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The site selection plays an important role in the entire life cycle of a tidal power plant (TPP) project. However, some problems decrease the evaluation quality of TPP site selection: (a) suitable and effective methods are scarce since the TPP site selection involves multiple forms of data; (b) there is no comprehensive evaluation index system due to the unilateralism of existing criteria. In this paper, we firstly propose a novel method based on interval number with probability distribution weighted operation and stochastic dominance degree. It takes all stakeholders' preferences into consideration and can simultaneously deal with different forms of data in the TPP site selection; then, a comprehensive evaluation index system for TPP site selection is constructed on the basis of academic literature, feasibility research reports and expert opinions in different fields. It takes the factors of construction conditions, existing policies, social impacts as well as ecological and environmental impacts which reflects the inherent characteristics of TPP site selection fully into account. Finally, a Chinese case study is given to illustrate the applicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France. [ABSTRACT FROM AUTHOR]