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The intermittent nature of renewable-based generation may cause the dip or rise generation and load imbalances. Besides, the higher penetration of Electric Vehicles can affect the voltage profile and imbalances. The main contribution of the proposed work is to determine (i) the realistic load model of electric vehicle (EV) charging station (ii) the size of battery energy storage (BES) considering the EV load demand with the most realistic ZIP load. (iii) the cost-benefit analysis and the probabilistic modeling of EV load demand are considered for BES sizing. (iv) minimization of the daily energy loss (v), maximizing the total benefit of the system, (vi) the nature of the electric vehicle load demand has been modeled for size determination of energy storage devices. The fuel cost of the thermal generator has been minimized, and the operating and maintenance cost of the Solar Photovoltaic (PV), and Wind Turbine (WT) have been considered in the objective function. The daily energy loss has been reduced to 30.085%. The voltage deviation has been reduced to 1.165%. The analysis has been carried out for the IEEE-33 bus test system. The iterative Monte Carlo Simulation and Nonlinear Programming have been used to solve the multi-objective problem. [ABSTRACT FROM AUTHOR]

Microgrid hybrid renewable energy sources with green energy have become a need for the environment. Electric vehicle (EV) contribution is another revolution in clean energy. The Micro-grid with hybrid renewable energy (HREs) is a cost-effective, eco-friendly solution. The main contribution of the proposed work is; (i) to analyze the impact of the operating reserve power, battery energy storage system (BESS), renewable penetration percentage, and demand response program (DRP) for the EVs load with HREs integration. (ii) minimization of the net present cost (NPC) and Levelized cost of energy (LCOE) of the system with zero unmet HREs, (iii) obtaining the techno-economic analysis of the different combinations of hybrid energy sources as Photovoltaic/Wind-Turbin/Diesel Generator and BESS, with EV load growth. The annual energy purchased and sold from the grid has been reduced by 15.35% and increased by 42.98%, respectively, considering DRP. The NPC of the system has been reduced by 62.62%, considering the BESS. The Hybrid Optimization Model for Electric Renewable (HOMER) software with MATLAB R21a interface has been used for the analysis. This paper represents the feasibility of energy storage with the HREs management study for EV load considering techno-economic, cost-effective, and economic dispatch for feasibility analysis of Hawai Island, USA. • The Demand Response Programme's (DRP) objectives are to reduce the peak demand and shift the load. • Time-of-use pricing DRP/Reserve/BESS/Electric Vehicles (EVs)/and Hybrid-RES are considered. • The JAYA algorithm is used to minimize the NPC and cost of energy for Techno-economic analysis. • The NPC of the system has been reduced by 62.62%, considering the DRP with battery energy storage. • Feasibility and Techno-economic analysis are essential for the distribution network operator. [ABSTRACT FROM AUTHOR]

Faster consumption of hydrocarbon fuel in the transport sector is posing global threat of depletion of fossil fuel reserves. Studies are being extensively done to search for an alternative energy source and/or to find out appropriate energy conversion system. Among various alternatives, the use of compressed atmospheric air in air turbine is an attractive option provided the atmospheric air is compressed by natural sources such as sun energy, wind energy, etc. It has the capability to produce shaft work with almost zero pollution in the environment. This paper details the mathematical modeling of a small capacity compressed air driven multivane air turbine. The effect of having different vane angles and inlet pressure on shaft work output has been studied and analyzed here. The study shows that the flow work has significant contribution in total work output and varies from 1.5% to 16.3% at different pressures, 2–6 bars, and injection angles, 30°–60°. The total shaft work is found to be maximum at vane angle θ=36° (ten vanes) when injection angle is kept at 60°, and it reduces at vane angle θ=45° (eight vanes) when injection angle is kept at 45° and further goes down at vane angle θ=60° (six vanes) when injection angle is 30°, if injection pressure is maintained at 6 bars and speed of rotation at 2500 rpm. [ABSTRACT FROM AUTHOR]

The burgeoning wind sector, and its increasing influence on the operation and control of power systems, has made grid integration a key recent concern. Different countries are taking different approaches in setting up new grid codes to address this issue. [Copyright &y& Elsevier]

• To propose novel techniques to predict greenhouse gas emissions and minimize energy consumption in renewable sources based on a sustainable environment. • The deep neural network has improved energy efficiency to predict greenhouse gas emissions using a statistic regression neural network. • This research proposed a novel technique in predicting greenhouse gas emission and minimization of energy consumption in renewable sources based on a sustainable environment. • Here, the greenhouse gas emission has been predicted using statistic regression neural network. The long-term viability of energy resources as a main input is essential to achieve long-term economic growth of a country and the energy efficiency significantly reduces energy consumption and greenhouse gas emissions, supporting environmental sustainability. As a result, a number of governments, led by those in the developed world, are making an effort to enact laws governing energy efficiency. This study suggests cutting-edge methods for forecasting greenhouse gas emissions and reducing energy demand from renewable sources based on a sustainable environment. Utilizing the statistical regression neural network (SRNN), greenhouse gas emissions have been predicted, and the deep neural network's (DNN) energy efficiency has increased. The SRNN_DNN intensity method out predicts evaluated MLR (multiple linear regression) and second- and third-order non-linear MPR (multiple polynomial regression) techniques according to MAPE (mean absolute percentage error) results. Furthermore, presented methods are considered suitable for computing GHG emissions due to the high accuracy of the SRNN DNN model. The anticipated greenhouse gas emissions related to energy were remarkably similar to the actual emissions of EU (European Union) nations. [ABSTRACT FROM AUTHOR]