Search

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. 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. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of 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 popular renewable energy evaluation methods. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Apply non-dominated sorting genetic algorithm II to determine the index weights. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, renewable energy, especially wind power, solar PV and hydropower, will become the most important power sources in the future low-carbon power system. Since wind power and solar PV are specifically intermittent and space-heterogeneity, an assessment of renewable energy potential considering the variability of wind power and solar PV with high temporal resolution in different regions will facilitate more accurate identification of the decarbonization pathway of power system. In this paper, the complementary output potential of wind-solar-hydro power every 15 min in 31 Chinese provinces is evaluated by developing a multi-objective optimization model based on Nondominated Sorting Genetic Algorithm II. The results show that the total annual complementary power generation potential in China is 17.57 PWh, of which the three components account for 47.8%, 25.3% and 26.9%, respectively. Inner Mongolia and Tibet are the major renewable energy source provinces with annual complementary power generation potential of 5330 TWh and 3907 TWh, accounting for 30.3% and 13.7% of the total power generation potential, respectively. The temporal potential of wind-solar-hydro power varies greatly, with daily potential is more volatile than monthly. Seasonal and spatial heterogeneity of the complemental renewable potential makes some provinces suffer power shortage during long-hours period especially in winter. Our findings will encourage a higher penetration of renewable energy, the promotion of multi-energy complementarity, and the development of inter-provincial power transmission and energy storage infrastructure in China's future power sector. • A multi-objective wind-solar-hydro complementary optimization model is developed. • Electricity supply and demand have gaps at different temporal and spatial scales. • Wind-solar-hydro complementary potential shows great temporal and spatial variation. • Renewable complementarity can improve China's future power system stability. [ABSTRACT FROM AUTHOR]

Carbon neutrality is one of the key issues in mitigating global climate change. The carbon neutrality process accelerates the global energy transformation from fossil energy to renewable energy. Among the main indicators related to carbon neutrality proposed by the Chinese government, those directly related to energy include carbon emission intensity, the proportion of nonfossil energy consumption, and the total installed capacity of wind and solar power generation. It can be seen that the transformation of energy development is crucial for realizing carbon neutrality. Meanwhile, the correlation between carbon neutrality and economic development is not antagonistic. On the contrary, carbon neutrality can not only enhance the quality and efficiency of traditional industries but also improve the overall development of society. Therefore, the coordinated and sustainable developments of the energy - economy-environment (3E) system is the priority for achieving the target of carbon neutrality. However, the impact of carbon neutrality on regional development requires long-term consideration, which increases the difficulties of comprehensive planning. In this study, a long-term comprehensive programming model of regional energy and economic development under carbon peaking and carbon neutrality targets was established. A carbon neutrality assessment indicator (CNAI) framework was constructed to assess the effect of carbon neutrality actions in different regions. Meanwhile, a LEAP model was developed to simulate energy demand and carbon emissions. The multi-target weights determined by assessment results of CNAI and the parameters range set by LEAP model were the basis of the multi-objective optimization model to support regional development. Furthermore, the proposed method was applied to Shaanxi province in China, which offered quantitative targets and suggestions for energy structure transformation and industrial structure adjustment. The results show that coal and oil will account for 6.25% of the total energy consumption and the proportion of secondary industry will drop to 25.40% by 2060. The method established in this study provides a new scientific approach for resolving multi-objective trade-offs in long-term programming and determining a reasonable optimization range for regional development under climate mitigation policies. [ABSTRACT FROM AUTHOR]