Search Results (113)

In recent years, DC grids have gained traction, and several proposals regarding measuring strategies and several Power Quality (PQ) parameters have been defined to be used in such networks that differ from traditional AC power grids. As a complement to all this preliminary work, this study on the effect of modifying the sampling frequency on some of those parameters has been conducted. For time series evaluation of mean and RMS voltage values, the Dynamic Time Warping (DTW) algorithm has been used. Additionally, the consequence of varying the sampling rate in voltage event detection has also been analysed. As a result, relevant advice regarding sampling frequency is presented in this paper for an effective and optimum evaluation of RMS or mean voltage values and its implementation in detecting voltage events (dips or swells). At least for the parameters in the monitored DC microgrid, a clue for the minimum sampling rate that guarantees accurate measurements is found. Full article

This paper presents a decentralized energy management system (EMS) based on Multilayer Perceptron Artificial Neural Networks (MLP-ANNs) and a Modified Cheetah Optimizer (MCO) to account for uncertainty in renewable generation and load demand. The proposed framework applies an MLP-ANN with Levenberg–Marquardt (LM) training for high-precision forecasts of photovoltaic/wind generation, ambient temperature, and load demand, greatly outperforming traditional statistical methods (e.g., time-series analysis) and resilient backpropagation (RP) in precision. The new MCO algorithm eliminates local trapping and premature convergence issues in classical optimization methods like Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). Simulations on a test microgrid verily demonstrate the advantages of the framework, achieving a 26.8% cost-of-operation reduction against rule-based EMSs and classical PSO/GA, and a 15% improvement in forecast accuracy using an LM-trained MLP-ANN. Moreover, demand response programs embodied in the system reduce peak loads by 7.5% further enhancing grid stability. The MLP-ANN forecasting–MCO optimization duet is an effective and cost-competitive decentralized microgrid management solution under uncertainty. Full article

The proliferation of power electronics in renewable-integrated grids exacerbates the challenges of nonlinearity and multivariable coupling. While the modal series method (MSM) offers theoretical foundations, it fails to provide tools to systematically quantify dynamic interactions in these complex systems. This study proposes a unified nonlinear modal analysis framework integrating second-order analytical solutions with novel nonlinear indices. Validated across diverse systems (DC microgrids and grid-connected PV), the framework yields significant findings: (1) second-order solutions outperform linearization in capturing critical oscillation/damping distortions under realistic disturbances, essential for fault analysis; (2) nonlinear effects induce modal dominance inversion and generate governing composite modes; (3) key interaction mechanisms are quantified, revealing distinct voltage regulation pathways in DC microgrids and multi-path dynamics driving DC voltage fluctuations. This approach provides a systematic foundation for dynamic characteristic assessment and directly informs control design for power electronics-dominated grids. Full article

To enhance the accuracy of short-term wind power forecasting, this study proposes a hybrid model combining Northern Goshawk Optimization (NGO)-optimized Variational Mode Decomposition (VMD) and an Improved Snow Ablation Optimizer (ISAO)-optimized Long Short-Term Memory (LSTM) network. Initially, NGO is applied to determine the optimal parameters for VMD, decomposing the original wind power series into multiple frequency-based subsequences. Subsequently, ISAO is employed to fine-tune the hyperparameters of the LSTM, resulting in an ISAO-LSTM prediction model. The final forecast is obtained by reconstructing the subsequences through superposition. Experiments conducted on real data from a wind farm in Ningxia, China demonstrate that the proposed approach significantly outperforms traditional single and combined models, yielding predictions that closely align with actual measurements. This validates the method’s effectiveness for short-term wind power prediction and offers valuable data support for optimizing microgrid scheduling and capacity planning in wind-integrated energy systems. Full article

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however, uncertainty remains about optimal grid architectures to minimize cost, including how and when to incorporate long-duration energy storage. This study implements a novel, multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue, which has already successfully deployed solar photovoltaics, wind turbines, and battery storage systems. Using real community load, resource, and generation data, we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation, considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs, but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase, but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue, we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability, sustainability, and security. Full article

This study proposes a forecast-aided energy management strategy tailored for industrial microgrids operating in Vietnam’s tropical climate. The core novelty lies in the implementation of a converter-based EMS that enables bidirectional DC power exchange between multiple subsystems. To improve forecast accuracy, an artificial neural network (ANN) is used to model the relationship between electric load and localized meteorological features, including temperature, dew point, humidity, and wind speed. The forecasted load data is then used to optimize charge/discharge schedules for energy storage systems (ESS) using a Particle Swarm Optimization (PSO) algorithm. The strategy is validated using real-site data from a Vietnamese industrial complex, where the proposed method demonstrates enhanced load prediction accuracy, cost-effective ESS operation, and multi-microgrid flexibility under weather variability. This integrated forecasting and control approach offers a scalable and climate-adaptive solution for EMS in emerging industrial regions. Full article

The adoption of wind energy is pivotal for advancing sustainable power systems, particularly in off-grid microgrids where infrastructure limitations hinder conventional energy solutions. The inherent variability of wind generation, however, challenges grid reliability and demand–supply balance, necessitating accurate forecasting models. This study proposes a hybrid framework for short-term wind speed prediction, integrating deep learning (Long Short-Term Memory, LSTM) and ensemble methods (random forest, Extra Trees) to exploit their complementary strengths in modeling temporal dependencies. A multivariate approach is adopted using meteorological data (including wind speed, temperature, humidity, and pressure) to capture complex weather interactions through a structured time-series design. The framework also includes a feature selection stage to identify the most relevant predictors and a hyperparameter optimization process to improve model generalization. Three wind speed variables, maximum, average, and minimum, are forecasted independently to reflect intra-day variability and enhance practical usability. Validated with real-world data from Cuenca, Ecuador, the LSTM model achieves superior accuracy across all targets, demonstrating robust performance for real-world deployment. Comparative results highlight its advantage over tree-based ensemble techniques, offering actionable strategies to optimize wind energy integration, enhance grid stability, and streamline renewable resource management. These insights support the development of resilient energy systems in regions reliant on sustainable microgrid solutions. Full article

This paper proposes a supercapacitor-battery hybrid energy storage scheme based on a series-parallel hybrid compensation structure and model predictive control to address the increasingly severe power quality issues in oilfield microgrids. By adopting the series-parallel hybrid structure, the voltage compensation depth can be properly improved. The model predictive control with a current inner loop is employed for current tracking, which enhances the response speed and control performance. Applying the proposed hybrid energy storage system in an oilfield DC microgrid, the fault-ride-through ability of renewable energy generators and the reliable power supply ability for oil pumping unit loads can be improved, the dynamic response characteristics of the system can be enhanced, and the service life of energy storage devices can be extended. This paper elaborates on the series-parallel compensation topology, operational principles, and control methodology of the supercapacitor-battery hybrid energy storage. A MATLAB/Simulink model of the oilfield DC microgrid employing the proposed scheme was established for verification. The results demonstrate that the proposed scheme can effectively isolate voltage sags/swells caused by upstream grid faults, maintaining DC bus voltage fluctuations within ±5%. It achieves peak shaving of oil pumping unit load demand, recovery of reverse power generation, stabilization of photovoltaic output, and reduction of power backflow. This study presents an advanced technical solution for enhancing power supply quality in high-penetration renewable energy microgrids with numerous sensitive and critical loads. Full article

In intelligent cluster systems, the spatio-temporal complexity of agent data collection and resource allocation, as well as the problems in collaborative organizations, present substantial challenges to efficient resource distribution. To address this, a novel self-organizing prediction method for spatio-temporal resource allocation is proposed. In the spatio-temporal modeling part, dilated convolution is applied for time modeling. Its dilation rate grows exponentially with the layer depth, allowing it to effectively capture the time trends of graph nodes and handle long time series data. For spatial modeling, an innovative dual-view dynamic graph convolutional network architecture is utilized to accurately explore the static and dynamic correlation information of the spatial layout of charging piles. Meanwhile, a composite self-organizing mechanism integrating a trust model is put forward. The trust model assists agents in choosing partners, and the Q-learning algorithm of the intelligent cluster realizes the independent evaluation of rewards and the optimization of relationship adaptation. In the experimental scenario of electric vehicle charging, considering charging piles as agents, under the home charging mode, the self-organizing charging scheduling can reduce the total load range by up to 90.37%. It effectively shifts the load demand from peak periods to valley periods, minimizes the total peak–valley load difference, and significantly improves the security and reliability of the microgrid, thus providing a practical solution for resource allocation in intelligent clusters. Full article

In order to improve the ability to suppress unbalanced voltage in bipolar DC microgrids, a comprehensive power regulation control of a novel shared energy storage system is proposed for a multi-scenario bipolar DC microgrid. The novel shared energy storage system is composed of an electric spring (ES) with a full-bridge DC/DC converter and non-critical load (NCL) in series, considering demand-side response. The proposed comprehensive power regulation control can enable the bipolar DC microgrid to deal with various scenarios. When operating in stand-alone mode, the unbalanced voltage caused by greater unbalanced power can still be suppressed under the proposed control of the shared energy storage. In case of distributed energy storage (DES) failure on the source side, the shared energy storage can realize DC voltage regulation and maintain system operation by reducing NCL power. In grid-connected operation, the shared energy storage can actively cooperate with the power dispatching of the utility grid for storage reduction of DES on the source side. Thus, the reliability and resilience of the bipolar microgrid have been improved. Finally, to verify the effectiveness of the proposed control strategy, hardware-in-the-loop experimental results are presented in this paper. Full article

Numerous cascaded inverter configurations have been developed to generate higher voltage levels, thereby improving performance and lowering costs. Comparing conventional delta-connected cascaded H-bridge (CHB) multilevel inverters to star-connected CHB multilevel inverters reveals a disadvantage. In conventional delta-connected CHB multilevel inverters, more switches are unavoidably needed to achieve the same line-to-line grid voltage, since more H-bridges cascaded in series are required than in a star-connected CHB. This paper presents a modified topology based on the delta-connected CHB multilevel configuration to provide the same number of line-to-line voltage levels as a star-connected CHB, using an equivalent number of switches. The number of switches in the proposed multilevel inverter is decreased compared to conventional delta-connected CHB MLIs at the same voltage levels. The mathematical modeling of the proposed topology and the simulation results using a fixed load and a PV-grid connection are provided to validate the efficacy and dependability of the proposed topology. To validate the usefulness of the proposed configuration, it was practically implemented in the laboratory. Data acquisition and generation of gating signals to fire the switches were implemented using a MicroLabBox real-time controller. The prototype was examined under a resistive–inductive load and tested under different modulation indices. To demonstrate the effectiveness and the functionality of the topology, the experimental results are also provided. Full article

Solar photovoltaic (PV) and wind power generation are key contributors to the integration of renewable energy into modern power systems. The intermittent and variable nature of these renewables has a substantial impact on the power system’s reliability. In time-series simulation studies, inaccuracies in solar irradiation and wind speed parameters can lead to unreliable evaluations of system reliability, ultimately resulting in flawed decision making regarding the investment and operation of energy systems. This paper investigates the reliability deviation due to modeling uncertainties in a 100% renewable-based system. This study employs two methods to assess and contrast the reliability of a standalone microgrid (SMG) system in order to achieve this goal: (i) random uncertainty within a selected confidence interval and (ii) splitting the cumulative distribution function (CDF) into five regions of equal probability. In this study, an SMG system is modeled, and loss of load probability (LOLP) is evaluated in both approaches. Six different sensitivity analysis studies, including annual load demand growth, are performed. The results from the simulations demonstrate that the suggested methods can estimate the reliability of a microgrid powered by renewable energy sources, as well as its probability of reaching certain levels of reliability. Full article

This paper introduces an alternative forecasting approach that leverages the application of visibility graphs in the context of multivariate energy forecasting for a regional airport, which incorporates energy demand of diverse types of buildings and wind power generation. The motivation for this research stems from the urgent need to enhance the accuracy and reliability of load forecasting in microgrids, which is crucial for optimizing energy management, integrating renewable sources, and reducing operational costs, thereby contributing to more sustainable and efficient energy systems. The proposed methodology employs visibility graph transformations, the superposed random walk method, and temporal decay adjustments, where more recent observations are weighted more significantly to predict the next time step in the data set. The results indicate that the proposed method exhibits satisfactory performance relative to comparison models such as Exponential smoothing, ARIMA, Light Gradient Boosting Machine and CNN-LSTM. The proposed method shows improved performance in forecasting energy consumption for both stationary and highly variable time series, with SMAPE and NMRSE values typically in the range of 4–10% and 5–20%, respectively, and an $R^2$ reaching 0.96. The proposed method affords notable benefits to the forecasting of energy demand, offering a versatile tool for various kinds of structures and types of energy production in a microgrid. This study lays the groundwork for further research and real-world applications within this field by enhancing both the theoretical and practical aspects of time series forecasting, including load forecasting. Full article

This paper presents a model predictive control (MPC)-based energy management system (EMS) for optimizing cooperative operation of networked microgrids (MGs). While the isolated operation of individual MGs limits system-wide optimization, the proposed approach enhances both stability and efficiency through integrated control. The system employs mixed-integer quadratic constrained programming (MIQCP) to model complex operational characteristics of MGs, facilitating the optimization of interactions among distributed energy resources (DERs) and power exchange within the MG network. The effectiveness of the proposed method was validated through a series of case studies. First, the performance of the algorithm was evaluated under various weather conditions. Second, its robustness against prediction errors was tested by comparing scenarios with and without disturbance prediction. Finally, the cooperative operation of MGs was compared with the independent operation of a single MG to analyze the impact of the cooperative approach on performance improvement. Quantitatively, integrating predictions reduced operating costs by 19.23% compared to the case without predictions, while increasing costs by approximately 3.7% compared to perfect predictions. Additionally, cooperative MG operation resulted in an average 46.18% reduction in external resource usage compared to independent operation. These results were verified through simulations conducted on a modified version of the IEEE 33-bus test feeder. Full article

Half-bridge converter series microgrid (HBCS-MG) is susceptible to a variety of uncertainties and disturbances during operation, and therefore, the use of the traditional integer-order models cannot accurately reflect the effects of environmental variations on internal components of the off-grid system, such as converters, filters, and loads, including factors like time delays, memory effects, and multi-scale coupling. The fractional-order control method is better equipped to deal with these disturbances, thereby enhancing the robustness and stability of the system. In the off-grid state, a fractional-order PI (FOPI) controller is employed for double-closed-loop control, and the load voltage feedforward control is utilized to offset the impact of load voltage fluctuations on the system. A new simplified equivalent circuit calculation method for the fractional-order inductor is proposed, and a complete fractional mathematical model of the system in the dq rotating coordinate system is established to obtain the transfer function between the load voltage and the input voltage. Furthermore, the impact of the fractional-order variation of the FOPI controllers and the fractional elements on system performance in the frequency domain and time domain is described in detail. The simulation results are compared with the theoretical analysis to demonstrate the accuracy of the mathematical model. The overshoot of the load voltage at the switching instant of 0.7 s is reduced by 4.2% compared with the integer-order PI controller, which proves that the fractional-order controller can improve the system control accuracy. Full article