FORECASTING OF WIND AND SOLAR POWER GENERATIONS FOR ENHANCING THEIR PENETRATIONS IN SMART GRID

Abstract

The vanishing conventional energy sources and global warming drive the world for the power generation from renewable energy sources. The main renewable sources namely solar power and wind power are uncertain and intermittent in nature. Wind & solar photo voltaic (PV) power forecasting with good accuracy promise the power sector for large scale integrations of wind & solar PV power generations into the grid. In the context of smart grid and deregulated electricity market, price forecasting is a challenging job for researchers. A rigorous literature review of wind power forecasting, solar PV power forecasting and price forecasting is conducted with focus on various statistical & learning forecasting methods. The data is collected from Belgium wind farms, US wind farms and Indian wind farms and Indian photo voltaic plants. The dependency of wind power generation and solar power generation is analyzed with the computation of correlation factors. Nonlinear autoregressive with external input (NARX) model is implemented to forecast wind power generation of Belgium wind farms by using historical data of wind speed and wind power. Further, NARX model is also used to forecast wind speed for US wind farms from the input data of wind direction, temperature and air density. Wind speed is predicted with good accuracy and minimum MAPE is 2.3%. The research work is continued to improve short term wind power forecasting accuracy by designing generalized regression neural network (GRNN) and radial basis function neural network (RBFN). A hybrid network of GRNN &RBFN is designed with parallel topology to forecast wind power for improved accuracy. Reliability of forecasting models is analyzed with the computation of confidence intervals on MAPE. As support vector machine (SVM) is very good at classification and regression analysis, in this work the support vector regression (SVR) model with tuned parameters is used to xiii forecast wind power generation and solar PV power generation. To achieve better accuracy and to retain the benefits of individual models, a hybrid approach K-means clustering based artificial neural network- particle swarm optimization (ANN-PSO) model is designed and proposed for solar PV power forecasting. In the context of smart grid, the uncertainty in wind & solar PV power generations increases the volatility of electricity price. A hybrid approach of K-means clustering based long short term memory (LSTM) network is proposed for short term electricity price forecasting of Austria by considering wind power generation in the market. The proposed model shows highest accuracy in prediction when compared against feed forward neural network-particle swarm optimization (FNN-PSO) and SVR models. In hour ahead price forecasting with the consideration of wind & solar PV power generations, bootstrap aggregation of ensemble model (proposed model) has outperformed with significant reduction in error. As renewable energy integration to the power grid is enhancing day by day, it becomes pertinent to introduce new market models to operate the renewable energy (RE) enabled restructured electricity market. For such an RE enabled Indian electricity market, seven various market models are developed and proposed along with their salient features. An operating mechanism for future RE enabled Indian electricity market is also proposed based upon the developed models.