MODELLING AND OPTIMIZATION OF HYBRID RENEWABLE ENERGY SYSTEMS AND APPLICATIONS

Abstract

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. Wind and solar energy is critically important for the social and economic growth of any country. Moreover, reliable and precise wind and solar power prediction is crucial for the dispatch, unit commitment, and stable functioning of power systems. This makes it easier for grid operators of the power system to support uniform power distribution, reduce energy loses, and optimize power output. Consequently, the integration of wind and solar power globally relies on correct wind and solar power forecasting. Current studies typically adopt machine learning algorithms (ML). The foremost contribution of this research is short-term wind power forecasting on the basis of the historical values of wind speed, wind direction, and wind power by using ML algorithms. In this study, regression algorithms such as random forest, k-nearest neighbor (k-NN), gradient boosting machine (GBM), decision tree, and extra tree regression are employed to enhance the forecasting accuracy for wind power production for a Turkish wind farm situated in the west of Turkey. Polar curves have been plotted and the impacts of input variables such as the wind speed and direction on wind energy generation is examined. Scatter curves depicting the relationships between the wind speed and the produced turbine power are plotted for all of the methods here and the predicted average wind power is compared with the real average power from a turbine with the help of the plotted error curves. The second contribution of this research is short-term solar power forecasting on the basis of the historical values of ambient temperature, irradiation, module temperature and solar power by using ML algorithms. In this study, regression algorithms such as random forest (RF) and k-nearest neighbor (k-NN) regression algorithms are employed to enhance the forecasting vii accuracy for solar power production for a Qassim University, KSA. The performance of all algorithms were estimated based on the various statistical indicators. As renewable energy sources (RES) provide intermittent power and are not available 24 hours a day, it is vital to build hybrid models based on RES to provide an uninterrupted, sustainable, eco-friendly, and cost-efficient power supply. The current research is also devoted to the development and design of an optimal hybrid model using locally accessible RES for selected locations. The evaluation of the potential of locally available RES for selected sites in Uttar Pradesh, India, is carried out to develop the hybrid model. To fulfil the energy demand of the selected site, a hybrid model was constructed using the Hybrid Optimization Model for Electrical Renewable (HOMER) software based on the feasibility analysis of RES at the selected site. To create a hybrid model, the electrical load demand for the specified location is evaluated while taking seasonal fluctuations, current and future power requirements, everyday hourly consumption patterns, living standards, and so on into account. The primary goal of this study is to develop an economic and optimal hybrid PV/Biogas configuration for power production for rural common facilities including one Primary school, Junior school and Panchayat Ghar buildings of Sarai Jairam village in Uttar Pradesh, India. The PV/biogas hybrid configuration was designed utilizing the Hybrid Optimization Model for Electric Renewable (HOMER) and techno-economic analysis is carried out to fulfill the load requirements. The HOMER analysis produced a solution that included total net present cost (NPC) and cost of electricity (COE), and these results were then further improved using sensitivity analysis. Based on the NPC and COE, this analysis evaluates the system performance and demonstrates that it is techno-economically feasible. In addition, for maximizing the solar power generated from solar photovoltaic system (SPV), the optimization of space and orientation of solar PV system are also done.