Journal of Green Energy Research and Innovation

Abstract Today, due to environmental and political reasons, countries around the world are required to use green energies, such as wind and solar energy. Also, most countries have switched to using electric vehicles (EVs) to reduce environmental pollution. Since smart distribution systems’ distributed generation (DG) power output is limited, this paper addresses this issue by planningcharging parking lots of EVs. The problem was formulated as a nonlinear optimization model. The objective function was to increase the power output, reduce the loss cost, and reduce the bus voltage deviations. Also, technical and economic limitations were considered in solving the planning problem. The uncertainty of consumption load, the behavior of EVs, and the output power of wind DGs were modeled using a combination of Monte Carlo and means methods. The improved gray wolf optimization (IGWO) algorithm was adopted to optimize the objective function. A standard IEEE 33-bus smart distribution system was studied to show the efficacy of the suggested solution. The results demonstrated the proposed solutions' high performance in improving the wind DG power output of the distribution system (PODS).

Abstract This study focuses on achieving a self-healing network with high reliability and minimal losses by discussing the optimal placement of distributed generation (DG) sources in the network using the particle swarm algorithm (PSO). Optimal placement not only reduces losses but also enhances the voltage profile and enhances system reliability. Consequently, the paper explores the concept of a self-healing smart grid. Smart grids possess a crucial attribute known as self-healing, designed to enhance the system’s dependability. The self-healing power grid can detect and rectify disruptions within its infrastructure with minimal or even absent human intervention. This study examines a standard IEEE 69-bus network and presents results demonstrating the network's safety, the prevention of blackouts, the reduction of losses, and the substantial enhancement of the voltage profile.

Abstract The main methodology in every wind power prediction model involves converting wind speed into power using the power output curve of the wind turbine. However, preceding studies that have introduced models for such curves have not considered the impact of wind direction and its recurring fluctuations over time on predicting wind turbine power output. The main focus of these studies has just been on the magnitude of wind speed and the relationship between wind speed and turbine power. The present study models the effect of wind direction on wind turbine power output and uses it to modify the quadratic power curve equations. Using these modified equations and considering the turbine mechanism to follow the wind direction, a method is presented for predicting wind turbine power output under frequent changes in wind direction over time. To deal with the lack of access to long term and high-resolution wind data, registered historical data and probabilistic distribution functions are used to produce lost data with software. To demonstrate the efficacy of the suggested approach, the real data recorded for a 1.5 MW turbine installed in Khaf in Razavi Khorasan, Iran, are used as a case study. Finally, the potential wind power and potential income of the four windy regions in Iran were assessed based on the payment mechanism of the Organization of Renewable Energy and Electricity Efficiency of Iran, assuming the same installed capacity. The effect of wind direction and its variations over time, which can affect power output of wind turbine and income, is the main focus of this section of paper.

Abstract This research designed and simulated CIGS/CIGS back-to-back solar cells using Silvaco-Atlas software. We considered CIGS absorbing layer thickness and sub-cells as critical parameters to optimize the performance of the CIGS/CIGS tandem solar cell. The research comparatively examined the effect of different electrode metals, such as molybdenum, aluminum, titanium, and silver, on the efficiency. The electrical parameters of the best CIGS/CIGS tandem solar cell configuration were a short-circuit current density (Jsc) of 15.65 mA/cm², an open-circuit voltage (Voc) of 1.86 V, a fill factor (FF) of 86.04%, and a conversion efficiency (η) of 27.12%. The optimal CIGS absorbing layer thickness of the top and bottom cells corresponding to the maximum conversion efficiency obtained were 0.17 and 6.3 μm, respectively. In contrast, the optimal thickness of the Cds layer was 0.04 µm. Silver had the best performance in connecting layers between several metals. The results can be used to develop low-cost and high-efficiency solar cells.

Abstract Microgrids (MGs), which can incorporate renewable energies such as wind and solar, can be divided into several sub-MGs with multiple connection points (MCPs) to the grids. However, this configuration is not ideal for MG operation due to the lack of adequate protection and operation mechanisms that ensure the safe and reliable functioning of distributed generation. A key issue with these MGs is the identification of islanding, which is challenging due to the presence of a broad non-detection zone (NDZ). Passive islanding identification approaches primarily depend on over/under voltage protection (OVP/UVP), over/under frequency protection (OFP/UFP), and monitoring metrics, such as phase jump at the point of common coupling (PCC). This study examines the power equations for real and reactive power in renewable-energy-based MGs (referred to as renewable MGs) with multiple connections to different grids and MGs, which are of significant size. The analysis focuses on the NDZ of OVP/UVP and OFP/UFP approaches. Passive approaches observe the changing system parameters that occur when the MG is isolated, while active methods depend on the system's reaction to a minor disturbance introduced to identify the isolation situation. Traditional passive islanding detection approaches exhibit a significant NDZ that may compromise the accuracy of islanding detection in these types of MGs. Even if one grid is disconnected, the MG remains connected to other grids, preventing islanding. Consequently, typical active islanding detection methods are unable to identify the off-grid status.

Abstract In this study, optimal energy management is addressed in the residential building. The residential building is equipped with renewable energies including wind turbines (WT) and photovoltaic (PV) systems. Stochastic programming is used to model the uncertainty of renewable energy resources. To manage these uncertainties and reduce the total daily cost of energy, the load control program is adopted. In this respect, five different types of loads are modeled in the building, including interruptible, uninterruptible, constant-energy, constant-power and movable loads. The above charges are properly adjusted and shipped to minimize energy costs and address the uncertainties of renewable energy by hybrid sine cosine shuffled frog leaping algorithm. The residential building is considered as later active in the network, which transfers energy from network to the building and vice versa. The simulation results show that the proposed model can efficiently harness all the energy possible from WT-PV systems, manage uncertainties, minimize total daily costs and operate as an island. All of these objectives are achieved by optimal load distribution and control within the proposed load control program.