Increasing concern about the shortage of energy resources and harmful outcome of fossil fuel emission has initiated new requirement of reliable and cleaner green power sources. Hence, solar photovoltaic and wind power system are fastest developing sources among different renewable energy sources. In the proposed work, the urgency of the national policy to upgrade the existing coal-based plant as integrated solar, wind and coal-based power plant to reduce the carbon emission and to evaluate the feasibility of developing grid-connected hybrid energy system in Ramapuram Chennai, India has been presented. Moreover, the regression models for estimation of global solar radiation using different metrological parameters are developed and compared with the results of other models. In this work, Ramapuram, Chennai, India area is chosen to install the wind and solar photovoltaic systems to feed three types of load (residential, commercial and industrial). In this study, ENNORE thermal power station, Chennai is considered to reduce the carbon emission as the integration of solar photovoltaic system and wind system to the grid reduce the units generation from this plant. Hence, study shows that 110329.56 kg emission is reduced from ENNORE thermal power station by using this system.

Hybrid electric vehicles are an effective alternative to the conventional fuel engine vehicles and thus efficient and intelligent energy management is the key for establishing a significant market for the hybrid electric vehicles globally. Recent developments in the field of intelligent techniques and demand to make the energy systems intelligent have become a means to develop energy efficient hybrid electric vehicles. The energy management issue becomes vital in order to enhance the autonomy of hybrid electric vehicles and to reduce the costs. Therefore, a novel approach with intelligent techniques, to control the plug-in hybrid electric vehicles, in front of different customer profiles has been presented. This paper presents the battery performance improvement of a plug-in hybrid electric vehicle using fuzzy logic controller and neural fuzzy logic controller with battery state of charge as a deciding parameter and consequently comparing the performance of both cases. The battery state of charge and engine speed as input has been selected and based on their values the advanced controller decides the accurate torque required to be converted to energy which could be used to charge the battery and this can be achieved by controlling the forward gain value. For this, an advanced fuzzy controller and advanced adaptive nuero fuzzy inference system controller are used to decide the value of forward gain. Simulink environment is used to simulate the performance of the proposed system. This could be helpful in deciding which type of intelligent system is to be used for the power efficient operation of the hybrid electric vehicle. The results of both the control techniques are compared and the better controller is recommended for energy management of a plug-in electric vehicles. The results indicate that advanced control techniques provide the good performance and improving the fuel budget of hybrid electric vehicles.

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The growth in sustainable generation technology such as fuel cell, wind energy conversion system, photovoltaic system, increase in fuel cost, energy necessity and the reduction in the fossil fuel reserve, for better power quality and reliability, is obliging the power sector to use the renewable based energy sources. In India, wind energy is gradually becoming an important and significant energy resource. Keeping in opinion the aforementioned wind energy prediction is becoming an essential study for harnessing the wind energy prospective. This paper proposes an effective technique based on intelligent approach for predicting wind power in different areas. This technique is based on using an intelligent model concerning the predicted gap to its similar one and two year old data. There are many intelligent and conventional models existed in literature for the wind power prediction like support vector machines (SVM), back propagation (BP) prediction etc. In this paper an effective fuzzy logic and model predictive control based models have been developed and offered for the wind power prediction for microgrid application by using air density and wind speed as the input parameters for fuzzy system. The outcomes are compared with the computed data and existing models and it can be observed that the different errors are found within the permissible limits. The outcomes obtained from fuzzy based technique are very close to calculated values if compared with model predictive based technique. Hence, the proposed models can be employed for the prediction of wind speed and wind power generation in the selected stations. The existing models results are compared with Kolkata city outcomes. The Error RMSE with Support vector machine, Back propagation, Model of forecast error correction +SVM and Model of forecast errors correction +BP, Neural Network method, model predictive based system, and proposed fuzzy logic based system are 30.48%, 32.83%, 26.81% , 28.58% , 1.1431% , 1.38% and 1.12% respectively. Therefore, the proposed techniques provide the best results and even these are observed within the suitable limits. Additionally, the achieved outcomes can be used for Microgrid/SmartGrid applications. INDEX TERMSWind energy, Model predictive control, fuzzy logic, Microgrid NOMENCLATURE Support vector machines Back propagation Artificial intelligence Energy taken from wind turbine 1 Upstream turbine speed 2 Wind speed at turbine 3 Downstream turbine speed Wind velocity Rotor shaft area Wind Power Maximum wind power Swept area Densityof air V k *

Sustainable microgrid primarily powered by renewable energy sources is a recent concept to fulfill the pledge of delivering reliable power supply for upcoming power systems. This study presents a microgrid system primarily powered by wind and solar energy sources and identifies the issues related to the design, operation, and control of the system. The system is designed and simulated to detect the practical issues involved in the control and operation of the sustainable microgrid system based on wind and solar sources. The technical challenges and a brief plan of conceptual methods to detect some of the technical issues are presented for further analysis. To achieve power quality improvement, effective control architecture is described here. Furthermore, an advanced random pulse position modulation technique for the voltage source inverter is proposed to influence the DC-link inductor which further reduces the harmonic distortion. A current injected control loop (CICL) is also proposed to improve the dynamic behavior of microgrid, due to change in solar radiation and wind speed, which causes DC bus voltage oscillation and, hence, affects in proper system operation. The simulation results report that the microgrid system powered by renewable energy sources have a good performance.