GM Shafiullah

Battery energy storage system (BESS) is a crucial technology for managing various uncertainties and key challenges particularly, peak shaving, inherent in regional distribution networks (RDNs). However, an improperly sized BESS can lead to unreasonable installation, operation, and maintenance costs. Considering that these costs may exceed the operational benefits of the battery, this work establishes an analytical approach for the optimal sizing of BESS aimed at cost-effective peak-shaving applications, especially in an Australian RDN. The procedure utilizes the RDN load profiles, characterized by a determined time resolution, while accounting for various billing rates and electricity costs. Utilizing real load and cost data, this approach systematically determines the optimal battery capacity from various BESS configurations, enhancing the overall efficiency and performance of the BESS. The proposed analytical method is evaluated using a rule-based technique, ensuring practical applicability and reliability. The results, tested on a real Australian RDN, demonstrate that the approach can significantly determine the most economically suitable BESS configuration, reduce system operational costs, and achieve effective peak shaving during high-demand periods. Additionally, to evaluate the feasibility of the technique, load profiles and associated cost factors also have been collected from a Malaysian RDN, tested in the case study.

The increasing adoption of electric vehicles (EVs) has led to significant challenges in the management of renewable-powered grid-connected electric vehicle charging stations (EVCS), particularly in maintaining grid stability. This paper introduces a novel Intelligent Model-Following Controller (IMFC) for EVCS integrated with a hybrid microgrid consisting of nuclear, photovoltaic (PV), and biogas power sources. The proposed IMFC aims to improve voltage and frequency stability, as well as overall energy management, compared to traditional controllers such as the PQ Droop Controller (PQDC). A comprehensive simulation study is conducted to evaluate the performance of both controllers under various dynamic conditions. A comparative analysis is conducted between IMFC and a PQDC to assess their performance in real-world scenarios to control the power system responses (active power, reactive power, voltage and frequency) of the hybrid system. Two consecutive three-phase faults have been implemented within the system and the transient response have been analyzed for both the controllers. The results show that the IMFC achieves a renewable fraction of 89.1%, with a cost of energy of $0.0132/kWh, and an internal rate of return (IRR) of 73%, demonstrating its economic feasibility and environmental benefits. The IMFC outperforms the PQDC in terms of transient response and system resilience, reducing the transient recovery time to 1.5 s, compared to 2.2 s for PQDC. Additionally, the IMFC provides better frequency regulation with a peak deviation of ±0.04 p.u., as opposed to ±0.1 p.u. for PQDC. These findings highlight the superiority of the IMFC in ensuring stable, efficient, and sustainable operation of hybrid renewable-powered EVCS.

Hydrogen-based Power Systems (H2PSs) are gaining accelerating momentum globally to reduce energy costs and dependency on fossil fuels. A H2PS typically comprises three main parts: hydrogen production, storage, and power generation, called packages. A review of the literature and Original Equipment Manufacturers (OEM) datasheets reveals that no single manufacturer supplies all H2PS components, posing significant challenges in system design, parts integration, and safety assurance. Additionally, both the literature and H2PS projects’ database highlight a gap in a systematic hydrogen equipment and auxiliary sub-systems technology selection process, and how this selection affects the overall H2PS Balance of Plant (BoP). This study addresses that gap by providing a guideline for available technology options and their impact on the H2PS-BoP. The analysis compares packages and auxiliary sub-system technologies to support informed engineering decisions regarding technology and equipment selection. The study finds that each package’s technology influences the selection criteria of the other packages and the associated BoP requirements. Furthermore, the choice of technologies across packages significantly affects overall system integrity and BoP. These interdependencies are illustrated using a cause-and-effect matrix. The study’s significance lies in establishing a structured guideline for engineering design and operations, enhancing the accuracy of feasibility studies, and accelerating the global implementation of H2PS.

Integration of renewable energy sources like solar and wind power into the power network has increased significantly in recent years. However, these sources are inherently variable and intermittent, which leads to challenges in maintaining grid stability and reliability. A promising solution to these challenges is the strategic deployment of battery energy storage systems (BESS). The BESS can support improving system voltage and frequency stability and increase system reliability because it can rapidly charge and discharge the grid when needed. To fully explore the advantages of BESS in power systems, it is crucial to determine their optimal allocation. Therefore, this paper presents a technique for optimal allocation of BESS in weak grids to bolster system voltage and frequency stability and enhance system reliability. The proposed method uses the recent adaptive grey wolf optimisation (AGWO) algorithm to identify the optimal capacity and placement of the BESS. The AGWO algorithm is a metaheuristic optimisation algorithm that uses a population of wolves to explore the solution space for the best outcome. The outcomes from the AGWO method are validated using grey wolf optimisation (GWO), beluga whale optimisation (BWO), and sparrow search algorithm (SSA). The efficacy of the proposed methodology is validated in a high renewable distributed generation (DG) penetrated weak IEEE-39 bus system using DIgSILENT PowerFactory software. Simulation findings demonstrate that integrating BESS at the optimal location and size can significantly improve the voltage and frequency stability of the grid and increase its reliability. The proposed methodology can help grid operators and system planners make informed decisions on integrating BESS into the grid.

This study presents an advanced hybrid energy management system (EMS) designed for isolated microgrids, aiming to optimize the integration of renewable energy sources with backup systems to enhance energy efficiency and ensure a stable power supply. The proposed EMS incorporates solar photovoltaic (PV) and wind turbine (WT) generation systems, coupled with a battery energy storage system (BESS) for energy storage and management and a microturbine (MT) as a backup solution during low generation or peak demand periods. Maximum power point tracking (MPPT) is implemented for the PV and WT systems, with additional control mechanisms such as pitch angle, tip speed ratio (TSR) for wind power, and a proportional-integral (PI) controller for battery and microturbine management. To optimize EMS operations, a novel hybrid optimization algorithm, the JSO-GJO (Jellyfish Search and Golden Jackal hybrid Optimization), is applied and benchmarked against Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA). Comparative analysis indicates that the JSO-GJO algorithm achieves the highest energy efficiency of 99.20%, minimizes power losses to 0.116 kW, maximizes annual energy production at 421,847.82 kWh, and reduces total annual costs to USD 50,617,477.51. These findings demonstrate the superiority of the JSO-GJO algorithm, establishing it as a highly effective solution for optimizing hybrid isolated EMS in renewable energy applications.

In recent years, research on Electric Vehicles (EVs) has gained considerable attention due to their potential to reduce reliance on fossil fuels and curb environmental pollution significantly. Various stakeholders play a pivotal role in the large-scale EV integration into the existing power grid by providing unique services, particularly in grid power management and charging of EV. This study provides a comprehensive analysis of the critical role played by key stakeholders in the large-scale integration of electric vehicles (EVs) into the power grid, with a particular focus on grid power management and charging infrastructure. It systematically examines the involvement of Distributed Network Operators (DNOs), EV Owners (EOs), and Charging Station Owners (CSOs), elucidating their respective objectives, responsibilities, and challenges. Furthermore, the paper delves into power management strategies, reactive power regulation, and advanced control methodologies aimed at enhancing grid stability. A case study on EV adoption in Western Australia is presented to contextualize current developments and future trajectories. The study concludes by identifying key challenges associated with EV-grid integration and providing strategic recommendations to facilitate a more resilient, efficient, and sustainable power system.
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This research presents an innovative Constant Reactive Power (Q) and Constant Voltage (V) controlled renewable energy-based Electric Vehicle (EV) Charging Station (EVCS). The proposed approach addresses challenges such as limited renewable energy sources, high operational costs, and grid instability, aiming to enhance EV charging efficiency. By integrating renewable resources like wind turbines, solar photovoltaic panels, and energy storage systems, the station can consume electricity during off-peak hours and supply power to the grid when demand is high. The innovation lies in applying Constant V and Q control to stabilize voltage and frequency, ensuring the system's resilience under fault conditions. Simulation results demonstrate the controllers' robustness, with voltage and frequency returning to nominal values within 0.4 seconds, indicating rapid recovery after faults. The proposed strategy offers a scalable and cost-effective framework for EV charging stations, adaptable to regions with similar challenges. Additionally, it contributes to grid stability, enhances energy efficiency, and reduces carbon footprints by integrating renewable energy. This solution optimizes renewable resource usage and supports the transition to sustainable energy systems, providing a reliable and efficient charging infrastructure for the future.

Regional distribution networks (RDNs) frequently encounter challenges related to peak load demands, such as increased system operational costs, grid instability, transmission and distribution line losses, and decreased system efficiency. Thus, this study specifically examines the practice of peak shaving for RDN by employing a battery energy storage system (BESS) in order to decrease overall operational expenses and improve system performance. A centralized peak shaving technique is developed using an economic size of BESS to provide optimal peak shaving performance. Real load data is utilized in this work to eliminate the forecasting errors which is collected from a real RDN located in Australia. The developed technique is applied to real RDN furnished with real data to undertake a variety of simulation tests aimed at assessing peak shaving performance. A comparison study is used to demonstrate the success of the improved peak shaving operation by comparing it with previous strategies. The result reveals that the developed method with an economic size of BESS can generate substantial electricity savings and economic benefits, with a net revenue over investment 2.2 times higher.

Maximizing power production in photovoltaic (PV) technology is a key strategy in the energy transition paradigm. Therefore, it is essential to consider the potential factors that influence PV efficiency. One of the primary factors is solar irradiation which is inherently variable and beyond human control. While higher irradiation levels can boost PV power output to some extent, they also lead to higher operating temperatures, which can negatively impact the efficiency of the PV panel. As a result, passive cooling emerges as a key solution to mitigate the efficiency drop caused by rising operating temperatures. This study aims to provide a comprehensive review of phase change materials (PCMs)-based passive cooling techniques, which hold significant potential for enhancing the efficiency of PV panels. The review begins by characterizing the ideal phase change material (PCM) and identifying various PCMs, along with their thermophysical properties, that have potential applications in PV panel cooling. An in-depth investigation is then conducted on several PV-PCM research studies, focusing on key aspects such as the design parameters of PCM containers and PV capacity, variations in PCM mediums, the impact of environmental conditions, and the integration of other passive cooling approaches with the PV-PCM system. This review is conducted by exploring the impact of various influencing factors on heat transfer modes, such as convection and conduction, and their effects on the performance of the PV-PCM system. To further improve the thermal performance of PCMs, a holistic overview of approaches to enhancing their thermophysical properties is provided. This includes the integration of PCMs with extended surfaces, heat pipes, and metal and non-metal foams. Additionally, the review explores the potential of doping PCMs with high thermal conductivity materials and discusses the benefits of PCM encapsulation. Based on key observations derived from experimental and simulation studies of PV-PCM research and thermal performance enhancement, a conceptual framework for selecting suitable PCMs for PV cooling applications is proposed. Furthermore, the review extends its contribution by offering research directions, suggestions, and design recommendations to guide prospective studies in PV-PCM systems.

The microgrid (MG) cluster is one of the feasible ways to integrate large-scale renewable energy sources, address the constrained energy exchange problems, and optimize power system efficiency, resiliency, and reliability. The present power system will probably shift into a network of MGs to bring about economic gains by employing demand-supply balance. However, various factors related to interconnected MG (IMG) infrastructure, such as the diversity of IMG architecture, economic viability, integration capacity, reliability, operational flexibility, and communication and protection schemes, can create great challenges in implementing efficient and resilient IMG infrastructure. Therefore, to address this concern, different IMG infrastructures are explored and analyzed to bring out the benefits, challenges, and constraints of the presented IMG architectures in this review. Accordingly, the concepts of IMGs and potential IMG architectures based on layout, line, and interface technologies are first discussed and compared. Then, numerous factors like cost, reliability, scalability, flexibility, complexity, and efficiency that play a significant role in evaluating the performance of IMG structures are identified to present a comparative analysis among various architectural topologies. All these analyses and comparisons are expected to serve as a reference in the field of IMG infrastructure to select a suitable framework based on the necessary functionalities. This paper also provides potential challenges related to IMG architecture and recommendations for future research studies to improve IMG infrastructure and operation.