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With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

Renewable energy is an effective solution to alleviate the energy crisis and achieve sustainable development, and is gradually growing into an important energy source to support economic and social development. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. Given this background, this paper proposes a novel evaluation method for renewable energy technical plans based on improved rough set theory and grey cloud model. Firstly, based on the normalized evaluation data, self-organizing map (SOM) clustering algorithm and system clustering analysis are adopted to discretize the evaluation attributes indexes and decision attributes indexes, respectively. Next, the improved rough set conditional information entropy method is used to calculate the index weights. Finally, the grey cloud model is employed to determine the grade classification of each evaluated plan. At the same time, each grade is quantified by assigning a corresponding score, and the comprehensive performances of renewable energy plans can be calculated, and then the ranking of different plans is achieved. Based on the actual renewable energy development data from a province in China, experiments were carried out to evaluate the performance of the proposed method. The results show that the proposed method performs better than some evaluation methods for renewable energy technical plans. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Use self-organizing map clustering algorithm to discretize the attributes indexes. • Apply improved rough set theory to determine the index weights. • Achieve the complete ranking of plans, and classify the plans into different levels. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

• The data envelopment analysis is introduced to renewable energy development. • The method has two stages: comprehensive evaluation and structure optimization. • Indexes from different aspects are quantified via intuitionistic fuzzy numbers. • The comprehensive performances are determined based on the relative efficiencies. • The proportions of renewable energy are optimized based on the whole efficiency. Different kinds of renewable energy resources have developed rapidly. For renewable energy planning, it is meaningful to assess the comprehensive performances of different schemes and then to determine the optimal design of the energy structure. This paper presents a two-stage method for the comprehensive evaluation and structure optimization of renewable energy plans. First, in the evaluation stage, multiple indexes from different aspects are taken into consideration, of which each qualitative index will be converted quantitatively, using the intuitionistic fuzzy number to describe the fuzziness and ambiguity in a qualitative index. Then, the superefficiency data envelopment analysis model is used to determine the comprehensive performances of different plans based on the concept of relative efficiency. Next, in the optimization stage, an optimal model combining multiple renewable energy resources is established based on the relative efficiency results of the evaluation process. This model aims at the maximum efficiency as a whole and can be used to optimize the proportions of different renewable energy resources. Finally, a real case of renewable energy development from a province in China is given to demonstrate the feasibility and effectiveness of the proposed method in this paper. The results show that it can overcome the shortcomings of the traditional basic model, obtain more objective evaluation results and provide beneficial references for the strategy making of renewable energy development. [ABSTRACT FROM AUTHOR]