The 2020 climate and Energy Rates of the WHO passed an act in 2009 that states three main targets for greener and cleaner energy production. First greenhouse gas emissions were cut by 20%, second renewable energy sources give 20% of energy production, and third energy efficiency was improved by 20%. Each country has set an annual target to gradually increase so that they can achieve it in 2020. To achieve this climate goal set for 2020, the electricity market has changed rapidly over the recent years mainly from fossil-based production that gives a cleaner energy mix including some technology like hydro, solar, and wind. Climate and topology differences available in natural resources lead to various changes in product mix in different markets at the time of new renewable technologies introduction. Some markets like Nord pool have a very large portion of supply covered by hydropower and its energy source easily gets into control and with high predictability. In the other case, other markets like the German apex have high intermittent energy sources like photovoltaic and wind. These are not controllable electric power which produces where and when the sun shines or the wind blows. This will not happen at the same time as the demand for electricity at present. Due to these factors, one must attain the challenge that facing different market factors like supply security, volatility and environmental concerns are different. The main purpose of this thesis was to investigate the factors that move towards cleaner energy in the world. If we identify some negative factors that also help to navigate to possible solutions.
Your energy. Your choice
The organization will investigate major things through the lens of econometric modeling. Statistical models were created for price formation in several European electricity markets so that we can isolate and investigate some important effects of renewable energy sources consisting of specific variables like price volatility and electricity price. It will help us to qualify and identify certain negative externalities that cause by transmitting to renewable energy production. It identifies the significant problem and also uses economic models to detail the economic viability of infrastructure-based solutions that helps to control the negative effects. This will raise two major research questions. First, By creating electricity rates format in many European markets, specifically by detailing the fundamental factors and times in price formation, can we find some important challenges introduced into markets from the recent transition to renewable energy. Second, is this possible to identify an economical solution that helps to hide the negative impact of starting stage of renewable energy, and possible to find how various markets can complement each other? This contribution to different fields took place randomly. Moreover, the field of electricity price modeling takes place first. The most notable model is the use of quantile regression models. Some other modeling techniques are also used randomly. In the real option, we can extend the literature by creating a real model to select between two different locations for transmission assets, on the other hand, this model determines the economic value of high-scale battery storage. At last electric rates may vary in different models and react model has unique features to control energy rates with a minimum amount.