Search Results (113)

Microgrid systems play a vital role in managing distributed energy resources like solar, wind, batteries, and supercapacitors. However, maintaining stable AC/DC bus voltages and minimizing grid reliance under dynamic conditions is challenging. Traditional control methods such as Sliding Mode Controllers (SMCs) suffer from issues like chattering and slow convergence, reducing practical effectiveness. This paper proposes a hybrid AC/DC microgrid that operates in both grid-connected and islanded modes while ensuring voltage stability and efficient energy use. A Conditional-Based Super-Twisting Sliding Mode Controller (CBSTSMC) is employed to address the limitations of conventional SMCs. The CBSTSMC enhances system performance by reducing chattering, improving convergence speed, and offering better tracking and disturbance rejection. To further refine controller performance, an Improved Grey Wolf Optimization (IGWO) algorithm is used for gain tuning, resulting in enhanced system robustness and precision. An Energy Management System (EMS) is integrated to intelligently regulate power flow based on renewable generation and storage availability. The proposed system is tested in real time using a Texas Instruments Delfino C2000 microcontroller through a Controller-in-the-Loop (CIL) setup. The simulation and hardware results confirm the system’s ability to maintain stability and reliability under diverse operating scenarios, proving its suitability for future smart grid applications. Full article

The integration of Internet of Things (IoT) technologies into islanded microgrids has increased their vulnerability to cyberattacks, particularly those targeting critical components such as power converters within an islanded AC microgrid. This study investigates the impact of False Data Injection (FDI) and Denial of Service (DoS) attacks on various power converters, including DC–DC boost converters, DC–AC converters, battery inverters, and DC–DC buck–boost converters, modeled in MATLAB/Simulink. A dataset of healthy and compromised operational parameters, including voltage and current, was generated under simulated attack conditions. To enhance system resilience, a deep learning-based detection and classification framework was proposed. After evaluating various deep learning models, including Deep Neural Networks (DNNs), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Long Short-Term Memory (LSTM), and Feedforward Neural Networks (FNNs), the final system integrates an FNN for rapid attack detection and an LSTM model for accurate classification. Real-time simulation validation demonstrated a detection accuracy of 95% and a classification accuracy of 92%, with minimal computational overhead and fast response times. These findings emphasize the importance of implementing intelligent and efficient cybersecurity measures to ensure the secure and reliable operation of islanded microgrids against evolving cyberattacks. Full article

Islanded DC microgrids face challenges in voltage stability and communication overhead due to renewable energy variability. A novel enhanced distributed coordinated control framework, based on adaptive event-triggered mechanisms, is developed for the efficient management of multiple hybrid energy storage systems (HESSs) in islanded DC microgrids (MGs). We propose a hierarchical distributed control framework integrating ANN-based controllers and adaptive event-triggered mechanisms to dynamically regulate power flow and minimise communication. This system utilises a hierarchical coordinated control method (HCCM) with primary virtual resistance droop control integrated with state-of-charge (SoC) management and secondary control for voltage regulation and proportional current distribution through optimised communication networks. The integration of artificial neural network (ANN)-based controllers alongside traditional PI control leads to an improvement in system responsiveness. The control approach dynamically adjusts the trigger parameters to minimise communication overhead with tight voltage regulation. An extensive simulation using MATLAB/Simulink shows how the system can effectively manage variability in renewable energy sources and maintain stable voltage profiles with precise power distribution and minimal bus voltage fluctuations. Simulations confirm enhanced voltage regulation (±0.5% deviation), proportional current sharing (98% accuracy), and 60% communication reduction under load transients (outcomes). Full article

The interlinking converter, an important device in a hybrid AC-DC microgrid, undertakes the task of power distribution between the AC sub-microgrid and DC sub-microgrid. To address the limitations of traditional bidirectional droop control in islanding mode, particularly the lack of consideration for regulation priority between AC frequency and DC voltage, this paper proposes an adaptive bidirectional droop control strategy. By introducing an adaptive weight coefficient based on normalized AC frequency and DC voltage, the strategy prioritizes regulating larger deviations in AC frequency or DC voltage. Interlinking converter action thresholds are set to avoid unnecessary frequent starts and stops. Finally, a hybrid AC-DC microgrid system in islanding mode is established in the Matlab/Simulink R2020a simulation platform to verify the effectiveness of the proposed control strategy. Full article

The emergence of highly efficient and cost-effective power converters, coupled with the growing diversity of DC loads, has elevated the importance of DC microgrids to a level comparable with AC microgrids in the modern power industry. DC microgrids are free from synchronization and reactive power dynamics, making them more reliable and cost-effective. In autonomous mode, achieving effective voltage regulation and satisfactory power sharing is critical to ensuring the overall stability of the microgrid. As the common DC bus of the microgrid connects distributed generators (DGs), storage devices, and loads through power electronic converters (PECs), the controllers of these PECs must regulate the bus voltage effectively, track reference signals to meet power demands, and enable satisfactory load sharing. In this work, a real time decentralized droop controller is implemented for an islanded DC microgrid to enhance the voltage regulation at the DC bus and current sharing efficacy between the sources subject to load transients. A novel control strategy is presented in which the conventional droop control is modified considering the load dynamics. The performance of the proposed control strategy is compared with the conventional voltage droop control strategy. The fluctuations in the DC bus voltage, which is the major cause of voltage instability of the DC microgrid is effectively reduced by the proposed strategy. The proposed strategy is validated by comparing it with the conventional fixed droop control method on the MATLAB Simulink platform. The variable droop control strategy outperforms the fixed droop method by addressing sudden voltage fluctuations in the DC bus, which occur due to the inherent load current dependency of the fixed droop approach. This technique achieves enhanced voltage regulation, which is crucial for microgrid stability. Full article

Nowadays, the rise of DC loads along with distributed energy resources (DERs) and energy storage systems (ESSs) have led to a growing interest in using direct current (DC) microgrid systems. Conventional droop control methods face significant limitations when applied to parallel-connected distributed generation (DG) units, particularly in achieving balanced power sharing and minimizing voltage deviations. To overcome this issue, an enhanced power sharing control method is proposed in this paper to address load sharing in parallel-connected DG units based DC microgrids, considering unmatched line impedance and load variation. The enhanced control method aims to achieve balanced load power sharing and voltage control through the use of a Luenberger observer to estimate the Point of Common Coupling (PCC) bus voltage and accordingly estimate the voltage deviation. The proposed method compensates for the effects of unmatched line impedances and dynamic load variations, enabling accurate power sharing and precise DC bus voltage regulation. Various scenarios are studied to evaluate the performance of the proposed method under different operating conditions including system and load parameters variations. Finally, the performance of the proposed control method was validated through real-time simulation using OPAL-RT target, and compared with conventional droop control approaches. Full article

The optimal operation of a hybrid AC-DC microgrid is investigated in this study. The operation of an AC microgrid connected to the main grid and an islanded DC microgrid has been examined under three management approaches. In the first approach, two microgrids are not connected, and the DC microgrid is operated in the islanded mode. In the second and third approaches, AC and DC microgrids are connected. The main difference between these two approaches is the energy management framework. In the second approach, each microgrid has its own management system, while the third approach integrates both into a single energy management system to form an AC-DC microgrid that minimizes overall operational costs. The main goal of the proposed model is to minimize the operating costs of two microgrids over a 24 h period. The investigated AC microgrid includes a microturbine, wind turbine and diesel generator in order to supply the residential load profile, and the DC microgrid includes an energy storage system, fuel cell, wind turbine and solar panel in order to supply the commercial load profile. Simulations are performed first with a wind and load scenario in order to show and compare the optimal points of using the decision variables in three approaches. Finally, in order to prove the effectiveness of the proposed method in the presence of uncertainties, the cost distribution function for the three approaches is presented by means of Monte Carlo simulation. Applying the proposed model results in the following the cost reduction: 67.9% in the DC microgrid, 14.2% in the AC microgrid and 24.4% overall. This reduction is primarily attributed to the microgrid central energy management system, which decreases reliance on the main grid and instead utilizes alternative sources such as fuel cells. Comparing the first and third approaches, the fuel cell’s contribution to supplying microgrid loads increased by 29%, while the main grid’s participation decreased by 26%. Full article

To address the stability of the power supply to agricultural facilities and greenhouses in remote areas, this paper proposes a solution based on the bus voltage fluctuation issue in an islanded photovoltaic-storage DC microgrid. Traditional power supply methods often struggle to meet demand due to significant fluctuations in solar irradiance and load. To resolve this, an improved sliding-mode linear active disturbance rejection control (ISMLADRC) strategy is designed, significantly enhancing the response speed of the microgrid control system while improving its adaptability in complex agricultural environments. The system integrates a hybrid energy storage system and photovoltaic power generation to optimize microgrid power compensation, ensuring the stability of the power supply to agricultural facilities and greenhouses. Simulation results demonstrate that the proposed control scheme enhances the robustness and efficiency of the original system, ensuring a reliable power supply for crop production in remote areas, advancing smart agriculture, and promoting the sustainable development of green agriculture. Full article

Microgrids are considered a practical solution to revolutionize power systems due to their ability to island and sustain the penetration of renewables. Most existing studies have focused on the optimal management of microgrids with a fixed configuration. This restricts the application of developed algorithms to other configurations without major modifications. The objective of this study is to design a rule-based modular energy management system (EMS) for microgrids that can dynamically adapt to the microgrid configuration. To realize this framework, first, each component is modeled as a separate entity with its constraints and bounds for variables. A wide range of components such as battery energy storage systems (BESSs), electric vehicles (EVs), solar photovoltaic (PV), microturbines (MTs), and different priority loads are modeled as modules. Then, a rule-based system is designed to analyze the impact of the presence/absence of one module on the others and update constraints. For example, load shedding and PV curtailment can be permitted if the grid module is not included. The constraints of microgrid components present in any given configuration are communicated to the EMS, and it optimizes the operation of the available components. The configuration of microgrids could be as simple as flexible loads operating in grid-connected mode or as complex as a hybrid microgrid with AC and DC buses with a diverse range of equipment on each side. To facilitate the realization of diverse configurations, a hybrid AC/DC microgrid is considered where the utility grid and interlinking converter (ILC) are also modeled as separate modules. The proposed method is used to test performance in both grid-connected and islanded modes by simulating four typical configurations in each case. Simulation results have shown that the proposed rule-based modular method can optimize the operation of a wide range of microgrid configurations. Full article

Research in renewable energy sources and microgrid systems is critical for the evolving power industry. This paper examines the operational behavior of both pico- and nano-grids during transitions between grid-connected and islanded modes. Simulation results demonstrate that both grids effectively balance the power flow, regulate the state of charge (SOC), and stabilize the voltage during dynamic operational changes. Specific scenarios, including grid disconnection, load sharing, and weather-based energy fluctuations, were tested and validated. This paper models both pico-grids and nano-grids at the Singapore Institute of Technology Punggol Campus, incorporating solar PVs, energy storage systems (ESSs), power electronic converters, and both DC and AC loads, along with utility grid connections. The pico-grid includes a battery storage system, a single-phase inverter linked to a single-phase grid, and DC and AC loads. The nano-grid comprises solar PV panels, a boost converter, a battery storage system, a three-phase inverter connected to a three-phase grid, and AC loads. Both the pico-grid and nano-grid are configurable in standalone or grid-connected modes. This configuration flexibility allows for a detailed operational analysis under various conditions. This study conducted subsystem-level modelling before integrating all components into a simulation environment. MATLAB/Simulink version R2024b was utilized to model, simulate, and analyze the power flow in both the pico-grid and nano-grid under different operating conditions. Full article

A microgrid (MG) is a unique area of a power distribution network that combines distributed generators (conventional as well as renewable power sources) and energy storage systems. Due to the integration of renewable generation sources, microgrids have become more unpredictable. MGs can operate in two different modes, namely, grid-connected and islanded modes. MGs face various challenges of voltage variations, frequency deviations, harmonics, unbalances, etc., due to the uncertain behavior of renewable sources. To study the impact of these issues, it is necessary to analyze the behavior of the MG system under normal and abnormal operating conditions. Two different tools are used for the analysis of microgrids under normal and abnormal conditions, namely, power flow and short-circuit analysis, respectively. Power flow analysis is used to determine the voltages, currents, and real and reactive power flow in the MG system under normal operating conditions. Short-circuit analysis is carried out to analyze the behavior of MGs under faulty conditions. In this paper, a review of power flow and short-circuit analysis algorithms for MG systems under two different modes of operation, grid-connected and islanded, is presented. This paper also presents a comparison of various power flow as well as short-circuit analysis techniques for MGs in tabular form. The modeling of different components of MGs is also discussed in this paper. Full article

This paper presents the design and validation of a novel adaptive islanding detection method (AIDM) for a hybrid AC/DC microgrid network using a combination of Artificial Intelligence (AI) and Signal Processing (SP) approaches. The proposed AIDM is aimed to detect and discriminate between the different fault/disturbance conditions that result in islanding and/or non-islanding conditions in a hybrid microgrid. For the islanding and non-islanding conditions detection by the AIDM, firstly, fault/disturbance signals are obtained from a test microgrid. Secondly, these signals are decomposed using Dual-Tree Complex Wavelet Transform. Thirdly, a Synthetic Minority Oversampling Technique (SMOTE) is applied for data preprocessing to increase the accuracy of the classifier. Finally, an artificial neural network (ANN) is used as the classifier for training and testing the proposed AIDM for different microgrid configurations and event scenarios. The proposed method is tested with different data categories from three different microgrid test systems with different scenarios. All modeling and simulations are executed in MATLAB Simulink Version 2023a. Results indicate that the proposed scheme could detect and discriminate between islanding and non-islanding conditions accurately in terms of dependability, precision, and accuracy. An average accuracy of 99–100% could be achieved when tested and validated with microgrid networks adapted from IEEE 13-bus systems. Full article

This paper presents a novel approach to manage distributed DC microgrids (DCMG) by integrating a time-of-use (ToU) electricity pricing scheme and an internal price rate calculation mechanism. The proposed power-management system is designed to effectively handle uncertainties such as utility grid (UG) availability, fluctuating electricity prices, battery state of charge (SOC) levels, and frequent plug-ins and plug-outs of electric vehicles (EVs). Uncertainties in DCMG systems often lead to inefficiencies, power imbalances, and inexact voltage regulation issues within DCMGs. In addition, to maintain the power balance and constant voltage regulation under various operational states, the proposed scheme also incorporates secondary control into the DCMG power-management system. Unlike the existing approaches that often fail to adapt dynamically to changing conditions, the proposed method is the first approach to consider the concept of internal price rate in designing the DCMG power management. To address this challenge, this approach proposes a more resilient power-management strategy to enhance the efficiency and adaptability of DCMG systems. Extensive simulations and experimental validations demonstrate the practicality and adaptability of the proposed control strategy under diverse test conditions, including operation transitions between grid-connected mode (GCM) and islanded mode (IM), low battery SOC condition, operation transition from the current control mode (CCM) to distributed secondary control mode (DSCM), and EV plug-in scenarios. The test results confirm that the proposed method enhances the reliability, efficiency, and economic viability of DCMG systems, making it a promising solution for future smart grid and renewable energy integrations. Full article

This research study presents a resilient control scheme for an islanded DC microgrid (DC MG) integrating solar photovoltaic (PV), battery storage (BESS), and piezoelectric (PE) energy harvesting modules. The microgrid (MG) case study represents an energy hub designed to provide electricity for lighting systems in transportation, roads, and other infrastructure. To enhance practicality, the PE is modeled using the real data captured from a traffic simulator. The proposed reinforcement learning (RL) method was tested against four severe and unexpected failure scenarios, including short circuit at the load side, sudden and severe change of load, open circuit, and converter failure. The performance of the controller was quantitatively compared with a conventional PI controller. The results show marginal improvement in one scenario and significant improvement in the other three, suggesting that the proposed scheme is a robust candidate for microgrids with high levels of uncertainty, such as those involving solar and PE harvesters. Full article

Islanded DC microgrids are poised to become a crucial component in the advancement of smart energy systems. They achieve this by effectively and seamlessly integrating multiple renewable energy resources to meet specific load requirements through droop control, which ensures fair distribution of load current across the distributed energy resources (DERs). Employing droop control usually results in a DC bus voltage drop. This article introduces a secondary distributed control approach aimed at concurrently achieving current distribution among the DERs and regulating the voltage of the DC bus. The proposed secondary control approach eradicates voltage fluctuations and guarantees equitable current allocation by integrating voltage and current errors within the designed control loop. A novel hybrid particle swarm optimization–grey wolf optimization (HPSO-GWO) has been proposed, which assists in selecting the parameters of the distributed control technique, enabling the achievement of the proposed control objectives. Eigenvalue observation analysis has been utilized through the DC microgrid state-space model designed to assess the influence of the optimized distributed secondary control on the microgrid stability. A real-time testing system was constructed within MATLAB/Simulink^® and deployed on Speedgoat™ real-time equipment to validate the operations of the proposed technique for practical applications. The results indicated that the proposed secondary control effectively enhances voltage recovery and ensures proper current distribution following various disturbances, thereby maintaining a continuous power supply. The outcomes also demonstrated the capabilities of the control approach in accomplishing the control objectives within the DC microgrid, characterized by minimal oscillations, overshoots/undershoots, and rapid time responses. Full article