Showing 27 results for Microgrid
Fatemeh Zare-Mirakabad, Mohammad Hosein Kazemi, Aref Doroudi,
Volume 19, Issue 3 (9-2023)
Abstract
This paper proposes a robust H ∞ -LMI-based primary controller using the Linear Parameter Varying (LPV) modeling for an AC islanded Micro-Grid (IMG). The proposed controller can regulate the frequency and voltage of the IMG under various scenarios, such as load changes, faults, and reconfigurations. Unlike most previous studies that neglected the nonlinearity and uncertainty of the system, this paper represents the system dynamics as a polytopic LPV model in the novel primary control structure. The proposed method computes a state-feedback control by solving the corresponding Linear Matrix Inequalities (LMIs) based on H ∞ performance and stability criteria. The robust primary control is applied to a test IMG in the SIM-POWER environment of MATLAB and evaluated under different scenarios. The simulation results demonstrate the effectiveness and efficiency of the proposed method in maintaining the stability of the frequency and voltage of the IMG.
Nasreddine Attou, Sid-Ahmed Zidi, Samir Hadjeri, Mohamed Khatir,
Volume 19, Issue 3 (9-2023)
Abstract
Demand-side management has become a viable solution to meet the needs of the power system and consumers in the past decades due to the problems of power imbalance and peak demand on the grid. This study focused on an improved decision tree-based algorithm to cover off-peak hours and reduce or shift peak load in a grid-connected microgrid using a battery energy storage system (BESS), and a demand response scheme. The main objective is to provide an efficient and optimal management strategy to mitigate peak demand, reduce the electricity price, and replace expensive reserve generation units. The developed algorithm is evaluated with two scenarios to see the behavior of the management system throughout the day, taking into account the different types of days (weekends and working days), the random profile of the users' demand, and the variation of the energy price (EP) on the grid. The simulation results allowed us to reduce the daily consumption by about 30% to 40% and to fill up to 12% to 15% of the off-peak hours with maximum use of renewable energies, demonstrating the control system's performance in smoothing the load curve.
S. Prasad Tiwari,
Volume 19, Issue 3 (9-2023)
Abstract
In spite of the numerous benefits over the traditional power distribution system, protection of the microgrid is a challenging and complex task. The varying fault resistances due to dissimilar grounding conditions can affect the performance of the protection scheme. Under such conditions, the magnitude of the fault current can vary from lower to higher level. In addition to the above, the dissimilar magnitude of fault current during grid connected and islanded mode demands a protection scheme that can easily discriminate the mode of operation. The magnitude of fault current in grid-connected and islanded modes needs a robust protection scheme. In this regard, an ensemble of subspace kNN based robust protection scheme has been proposed to detect the faulty conditions of the microgrid. The tasks of the mode detection, fault detection/classification as well as faulty line identification has been carried out in the proposed work. In the proposed protection scheme, discrete wavelet transform (DWT) has been used for processing of the data. After recording the voltage and current signals at bus-1, the protection scheme has been validated. The validation of the protection scheme in Section 6 reveals that the protection scheme is efficiently working.
Jayati Vaish, Anil Kumar Tiwari, Seethalekshmi K.,
Volume 19, Issue 4 (12-2023)
Abstract
In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.
Shankarshan Prasad Tiwari,
Volume 20, Issue 1 (3-2024)
Abstract
In recent years, due to the widespread applications of DC power-based appliances, the researchers attention to the adoption of DC microgrids are continuously increasing. Nevertheless, protection of the DC microgrid is still a major challenge due to a number of protection issues, such as pole-to-ground and pole-to-pole faults, absence of a zero crossing signal, magnitude of the fault current during grid-connected and islanded mode, bidirectional behaviour of converters, and failure of the converters due to enormous electrical stress in the converter switches which are integrated in the microgrid. Failure of the converter switches can interrupt the charging of the electrical vehicles in the charging stations which can affect transportation facilities. In addition to the above mentioned issues protection of the DC microgrid is more challenging when fault parameters are varying due to dissimilar grounding conditions and varying operational dynamics of the renewable sources of energy. Motivated by the above challenges a support vector machine and ensemble of k-nearest neighbor based protection scheme has been proposed in this paper to accurately detect and classify faults under both of the modes of operation. Results in the section 5 indicate that performance of the protection scheme is greater as compared to other algorithms.
Nurul Husna Abd Wahab, Mohd Hafizuddin Mat, Norezmi Md Jamal, Nur Hidayah Ramli,
Volume 21, Issue 2 (6-2025)
Abstract
In islanded microgrids, circulating currents among parallel inverters pose significant challenges to system stability and efficient power distribution. Traditional droop control methods often struggle to manage these currents effectively, leading to inefficiencies and potential system damage. This study introduces an advanced fuzzy-robust droop control strategy that integrates fuzzy logic with robust droop control to address these challenges. By incorporating fuzzy logic, the proposed strategy enhances the adaptability of droop control to varying system conditions, improving the management of circulating currents and ensuring more accurate power sharing among inverters. Comprehensive mathematical modeling and extensive simulation analyses validate the performance of this control strategy. The results show that the fuzzy-robust droop control method significantly outperforms conventional approaches, achieving up to a 70% reduction in circulating currents. This improvement leads to a substantial reduction in power losses and enhances the dynamic response under varying load conditions. Additionally, the strategy improves voltage and frequency regulation, contributing to the overall stability and reliability of the microgrid. The findings provide a robust solution to the longstanding issue of circulating currents, optimizing microgrid operations, and paving the way for more efficient and resilient distributed energy systems. The advanced control strategy presented in this study not only addresses critical challenges but also demonstrates the potential for innovative methodologies to meet the growing demands of future energy infrastructures, where reliability and efficiency are essential.
Mahdi Arabsadegh, Aref Doroudi,
Volume 21, Issue 4 (11-2025)
Abstract
This paper presents an advanced methodology for post-storm power system restoration. A real-time Condition Index (CI)-based classification scheme is introduced to categorize circuit breakers into high-reliability (Type A) and moderate-reliability (Type B) groups. Leveraging this classification, a genetic algorithm (GA) optimizes microgrid configurations to maximize power restoration probabilities by explicitly modeling the stochastic failure risks associated with circuit breakers under severe weather conditions. The approach was validated on the IEEE 118-bus system with five critical breakers deactivated due to storm conditions. The GA achieved a 92.5% load restoration after 200 iterations, surpassing a baseline Monte Carlo simulation that attained 85.2%. Computational efficiency was significantly improved, reducing execution time to approximately 15 minutes compared to 60 minutes for traditional methods, with enhanced accuracy indicated by a 1.8% error margin versus 7.5%. Key contributions include utilizing live CI data for dynamic breaker classification, which resulted in a 20% reduction in computational time, and demonstrating scalability and effectiveness on large-scale test systems such as the 118-bus network. The methodology's performance decreases to 78.3% load restoration when more than 14 breakers are compromised. Future research will focus on integrating detailed storm modeling—including wind speed profiles—and incorporating renewable energy resources to enhance grid resilience.
