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.

Power generation using wind has been extensively utilised, with substantial capacity add-on worldwide, during recent decades. The wind power energy sector is growing, and has turned into a great source of renewable power production. In the past decades of the 21st century, the capacity of installed wind energy has almost doubled every three years. This review paper presents the crucial facets and advancement strategies that were approved and adopted by the Government of India for intensifying the country’s own power safety, by the appropriate use of existing power sources. From India’s viewpoint, wind energy is not only utilized for power production but also to provide power in a more economical way. The particulars of India’s total energy production, contributions of numerous renewable sources and their demand are also encompassed in this paper. After an exhaustive review of the literature, detailed facts have been identified about the present position of wind energy, with an emphasis on government achievements, targets, initiatives, and various strategic advances in the wind power sector. Wind power potential is discussed, which can assist renewable power companies to select efficient and productive locations. All analyses carried out in this paper will be incredibly valuable to future renewable energy investors and researchers. The current scenario of wind power production in India is also paralleled with that of other globally prominent countries.

This paper introduces a new hybrid technique for investigating the optimal location of electric vehicle charging stations. Reactive power compensation is also provided along with charging station to deal with power loss issues and retain the distribution network’s reliability. Moreover, vehicle to grid facility has been thoroughly examined in this article. The suggested algorithm is the hybrid of gray wolf optimization (GWO) and particle swarm optimization (PSO). The hybridization of two algorithms combines the desirable attributes of both algorithms and maintains a strong balance between the exploration and exploitation capability. The proposed hybrid approach is applied on IEEE-33 bus and 34-bus distribution systems to minimize the active power loss, maximize the net profit, and improve the reliability of electric grid network. Furthermore, the outcome attained using the anticipated approach is equated with conventional GWO and PSO. For 33-bus system, the proposed technique results in loss reduction of 30.67% and maximizing the net profit by 1142 $ and 1560 $ as compared to GWO and PSO, respectively. Likewise, power losses have been reduced to 27.6% and maximizing the net profit by 1726 $ and 3149 $ as compared to isolated GWO and PSO, respectively, for 34-bus network. The achieved outcomes prove the supremacy of the suggested approach over GWO and PSO to evaluate locations of charging stations’ and reactive power source in radial distribution network.

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.

The utilisation of renewable energy sources (RES) in increasing drastically because of various issues including depletion of fossil fuels, greenhouse gas emissions, climate change and so on. As the power generated from RES is fluctuating in nature, therefore, the appropriate sizing of the hybrid model based on RES is utmost important. In this study, the grey wolf optimisation, a newly developed approach is used for the optimal sizing of the hybrid model. In this work, the optimal design of solar/biomass/biogas/battery‐based hybrid system has been carried out to supply continuous electricity to various households of a cluster of villages of Haryana state of India. The results obtained from the proposed model have been compared with harmony search and particle swarm optimisation and found better.