kg CO₂-e/m³) arising from the chloride content of the Sorel cement binder are charged to the MOA system. Under landfill, both products behave similarly, as wood carbon dynamics dominate. A break-even analysis shows that the service life of MOA plywood would have to fall below 25.3 years (against a 30-year reference) for its cradle-to-gate advantage to be erased. These findings clarify the lifecycle trade-offs of inorganic adhesive plywood and provide actionable data for environmental product declarations and procurement frameworks. Full article

AC/DC hybrid building microgrids require an energy management system (EMS) that coordinates distributed energy resources while maintaining fast local responses to communication and device faults. This study proposes a knowledge-graph-enhanced edge-cloud EMS for an AC/DC hybrid building microgrid. A 24 h linear-programming scheduler coordinates multi-resource dispatch in the cloud, while edge controllers enforce local safety constraints, correct setpoints and maintain fallback operation during link interruptions. The knowledge graph provides semantic context by linking assets, constraints, faults and admissible actions. Six operating scenarios, controlled V2G ablation tests and workday–weekend validation were used to assess the framework. Compared with rule-based EMS, the proposed method reduced peak demand by 28.0% and increased PV self-consumption from 78.4% to 95.4%. Compared with cloud-only MPC, it reduced the simulated mean control-loop latency from 7.54 s to 0.81 s. The eight-rule fault evaluation achieved a mean trigger accuracy of 94.6%. Under normal operation, however, the higher PV self-consumption was accompanied by a modest increase in operating cost and peak demand relative to non-KG edge-cloud MPC. These results support the complementary use of cloud scheduling, edge autonomy and semantic context within the tested simulation scope. Full article

This study proposes a stability-aware load-shaping energy management system (EMS) for a hybrid electric shipboard power system. The proposed EMS uses the energy storage system (ESS) as a dynamic load-shaping buffer to reduce active diesel-generator (DG) low-load exposure and electrical power fluctuations. A supervisory reference-generation procedure integrating low-pass filtering, ESS state-of-charge (SOC) compensation, DG ramp-rate limiting, residual-power calculation, and explicit power and SOC constraints was implemented on a real-time controller. Comparative experiments were conducted on an MW-class platform comprising one active 600 kW DG, a 400 kW/400 kWh ESS, two 450 kW propulsion-load channels, and a 100 kW service-load channel connected to a 750 V DC bus. The second installed DG remained offline during all comparative experiments. Under a common one-hour ship-load profile, the proposed EMS reduced the low-load exposure ratio from 0.1320 to 0.00139, the DG power variance from 3.06 × 10⁴ to 1.37 × 10⁴ kW², and the mean DG ramp rate from 13.8 to 0.776 kW/s relative to the rule-based EMS. These values correspond to reductions of approximately 98.9%, 55.2%, and 94.4%, respectively. After terminal-SOC correction, the BSFC-map-estimated equivalent fuel consumption decreased from 90.4 to 88.2 kg. Experimental parameter-sensitivity tests demonstrated the trade-offs among DG power smoothing, low-load exposure, SOC regulation, and ESS participation. A supplementary offline Monte Carlo analysis further indicated that the principal comparative benefits were maintained under bounded variations in load magnitude and fluctuation amplitude. The results demonstrate that the proposed EMS improves supervisory DG loading quality while maintaining the ESS within its prescribed power and SOC limits. Full article

Energy storage systems (ESSs) play a crucial role in mitigating the intermittency and variability of renewable energy sources (RESs) and enhancing the stability and reliability of modern power systems. However, the inherent limitations of individual storage technologies, particularly the trade-off between energy density and power density, restrict their ability to satisfy diverse operational requirements. In this context, hybrid energy storage systems (HESSs), which combine complementary storage technologies, such as batteries, supercapacitors, and flywheels, have emerged as an effective solution capable of simultaneously delivering high-energy and high-power performance. This paper presents a comprehensive review of HESS architectures, converter topologies, energy management strategies (EMSs), and applications. The EMS taxonomy is organized into classical and intelligent control. Classical EMS approaches are categorized into filtration-based, rule-based, deadbeat, droop, sliding mode, and fuzzy logic control, whereas intelligent EMS approaches encompass optimization-based methods, including model predictive control, as well as learning-based techniques such as supervised and reinforcement learning. Moreover, HESS applications are examined across grid-scale systems, microgrids, renewable energy systems, transportation, power quality improvement, frequency regulation, peak shaving, and uninterruptible power supply systems. Representative implementations are also reviewed to identify current technological trends, operational challenges, and performance trade-offs. Finally, future research directions are outlined, with emphasis on digital twins, privacy-preserving and explainable learning frameworks, cyber–physical security, and adaptive and scalable EMSs. Full article

In this paper, a route- and environment-aware rule-based energy management system (EMS) strategy for EVs equipped with HESSs consisting of a lithium-ion battery and a supercapacitor is proposed. The proposed rule-based EMS integrates driving mode classification, traffic conditions, road gradient, wind resistance, ambient temperature, and the supercapacitor (SOC) to determine the optimal power-sharing strategy between the battery and the supercapacitor under various driving conditions, including city, highway, and stop-and-go traffic. A mathematical model of the EV powertrain, battery, and supercapacitor is developed. The performance of the proposed rule-based EMS strategy is validated in MATLAB/SIMULINK under three representative driving cycles: the Urban Dynamometer Driving Schedule (UDDS), Artemis Motorway 130, and a Central Business District (CBD) driving cycle. It is shown that the proposed rule-based EMS strategy significantly reduces the battery current stress while increasing supercapacitor usage. Full article

This study considers the multi-objective optimization of a standalone hybrid nanogrid system (HNGS) providing electricity power to a residential load in an off-grid area of Djelfa Province, Algeria. The focus of this study is to obtain the optimum design of a standalone hybrid nanogrid system consisting of photovoltaic (PV) panels, wind turbines (WTs), battery storage (BT), diesel generators (DGs), and power converters to satisfy the energy demand of residential consumers in Djelfa Province, Algeria. In this context, four multi-objective optimization algorithms (MOPs), NSGA-II, MOPSO, MOSSA, and MODE, are used to solve the optimal sizing problem of the proposed system. The formulated multi-objective optimization problem takes into account multiple performance criteria such as cost of energy (COE), loss of power supply probability (LPSP), renewable energy penetration, and diesel generator usage reduction, balancing economic, reliability, and sustainability aspects. The optimization process optimizes critical design parameters, including the size of the PV system, the number of wind turbines, and the size of the battery storage system, for a realistic operating scenario. The optimization algorithms are combined with an energy management strategy (EMS) that helps to coordinate the power flow distribution between various parts of the system to achieve optimum system performance. The effectiveness of each of the proposed approaches is analyzed based on the obtained results, where it was found that the MODE algorithm provides the best compromise solution, with a COE of 0.167 USD/kWh and an LPSP of 6.372%, and the lowest carbon dioxide emissions of 205.1 kg/year compared to MOPSO, NSGA-II, and MOSSA. Moreover, the results obtained from this process will provide a set of feasible design solutions, which will allow decision-makers to choose the most suitable design solution based on technical and economic specifications. Full article

The development of fuel cell electric vehicles (FCEVs) remains challenged by complex system integration, powertrain design, and the limited availability of experimental data under realistic operating conditions, which constrains the validation of energy management systems (EMSs) and system-level performance assessment. To address this gap, this study presents a validated test bench platform for fuel cell powertrains that combines a hardware-based powertrain test bench with a simulation environment for EMS analysis. The platform enables the integration and testing of a fuel cell powertrain in an electric van under realistic operating conditions. Validation under the US06 driving cycle shows an equivalent hydrogen consumption deviation of only 5.1 g (2.4%) between the hardware and simulation environments, demonstrating high platform reliability. A comparative analysis of load-following and average load power strategies is conducted. Results indicate that the average load power strategy achieves higher energy efficiency, reducing equivalent hydrogen consumption by 1.8%, 3.8%, and 6.4% under city, rural, and highway conditions, respectively. The efficiency advantage becomes increasingly pronounced as power demand rises. The proposed platform provides a validated framework for system-level development, validation, and evaluation of fuel cell powertrain systems. Full article

Fuel cell electric vehicles represent a promising option for reducing greenhouse gas emissions from heavy-duty transport. In this context, integrating Hydrogen Refueling Stations (HRSs) into renewable-based microgrids represents a key strategy for ensuring sustainable hydrogen production. This study investigates the integration of an HRS into a photovoltaic-based microgrid supplying a fleet of 21 urban buses. A detailed hourly model of the photovoltaic system, battery storage, hydrogen generator, hydrogen storage, compression and refueling processes was developed. A multi-objective optimization was performed to minimize the Levelized Cost of Hydrogen (LCOH) while maximizing the Self-Sufficiency Rate (SSR). Three Energy Management Strategies (EMSs) were compared: hydrogen production using only renewable electricity, mixed renewable and grid electricity and grid-only electricity. Results reveal a marked economic penalty associated with achieving full self-sufficiency. Under the renewable-only EMS, the LCOH increases from 16.8 €/kg at an SSR of about 80% for the minimum-LCOH solution to 24.3 €/kg at an SSR of 100%. Under the MIXED-EMS, it increases from 12.3 €/kg at an SSR of about 47% to 22.7 €/kg at an SSR of 100%. When revenues from surplus electricity export are included, the corresponding LCOH values at 100% SSR decrease to approximately 15 €/kg, regardless of the EMS adopted. Compared with the emissions from the diesel-bus fleet, hydrogen buses could reduce emissions by about 12% with grid-based production and up to 99% with renewable hydrogen. Full article

Energy forecasting in smart buildings is a critical aspect of optimizing energy consumption and improving sustainability in modern urban environments. This systematic literature review aims to provide a comprehensive analysis of the current research trends, datasets, forecasting methods, and models used in the context of smart building energy forecasting. We reviewed studies published between 2010 and 2024, focusing on the methodologies and techniques applied to predict energy usage in buildings equipped with advanced technologies such as Internet of Things (IoT) devices, energy management systems (EMSs), and renewable energy sources. This review highlights a shift from traditional statistical methods to more advanced machine learning (ML) and deep learning (DL) models, with notable improvements in forecasting accuracy. We also examine the datasets commonly used in these studies and identify key challenges such as data availability, model generalization, and the integration of renewable energy. The findings indicate a growing trend towards hybrid models that combine various forecasting techniques, with a particular focus on real-time prediction and optimization. This review also identifies several research gaps, including the need for larger, more diverse datasets, improved model interpretability, and the integration of renewable energy in forecasting models. Ultimately, this review offers insights into the state of the field and provides guidance for future research directions in energy forecasting for smart buildings. Full article

The increasing integration of renewable energy sources into microgrid systems has driven significant advances in energy management system (EMS) design, yet the diversity of proposed approaches makes systematic comparison challenging. This paper presents a comprehensive review of decision-making approaches for microgrid EMSs, organized along three dimensions: architecture-based, decision-method-based, and application-context classification. The review covers deterministic mathematical programming; heuristic and meta-heuristic optimization; stochastic and robust optimization; data-driven intelligent and agent-based methods, including fuzzy logic, machine learning, reinforcement learning, and multi-agent systems; and model predictive control and its variants. For each category, representative studies are analyzed with respect to optimization objective, uncertainty handling, key components, and control architecture. The results show that classical methods offer transparency and optimality guarantees but are limited under uncertainty and nonlinearity conditions, while AI-based and MPC approaches provide adaptability and real-time performance at the cost of higher data requirements. This comparative analysis aims to guide researchers and practitioners in selecting appropriate EMS strategies for microgrid applications. Full article