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Distributed photovoltaic power generation can efficiently utilize idle resources and reduce carbon emissions. In order to reduce the impact of grid-connected distributed photovoltaic power fluctuations on grid operation, this paper simultaneously exploits the temporal dependence of power series and the spatial correlation of meteorological data to propose a combined prediction model with temporal characteristics and spatial relationships fused for distributed photovoltaic power plants with spatiotemporal information. First, in the study of time-dependent prediction, we propose a long and short-term memory neural network ensemble prediction model based on genetic algorithm-natural gradient boosting, which efficiently fits multiple sets of temporal characteristics of distributed photovoltaic. In the study of spatial correlation prediction, the meteorological data affecting photovoltaic power generation are selected by κ correlation coefficients, the target power plant and spatial reference power plant meteorological data are reconstructed into a two-dimensional matrix, and a two-dimensional convolutional neural network spatial feature extraction power prediction model is designed. Finally, the advantages of the two prediction models of temporal information and spatial features are fused by multiple error evaluation criteria improved information entropy, and a distributed photovoltaic power plant is constructed and implements highly accurate spatiotemporal information combination prediction model. The effect of the forecasting model in this study is validated using the photovoltaic cluster dataset in Hebei Province, China. Compared with other models, the results of this study show that the five prediction performance evaluation metrics of the proposed combined spatiotemporal information model are better. [ABSTRACT FROM AUTHOR]

Given the pillar role of renewable energy in the low-carbon energy transition and the balancing role of energy storage, many supporting policies have been promulgated worldwide to promote their development. To achieve the ambitious goal of no less than 1200 GW of wind and solar by 2030, China has also introduced policies to encourage the deployment of energy storage for the grid integration of renewable energy. The national policy is conducive to enhancing system flexibility for renewable integration, but it will also add the costs of renewable energy, especially when counting the regional differences. This paper adopts an improved levelized cost of electricity model to examine the total costs of renewable power co-deployed with energy storage in different provinces of China. The results show that the nationally unified energy storage co-deployment requirement, namely, 15% capacity ratio of renewable installation and 4 h duration, will negatively affect the economics of renewable generation, leading to an average cost increase in 15% and 21% for wind and photovoltaic generation, respectively. The economics of co-deploying energy storage under current market mechanism is inferior, but it can be effectively improved when energy storage participates in ancillary services market. With the revenue of frequency regulation, the cost of renewable co-deployed with energy storage can be even less than that without co-deployment in most provinces, except for Hebei, Jiangxi, and Gansu. An independent market entity status is thus conducive to encourage the co-deployment. Our finding also implies for a province-specific energy storage co-deployment policy. [ABSTRACT FROM AUTHOR]

As the largest energy consumption country, China now pays more and more attention to photovoltaic power generation because solar energy is the largest renewable and sustainable energy reserve in the world. In this case, the photovoltaic power generation plant project plays a crucial role in sustainable development, especially in the underdeveloped northwest region of China. This paper focuses on applying bi-level programing to the multimode resource-constrained project scheduling problem (MRCPSP) in photovoltaic power generation plant construction, which simultaneously considers the practical hierarchical organization structure and an uncertain decision-making environment. A bi-level fuzzy random multiple objective model is developed, wherein the government agency is in the upper decision level, while the contractor is in the lower decision level. Then, motivated by the particular mathematical nature of the proposed bi-level MRCPSP model, a hybrid intelligent algorithm is designed. Finally, a practical case from the Shenneng Futa Kashi-Tashi-Kuergan photovoltaic power generation plant project in a Chinese energy company is applied, and the results validate the practicability of the proposed model and solution algorithm for solving practical photovoltaic power plant project scheduling problems. [ABSTRACT FROM AUTHOR]

Under the global climate change and energy crisis, solar energy has become the focus of research, development, and utilization throughout the world. Therefore, an empirical study on the awareness of residents for solar photovoltaic power generation is of great significance. Based on the 330 questionnaire data, by adopting the single factor variance analysis (S-N-K test) methodology, this paper first analyzed the differences between various groups of residents from varied perspectives of Chinese residents, i.e., different ages, different education attainments, different income levels, different genders, and different industries. Then, it conducted a regression analysis of the demographic variables by using linear regression models. The results indicate that: (1) the awareness of different types of Chinese residents is significantly different; (2) education and income levels have significant impacts on their awareness. Finally, this paper proposed corresponding policy recommendations based on these findings. [ABSTRACT FROM AUTHOR]

The technologies and application of distributed generators (DGs) are progressing rapidly due to the advantages of DGs. The penetration of renewable resources becomes higher and higher, which brings a lot of problems in distribution networks (DNs). The selection of interconnection point for DGs is required to be solved urgently. In this paper, the factors impacting on the interconnection includes host DN structure, DG characteristics, and DG function, which are analysed in order to provide the constraints for the selection strategy. The objective of the selection strategy is to search the interconnection point with the minimum equipment and operation costs. The IEEE 34-node test bed and a practical project of the building integrated photovoltaic in Shanghai are utilized to demonstrate the effectiveness of the selection strategy. [ABSTRACT FROM AUTHOR]