An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production

The stochastic and erratic behavior of solar photovoltaic (SPV) is a challenge, especially due to changing meteorological conditions. During a partially irradiated SPV system, the performance of traditional maximum power point tracking (MPPT) controllers is unsatisfactory because of multiple peaks in the Power-Voltage curve. This work is an attempt to understand the performance uncertainties of the SPV system under different shading conditions and its mitigation. Here, a novel hybrid metaheuristic algorithm is proposed for the effective and efficient tracking of power. The algorithm is inspired by the movement of grey wolves and the swarming action of birds, and is thus known as the hybrid grey wolf optimizer (HGWO). The study focuses on the transient and steady-state performance of the proposed controller during different conditions. A comparative analysis of the proposed technique with incremental conductance and a particle swarm optimizer for different configurations is presented. Thus, the results are presented based on power extracted, shading loss, convergence factor and efficiency. The proposed HGWO–MPPT is found to be better as it has a maximum efficiency of 94.30% and a minimum convergence factor of 0.20 when compared with other techniques under varying conditions for different topologies. Furthermore, a practical assessment of the proposed controller on a 6.3 kWp rooftop SPV system is also presented in the paper. Energy production is increased by 8.55% using the proposed approach to the practical system

Grey Wolf Optimizer-Based Array Reconfiguration to Enhance Power Production from Solar Photovoltaic Plants under Different Scenarios

The extraction of maximum power is a big challenge in solar photovoltaic-based power plants due to varying atmospheric and meteorological parameters. The concept of array reconfiguration is applied for the maximum power extraction in solar PV plants. Using this approach, the occurrence of multiple peaks in P-V and I-V characteristics during partial shade can be smoothened and reduced significantly. Partial shading due to the movement of the cloud is considered in the research. The cloud movement mainly because of velocity and wind direction is used for creating various shading conditions. The main focus is to reduce the power losses during partial shading using a nature-inspired optimization approach to reconfigure the array for different types of shading conditions. A grey wolf optimizer-based bridge-linked total cross-tied (GWO-BLTCT) configuration is proposed in this paper. The performance of the proposed topology is compared with standard and hybrid topologies, namely, series-parallel, total cross-tied, BLTCT, and SuDoKu-BLTCT, based on performance indicators such as fill factor, performance ratio, power enhancement, and power loss. The proposed GWO-BLTCT outperforms the remaining topologies due to the least power loss and high fill factor. It also has the highest average power enhancement and performance ratio with 23.75% and 70.02% respectively

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

In the last few years, several countries have accomplished their determined renewable energy targets to achieve their future energy requirements with the foremost aim to encourage sustainable growth with reduced emissions, mainly through the implementation of wind and solar energy. In the present study, we propose and compare five optimized robust regression machine learning methods, namely, random forest, gradient boosting machine (GBM), k-nearest neighbor (kNN), decision-tree, and extra tree regression, which are applied to improve the forecasting accuracy of short-term wind energy generation in the Turkish wind farms, situated in the west of Turkey, on the basis of a historic data of the wind speed and direction. Polar diagrams are plotted and the impacts of input variables such as the wind speed and direction on the wind energy generation are examined. Scatter curves depicting relationships between the wind speed and the produced turbine power are plotted for all of the methods and the predicted average wind power is compared with the real average power from the turbine with the help of the plotted error curves. The results demonstrate the superior forecasting performance of the algorithm incorporating gradient boosting machine regression

Advanced Intelligent Approach for Solar PV Power Forecasting Using Meteorological Parameters for Qassim Region, Saudi Arabia

Solar photovoltaic (SPV) power penetration in dispersed generation systems is constantly rising. Due to the elevated SPV penetration causing a lot of problems to power system stability, sustainability, reliable electricity production, and power quality, it is critical to forecast SPV power using climatic parameters. The suggested model is built with meteorological conditions as input parameters, and the effects of such variables on predicted SPV power have been studied. The primary goal of this study is to examine the effectiveness of optimization-based SPV power forecasting models based on meteorological conditions using the novel salp swarm algorithm due to its excellent ability for exploration and exploitation. To forecast SPV power, a recently designed approach that is based on the salp swarm algorithm (SSA) is used. The performance of the suggested optimization model is estimated in terms of statistical parameters which include Root Mean Square Error (RMSE), Mean Square Error (MSE), and Training Time (TT). To test the reliability and validity, the proposed algorithm is compared to grey wolf optimization (GWO) and the Levenberg–Marquardt-based artificial neural network algorithm. The values of RMSE and MSE obtained using the proposed SSA algorithm come out as 1.45% and 2.12% which are lesser when compared with other algorithms. Likewise, the TT for SSA is 12.46 s which is less than that of GWO by 8.15 s. The proposed model outperforms other intelligent techniques in terms of performance and robustness. The suggested method is applicable for load management operations in a microgrid environment. Moreover, the proposed study may serve as a road map for the Saudi government’s Vision 2030

Energy Production Forecasting From Solar Photovoltaic Plants Based on Meteorological Parameters for Qassim Region, Saudi Arabia

Due to the increasing cost of crude oil because of pandemic COVID-19 and global environmental threats, the exploitation of fossil fuels for power generation is discouraged. Further, the demand for electrical power is increasing drastically, and therefore, the exploitation of renewable energy resources, particularly solar photovoltaic-based technology for power generation is invigorated. However, the large-scale penetration of solar photovoltaic is becoming a major challenge in terms of stability, reliability of power when integrated with the grid. Thus, it becomes important to develop a novel approach or strategy which is useful to improve power quality, reliability, and grid stability. Solar photovoltaic power forecasting is a key tool for this new era and becoming the main component for a smart grid environment. Here, in this paper, the ensemble trees approach-based machine learning approach is utilized to forecast the solar photovoltaic power with the help of various meteorological parameters. The high-quality measured data for meteorological parameters for Qassim, Saudi Arabia is used in this research. The performance of the proposed model is evaluated with the help of statistical indices such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Training Time (TT) and found within the desired limits. To validate the obtained results a comparative analysis with other machine learning models is carried out. Moreover, the proposed research may provide the roadmap in achieving the vision 2030 of the government of Saudi Arabia

An Intelligent PI Controller-Based Hybrid Series Active Power Filter for Power Quality Improvement

The quality of power that is degrading day by day is an important issue for all the consumers. The important factor for this is harmonics in the voltage and current waveforms which can be resolved by the use of hybrid series active power filter. The combination consists of a series active power filter and a shunt passive filter connected in parallel to the load. The method used in this paper is for the purpose of achieving good harmonic compensation and reduced total harmonic distortion for various types of nonlinear loads as per the standards of IEEE 519. The proposed HSAPF technique uses the synchronous reference frame method for generating the compensating signal with an intelligent PI controller that uses particle swarm optimization (PSO) technique to obtain the required gain values needed to improve the steady state response of the system. The concept of vigorous HSAPF has been authenticated through MATLAB simulation analysis, and the results obtained validate the accuracy of the method for the different load conditions

An Advanced and Robust Approach to Maximize Solar Photovoltaic Power Production

The stochastic and erratic behavior of solar photovoltaic (SPV) is a challenge, especially due to changing meteorological conditions. During a partially irradiated SPV system, the performance of traditional maximum power point tracking (MPPT) controllers is unsatisfactory because of multiple peaks in the Power-Voltage curve. This work is an attempt to understand the performance uncertainties of the SPV system under different shading conditions and its mitigation. Here, a novel hybrid metaheuristic algorithm is proposed for the effective and efficient tracking of power. The algorithm is inspired by the movement of grey wolves and the swarming action of birds, and is thus known as the hybrid grey wolf optimizer (HGWO). The study focuses on the transient and steady-state performance of the proposed controller during different conditions. A comparative analysis of the proposed technique with incremental conductance and a particle swarm optimizer for different configurations is presented. Thus, the results are presented based on power extracted, shading loss, convergence factor and efficiency. The proposed HGWO–MPPT is found to be better as it has a maximum efficiency of 94.30% and a minimum convergence factor of 0.20 when compared with other techniques under varying conditions for different topologies. Furthermore, a practical assessment of the proposed controller on a 6.3 kWp rooftop SPV system is also presented in the paper. Energy production is increased by 8.55% using the proposed approach to the practical system

Design and Development of a Proficient Converter for Solar Photovoltaic Based Sustainable Power Generating System

The demand for renewable energy resources is invigorated due to various issues primarily related to energy security, energy sustainability, and greenhouse gas emissions. The proper utilization of renewable energy resources has certain challenges due to the varying nature of meteorological parameters and atmospheric aerosols. Therefore, it is of utmost importance to extract the maximum power from the photovoltaic systems with the help of proficient converters. Here, a coupled inductor based highly efficient synchronous interleaved boost converter is designed and developed for the grid integrated photovoltaic system. The proposed converter contains an Insulated Gate Bipolar Transistor (IGBT) switch in place of the conventional diode to maintain zero leakage current, bared minimum switching losses, and improved conduction time thereby enhancing the overall efficiency and reliability of the system. In addition, the proposed system topology is simple to implement and is capable of controlling power at both the generation as well as the load sides. The results obtained are validated with the help of a laboratory prototype under practical scenarios like varying temperature and solar irradiance. Further, an intelligent technique based on modified particle swarm optimization is utilized to extract the maximum power from the photovoltaic system. Moreover, the power quality indices were monitored and analyzed at the point of common coupling and at the grid level which vets the effectiveness of the proposed topology

Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components

Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components