Chapters authored
Economic Future of Concentrating Solar Power for Electricity Generation
Worldwide, the climate change is a major universal concern. CO2 is the main cause of international warming, and at least 85% emission results of CO2 came from conventional energy depleted sources (oil, natural gas and coal) for energy generation. Hence, renewable energy has been the focal point of most regulations of governments to aim at greenhouse gas reduced. In Egypt, greenhouse gas emissions from rural activities amount to some 25% of national greenhouse gas emissions, amounting to approximately 27 million t CO2 equivalent annually. Moreover, these emissions are supposed to increase rapidly in the coming decades, more than doubling in the next 15 years, as rural populations grow and activities become increasingly energy intensive. The Mediterranean region embraces Europe, North Africa and Middle East and has enormous potential in solar energy. It has abundant solar radiation, cheap land and high electricity demand, which could make this region the universal hub for concentrating solar power (CSP) generation. This chapter discusses the Egypt market potential of CSP. The chapter covers recent CSP trends and discusses in detail the CSP market development. The chapter aims to obtain the data sources to compare the CSP and levelized electricity cost. Enas Shouman presents a strategy for CSP plant market entrance in Egypt and a comparison between the electricity cost for Egypt model case and the cost evolution of CSP plants on the basis of expectations for the expansion as an international level. This chapter proposes a concept strategy for management CSP in Egypt. The chapter included two applied parts. The first part is to calculate the generating electricity cost from conventional power sources and its expansion in the future. Then, the second part will be followed by identifying the CSP cost and its growth in the future.
Part of the book: Thermal Power Plants
Global Prediction of Wind Energy Market Strategy for Electricity Generation
Global warming and increasing electricity consumption trends in many parts of the world pose a serious challenge to most countries from a climate change and energy security perspective. Wind power is the only one that offers a mature technique, as well as promising commercial prospects, and is now generally applied in large-scale electricity generation. Continued technological improvements will assist to boost the on-shore and off-shore wind farms’ ability by improving micro turbine, enhancing reliability with predictive maintenance models. At the same time, as global and regional markets for wind power technologies grow, economies of scale are being reaped in manufacturing. With increased market scale, opportunities to improve the efficiency of supply chains arise. Technological improvements and cost reductions have led wind energy to become one of the most competitive options for new generation capacity. Wind energy still has significant potential for cost reduction. Indeed, by 2025, the global weighted average levelized cost of electricity (LCOE) of onshore and offshore wind could see declines of 26 and 35%, respectively. This chapter aims to provide an overview of the world wind energy market, current and forecasting development globally of wind energy, and LCOE historical growth Ffor wind energy.
Part of the book: Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems
Wind Power Forecasting Models
The rising costs and undesirable environmental effects of traditional, nonrenewable energy sources have led to increased research regarding the viability of renewable energy sources. Wind has been the fastest-growing source of electricity generation in the world since the 1990s. One of the primary limiting constraints of wind energy is its reliability and there is no cost-effective mechanism for storing wind energy generated by a wind turbine, thus it must be quickly integrated into the electrical grid. The financial implications of wind forecasting are also of great consequence. A 1% error in forecasted wind speeds can result in a loss of $12,000,000 during the facility’s life time. As more wind power is incorporated into electricity markets, the capacity to correctly and precisely estimate wind speeds has become increasingly vital. Hence, the importance of this chapter by addressing the different divisions related to wind speed prediction into two overall groups. The first group is based upon analysis of historical time series of wind energy forecasting explanatory variables which are generated from a meteorological model of wind dynamics and the second uses forecasted values from a numerical weather prediction (NWP) model as an input to utilize a statistical approach to anticipate energy predication.
Part of the book: Wind Turbines
Solar Power Prediction with Artificial Intelligence
Solar power prediction is a critical aspect of optimizing renewable energy integration and ensuring efficient grid management. The chapter explore the application of artificial intelligence (AI) techniques for accurate solar power forecasting. The AI models considered include Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest, and Gradient Boosting. These models are selected based on their ability to capture complex patterns and non-linear relationships present in the solar energy data. The solar power forecasting process involves data preprocessing, feature selection, model training, and evaluation. Data preprocessing techniques are applied to handle missing values and normalize the data to improve model performance. Feature selection methods are utilized to identify the most relevant features that influence solar power generation. The AI models are trained using historical data, where they learn the relationships between input features and solar power generation. Model evaluation is carried out using metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) to assess the accuracy of the forecasts. Furthermore, the forecasted results are visualized through line plots and error plots to provide valuable insights to stakeholders. A comprehensive report detailing the forecasting process, methodology, and results is generated, allowing decision-makers to make informed choices based on the forecasted solar energy data.
Part of the book: Advances in Solar Photovoltaic Energy Systems