MD Shoeb

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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Solar photovoltaic generation has become the dominant global method of producing renewable electricity around the globe. However, solar PV farms require a considerable amount of land. Agrivoltaics has been a promising field of interest recently as this system maximizes the land's utilization by producing crops beneath the photovoltaics panels. This paper proposes a new agrivoltaics system that simultaneously produces crops and electrical power by installing PV panels on agricultural land in such a way that the production of regular crops does not get curtailed and can still grow around and beneath the panels, avoiding any reduction in crop yield. The architectural design of the proposed mounting structure and the installation method are discussed, ensuring full utilization of the land area under the panel with no crop limitations. Bangladesh is considered a case study location as its economy is mostly dependent on agriculture. The country started allocating enormous amounts of farmland for solar photovoltaic farms to mitigate the energy crisis. After a preliminary survey, an agrivoltaics system was designed, developed and installed in the Chuadanga District of Bangladesh. Then a detailed techno-economic analysis was performed to evaluate the feasibility and economic viability of the implemented agrivoltaics project. A comparative analysis of seven different scenarios is demonstrated in terms of equity payback, internal rate of return, modified internal rate of return, net present value, annual life cycle savings, and benefit-cost ratio to determine the optimum agrivoltaics approach as well as to showcase the superiority of the proposed system. The results demonstrate that the proposed agrivoltaics system achieves full land utilization, by producing crops along with electricity generation with the lowest payback period, highest profit margin, and highest benefit-cost ratio over the project lifetime.

Electric Vehicles (EVs) play a crucial role in advancing environmental and economic sustainability, yet their widespread adoption poses risks to the electrical grid, including voltage instability and increased peak load stress. Research has highlighted the potential of V2G-enabled EV chargers to mitigate these issues by providing reactive power support to the grid. Despite these advancements, the impact of reactive power injection on different types of networks, such as resistive versus inductive distribution networks, remains inadequately studied. This paper addresses this gap by investigating how reactive power affects various low-voltage (LV) distribution networks, providing insights into optimizing EV integration and enhancing grid stability. The analysis employs a modified IEEE 13-bus network to evaluate the effects of V2G support by EVs across different network types. The results demonstrate a strong correlation between the type of distribution network and key performance metrics, including the grid's voltage profile, charging rates, and EV charging times. These findings emphasize the importance of considering distribution networks when assessing the potential benefits of V2G technology for grid stability and EV charging efficiency.

The rapid integration of Electric Vehicles (EV s) poses a significant concern for energy producers, transmitters, and distributors regarding the capability of the existing grid topology and technology. EV owner prefers to use high-rated chargers to reduce the charging time, negatively impacting the grid. This paper focuses on the impact of different power ratings of chargers and the abilities of EV s for reactive power support to the utility grid. This analysis considers two sizes of popular EV chargers to assess their impact on the utility grid. An IEEE 14 bus network is employed in this study to investigate the grid performance with three different levels of EV penetration (small, medium, and high). The simulation results show that the fast chargers can significantly impact the power system, mainly when the EV penetration is very high.

A microgrid (MG) is always prone to the uncertainties of its demand variation and the generation of its non-dispatchable renewable sources when operating in islanded mode. Such variation events can lead to the voltage or frequency (VF) violation in the MG. However, there are some techniques available in the literature that can predict these events a few minutes ahead. Using such techniques, the VF violation can also be predicted and prevented with the introduction of a suitable preventive controller. Hence, this paper proposes a look-ahead controller that uses the short-horizon prediction data of demand and renewable generation to determine any prospective VF violation. If a violation is predicted, the proposed technique will aim to define the most optimal generation level of dispatchable sources, the MG’s best network configuration and the engagement level of the supportive actions, such as exchanging power with neighboring microgrids, utilizing energy storages, a demand response and renewable energy curtailment (if and when available). The technical, reliability and environmental aspects of the MG are considered within the proposed technique along with the operational cost. The determined optimal control variables are then sent to the local controllers to apply the proper arrangements in the system to retain the VF within the desired range. The performance of the developed technique is validated through extensive numerical analyses in MATLAB.

The rapid increase in solar photovoltaic (PV) integration into electricity networks introduces technical challenges due to varying PV outputs. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (IMGs) with high solar PV penetration. PV systems and optionally controllable distributed energy resources (DERs) in IMGs can be operated in an optimised way based on nowcasting (forecasting up to 60 min ahead). This study aims to evaluate the performance under Perth, Western Australian conditions, of an all-sky imager (ASI)-based nowcasting system, installed at Murdoch University in Perth, Western Australia (WA). Nowcast direct normal irradiance (DNI) and global horizontal irradiance (GHI) are inputted into a 5 kWp solar PV system with a direct current (DC) power rating/alternating current (AC) power rating ratio of 1.0. A newly developed classification method provided a simplified irradiance variability classification. The obtained nowcasting system evaluation results show that the nowcasting system’s accuracy decreases with an increase in lead time (LT). Additionally, the nowcasting system’s accuracy is higher when the weather is either mostly clear (with a recorded LT15 mean absolute deviation (MAD) of 0.38 kW) or overcast (with a recorded LT15 MAD of 0.19 kW) than when the weather is intermittently cloudy with varying cloud conditions (with a recorded LT15 MAD of 0.44 kW). With lower errors observed in lower LTs, overall, it might be possible to integrate the nowcasting system into the design of IMG controllers. The overall performance of the nowcasting system at Murdoch University was as expected as it is comparable to the previous evaluations in five other different sites, namely, PSA, La Africana, Evora, Oldenburg, and Julich.

The concept of maintaining indoor environmental quality comprising building indoor temperature, relative humidity, CO2, and volatile organic compound (VOC) level poses new challenges to the optimal operation of heating, ventilation and air-conditioning (HVAC) systems. While existing case studies demonstrate the energy-saving potentials for efficient HVAC operation, there is a lack of studies quantifying energy savings whilst considering indoor environmental conditions. This study proposes a state-of-the-art modelling and optimization approach to minimize the energy consumption of the HVAC systems without compromising indoor environmental quality. While the primary objective of ensuring optimal operation of HVAC systems is to minimize energy consumption, controlling indoor environmental parameters to remain within the acceptable range imposes excess energy use. These two conflicting objectives constitute a multi-variable constrained optimization problem that has been solved using a particle swarm optimization (PSO) algorithm. Real-time predictive models are developed for the individual indoor environmental parameters and HVAC energy consumption using a Nonlinear Autoregressive Exogenous (NARX) neural network (NN). During model development, models' performance is optimized in terms of complexity, predictive accuracy, and ease of application to a real system. The proposed predictive models are then optimized to provide an optimal control setting for the HVAC systems considering seasonal variations. The results indicate that it is possible to reduce 7.8% of total energy, without negotiating indoor environmental conditions, e.g., air temperature 19.60–28.20°C and relative humidity 30–65% as per ASHRAE Standard 55, and CO2 ≤ 800 ppm and VOC ≤1000 ppm as per AS 1668.2.

Microgrids (MGs) are promising approaches to proliferate distributed energy resources for electrification in remote areas. However, to deal with the uncertainties of renewables and energy demand, special measures are required. Energy storages, controllable loads and reconfigurable networks are some of those measures that improve MGs’ flexibility. Another alternative is temporarily coupling the adjacent MGs to support each other and form coupled MGs (CMGs). This paper proposes a look-ahead technique to form CMGs while reassuring the optimal performance of all MGs. Preserving the voltage and frequency of each MG is also another key objective. The proposed optimization approach tries to solve the voltage/frequency problem by forming the CMGs when the local actions (such as energy storages) are inadequate or cost-ineffective. The proposed technique considers the operational cost, technical and environmental aspects, reliability and losses in the CMG formation. This technique is general and can be used for complex topologies and also to form multiple CMGs if deemed more suitable. The performance of the developed technique is validated through extensive numerical analyses in MATLAB.

The integration of solar photovoltaic (PV) into electricity networks introduces technical challenges due to varying PV output. Rapid ramp events due to cloud movements are of particular concern for the operation of remote islanded microgrids (MGs) with high penetration of solar PV generation. PV plants and optionally controllable distributed energy resources (DERs) in MGs can be operated in an optimized way based on nowcasting, which is also called very short-term solar irradiance forecasting up to 60 min ahead. This study presents an extensive literature review on nowcasting technologies along with their current and future possible applications in the control of MGs. Ramp rates control and scheduling of spinning reserves are found to be the most recognized applications of nowcasting in MGs. An online survey has been conducted to identify the limitations, benefits and challenges of deploying nowcasting in MGs. The survey outcomes show that the incorporation of nowcasting tools in MG operations is still limited, though the possibility of increasing solar PV penetration levels in MGs if nowcasting tools are incorporated is acknowledged. Additionally, recent nowcasting tools, such as sky camera-based tools, require further validation under various conditions for more widespread adaptation by power system operators.

This paper proposes the novel application of a genetic algorithm optimized fuzzy logic controller as a nonlinear controller for capacitive bridge type fault current limiter (CBFCL) to improve the stability performance of the power systems. The proposed controller provides fast convergence for the system and uses data from the system as a feedback in the controller loops. The performance of the proposed genetic algorithm optimized fuzzy logic controlled CBFCL is compared with that of a static nonlinear controller based CBFCL and a static nonlinear controller-based bridge type fault current limiter (BFCL). The detail controller design and stability analysis are carried out on the IEEE 39 bus power system in MATLAB/SIMULINK. To capture a realistic system's response, a wind farm is connected to bus one in the IEEE 39 bus system. From the simulation results and several quantifying parameters, it is shown that the proposed genetic algorithm optimized fuzzy logic controlled CBFCL can effectively improve the stability and the performance of the power system as well as the grid connected wind farm. Further, the proposed controller performs better than the static nonlinear controller based CBFCL and the static nonlinear controller based BFCL.