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The pulp and paper industry faces several challenges linked to climate and environmental impact, resource efficiency, rising energy prices, increased competition for biomass resources, and declining demand for traditional printed paper products. However, these challenges also offer strategic opportunities for the industry to develop into a competitive, resource-efficient, and low-carbon industry in line with a biobased economy. Against this background, this paper aims to analyse current energy strategies in the pulp and paper industry in Sweden. Specifically, the paper analyses how companies combine continuous process efficiency to reduce energy costs with activities that could be developed into new energy-related products to increase revenue. Most of the analysed companies work to various degrees with both these strategies, employing methods that include improving energy efficiency, energy security, and energy conversion, as well as developing a wide range of biobased energy products. However, our study indicates that there is an untapped potential associated with energy product development, and we conclude that energy efficiency measures can free up resources, enabling the development of new energy products. Finally, several potential managerial outcomes and implications are outlined. • The paper analyses the energy strategies in the pulp and paper industry in Sweden. • Energy strategies focus both to improve energy efficiency and develop new energy-products. • The companies' energy strategies combined exploitation and exploration. • A number of managerial implications are presented. [ABSTRACT FROM AUTHOR]

Abstract: Alarmingly low pulp prices in early 2009 left pulp and paper mills across North America desperate for any way to improve thin profit margins. One solution that continues to gain popularity among the industry is improved energy management systems for cogeneration systems, which use steam for two purposes – to provide heat for the pulping process and to generate electricity for sale to regional providers. This paper presents an energy optimization algorithm for use in a pulp and paper mill cogeneration system. The algorithm is applicable to a number of popular mill configurations, power sale contracts, and fuel purchasing scenarios. The method is also extended to address weather-dependent cooling limitations encountered by a mill cogeneration facility, in which case an iterative solution is proposed in order to maintain convexity of the optimization problem. Results are presented in the form of three case studies. [ABSTRACT FROM AUTHOR]

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]

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]

• •A full-time scale resilience enhancement framework is proposed. • •A minimax regret robust optimization model is established to make defensive strategy. • •The influence of gas and transportation networks is jointly considered. • •Two master-subproblem frameworks are developed for solving the proposed models. The frequent occurrence of extreme disaster events with low probability and high risk poses a serious threat to the safe and stable operation of power grids. With the development of energy internet technology, the deep integration of power distribution, natural gas and transportation networks has opened a promising way for resilient distribution networks. This paper proposes a full-time scale resilience enhancement framework for an integrated electricity-gas-transportation coupled system oriented to energy internet. In this framework, a minimax regret robust optimization model considering the uncertainty of extreme disaster attacks is developed to make the line hardening strategy in the pre-disaster stage, which is solved by the column-and-constraint generation algorithm. Furthermore, the synthetic restoration model considering the co-optimization of dynamic dispatch of gas-fired units, network reconfiguration and damaged component repair is established, in which the coupling influence of the gas and transportation networks on the restoration strategy during the disaster and post disaster stages is taken into account. And the sequential cone programming method and generalized Benders decomposition algorithm is implemented to solve the above model. Finally, the proposed model and solution method are validated via a modified 33-bus power distribution network coupled with a 12-node transportation network and a 7-node gas network. The simulation results demonstrate that the proposed minmax regret robust optimization model can achieve a pre-disaster defensive strategy with less conservativeness in contrast to the conventional robust optimization model. Specifically, the cost under the worst-case attacks by the former has reduced by 11.52–22.29% compared to the latter. Moreover, the influence of the interdependent critical infrastructure networks on the synthetic restoration strategy in the disaster and post-disaster stages has been investigated. In particular, the power output by gas-fired units has reduced by 2699 kW when considering the operation constraints of the gas network. Additionally, due to the time-varying characteristics of traffic flow and commuting time in the transportation network, the repair time for the damaged components is shortened by 1.09 h while the preparation time of crews changes from 2.5 to 1.5 h. [ABSTRACT FROM AUTHOR]

Over the past few years, energy communities initiatives have gained traction in Europe as a means to achieve energy transition goals, allowing citizen involvement in energy production, consumption, and distribution. Following the 2018 European directive RED II, these projects have rapidly increased across the continent, showcasing their potential for creating sustainable local energy systems. While studies highlight economic, environmental, and social benefits, barriers such as regulatory challenges, finance access, and low public awareness remain. This paper presents the results of a systematic literature review that explores the state-of-the-art of the academic research on energy communities. The study adopts a systematic approach to examine the body of literature on the topic to provide a comprehensive analysis of the phenomenon on the basis of rigorous and replicable research criteria. The reviewed material consists of 140 articles collected through the Scopus database, evaluated using specific structural dimensions to group the literature into analytical categories. The analysis of scientific papers published between 2018 and 2022 shows a growing attention towards Renewable Energy Communities, particularly those based on solar energy for electricity production, often combined with storage systems and heat pumps, whereas attention towards energy communities based on thermal energy production is still limited. Furthermore, most scholars focused on analysing specific territorial contexts; comparisons between different regions allows to highlight common features and elements characterizing each model. It emerged that the development of energy communities depends mainly on regulatory, financial, and managerial barriers that require an adequate institutional and legal framework capable of promoting their diffusion. Most studies address the topic from an economic perspective, often in association with incentivizing community models. However, a social perspective should be included by introducing the concept of energy sharing and methodologies for its distribution among all members of the community. The scientific community is called upon to continue investigating and supporting the development of community energy initiatives by providing evidence-based recommendations to policymakers, industry stakeholders, and local communities. This can help to overcome existing barriers and facilitate the diffusion of energy communities models, contributing to the achievement of sustainable and inclusive energy transition goals. • Systematic literature review exploring academic research on energy communities in Europe. • Structural dimensions and related analytical categories as framework to review 140 journal articles. • Most studies concern ECs based on renewable energy sources, with solar power being the predominant source. • Most employed technologies are related to the production of electricity from photovoltaic with batteries and heat pumps. • Most studied focus on Renewable Energy Community, while Citizens Energy Community are less considered in academic studies. [ABSTRACT FROM AUTHOR]

Pumped storage technology plays a crucial role in achieving balance in power systems and enhancing the stability of energy systems. Scientific planning can help optimize the operation of power systems, promote the development of renewable energy, and conserve energy. This paper addresses the capacity planning problem of pumped storage stations in hybrid operation systems considering wind power uncertainty. A comprehensive decision-making method is proposed, based on the Delphi method, interval intuitionistic fuzzy theory, grey relational theory, entropy weight method, and prospect theory. This method comprehensively evaluates decision alternatives by considering various factors such as the uncertainty of qualitative indicators, the interrelationships between indicators, and the impacts of different risk environments. The test system in this paper is conducted on a hybrid wind-thermal-pump storage output model based on the IEEE 30-bus system. The optimal capacity planning scheme determined by the proposed method is 190 MW when decision-makers are risk-neutral or risk-averse, and 200 MW when they are risk-appetite. Subsequently, the case study verifies the impact of different load levels and proportions of wind power access on the optimal decision-making scheme, and compares it with decision-making method such as principal component analysis, technique for order preference by similarity to an ideal solution, and a combined method of Pythagorean fuzzy theory and cumulative prospect theory, validating feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Uranium extraction from seawater is essential for the sustainable development of nuclear energy. Nevertheless, the complex coexistence of ions in seawater can significantly impact the practical uranium species distribution and uranium adsorption process. In this paper, a novel g-C 3 N 4 /ZrO 2 -loaded amidoxime polyacrylonitrile (g-C 3 N 4 /ZrO 2 -AO) was synthesized and investigated as an adsorbent for uranium in U(VI)–CO 3 , Ca/Mg–U(VI)–CO 3 , U(VI)–P–CO 3 and U(VI)–X–CO 3 (X = F, Cl, Br, I) solutions. The uranium adsorption capacity (q e) of g-C 3 N 4 /ZrO 2 -A reached ∼104.5 + 1.5 mg·g 1 in U(VI)–CO 3 solution, which correlated with a monolayer chemisorption process. High pH and [NaHCO 3 ] decreased q e values because of the electrostatic repulsion and occupation of adsorption sites by excess CO 3 2-. Adsorption of uranium by g-C 3 N 4 /ZrO 2 -AO was impeded in the Ca/Mg–U(VI)–CO 3 , U(VI)–P–CO 3 and U(VI)–X–CO 3 (X = F, Cl, Br, I) solutions, possibly due to the formation of various U(VI) complexes. The amidoxime groups inside the g-C 3 N 4 /ZrO 2 -AO have been proved with good uranium selectivity. FTIR and XPS verified the NH 2 -C N-OH and Zr-OH acted as the primary U(Ⅵ) adsorption sites. This paper opens up opportunities for practical applications of amidoxime polyacrylonitrile-based composites towards the uranium recovery from seawater. [Display omitted] [ABSTRACT FROM AUTHOR]

The selection of optimal design and the most efficient operational parameters for energy devices constitute a priority task for sustainable development and increasing energy efficiency within the net-zero emissions strategy. This is particularly important in adsorption cooling and desalination systems with poor performance due to unfavourable heat transfer conditions in conventional packed beds of adsorption chillers (ACs). Therefore, looking for additional ways of performance improvement is still challenging, especially covering different design variants and operational strategies. The existing complex, time-consuming and costly analytical, numerical and experimental methods, usually focused on a specific design and operating parameters of conventional packed adsorption beds, cannot tackle these comprehensive problems. Since artificial intelligence (AI) based models are considered tools that sometimes may overcome the shortcomings of the programmed computing approach and the experimental procedures, the paper introduces automated machine learning (AutoML) as a general approach for the design and optimization study of adsorption cooling and desalination systems. The double-effect, i.e. specific cooling capacity (SCP) and specific daily water production (SDWP) of various adsorption chillers (ACs) operating in large-, pilot- and small-scale adsorption cooling and desalination systems, is considered in the study. The paper also presents a novel big data optimization procedure for selecting the best operating and design strategy in adsorption cooling and desalination technology. Finally, a new concept of fluidized bed-type application in adsorption chillers is proposed, which allows for enhancing the performance of ACs. The presented approach can be referred to as a complementary design technique in adsorption cooling and desalination systems, besides the existing complex analytical and time-consuming numerical methods and expensive experiments. • Double-effect desalination and cooling production in adsorption chillers (AC) are evaluated • The new approach allows considering various cooling and desalination adsorption systems. • The novel concept of fluidized bed application in adsorption systems is proposed. • The developed models constitute powerful tools for optimizing ACs systems' performance. [ABSTRACT FROM AUTHOR]

This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution with specific evidence from ASEAN countries. The study applies an autoregressive distributed lag (ARDL) approach. The paper also uses the unrestricted fixed and random panel data methods. The manuscript identifies significant factors such as technological innovation, governmental policies, public awareness, electricity consumption, country population, foreign direct investment inflows, imports, exports, and economic growth that have influenced the renewable energy sector in these nations. This study also investigates the determinants that influence renewable energy in the context of international integration and the fifth technology revolution in ASEAN countries. The research analyzes data spanning two decades, from 2000 to 2022, through a combination of econometric modeling and machine learning techniques. Preliminary findings indicate that technological innovation, government policies, and public awareness significantly affect renewable energy consumption in these countries. The empirical results offer insights for policymakers and stakeholders in leveraging technological advancements for sustainable energy development. The findings from this study on the determinants of renewable consumption in the context of the Fifth Technology Revolution in ASEAN countries also have several important policy implications for the sustainable development goal. • This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution. • The study uses panel data analysis, and the paper identifies significant factors that have influenced the renewable energy sector. [ABSTRACT FROM AUTHOR]

• Recent advances in discovery and performance prediction of energy storage materials relying on ML. • An overview of the current status and dilemmas of ML databases commonly used in energy storage materials. • ML in energy storage material discovery and performance prediction: typical applications and examples. • Dilemmas to be faced in the development of ML-assisted or led energy storage materials, and corresponding prospects. Energy storage material is one of the critical materials in modern life. However, due to the difficulty of material development, the existing mainstream batteries still use the materials system developed decades ago. Machine learning (ML) is rapidly changing the paradigm of energy storage material discovery and performance prediction due to its ability to solve complex problems efficiently and automatically. Various excellent works are constantly emerging in the field of ML assisted or dominated development of energy storage material, such as exploring of new materials, studying of battery performance, investigating of battery aging mechanism. In this paper, we methodically review recent advances in discovery and performance prediction of energy storage materials relying on ML. After a brief introduction to the general workflow of ML, we provide an overview of the current status and dilemmas of ML databases commonly used in energy storage materials. The typical applications and examples of ML to the finding of novel energy storage materials and the performance forecasting of electrode and electrolyte materials. Furthermore, we explore the dilemmas that will be faced in the development of applied ML-assisted or dominated energy storage materials and propose a corresponding outlook. This review systematically summarizes the current development of ML-assisted energy storage materials research, which is expected to point the way for its further development. [ABSTRACT FROM AUTHOR]

The power industry in China is the foremost emitter of carbon, necessitating a clean transition in its power generation segment for successful decarbonization. Identifying and quantitative analysis the critical factors that shape the transition and their operational mechanisms will help achieve a secure low-carbon transformation of the energy system. Nonetheless, the body of extensive research in this particular field remains notably limited. To bridge this gap, this paper initiates by developing a comprehensive mechanism framework (REEP) for the transition towards cleaner power generation including Resource endowment (abbreviated as R), Electricity market (abbreviated as E), Economic development (abbreviated as E) and Policy implementation (abbreviated as P) four fields. This framework is based on monthly power generation data derived from four categories of power generation sources at the provincial level in China, spanning the years 2014–2019. It dynamically and quantitative elucidates the multifaceted impacts of various influencing factors on the evolution of the power generation structure. Based on the REEP framework, we observe the power generation structure evolution trends. The study's findings underscore the significance of resource endowment as a paramount internal factor influencing the clean evolution of the power generation structure, while economic development emerges as a pivotal external driving force propelling its transformation. Despite wind and solar power generation accounting for a relatively small proportion at the end of the 14th Five Year Plan period, it is imperative for China to continue promoting the extensive development of renewable energy. While we should coordinate national power transmission and distribution on the foundation of incentivizing technological innovation in emission reduction and the development of energy storage technology. This approach is essential to comprehensively advance the low-carbon and stable development of the energy system, ultimately facilitating the realization of the dual-carbon goal. [ABSTRACT FROM AUTHOR]

One of the most important areas of infrastructure projects that can contribute to the goals of sustainable development is the energy sector. Forming the right set of energy projects can lead to developing a sustainable environment. Another important aspect is the resilience of energy projects. This paper introduces a novel approach that simultaneously considers resilience and sustainability in forming energy project portfolios. In the first part, the relative importance of each project is computed under a triangular neutrosophic (TN) environment. This approach keeps the vagueness of data while evaluating projects versus resiliency and sustainability criteria. In the second part, an interval mathematical model is presented to maximize the total value of the selected portfolio while addressing bi-level budgeting and skill utilization. In this study, uncertainty is expressed in a hybrid way which enhances the flexibility of computations in addition to providing experts with more freedom in expressing their opinions. To present the application of this method, data from an existing case study has been used, and the process has been implemented step by step. This application demonstrates the importance of addressing resilience and sustainability in energy projects. Moreover, it shows the benefits of applying a hybrid solution approach in presenting the vagueness of energy projects in addition to assigning the budget in a bi-level process. • Introducing a new energy project evaluation approach under neutrosophic fuzzy sets. • Considering resilience and sustainability in energy project portfolio evaluation. • Presenting a new model by bi-level budgeting and skill utilization. • Providing project manager assignments through skill improvements and declines. • Using data from a case study to demonstrate the applicability of proposed approach. [ABSTRACT FROM AUTHOR]

• The self-assembly of supramolecular hydrogels and their physio-chemical properties are described. • The applications in stimuli responsiveness, energy storage and energy conversions, etc., are summarized. • For supramolecular hydrogels, none of the review papers provide comprehensive summaries of recent advancements and applications. This review article highlights the potential of supramolecular hydrogels as emerging energy materials. Supramolecular hydrogels are a class of soft materials with unique properties, such as self-healing, self-assembly, stimuli responsiveness, and biocompatibility, making them an attractive platform for various energy applications. The article discusses the definition, properties, and advantages of supramolecular hydrogels in energy materials. It further explores the recent advances in the field, such as self-healing properties, surface functionalization, and stimuli-responsive behavior. The article concludes by discussing the current challenges and future perspectives on using supramolecular hydrogels in energy materials. Overall, supramolecular hydrogels hold great potential as versatile and sustainable materials for energy storage and conversion, and further research in this area can lead to the development of novel energy solutions. [ABSTRACT FROM AUTHOR]

Recently, with the development of new energy technologies, all-electric ships (AESs) with hybrid energy storage system (HESS) are becoming a promising solution to reduce fuel consumption and emissions. However, the high maneuverability of ships during the actual navigation places higher performance requirements on the HESS, which presents a nonlinear and multi-objective challenge for the HESS design. Therefore, it is necessary to consider the coupling between HESS sizing and energy management strategy (EMS). In this paper, a self-adaptive joint optimization framework (SJOF) for marine HESS design considering load fluctuation characteristics is proposed, which can find the optimal decision solution with excellent system economic and battery life performance for AES HESS design. Based on the rain flow counting (RFC) method, a multi-objective joint optimization method considering life cycle cost (LCC) and battery degradation index (BDI) is introduced into SJOF. Besides, a novel EMS including the self-adaptive segmentation mechanism (SSM) and power allocation is proposed, which can achieve the most efficient energy scheduling. • A self-adaptive working condition classification approach is proposed. • A power allocation strategy with optimal filter matching is proposed. • A reconstruction method for obtaining optimal solution set is proposed. [ABSTRACT FROM AUTHOR]

• A stochastic coordinated generation and transmission expansion planning model is proposed. • The planning model takes into account the impact of demand response programs and renewable energy development. • The detailed demand response model with operating characteristics such as power, energy, reserve, response period, and response number is established. • The chance-constrained method is introduced into the planning model to flexibly control the participation level of DR programs. The integration of high-penetration renewable energy has led to a significant increase in the uncertainties of power systems, and coordinated generation and transmission expansion planning has attracted more and more attention. Demand response resources are regarded as a kind of virtual power supply, which can improve the flexibility of power systems and delay generation and transmission investment to a certain extent. This paper establishes a stochastic coordinated generation and transmission expansion planning model taking into account the impact of demand response programs. In particular, the demand response model describes the operating characteristics such as power, energy, reserve, response period, and response number in detail. Considering the difference in the degree of invocation of demand response programs by different power systems, the chance-constrained method is adopted, which can make the planning schemes flexibly adapt to different demand response levels and improve the practicability of the model. In the case study, numerical results of construction and operation indicate that demand response programs can play a role in replacing generation and transmission capacity. Specifically, although the response energy does not exceed 0.3% of the total load energy, the investment cost can be saved up to more than 20%. Moreover, the response level, flexibility parameters, response capacity, and the proportion of renewable energy will all affect the capacity substitution effect of demand response, which verifies the rationality and necessity of considering these factors in this paper. [ABSTRACT FROM AUTHOR]

With the promotion of emerging technologies in the era of big data, there has been a diversification in user participation in product requirement development. This has resulted in many complex, diverse, and rapidly iterated user requirements, greatly reducing the applicability of traditional requirement development methods. In this paper, to address the complex and ever-changing relationships among users, requirements, products, and requirement development teams in the product development process, a multilayer dynamic heterogeneous network node influence analysis method is constructed for product requirement development. This method establishes a product requirement development framework based on a multilayer dynamic heterogeneous network consisting of a user layer, requirement layer, component layer, and expert layer in terms of three dimensions: requirement development, requirement evaluation, and time. Based on this framework, indicators such as comprehensive node significance, layer significance, and node influence are designed. To address the issues encountered in small sample data testing, three measurement models are further designed in this paper, effectively improving the sampling efficiency of the multilayer dynamic heterogeneous network. Additionally, calculation models for the comprehensive node influence and comprehensive node influence variation rate are improved and constructed, and corresponding algorithms are designed. This method is applied in an experimental study of product requirement development for new energy vehicles, providing high-quality recommendations for new energy vehicle requirement developers, including functional requirements and product components that need close and timely attention. Moreover, accuracy tests show that the proposed algorithm achieves an average accuracy rate of 0.8611, which is significantly higher than those of other network node centrality measurement methods. This demonstrates the advanced nature and applicability of the proposed method in the field of product requirement development, providing a new way for requirement developers to improve work efficiency. [ABSTRACT FROM AUTHOR]

• The proposed joint demand response and dynamic pricing strategies can make the net grid demand curve smoother and have practical significance. • The dynamic pricing strategy helps to realize the joint dispatch of multiple demand response resources in the region and alleviate the grid pressure during peak hours. • A two-layer optimization model is proposed to not only realize the economy of joint operation of multiple energy stations, but also make the net grid demand curve smoother through joint demand response. In recent years, countries around the world have made great efforts to coordinate energy green and low-carbon development and energy supply. In this context, it is of great significance to build energy stations that can greatly absorb renewable energy. The coordinated operation of multi-energy stations in the region can expand the role of energy stations and strengthen energy complementarity between regions. Aiming at the problem of energy interaction and coordinated operation of multi-energy stations in regional integrated energy system, this paper proposes a two-layer scheduling optimization model, which can effectively reduce the system operation cost and carbon emissions. At the same time, considering the joint demand response, it can effectively reduce the peak power supply pressure of the power grid. In addition, in order to ensure the accuracy and efficiency of the solution, this paper improves the traditional NSGA-II algorithm, greatly improving the overall performance of the algorithm. Finally, the simulation analysis is carried out through an example. The results show that the operation optimization model proposed in this paper can effectively reduce the total energy cost of the system, reduce carbon dioxide emissions, and reduce the peak pressure of the power grid. The coordinated operation optimization method is effective and feasible. [ABSTRACT FROM AUTHOR]

Carbon trading market is a central policy tool for promoting cleaner enterprises' energy consumption and investment behaviors. It is crucial for enterprises to establish a green and low-carbon development model by clarifying carbon trading impact mechanism on enterprise behaviors and the interactive relationship between entities in the carbon market. China's contract-based carbon market is still in the development stage, the impact of the mainstream allowance mechanism and government regulation on enterprises' energy behaviors has yet to be determined. In view of this, this paper describes mathematical mechanisms of the influence of carbon prices and carbon quotas on enterprises' energy consumption and investment, and the evolutionary game model of government-enterprise behaviors is constructed, the evolution process and appropriate strategies of enterprises' energy behaviors in the development of the carbon market are discussed. This paper uses real data of China carbon market to simulate the model, it is found that strategies such as relaxation of carbon quotas and carbon subsidies dynamization are needed during the initial development of the market. In the mature stage of the carbon market, strategies are needed to increase the proportion of government carbon revenue and reduce its regulatory cost. The higher the initial willingness of government-enterprise, the better the effect of strategies and the constraints of public willingness. The model and results have theoretical implications for enterprises' energy transformation and government regulation, and provide a reference for the development of China's carbon market. • Constructed benefit-cost analytical framework for enterprises' energy behaviors. • Described mechanisms of carbon trading affecting enterprises' energy behaviors. • Constructed government-enterprise behaviors model by the evolutionary game theory. • Found new conclusions of enterprises' energy behaviors under the carbon market. [ABSTRACT FROM AUTHOR]

• A novel concept of mobile energy storage is proposed in this paper. • A detailed modeling of mobile compressed air energy storage with higher dispatchability and storage capacity is presented. • In concern to routing challenges, Google map's Application Programming Interface is used to find the target location. • A new heuristic-based technique that overcomes deficiencies of traditional constraint handling methods is proposed. • The proposed framework is tested through the IEEE 33-bus and 136-bus networks with different scenarios. • Various reliability indices and operating profits are measured to demonstrate the applicability of the proposed technology. Efficient energy storage technology is one of the key elements to enhance the flexibility, economy, and security of the power system. With the continuous development of energy storage technology, containerized mobile energy storage is coming into view, which has offered promising opportunities to improve distribution network (DN) performances and grid operating factors against emergencies. This paper proposes the concept of mobile compressed air energy storage (CAES) for an electric DN. The movable air storage tanks with stored energy are transported by trucks and placed at some distribution nodes/buses to improve DN performance. To overcome routing challenges for trucks, the configuration of the grid is mapped on the urban region of the city of Sydney, Australia, using Google map's Application Programming Interface. This approach can accurately model distances between current and targeted locations, unavailability of tanks during traveling, route congestions, and fuel consumptions. A new heuristic mathematical method is proposed in this paper to convert the constraints of the mobile CAES (MCAES) model into feasible search spaces, which significantly improves the convergence quality and speed. Operating results for both stationary and mobile CAESs are presented and compared. The methodology is applied to IEEE 136-bus DN in addition to IEEE 33-bus DN to demonstrate the competence of MCAES for larger-scale grids in optimizing the total operating profit, active power loss, energy not supplied, and voltage stability index of the grid. The crucial application of the proposed model can deal with natural disasters to avoid large-scale power outages and, eventually, mitigate the power system damages. [ABSTRACT FROM AUTHOR]

• A hierarchical approach is proposed considering quartile method for optimization. • Proposed CEEMDAN-DMD can regress the energy based on pre-set energy proportion. • Proposed hierarchical allocation strategy can be used for severe power volatility. The development of renewable energy dominated power system and the integration of large-scale wind-storage energy system are inevitable trends of the future society. This paper developed a new methodology of hierarchical power allocation strategy considering wind power fluctuation. A novel idea is presented that fully considers the fluctuation and energy proportion. Based on the Quartile Method (QM) and Hampel-Savitzky Golay (HSG) strategy, a layered-based method that considers the volatility interval to effectively overcome the limitations of a single strategy caused by severe volatility interval is proposed. Then, an improved power division method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Dynamic Mode Decomposition (DMD) is proposed where CEEMDAN is used to decompose the Intrinsic Mode Function (IMF) of the remaining fluctuations. Furthermore, according to the required setting of energy proportion, DMD strategy is used for power fitting of the hybrid energy storage system (HESS). Through experimental verification, the hierarchical strategy proposed in this paper can effectively reduce the probability of severe volatility interval or distortion interval and ensure the sequence in the ideal interval. Moreover, the proposed CEEMDAN-DMD strategy can realize the power division according to the energy proportion demand. In model verification, the volatility improvement ζλ 1 min = 85.4766%. Set the energy demand ≥ 80%, the fitting energy proportion accounts for 87.7533%, which outperforms the traditional power division method. In addition, it is demonstrated that the theoretical method proposed in this paper is reliable and promising. [ABSTRACT FROM AUTHOR]

Photocatalytic technology is the recent investigation focus because of the wide applications in sewage disposal and new energy generation. In this paper, the optical and photocatalytic properties of TiO 2 NTAs were enhanced by the hydrothermal deposition of CuBi 2 O 4 by regulating the precursor concentration. The results indicated that the precursor concentration played remarkable influences on the morphology and photocatalytic ability. The mild reagent concentration was in favor of super-thin CuBi 2 O 4 nanosheet formation. CuBi 2 O 4 /TiO 2 NTAs with high reagent concentration exhibited high photocurrent and hydrogen production, and showed outstanding performance in the removal of organic dyes and heavy metal ions. The free radical production and the photocatalytic progress were introduced in detail. The high photocatalytic activity exhibits prospective foreground in industrial waste water treatment and novel energy resource development. [ABSTRACT FROM AUTHOR]

The recent international agreements signed by the majority of developed countries, such as those reached at the Climate Change Conferences in Paris'15 and Dubai'23, propose an increasingly rapid transition toward an energy ecosystem with a clear predominance of renewable energy in electrical power systems. The development of such ambitious energy programs should deal with the inherited stochasticity that renewable energy systems entail, which, when coupled with uncertainty about the capacity of the grid to maintain a power supply owing to increasing demands, decarbonization processes and the widespread closure of nuclear power plants, pose a serious threat to the normal functioning of energy systems. When combined with the escalation of armed conflicts that imply the loss of supply as a result of attacks or cyberattacks on power plants and distribution networks, it becomes clear that the current energy paradigm in which there is a centralized grid supplying numerous consumers, many of whom do not have their own generation capacity, must shift toward increasing the deployment of renewable-energy-based self-consumption facilities. The continuous advances toward a decentralized energy system of this nature will also lead to more cooperation, increasing the presence of energy communities with a great need to strengthen internal resilience as a sustaining factor. Considering the challenging framework of smart grids and energy transition, Microgrids would appear to be the key technology for the aggregation of generation, load and energy storage systems, and a cornerstone with which to provide the resilience and flexibility required for this new renewable-energy-based scenario. In addition to the complexity of the microgrid control problem, the issue of resilience energy management also has to be considered, which refers to the ability to adapt and supply loads during a specified period after a disruptive event with a loss of grid supply. This paper introduces an innovative method based on Model Predictive Control (MPC) techniques with the aim of enhancing resilience in microgrids by maximizing the energy surplus and reducing the aforementioned critical instants at which the capacity to feed loads is minimum in the case of power grid outages. The results obtained show that the proposed algorithm enhances the energy resilience in microgrids while the overall operational cost is optimized. A method with which to enhance the resilience of interconnected microgrids through cooperative optimization methods is also developed and validated. [Display omitted] • New techniques for resilience enhancement in microgrids/ networks of microgrids. • Optimizing energy management by enhancing surplus during critical resilience points. • Cooperative resilience management in networked microgrids with cost efficiency. [ABSTRACT FROM AUTHOR]

Based on China's provincial panel data from 2001 to 2015, the drivers of energy technological innovation, e.g., energy price, public financial policy, knowledge stocks, energy structure, and environmental regulation, are analyzed by using the generalized method of moment (GMM) in this paper. Through the construction of one kind of geometric distribution lag model, which is also called partial adjustment model, we empirically analyze and compare the influencing factors of energy technological innovation from the perspectives of fossil energy and renewable energy. The specific conclusions obtained in this paper are: (1) The impact of the energy price on fossil energy technological innovation is greater than renewable energy, which means that the current energy price in China is much lower than its optimal level and the development of renewable energy technology needs the support of price mechanism. (2) The development of these two kinds of energy technology innovation heavily relies on governmental policy support. (3) The accumulation of energy technology innovations will be conducive to the vertical spillover effect of knowledge, and further encourage the development of energy technology. Based on the above findings, we then propose some relevant policy proposals to promote energy technological innovation in China. [ABSTRACT FROM AUTHOR]

The aim of the paper is to present renewable energy market development with particular regard paid to biofuels in the EU. The analysis included data on the share of renewables in Gross Island energy consumption, changes of renewable energy in the years 2004–2016 and the amount of liquid biofuels. The authors of the paper used descriptive and statistical methods to describe the changes in bioenergy development in the European Union (EU). The biggest share of biofuels and renewable waste can be seen in Latvia (31.2%), Finland (26.7%) and Sweden (24.8%). The highest percentage of wind energy in 2015 was found in: Denmark (7.2%), Portugal (4.3%), Ireland (4.0%) and Spain (3.5%). The highest share of solar energy in 2015 was found in Cyprus (3.5%), Spain (2.6%) and Greece (2.2%). The highest contribution of geothermal energy was found in 2015 in Italy (3.5%), Portugal (0.8%) and Slovenia (0,7%). Hydropower was the biggest in 2015 in Sweden (14.2%), Austria (9.6%) and Slovenia (5.0%). The highest coefficients of variation of the share of electricity from renewable energy sources were found in the years 2004–2017 in Malta (140.3%), Cyprus (101.1%) and United Kingdom (71.9%). In addition, the highest coefficients of variation of share of renewable energy sources in heating and cooling in the years 2004–2017 were found in Malta (72.4%), United Kingdom (69.81%) and Hungary (44.91%). Moreover, the highest coefficients of variation of share of renewable energy sources in transport in the years 2004–2017 were found in Finland (113.78%), Malta (115.52%) and Belgium (96.53%). The biggest producers of ethanol and biodiesel in EU were Germany, France and Poland. Cluster analysis data show that Germany and France are of key importance in the production of biodiesel and ethanol. The biodiesel production increased in the years 2003–2017 from 719,32 million liters to 13323 million liters (increase 1852.2%). However, in the years 2014–2017 a stagnation in biofuel production was observed from 13673 million liters to 13323 million liters (−2,56%). The situation on the market and the increasing demand for green energy suggest that the production of ethanol and esters of vegetable oils will increase by 2030, which will contribute to the development of this sector. [ABSTRACT FROM AUTHOR]

Low-carbon technology has become the focus of addressing global climate challenges but is facing the supply restriction of critical high-tech metals. Recycling has become a powerful measure to solve this issue. In the paper, a comprehensive recycling potential index (RPI) is constructed to evaluate the recycling potential of high-tech metals for low-carbon energy technologies in China from 2020 to 2050, and development strategies are proposed based on the evaluation results. The results indicate that the future trends of recycling potential show four evolution modes – either reaching or rapidly rising to a high level, increasing to the middle and middle-high level, unsteadily staying at the low and middle-low level, or remaining at a low and continuously decreasing level. According to the evolution trend, different strategies, including key development, reserves, digestion, and transfer, should be adopted in different stages. Co, Li, Dy, Nd, Tb, and Eu should be the focus of technology, funds and policy from both the government and enterprises due to their high recycling potential in the long term. Moreover, measures should be taken to encourage recycling and mitigate the supply risk of high-tech metals for low-carbon energy technologies, including tracking the development of low-carbon energy technologies, adjusting the list of critical high-tech metals, promoting recycling technology, and exploring substitute resources. [Display omitted] • A comprehensive recycling potential index is constructed to evaluate the recycling potential of high-tech metals. • The future trends of recycling potential of high-tech metals show four evolution modes. • Strategies like key development, reserves, digestion, and transfer should be adopted in different stages. [ABSTRACT FROM AUTHOR]

The changes in energy use and energy-mix are important in ensuring the implementation of the sustainability goals, especially those related to the climate change. Even though there have been attempts to analyse the energy-related greenhouse gas (GHG) emission in the European Union, there is still a lack of research that considers structural change, fossil energy consumption, and energy-mix simultaneously. In this context, the present paper addresses the growth in energy-related greenhouse gas (GHG) emissions in the European Union (EU) agriculture. The index decomposition analysis based on the logarithmic mean Divisia index is implemented over the period of 2004–2021. The change in the GHG intensity at the EU level is factorized with respect to the structural effect and those related to the energy intensity, the share of the fossil energy, and carbon factor for the fossil energy. The results suggest that a decline in the energy intensity appeared as the main driver pushing the energy-related GHG emission intensity down in the EU agriculture. This calls for further actions directed towards the increase of the use of renewables. [ABSTRACT FROM AUTHOR]

The rapid development of renewable energy sources (RESs) facilitates the coordinated operation of different energy sources to hedge against the uncertain and non-dispatchable nature of RESs. In this paper, we propose an effective approach for ultra-short-term optimal operation of a photovoltaic-energy storage hybrid generation system (PV-ES HGS) under forecast uncertainty. First, a generic approach for modelling forecast uncertainty is designed to capture PV output characteristics in the form of scenarios. Then, stochastic model predictive control (SMPC) is utilized in the coordinated operation of PV-ES HGS for load shifting under forecast uncertainty. Finally, a PV-ES power station located in Northeast China is selected as a case study to validate the feasibility and effectiveness of the proposed method in detail. The results show that: (1) The forecast uncertainty modelling method, which incorporates adaptive kernel density estimation (KDE) and two-layer nested copula, could capture the characteristics of PV output effectively. (2) The proposed sampling procedure ensures the representativeness and temporal dependence of scenarios extracted from the joint predictive density, leading to stability and efficiency in solving the stochastic optimization problems. (3) The SMPC-based coordinated operation strategy exhibits significant superiority in load shifting performance and resistance to risks. The standard deviation of residual load (SDORL) is reduced by 17.5% on average compared to the conventional scheme. Thus, this work provides operators with guidance in the ultra-short-term optimal operation of a PV-ES HGS under forecast uncertainty. [Display omitted] • Integration of adaptive KDE and two-layer copula to model PV forecast uncertainty. • Proposal of a representative scenario generation method incorporating LH and CD. • Utilization of scenario-based SMPC to optimize ultra-short-term PV-ES HGS operation. • Identification of trade-off between load shifting performance and operational risks. [ABSTRACT FROM AUTHOR]

With the increasing penetration of electric vehicles into the existing power networks, the development of proper energy management tools become essential to mitigate the damaging effects of emerging high-power charging modes. To facilitate the management of vehicles, they can be gathered into clusters in order to aggregate their charging profiles. This paradigm is called cluster of vehicles and can be forward developed by gathering a group of parking, thus forming a parking cluster where the characteristics of different parking lots are aggregated. In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The new proposal is casted as a game-based framework which seeks for the equilibrium between the cluster coordinator and parking lots, instead of conventional tools where the pricing strategy is decided by a central entity disregarding the interests of users. The developed model is established as a bi-level optimization problem, which is further linearized to be tractable and solvable by off-the-shelf solvers. The implications of vehicle-to-grid are also studied by fully modelling bi-directional power flows in chargers. In addition, two uncertainty-aware strategies are proposed to cope with uncertain energy margins in parking lots. A case study is conducted and various results are analysed and discussed. It is shown that enabling vehicle-to-grid characteristics discloses significant economic benefits for users and the cluster coordinator. In addition, vehicle-to-grid impacts notably on the risk-averse character of the uncertainty-aware strategies proposed. The developed pricing mechanism is also compared with the conventional pricing policies based on key of repartitions, showing that the new proposal is able to reduce the cost for users, avoiding to directly translate the energy cost to charging points. • A new game-based pricing mechanism for parking clusters is developed. • Two uncertainty-aware strategies are proposed and compared. • Vehicle-to-grid helps to improve the economy of the cluster notably. • The impact of uncertainties strongly depends on vehicle-to-grid availability. • The new proposal reduces the cost of users compared with other approaches. [ABSTRACT FROM AUTHOR]

In the present situation, renewable energy has become a vital source to mitigate energy crises and environmental threats. In order to ensure that women's contributions, knowledge and opinions represent a significant portion of the expanding energy sector, it is crucial to approach the development of renewable energy from a gender perspective. Bibliometric analysis was performed to identify prior research on two primary terms, "Gender" and "Renewable Energy", from 1995 to 2022. Identifying the most influential countries, authors, publications, journals, and co-occurrence of keywords helps to understand the evaluation and trends of the research through network visualisations by bibliometric approach. This paper presents a comprehensive bibliometric review of the influence of gender on renewable energy. The result of this paper gives details picture of the gender impact on renewable energy during the period from 1995 to 2022. The paper centred on bibliometric and cluster analysis based on the two most important databases, Web of Science and Scopus. The study involved utilising various bibliometric tools, including R Studio, VOSviewer, and Biblioshiny. The study revealed an increase in publications and citations during the third period since 2017, after the United Nations set the target of the Sustainable Development Goal. Most influential countries and productive authors belong to China, the United States of America, and the United Kingdom. Energy Research and Social Science are predominated in publications on the research topic. In addition, the co-occurrence of keywords found major keywords related to this topic, such as renewable energy, gender, women, knowledge, sustainability, climate change and sustainable development. Finally, the study observed a huge research gap because developing nations are more affected by the lack of gender-influencing policies on the growth of renewable energy. Thus, the study provides a comprehensive overview of research in this field that can help researchers and stakeholders identify potential ways for future research in gender considerations on renewable energy access by formulating effective policies to change society and reach the Sustainable Development Goals. • Gender has become a focal point of interest within the renewable energy sector. • Using network visualisation, bibliometric analysis examines research trends by keyword occurrence and country collaborations. • Publication, citation, journal, and authorship contribution trends are to be analysed. • The research growth across different periods was discussed for potential future studies. [ABSTRACT FROM AUTHOR]

Clean energy plays an essential role in responding to environmental crises. Accurate forecasting of clean energy power generation can provide necessary references for the formulation of energy policy. This paper proposes a novel fractional discrete grey model with a dynamic time delay function (DTDFF-DGM (1,1)) to forecast clean energy power generation. This model introduces the fractional accumulation operator and the dynamic time-delay function into the discrete grey model, which ensures the priority of new information in the original data and improves the model's adaptability to different sample data. The optimal parameters of the model are calculated by the dynamic linkage between the fitting error and the test error, which effectively avoids the overfitting problem. Empirical studies have proved that the model has better prediction accuracy compared to other methods. Finally, the proposed model is employed to forecast clean energy power generation in China. The results show that from 2020 to 2025, the overall growth rate of hydropower, wind power and nuclear power in China will be 9.27%, 119.61% and 36.38%, respectively. Based on the discussion of the forecast results, relevant policy suggestions were made. This paper realizes the transformation from a static model to a dynamic model in methodology and promotes the sustainable development of clean energy power generation in application. [Display omitted] • A novel fractional discrete grey model is established. • The model is the general paradigm of the existing six discrete grey models. • The jump error is avoided in the transition from discrete to continuous. • The dynamic time-delay function is introduced. • China's clean energy power generation is effectively predicted by the model. [ABSTRACT FROM AUTHOR]

Clean energy has various environmental and economic benefits, including reducing air pollution. Also, due to its characteristics, it can be more compatible with the environment than fossil fuels. Industry leaders believe that high costs, technological shortcomings, and constraints on transmission infrastructure are hampering the growth of clean energy. In this paper, a new approach is presented to cover some of the drawbacks of the Failure Mode and Effects Analysis (FMEA) technique. The presented approach addresses the shortcomings of the FMEA method and applies complete prioritization for organizations. In this paper, a developed approach based on multi-criteria decision-making (MCDM) methods in a Spherical fuzzy set (SFS) to assess barriers to clean energy development has been stated. Three main criteria, severity, detection, and occurrence, are used to assess clean energy barriers and are weighed using the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on SFS. In the next phase, clean energy development barriers in five categories: technical, economic, social and cultural, administrative, legal, and political, are prioritized using the Combinative Distance-based Assessment (CODAS) method in the SFS environment, which reduces uncertainty. The results show that incomplete markets and consecutive changes in the administrative system are critical barriers. Finally, to confirm the effectiveness of the results, the sensitivity analysis was performed with other common methods. The results show the performance of the developed approach and its ranking stability. Also, the results of the MOORA, COPRAS and CODAS methods were compared and CODAS was superior to the other methods with a correlation of 0.981. [ABSTRACT FROM AUTHOR]

Abstract Currently, wind and solar energy have not been well utilized in China, and the rate of abandoned wind and solar facilities in China is increasing annually. Renewable portfolio standards (RPS) are an effective system for promoting large-scale renewable energy integration and consumption and realizing the goal of renewable energy development. To clarify the interactive relationship between the different levels and multiple actors in RPS, this paper uses policy network theory to analyze the interactive relationship of stakeholders from the three aspects of network subject, network structure and network interaction. The policy network of the RPS consists of the policy community, professional network, intergovernmental network, producer network and issue network. Under the RPS, to meet their own needs, participants affect the formulation and implementation of the RPS through the actions of confrontation, alliance, competition and cooperation. In different stages of developing the RPS, the openness and integration of policy networks change accordingly. This study overcomes the limitations of past simple analyses of the actor relationship in the policy process and thereby offers certain reference significance. Highlights • The actors of RPS policy networks are analyzed. • This paper analyze the game relationship of the stakeholders from three aspects. • This research reveals the network relationship between multiple stakeholders. • This study breaks through the limitations of past analysis of actor relationship. [ABSTRACT FROM AUTHOR]

Highlights • A novel quantitative life cycle sustainability assessment framework is developed. • Sustainable performance is assessed under normal condition and disruption scenarios. • Environmental, economic, and resilience aspects are quantified in monetary terms. • Innovative way of demonstrating results, named Sustainability Compass, is proposed. • The method is applied to IES of a university campus in support of decision analysis. Abstract There are two dimensions to "sustainability" as it applies to an engineered system: (i) it needs to be designed, operated, and managed such that its environmental impacts and costs are minimal, and, (ii) it ought to be designed and configured in such a way that it is robust to extreme disruptions and shocks posed by natural, manmade, or random events, i.e. be resilient under extreme events. These somewhat conflicting attributes call for a multi-criteria analysis framework. This paper proposes such an assessment framework for engineered systems involving three new location and circumstance-specific sustainability indices that monetize the economic, environmental and resiliency characteristics throughout the lifecycle of the system components. The proposed framework, thus, allows translating sustainability goals into engineering practice, and is applicable to: (i) the design of new systems, (ii) assessing performance of an existing system, (iii) day-to-day scheduling and operation of the system, and (iv) future growth planning. A novel presentation method, called "Sustainability Compass", is also proposed which allows decision-makers to visually track the direction and magnitude of changes in the individual sustainability indices of different scenarios; this would also allow easier communication with other stakeholders. The proposed methodology and the usefulness of the Sustainability Compass diagram have been illustrated using end-use monitored data for a whole year of operation of a university campus integrated energy system. The results of five alternative energy development scenarios involving a combined heat and power system and solar photovoltaic systems with different penetration levels have been investigated and are reported in this paper. [ABSTRACT FROM AUTHOR]

[Display omitted] • A high-efficiency and environment-friendly process for treating braunite was proposed. • The selective extraction of Mn from braunite was achieved. • The additives used in the roasting process can be regenerated. • The reaction mechanism was explored based on thermodynamics and phase conversions. • The proposed process can be used for the treatment of various manganese oxide ores. Manganese ore is a key raw resource for the development of new energy, new materials, and other emerging industries, as well as a basic raw material for the steel industry. As resources of the main high-grade pyrolusite are depleting, it is of strategic importance to develop and utilize other manganese oxide ores. This paper proposed the two-stage ammonium sulfate roasting-water leaching process for high-efficiency and environment-friendly separation and recovery of manganese from braunite ((Mn 2 O 3) 3 MnSiO 3). The first-stage was sulfation roasting, which could convert Mn and Fe in braunite into (NH 4) 2 Mn 2 (SO 4) 3 , (NH 4) 3 Fe(SO 4) 3 , NH 4 Fe(SO 4) 2 and Fe 2 (SO 4) 3 ; the second-stage was selective roasting, that could decompose (NH 4) 2 Mn 2 (SO 4) 3 into MnSO 4 , and decompose (NH 4) 3 Fe(SO 4) 3 , NH 4 Fe(SO 4) 2 and Fe 2 (SO 4) 3 into Fe 2 O 3. Under the optimum leaching conditions, the leaching efficiency of Mn reached 97.79%, while the leaching efficiency of Fe was only 1.52%, and a solution with a concentration of 93.36 g/L for Mn was obtained. The NH 3 volatilized during roasting was absorbed by the water solution to get an ammonia solution which was used to precipitate Mn from the leaching solution. The precipitation efficiency of Mn was 99.48% under optimal conditions. Manganese oxide products such as Mn 3 O 4 or Mn 2 O 3 could be obtained by drying and calcining Mn-rich precipitates. The final filtrate was neutralized with H 2 SO 4 and then evaporated and crystallized to give an (NH 4) 2 SO 4 product, enabling the recycling of ammonia. The process proposed in this paper is not limited to the treatment of braunite, but can also be applied to the processing of other manganese oxide ores and has a wide application potential. Compared to the traditional reduction roasting-acid leaching process, this process is more efficient and environmentally friendly. [ABSTRACT FROM AUTHOR]

In this paper, we suggest how India can become atmanirbhar (i.e. self-reliant) in development of green energy technologies- solar, battery storage and green hydrogen, given its stated commitments in clean energy. First, we introduce a generalized policy design – a set of ten policy measures: seven necessary and three sufficient- with the former being on the supply side and the latter on the demand side. The paper argues that while the ideal policy mix would be technology dependent, based on technology maturity and the strategic positioning of the country, it should draw both from supply side (i.e., for domestic development) and demand side (i.e., for scale deployment). Then, we suggest the required individual strategies for each of the three technologies- such as a manufacturing focused policy for solar solely to develop competence in manufacturing; a bottom-up, manufacturing-led policy for battery storage with focus on deployment in parallel with technology development; and traditional top-down, research-led strategy for green hydrogen by entering via development through supply side and complementing via deployment through demand side. Finally, we compare these with the existing domestic energy industrial policies as well as those prevalent in other jurisdictions such as the US and China. • Create self-reliance for India in green energy technologies. • Develop generalized policy design with supply and demand side measures. • For solar, manufacturing focused policy for competence solely in manufacturing. • For battery storage, bottom-up manufacturing-led policy with deployment alongside development. • For green hydrogen, top-down research-led strategy where deployment follows development. [ABSTRACT FROM AUTHOR]

Factors influencing the development of renewable energy have emerged as a hot topic of policy discussion in recent years. However, the direct impact of natural resource dependence on renewable energy has been overlooked, especially in emerging economies. Using Chinese province data from 2005 to 2019, this study employs a nonlinear panel model and a Quantile Regression with Nonadditive Fixed Effects for Panel Data (QRPD) model to examine the impact of natural resource dependence on renewable energy development and explores the great importance of government policy support. The study indicates that the influence of natural resource dependence on renewable energy development supports the "conditional resource curse" hypothesis, showing a U-shaped nonlinear relationship. The effect of natural resource dependence and government policy support on the development of renewable energy shows a dynamic change trend. In the early stage of renewable energy development, policy support is crucial, while during the growing and mature stages, natural resource dependence exerts a stronger driving force. The results of the mechanism test indicate that natural resource dependence can boost renewable energy development through green technology innovation and environmental regulation channels. This paper's conclusion provides a theoretical foundation for fostering the sustainable development of the renewable energy industry and is vital to China's efforts to reach peak carbon and carbon neutrality. • Nonlinear panel and QRPD models are employed. • Impact of natural resource dependence (NRD) on renewable energy development is revealed. • Dynamic effect of NRD and government policy support is discussed. • NRD has a negative and then positive impact on renewable energy development. • Pathways to improve renewable energy development through NRD are identified. [ABSTRACT FROM AUTHOR]

An emerging question for photovoltaic (PV) solar power development is how to ascertain the optimal choice from a finite set of available alternatives under numerous conflicting criteria as well as high levels of imprecise, vague, and uncertain information. For the first time, we investigate the prioritization of the alternatives for the development of PV solar power via the interval-valued intuitionistic fuzzy sets (IVIFSs), which show great power in capturing ambiguous, uncertain, and vague information, and mitigating information loss. This paper presents a novel integrated intelligent decision support system comprising of Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, Analytic Hierarchy Process (AHP), and Combined Compromise Solution (CoCoSo) within an interval-valued intuitionistic fuzzy (IVIF) framework. Four alternatives are considered. To rank these alternatives, twelve criteria are defined under four aspects of SWOT analysis based on literature review and discussion with decision-makers. Subsequently, the IVIF-AHP method is utilized to determine the weights assigned to each criterion and sub-criterion. Finally, the IVIF-CoCoSo method is employed to rank four alternatives. The results showed that giving proactive attention to mitigating potential adverse environmental impacts of PV solar systems is the most highly prioritized strategy for PV solar power development. The results of the comparative and sensitivity analyses showed that the proposed method generates highly robust outcomes. The formulated integrated intelligent decision support system can help energy policy authorities with a valuable resource to craft optimal techniques for developing PV solar power. • A novel integrated MCDM approach for the development of solar energy is presented. • Challenges to solar energy development are identified and evaluated. • Strategies to overcome these challenges are provided. • Sensitivity analysis is done to validate the stability of the integrated approach. [ABSTRACT FROM AUTHOR]

• 1. To propose a quasi-quantum model, which can reasonably divide and guide the planning period to reduce uncertainty. • 2. To propose an evaluation model, which can analyze the uncertainty potential energy function before and after the planning to assist the quasi quantum model, so as to reasonably divide the planning cycle. • 3. To propose a three-layer planning framework, including planning cycle division, stochastic planning, and rolling planning. With the rapid development in social informatization, more and more factors, such as regional economy, technological development, and people's living needs, will affect the supply–demand relationship of regional integrated energy systems (RIES), which involve multiple energy forms. These factors turn the supply–demand relationship of an energy system into a nonlinear and time-varying chaotic system. This makes it difficult to predict and balance these relationships, which poses a huge challenge to the prediction, planning, operation, etc. Existing conventional methods attempt to quantify the prediction bias caused by various external environmental factors of energy systems and to weaken the uncertainty caused by multiple energy loads through random programming. However, uncertainty factors increase with the development of an energy system, thereby inreasing the requirements of such uncertainty quantification methods. Furthermore, in conventional methods, the prediction or planning decisions are often made by an observer while neglecting the impact of the observer's decision-making behavior on prediction and planning. Therefore, this paper proposes a three-layer planning framework for to solve the above problems. This architecture includes quasi-quantum uncertainty periodic evaluation, stochastic planning based on information gap decision theory, and rolling planning based on model predictive control. First, we establish a quasi-quantum model of multi-energy system prediction error based on the quasi-quantum wave function model to qualitatively analyze prediction errors affected by uncertainty before and after planning. Simultaneously, combined with the evaluation model of the quasi-quantum potential energy function, a quasi-quantum uncertainty period evaluation model is proposed. Based on the minimum equivalent planning entropy, the planning cycle of multistage rolling planning is divided to minimize uncertainty. Second, the information gap decision theory is used to quantify the influence range of source and load uncertainty in the planning process and the multistage planning cycle is randomly planned. Then, the model predictive control method is used to control the actual error, and the planning strategy is changed in time to reduce the planning deviation caused by the prediction error. Finally, adopting a Beichen demonstration area in Tianjin, China, as a case study, the effectiveness of the proposed method in uncertainty analysis and long-term planning improvement is verified. The three-layer planning framework can improve the adaptability of the regional integrated energy system in the long-term planning process, and can timely adjust the planning scheme to cope with the impact of unpredictable uncertainties in the planning process. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• Proposing an new integration method that considers the relationship between simulation time-step and time constants of elementary transfer function blocks (ETFBs), including three integration modes: single-step integration, multiple-step integration, and static approximation. • Giving the implementation of static approximation for ETFBs with small time constants. • Discussing the determination of the threshold values of internal time-step for ETFBs. • Applying the proposed method to an open-source Simulation Toolkit for Electrical Power Systems (STEPS). With the development of renewable energy sources, the scale and complexity of power systems are continuously increasing, resulting in a high requirement for the efficiency of dynamic simulation. Increasing the simulation time-step is a possible measure for the fixed time-step (FTS) method to accelerate the simulation, yet also a potential cause for deteriorating the simulation accuracy. To improve the overall power system dynamic simulation speed without jeopardizing the expected accuracy, this paper proposes a novel Uneven Internal Time-Step Adjustment method based on Trapezoidal integration of elementary transfer function blocks (ETFBs). For ETFBs with different time constants, three different integration modes are defined, including single-step integration, multiple-step integration, and static approximation. Each ETFB can select an appropriate integration mode and adjust its internal time-step, automatically expanding the size of the simulation time-step. Implementation of static approximation for ETFBs with small time constants is given. Discussion on the determination of threshold values of internal time-step is provided, which mainly affect the accuracy of the system. The proposed method is applied to the open-source Simulation Toolkit for Electrical Power Systems (STEPS). Different cases are tested to obtain the maximum simulation time-step that can maintain numerical stability and validate the efficiency and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

Clarifying the clean energy (CE) development path is significant for realizing energy system transformation, coping with climate risks, and ensuring energy and ecological security. This paper uses national data from China from 1978 to 2021 as a sample. It considers the year-by-year dynamic indicators of changes in technology, population, industry, and economic factors in the short term. It takes the mandatory carbon emission reduction targets set by the country as the basis for carbon constraints. The Markov chain model was introduced to construct a power demand structure prediction model under scenarios to more accurately quantify the energy-saving and emission-reduction paths under different carbon constraints and economic development scenarios. The results show that China's electricity demand will rapidly grow in the short term. The upper limit constraint of thermal power generation under the high and normal economic growth models still has room to grow in the short term under the target carbon emission constraint scenario. During 2025–2030, the overall lower limit growth rate of the CE power generation structure under the target emission reduction scenario must exceed 8%, and the overall growth rate under the high-efficiency emission reduction scenario must exceed 12%. By 2030, the proportion of hydropower in CE generation is estimated to decrease to 33.7%, while nuclear power, wind power, and photovoltaic power generation will increase to 16.3%, 30.5% and 19.5%, respectively. These results provide a reference for the government to formulate a CE development path against the background of carbon constraints. [Display omitted] • Clarified China's short-term energy development path under carbon constraints. • Thermal power will not exit the market quickly in 2030. • Constructed an energy and power prediction model under carbon constraints. • Wind energy and photovoltaic energy will become the main development energy sources. • Support low-carbon power sector transformation under carbon policies. [ABSTRACT FROM AUTHOR]

Although the carbon reduction role of green finance has been highlighted in the context of global warming, discussions on green finance have primarily focused on size rather than structure. Based on Chinese panel data from 2008 to 2019, this paper distinguishes itself by investigating the relationship between green financial structures and carbon emission intensity from a structural matching perspective using the two-way fixed effect model, system generalized method of moments model, quantile regression, and the mediating effect model. Our findings indicate that a green financial structure can lower overall carbon emission intensity and that an inverted U-shaped carbon emission mitigation effect results from a green financial structure. Additionally, green financial structures alleviate carbon emission intensity by facilitating the development of energy technology. Furthermore, greater degrees of finance-technology matching effectively lower carbon emission intensity. These findings provide lessons for countries striving to construct comprehensive and reasonable green financial systems to address climate change. [ABSTRACT FROM AUTHOR]

This research investigates the relationship between greenhouse gas (GHG) emissions and energy production and consumption patterns in 122 countries over the period from 1990 to 2020. Particular attention is paid to the role of different types of energy, including renewable, hydro and nuclear energy, as well as oil, gas and coal, in shaping GHG emissions. Unlike previous studies, the paper analyzes the full range of energy resources and focuses on different quantiles of data using the quantile-quantile regression (QQR) method. The results of this approach are compared with traditional quantile regression (QR) to assess its validity. Among the findings of this study are: (1) different parts of the energy mix have a heterogeneous impact on GHG emissions in different quantiles; (2) energy consumption patterns play a more significant role in shaping GHG emissions than production patterns; (3) increasing production and consumption of fossil fuels does not necessarily increase GHG emissions; (4) similarly, the development of renewable energy sources is not always accompanied by a reduction in GHG emissions. Our general conclusion is that existing climate policy requires significant diversification to not be focused only on renewable energy, which impact on GHG emissions is not as homogeneous as it usually considered, and which cannot solve the problem of raising emissions solely. Instead of this, it could be more rational to focus on the searching the ways of hydrocarbon industries sustainable development and support them in this journey, especially in the field of Scope 2 and Scope 3 operations management. • Impact of energy consumption and production on GHG emissions were analyzed for 122 countries in 1990–2020. • Quantile and Quantile-on-Quantile regression were used to catch the specifics of energy consumption and production patterns. • Two temporal breakpoints (1999/2000 and 2009/2010) were introduced to catch shifts in variables dependencies. • The influence of fossil fuels on GHG emissions is not homogeneous as it usually considered, the same is fare for renewables. • A promising option to reduce GHG emissions is carbon capture, utilization and storage (CCUS). [ABSTRACT FROM AUTHOR]

Biotechnology has revolutionized the development of sustainable energy sources by harnessing biomass as a feedstock for energy production. However, challenges such as recalcitrant feedstocks and inefficient metabolic pathways hinder the large-scale integration of renewable energy systems. Enzyme engineering has emerged as a powerful tool to address these challenges by enhancing enzyme activity, specificity, and stability. Generative machine learning (ML) models have shown great promise in accelerating protein design, allowing for the generation of novel protein sequences with desired properties by navigating vast spaces. This review paper aims to summarize the state of the art in generative models for protein design and how they can be applied to bioenergy applications, including the underlying architectures and training strategies. Additionally, it highlights the importance of high-quality datasets for training and evaluating generative models, organizes available datasets for generative protein design, and discusses the potential of applying generative models to strain design for bioenergy production. • Challenges in renewable energy production require novel bioengineering approaches. • Generative models for novel enzymes present a big advancement in enzyme engineering. • We discuss how VAEs, GANs, and transformers enable improved enzyme production. • The potential of generative model integration into strain design is emphasized. • The role of high-quality, diverse datasets in training & evaluation is highlighted. [ABSTRACT FROM AUTHOR]

The development of renewable energy is the key to tackling climate changes and ensuring energy security. Meanwhile, the process is usually associated with a huge investment and financing gap, especially for transitional countries like China. Hence, developing and innovating the traditional financial system is urgent. However, existing studies have mainly focused on traditional financial perspectives, and few papers have critically examined how digital financial inclusion affects the growth of renewable energy industries. To this end, using Chinese provincial data from 2011 to 2020 and applying Feasible Generalized Least Squares (FGLS) method, this study explores the effect of financial inclusion on renewable energy and its underlying mechanisms from the production and consumption perspectives. It is found that the impact of financial inclusion on the production side of the renewable energy industry is significantly better than on the consumption side, and there is heterogeneity effect in different energy resource varieties, different regional locations, and different financial regulation intensities. The financial inclusion on renewable energy has a significant pulling effect in photovoltaic and wind provinces, but not in large hydropower provinces. Then, its driving effect is particularly pronounced in the northern region. Nonetheless, it is not significant for renewable energy consumption in the southern region. Besides, this positive effect cannot be achieved without stronger financial regulation. The mechanism of action assessment finds that optimizes the consumption pattern, increase the competition of commercial banks, and improve technological innovation all contribute to the positive effect whereas, FDI shows the negative effect. This study provides new evidence on China's achievement of the energy transition, suggesting the need to further improve incentives for FDI firms and to strengthen consumption-side guidance. [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]

• A high W rec = 7.3 J/cm3 is obtained in BT-based ceramics by "high-entropy" strategy. • The highest η = 94.3 % reported so far has been achieved in BNBSCTN ceramics. • The correlation between four effects and energy storage performance is emphasized. One of the major problems in ceramic capacitors is that their limited energy storage density (W rec) and efficiency restrict the development in cutting-edge energy storage applications. In this paper, the non-equimolar ratio high-entropy ceramics are designed using the "entropy" strategy based on the traditional ferroelectric BaTiO 3. Ultimately, the superparaelectric region of BaTiO 3 is extended successfully, forming the local polymorphic distortion structure and realizing the transformation of ferroelectrics-relaxor ferroelectrics. Here, the outstanding W rec of 7.3 J/cm3, the excellent ξ of 214.71 J/kV·m2, and the highest energy storage efficiency (η = 94.3 %) in high-entropy ceramics are obtained under moderate electric fields, indicating a breakthrough progress in energy storage applications of high-entropy relaxor ferroelectrics. Additionally, our work compared performance across multiple dimensions using measurement parameters, giving readers a more comprehensive reference. [ABSTRACT FROM AUTHOR]

Uranium and thorium are strategic metals, and the mining and utilization of uranium and thorium produce a large amount of radioactive wastewater. In this paper, a new process combining electrolysis and leaching is proposed to achieve resource regeneration of uranium and thorium from radioactive wastewater. The results revealed that carbonate was the optimal separation system. The uranium-thorium separation was based on the formation of soluble UO 2 (CO 3) 3 4− and insoluble ThO 2. And the separation efficiency was determined to reach a maximum value of 4.8 × 104 at pH 10.5. Surprisingly, UO 2 (CO 3) 3 4− could be effectively electrochemically recovered, and more than 90% of uranium could be recovered within 3 h, which indicated that uranium could be efficiently recovered. In addition, U–Th leaching studies were performed, and the results showed that selective leaching of uranium could be achieved at 0.01 M, while effective leaching of Th could be achieved at 0.3 M, thus establishing a graded-leaching process for U–Th. To reduce the impact of Ba2+ on the uranium electrochemical separation process, the SO 4 2--CO 3 2- combined system was developed, and the concentration of Ba2+ was reduced by two orders of magnitude. The final result revealed that the purity had been roughly tripled via this process. This study provides an effective strategy to reduce the waste of non-renewable uranium-thorium resources and process wastewater, achieving the recycling of non-renewable resources and the sustainable development of nuclear energy. • The combined process of electrolysis and leaching has been developed. • This process can realize the regeneration of uranium and thorium resources. • The carbonate had the highest uranium-thorium separation efficiency. • The establishment of SO 4 2-- CO 3 2− system greatly reduced the impact of Ba2+. • The process can be applied in a wide concentration range of 0–4.2 mM. [ABSTRACT FROM AUTHOR]

The pursuit of sustainable development has become a prevalent goal in numerous countries. Therefore, renewable energy development is deemed the prime alternative, as it not only curtails CO2 emissions but also stimulates economic prosperity. Hence, there is a need to investigate rational options for renewable energy development. This paper utilises a research framework that combines linear and non-linear modelling. The specific results are as follows. The study's findings reveal that wind, solar and hydro energy can each promote economic growth and lower carbon emissions. Nevertheless, wind energy exhibits the most effective reduction in emissions, followed by solar energy, and lastly, hydro energy. When it comes to promoting economic development, hydro energy proves to be the most effective, followed by wind energy and then solar energy. Additionally, a panel threshold model is utilised to assess further the CO 2 reduction for solar, wind, and hydro energy across a range of economic levels. The results indicate a double threshold effect for wind energy, whereby the effectiveness of CO 2 suppression initially increases and then decreases as the economic level rises. In contrast, the use of solar power displays a sole threshold effect that incentivizes CO 2 emissions at sub-threshold economic levels, yet significantly curbs them beyond it. Similarly, hydro energy also demonstrates a single threshold effect, but lacks statistical significance below the established threshold and actually heightens carbon emissions above it. Our observations can assist in creating sustainable renewable energy development strategies. [Display omitted] • Investigating the impacts of solar, wind, and hydro on economic growth and carbon emission. • Using the linear and nonlinear panel data in 22 countries from 2001 to 2020. • Solar, wind, and hydro contribute to economic growth and carbon emissions reduction. • Countries with a good economy prioritize wind and solar, while others prioritize hydro and wind. [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]