2 resultados para power system security
em Galway Mayo Institute of Technology, Ireland
Resumo:
Although the ASP model has been around for over a decade, it has not achieved the expected high level of market uptake. This research project examines the past and present state of ASP adoption and identifies security as a primary factor influencing the uptake of the model. The early chapters of this document examine the ASP model and ASP security in particular. Specifically, the literature and technology review chapter analyses ASP literature, security technologies and best practices with respect to system security in general. Based on this investigation, a prototype to illustrate the range and types of technologies that encompass a security framework was developed and is described in detail. The latter chapters of this document evaluate the practical implementation of system security in an ASP environment. Finally, this document outlines the research outputs, including the conclusions drawn and recommendations with respect to system security in an ASP environment. The primary research output is the recommendation that by following best practices with respect to security, an ASP application can provide the same level of security one would expect from any other n-tier client-server application. In addition, a security evaluation matrix, which could be used to evaluate not only the security of ASP applications but the security of any n-tier application, was developed by the author. This thesis shows that perceptions with regard to fears of inadequate security of ASP solutions and solution data are misguided. Finally, based on the research conducted, the author recommends that ASP solutions should be developed and deployed on tried, tested and trusted infrastructure. Existing Application Programming Interfaces (APIs) should be used where possible and security best practices should be adhered to where feasible.
Resumo:
Due to the global crisis o f climate change many countries throughout the world are installing the renewable energy o f wind power into their electricity system. Wind energy causes complications when it is being integrated into the electricity system due its intermittent nature. Additionally winds intennittency can result in penalties being enforced due to the deregulation in the electricity market. Wind power forecasting can play a pivotal role to ease the integration o f wind energy. Wind power forecasts at 24 and 48 hours ahead of time are deemed the most crucial for determining an appropriate balance on the power system. In the electricity market wind power forecasts can also assist market participants in terms o f applying a suitable bidding strategy, unit commitment or have an impact on the value o f the spot price. For these reasons this study investigates the importance o f wind power forecasts for such players as the Transmission System Operators (TSOs) and Independent Power Producers (IPPs). Investigation in this study is also conducted into the impacts that wind power forecasts can have on the electricity market in relation to bidding strategies, spot price and unit commitment by examining various case studies. The results o f these case studies portray a clear and insightful indication o f the significance o f availing from the information available from wind power forecasts. The accuracy o f a particular wind power forecast is also explored. Data from a wind power forecast is examined in the circumstances o f both 24 and 48 hour forecasts. The accuracy o f the wind power forecasts are displayed through a variety o f statistical approaches. The results o f the investigation can assist market participants taking part in the electricity pool and also provides a platform that can be applied to any forecast when attempting to define its accuracy. This study contributes significantly to the knowledge in the area o f wind power forecasts by explaining the importance o f wind power forecasting within the energy sector. It innovativeness and uniqueness lies in determining the accuracy o f a particular wind power forecast that was previously unknown.