Search Results (66)

The Greek power system includes 42 non-interconnected islands grouped into 28 autonomous electrical systems operated by the Hellenic Electricity Distribution Network Operator. Although these systems possess substantial wind and solar potential, the technical constraints of isolated microgrids lead to systematic renewable energy curtailment. Building on our previous methodology for estimating curtailed wind energy and hydrogen production, this study develops and evaluates a dispatch-oriented power-to-power pathway in which curtailed wind electricity is converted into hydrogen and subsequently reconverted into electricity. The study integrates hydrogen-to-power technology selection, annual energy recovery, dispatch strategy, and operational environmental and economic benefits for a representative non-interconnected island. A comparative assessment of commercially relevant hydrogen-to-power technologies identified proton exchange membrane fuel cells as the most suitable option because of their absence of direct CO₂ and NO_x emissions, rapid start-up, load-following performance, modularity, and compatibility with remote island operation. Applying the previously developed curtailment methodology to 2024 data yielded 9334.5 MWh of exploitable curtailed wind energy. This energy could produce 155.6–233.4 tonnes of hydrogen and recover 2437.1–4277.9 MWh of electricity annually. Two dispatch strategies were evaluated: continuous integration of hydrogen-derived electricity into the island’s generation mix, and strategic hydrogen storage with priority dispatch during periods of emergency diesel generator operation. Under the reference case, both strategies recovered approximately 2935.1 MWh annually, avoided 1868.9 tonnes of CO₂ emissions, and reduced fuel expenditure by €359,000. Full article

With the continuous penetration of renewable energy and power electronic equipment into modern power systems, the occurrence frequency of composite power quality disturbances has increased significantly. The accurate classification of various composite disturbances under strong noise remains a critical technical challenge. The existing single time–frequency transformation methods cannot simultaneously capture transient time-domain details and fine frequency-domain features of steady-state harmonics, while mainstream deep learning classification networks contain redundant parameters and introduce excessive computational overhead, failing to meet the real-time deployment requirements of power edge terminals. To address these limitations, a lightweight Coordinate Attention ResNet network named ResNet–LCA is proposed based on the dual-modal time–frequency fusion of the Continuous Wavelet Transform and Short-Time Fourier Transform. First, the two transforms are implemented separately to generate two groups of complementary time–frequency maps, which are concatenated along the channel dimension to fully extract the coupling features between the steady-state harmonics and the transient impulses. Second, a Haar wavelet subband mean aggregation module is designed for dimensionality reduction with negligible information loss. This module eliminates the channel redundancy introduced by the multimodal fusion and reduces the overall computational overhead at the input stage. Finally, a lightweight residual network integrated with Coordinate Attention is constructed, with Grouped Half-Convolution adopted to compress the model parameters. CA offsets the feature attenuation induced by the lightweight structural design and further improves the model’s noise immunity. A simulation verification was carried out on a simulated dataset covering 25 types of single and superimposed composite disturbances. At a signal-to-noise ratio of 20 dB, the proposed method achieved an average classification accuracy of 97.92%, with only 5.32 M total parameters and a single-sample GPU inference latency of 0.33 ms. Compared with standard ResNet-18 under 20 dB noisy conditions, the total parameter volume was reduced by 52.7%, the inference latency was shortened by 0.13 ms, and the classification accuracy was improved by 0.60 percentage points. The proposed method achieves coordinated optimization of classification accuracy, noise immunity and inference efficiency, and it can provide lightweight technical support for online intelligent power quality monitoring at the edge nodes of microgrids and islanded power systems. Full article

Reliable electric-power restoration is important for autonomous ships because propulsion, navigation, communication, and emergency loads depend on a compact shipboard distribution network with limited generation redundancy. This paper studies the fault reconfiguration of an autonomous shipboard meshed microgrid under generator outage, branch fault, and dynamic-load disturbance conditions. A multi-objective model is established by considering priority-based load restoration, switching-operation cost, and generator load balancing. To represent emergency load management more realistically, a continuous restoration ratio is introduced for aggregated shipboard load groups, so that full restoration, derated operation, and load shedding can be described in one formulation. A Q-learning-guided ant colony optimization method (QL-ACO) is then proposed. In this method, Q-learning is used as an adaptive parameter controller for the pheromone factor, heuristic factor, and greedy selection probability, rather than as a direct switch-action selector. Elite reinforcement and pheromone smoothing are also introduced to reduce premature convergence. Four shipboard fault scenarios are simulated, including a single-branch fault, a single-generator outage, a combined branch–generator fault, and a dynamic-load–branch-fault case. The results show that the proposed method maintains critical-load restoration, improves Class-III load recovery in complex scenarios and obtains feasible reconfiguration schemes with fewer switching operations than fixed-parameter ACO, NSGA-II, PSO, and a compact direct RL reference baseline. Runtime, scalability, statistical, and sensitivity analyses are also provided to examine online applicability and robustness. Full article

Microgrid scheduling is a challenging optimization problem because renewable energy generation, energy storage behavior, load demand, and grid interaction must be coordinated under nonlinear and constrained operating conditions. To improve the search performance of the original Butterfly Optimization Algorithm (BOA), this paper proposes an Improved Butterfly Optimization Algorithm (IBOA) for global optimization and microgrid scheduling. Three strategies are embedded into the BOA framework. First, the Dance Synchronization Guidance Strategy uses both the current global best solution and the dominant-group center to reduce excessive dependence on a single leader and improve population cooperation. Second, the Dance Emotion Disturbance Strategy introduces an adaptive perturbation term into the local search process, which helps the algorithm escape stagnant regions. Third, the Exponential Fragrance Decay Strategy dynamically adjusts the sensory modality parameter, allowing the search process to gradually shift from global exploration to local refinement. The performance of IBOA is evaluated through the IEEE CEC2017 and CEC2022 benchmark suites under different dimensions. The Friedman ranking results show that IBOA achieves the best mean ranks on CEC2017 with values of 1.13, 1.23, and 1.87 for 30-, 50-, and 100-dimensional cases, respectively. On CEC2022, IBOA also ranks first, with mean ranks of 1.50 and 1.00 for 10- and 20-dimensional cases. In the microgrid scheduling case, IBOA obtains the lowest average operating cost of 1443.56 with a standard deviation of 69.61 over 30 independent runs. Compared with CCO, CBSO, and GWCA, the average cost is reduced by approximately 13.58%, 14.98%, and 15.39%, respectively. Moreover, compared with the original BOA, the average cost is reduced from 28,338.69 to 1443.56. These results indicate that IBOA provides a more stable and cost-effective optimization approach for both benchmark optimization and microgrid scheduling problems. Full article

The electrification of future aircraft poses significant challenges to existing electrical power system (EPS) architectures, particularly due to increasing installed power levels, the introduction of electric flight control, and the (partial) electrification of propulsion systems. The transition to AEA requires more than simply replacing conventional systems with electrical counterparts. It demands a fundamental redesign of the electrical system architecture. This study investigates three novel EPS architectures for More Electric Aircraft (MEA) and three corresponding ones for All Electric Aircraft (AEA). All concepts are based on the segmentation of the EPS into electrically isolated microgrids and the separation between propulsion and on-board systems, aiming to improve system reliability, efficiency, fault management, and certification flexibility. The disruptive architecture proposes islanded microgrids, where electrical loads are grouped by Design Assurance Level (DAL) and spatial distribution. Each microgrid is powered locally by batteries, which significantly reduces cabling mass, electromagnetic interference (EMI), and system complexity. By decoupling safety-critical from non-critical loads and reducing reliance on centralized distribution, the proposed architectures increase reliability and reduce complexity. Full article

Accurate short-term load forecasting at disaggregated levels is critical for energy management in microgrids and institutional environments, yet it remains a challenge due to high consumption variability and limited contextual information. This paper proposes a hybrid model that combines Fuzzy ARTMAP neural networks with K-means clustering to improve hourly load forecasting using real data from a university microgrid. The methodology includes key preprocessing steps such as filtering low-load records, removing holidays, interpolating missing values, and applying cyclic encoding to standardize the data into 96 time intervals per day (15-min resolution). For each prediction, the average load profile of the five most recent weekdays is computed and compared to cluster centroids to identify the most similar group, which is then used to train the neural network. Results demonstrate consistent improvements in MAPE, RMSE, and MAE compared to the non-clustered baseline. The model showed robustness to non-stationary behavior and atypical patterns, even when relying solely on timestamp and load data. The proposed strategy outperformed conventional approaches and proved suitable for complex, data-limited environments. Full article

The deployment of grid-forming microgrids has attracted growing attention as a pathway toward improving energy system resilience and supporting low-carbon transitions in decentralized power systems. However, the relative influence of distinct stakeholder groups on microgrid development performance remains inadequately understood in the extant literature. Grounded in stakeholder theory and informed by behavioral economics, this study develops and empirically tests a stakeholder-based framework that examines the effects of government support, investor participation, user acceptance, and utility participation on microgrid development performance. Survey data were collected from 200 stakeholders engaged in microgrid-related activities and analyzed using consistent Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model accounts for a substantial proportion of the variance in microgrid development performance (R² = 0.647). The quantitative results indicate that all four stakeholder constructs exert statistically significant positive effects on microgrid development performance. Investor participation emerges as the strongest driver (β = 0.399, p < 0.001), followed by user acceptance (β = 0.190, p < 0.001), government support (β = 0.175, p = 0.015), and utility participation (β = 0.170, p = 0.003). Interpreted through a behavioral economics lens, these findings demonstrate that development performance is governed primarily by behavioral and perceptual factors, namely capital confidence, risk tolerance, and demand-side acceptance, rather than by technical preparedness alone. Conventional assumptions of linear adoption driven by technical superiority are therefore insufficient to account for observed development outcomes in complex, decentralized energy systems. This study advances a stakeholder-centered and behaviorally grounded understanding of grid-forming microgrid development and offers empirical guidance for designing governance frameworks that align regulatory structures with market and user behavioral dynamics. Full article

The increasing penetration of renewable energy sources, distributed generation, and advanced control technologies has transformed microgrids into complex, dynamic systems that pose significant challenges for protection coordination. This paper presents a comprehensive bibliometric analysis of the scientific literature on protection strategies in modern microgrids. Using a curated dataset from the Scopus database, four types of analyses were conducted: trend topic analysis, dendrogram clustering, co-occurrence network mapping, and thematic map analysis. The trend topic analysis highlights the temporal evolution of specific topics. The dendrogram analysis reveals thematic groupings and highlights concepts that have received limited attention. The co-occurrence network analysis reveals interactions between terms, and the thematic map analysis identifies basic, niche, and motor themes, as well as emerging or declining themes. These insights provide a structured overview of current knowledge and potential future research directions in microgrid protection. This study serves as a valuable reference for researchers and practitioners aiming to understand and address the evolving challenges associated with protection coordination in modern microgrids. Full article

Sub/super-synchronous oscillations induced by the interaction between wind turbines and the grid pose increasing challenges to the dynamic analysis of power-electronics-dominated power systems. For microgrids comprising a large number of distributed direct-drive wind turbines (DDWTs), detailed electromagnetic transient modeling becomes computationally prohibitive, while conventional single-machine equivalent models often fail to capture critical oscillatory characteristics. To address these issues, this paper proposes an impedance-sensitivity-based clustering and equivalent modeling method for DDWT groups in a microgrid. First, a frequency domain impedance model of DDWTs is established, and the impedance sensitivities of key control parameters are analyzed under various steady-state operating conditions. By jointly considering the absolute magnitude of impedance sensitivity and its variation across operating points, a sensitivity-informed criterion is developed to select physically meaningful clustering indices capable of distinguishing wind turbines with different operating conditions. Based on the selected indices, a k-means clustering algorithm is employed to group distributed DDWTs, and a multi-machine equivalent model is constructed accordingly. Simulation studies under impedance disturbances validate the effectiveness of the proposed equivalent model in accurately reproducing the oscillation characteristics of a microgrid with multiple DDWTs. Full article

Photovoltaic (PV) generation, a vital component of renewable energy, is key to supporting energy supply and reducing reliance on traditional energy sources. Given the substantial energy consumption of oilfield well groups, increasing the proportion of PV energy is imperative. Furthermore, as oilfields enter mid-to-late production stages, wells experience reduced oil production with increased energy consumption, necessitating intermittent pumping schedules. This paper addresses the optimized scheduling of pumping unit well groups within a photovoltaic-grid microgrid. The article aims to minimize the difference between the well group system’s total energy consumption and the PV power generation. A nonlinear mixed-integer programming (NMIP) model is constructed, incorporating a PV power forecasting model, a well group energy consumption model, and relevant constraints. An improved Particle Swarm Optimization (PSO) algorithm, integrating a hybrid coding scheme and multiple improvement strategies, is proposed to efficiently solve the NMIP model. The resulting optimal intermittent pumping schedule maximizes on-site PV power consumption, effectively mitigating PV energy wastage and potential grid stability issues associated with direct grid integration. The effectiveness of the proposed optimization algorithm is validated through numerical simulation case studies. Full article

The multi-microgrid integrated energy system (MM-IES) plays a vital role in enhancing energy utilization efficiency and promoting the coordinated consumption of renewable energy. However, the realization of low-carbon dispatch in MM-IES is hindered by multi-energy coupling and the need for distributed coordination under increasingly stringent carbon emission constraints. To address these issues, a distributed scheduling strategy that integrates demand response and green certificate trading mechanisms is proposed. Firstly, a low-carbon integrated energy microgrid (IEM) model integrating carbon capture and storage (CCS) and power-to-gas (P2G) technologies is proposed to improve the system’s low-carbon regulation capability and mitigate the impact of multi-energy coupling in MM-IES. This integration enhances the system’s low-carbon regulation capability. Secondly, to incentivize user participation in system optimization, a demand response mechanism and a tiered green certificate trading model are introduced. On this basis, an MM-IES low-carbon economic dispatch model is established with the goal of minimizing total operating costs, carbon trading costs, and green certificate trading costs. To further protect the privacy of each microgrid and achieve efficient coordination, distributed algorithms are used to solve the model. This method only requires exchanging boundary information to achieve collaborative optimization between microgrids. Finally, the simulation results indicate that the proposed strategy can effectively reduce system operating costs and carbon emissions. Furthermore, the effectiveness of demand response and green certificate trading in promoting low-carbon economic operation of multi microgrid systems is verified. Full article

In order to improve energy utilization efficiency and the flexibility of resource transfer in oceanic-island-group microgrids, a water–electricity–hydrogen flexible scheduling strategy based on a multi-rate hydrogen-powered ship is proposed. First, the characteristics of the seawater desalination unit (SDU), proton exchange membrane electrolyzer (PEMEL), and battery system (BS) in consuming surplus renewable energy on resource islands are analyzed. The variable-efficiency operation characteristics of the SDU and PEMEL are established, and the effect of battery life loss is also taken into account. Second, a spatio-temporal model for the multi-rate hydrogen-powered ship is proposed to incorporate speed adjustment into the system optimization framework for flexible resource transfer among islands. Finally, with the goal of minimizing the total cost of the system, a flexible water–electricity–hydrogen hybrid resource transfer model is constructed, and a certain island group in the South China Sea is used as an example for simulation and analysis. The results show that the proposed scheduling strategy can effectively reduce energy loss, promote renewable energy absorption, and improve the flexibility of resource transfer. Full article

Microgrids (MGs) technologies, with their advanced control techniques and real-time monitoring systems, provide users with attractive benefits including enhanced power quality, stability, sustainability, and environmentally friendly energy. As a result of continuous technological development, Internet of Things (IoT) architectures and technologies are becoming more and more important to the future smart grid’s creation, control, monitoring, and protection of microgrids. Since microgrids are made up of several components that can function in network distribution mode using AC, DC, and hybrid systems, an appropriate control strategy and monitoring system is necessary to ensure that the power from microgrids is delivered to sensitive loads and the main grid effectively. As a result, this article thoroughly assesses MGs’ control systems and groups them based on their degree of protection, energy conversion, integration, advantages, and disadvantages. The functions of IoT and monitoring systems for MGs’ data analytics, energy transactions, and security threats are also demonstrated in this article. This study also identifies several factors, challenges, and concerns about the long-term advancement of MGs’ control technology. This work can serve as a guide for all upcoming energy management and microgrid monitoring systems. Full article

In recent years, the growth of utilizing rural microgrids (RMGs) has been accompanied by various challenges. These necessitate the development of appropriate models for optimal generation in RMGs and RMGs’ coordination. In this paper, two distinct models for RMGs are presented. The first model includes an islanded rural microgrid (IRMG) and the second model consists of three RMGs that are interconnected with one another and linked to the distribution network. The proposed models take into account the uncertainty in load, photovoltaics (PVs), and wind turbines (WTs) with consideration of their correlation by using a scenario-based technique. Three objective functions are defined for optimization: minimizing operational costs including maintenance and fuel expenses, reducing voltage deviation to maintain power quality, and decreasing pollution emissions from fuel cells and microturbines. A new optimization method, namely the Improved Multi-Objective Crow Search Algorithm (IMOCSA), is proposed to solve the problem models. IMOCSA enhances the standard Crow Search Algorithm through three key improvements: an adaptive chaotic awareness probability to better balance exploration and exploitation, a mutation mechanism applied to the solution repository to prevent premature convergence, and a K-means clustering method to control repository size and increase algorithmic efficiency. Since the proposed problem is a multi-objective non-linear optimization problem with conflicting objectives, the idea of the Pareto front is used to find a group of optimal solutions. To assess the effectiveness and efficiency of the proposed models, they are implemented in two different case studies and the analysis and results are illustrated. Full article

The research focuses on incorporating microgrids into engineering curricula for achieving voltage stability in today’s power systems. This helps to meet the increasing demand for engineers to integrate distributed power generation and renewable energy sources. Some limitations of the current literature include the absence of models outlining approaches to microgrid education and limited insight into teaching strategies for electrical power systems. The research used a quantitative methodology to survey 100 engineering students enrolled in a microgrid modeling class to achieve the study’s objectives. The data analysis involved machine learning models such as Random Forest, Gradient Boosting, K-Means, hierarchical clustering, and regression models. The major findings identified exam score as the most significant determiner of student performance (weight ≈ 0.40). Based on the clustering analysis, it was found that microgrid systems can be grouped into four operational states. It was also seen that linear regression models were highly accurate and better than other highly complex models, like Decision Tree, with a model accuracy of R² ≈ 0.4. One of the study’s major strengths is the potential impact of the proposed framework for integrating microgrids into engineering education on the professional training of engineers. This framework, based on theoretical knowledge and practical experience as well as on developing advanced analytical skills, can significantly enhance the professional training of engineers to deal with the complexities of contemporary power systems, including microgrids and sustainable energy progress. Full article