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The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

• The method to effectively capture the key information of faults and improved model efficiency. • The method based on meta-learning represents an innovative path for the inverter output decision-making. • The method can quickly and accurately fit the mapping between features and decisions. With the increase of energy demand and the continuous development of renewable energy technology, active distribution networks have become increasingly important. However, the introduction of a large amount of renewable energy has made the structure of ADN increasingly complex and fragile, and emergency fault caused by emergencies may often occur. Voltage control in the emergency fault event is particularly important. In this context, this paper presents a meta-learning based voltage control strategy for renewable energy integrated active distribution network. A general regression neural network is first applied to extract features from the operation data. Then, the local cross-channel interaction network is adopted to capture targeted information that is most related to emergency fault from the features and induce knowledge transfer to update the voltage control strategy. This allows the proposed strategy to make optimal decisions quickly when only limited data are available under an emergency fault that has never occurred. Comparison results based on a 69-bus distribution network validate the effectiveness and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Integrated Assessment Models (IAMs) are omnipresent in energy policy analysis. Even though IAMs can successfully handle uncertainty pertinent to energy planning problems, they render multiple variables as outputs of the modelling. Therefore, policy makers are faced with multiple energy development scenarios and goals. Specifically, technical, environmental, and economic aspects are represented by multiple criteria, which, in turn, are related to conflicting objectives. Preferences of decision makers need to be taken into account in order to facilitate effective energy planning. Multi-criteria decision making (MCDM) tools are relevant in aggregating diverse information and thus comparing alternative energy planning options. The paper aims at ranking European Union (EU) energy development scenarios based on several IAMs with respect to multiple criteria. By doing so, we account for uncertainty surrounding policy priorities outside the IAM. In order to follow a sustainable approach, the ranking of policy options is based on EU energy policy priorities: energy efficiency improvements, increased use of renewables, reduction in and low mitigations costs of GHG emission. The ranking of scenarios is based on the estimates rendered by the two advanced IAMs relying on different approaches, namely TIAM and WITCH. The data are fed into the three MCDM techniques: the method of weighted aggregated sum/product assessment (WASPAS), the Additive Ratio Assessment (ARAS) method, and technique for order preference by similarity to ideal solution (TOPSIS). As MCDM techniques allow assigning different importance to objectives, a sensitivity analysis is carried out to check the impact of perturbations in weights upon the final ranking. The rankings provided for the scenarios by different MCDM techniques diverge, first of all, due to the underlying assumptions of IAMs. Results of the analysis provide valuable insights in integrated application of both IAMs and MCDM models for developing energy policy scenarios and decision making in energy sector. [ABSTRACT FROM AUTHOR]

With the increasing negative effects of fossil fuel combustion, many countries have paid more attention on supporting environmentally friendly energy generation, particularly renewable and sustainable energy sources (RES). However, renewables still cannot economically compete with fossil-fuels and are facing new challenges. Appropriate electricity pricing mechanism plays a vital role on mitigating the stress of limited fossil fuels and can promote renewable energy consumption. As one major type of renewable energy, wind power has been globally promoted. In order to promote the application of wind power, appropriate wind power prices (WPP) should be established by considering the local conditions. This paper targets such a field and conducts a bibliometric and network analysis based on the data from Scopus. The results show that the numbers of total related publications are gradually increasing, with the US as the leading country. European countries also have outstanding achievements. Moreover, both the most cited articles and keywords distribution offer future research directions. In general, this study provides valuable insights to both wind power researchers and practitioners. [ABSTRACT FROM AUTHOR]

Energy sectors of all the countries around the world are faced with a big challenge: the energy transition under the imperative of continuous reduction of carbon dioxide emissions. The energy transition and the sustainability constraints pose a significant challenge for engineers, researcher and scientists developing new and clean energy-efficient technologies. Energy production and transformations, along with its storage, distribution and consumption, are achieved by utilising new and clean energy technologies, with the goal of the continuous increase of energy efficiency, the growth of renewable energy sources utilisation, the uninterrupted switch from fossil fuels to alternative energy sources, and the continued reduction of CO2 emissions. This editorial paper addresses the latest progress and the findings in research and developments in the sustainable energy technologies, energy and environment systems from selected papers of the conference on Sustainable Development of Energy, Water and Environment Systems held in 2019. [ABSTRACT FROM AUTHOR]