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

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]

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]

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]

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]

• 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]

• 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]

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]

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]

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]

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]

• 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]

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]

• 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]

Among the regulatory policies, feed-in tariffs (FIT) and renewable portfolio standards (RPS) are the most popular to promote the development of renewable energy power industry. This paper uses system dynamics (SD) to establish models of long-term development of the renewable energy power industry under FIT and RPS schemes, and provides a case study of China's wind power industry by using scenario analysis method. The model, on the one hand, not only clearly shows the complex logical relationship between the factors but also reveals the process of coordination between the two policy tools in the development of the renewable energy power industry. On the other hand, it provides a reference for scholars to study similar problems in different countries, thereby facilitating an understanding of the renewable energy power's long-term sustainable development pattern under FIT and RPS schemes, and helping to provide references for policy-making institutions. The results show that the integrate implementation of FIT and RPS can promote long-term and rapid development of China's wind power industry given the constraints and actions of the mechanisms of RPS quota proportion, the tradable green certificates (TGC) valid period, and fines. In addition, in the period of the construction of the TGC market, RPS quota growth rate and fine have a no significant effect on industrial development, however, with TGC market game becoming more and more fierce, both of them have a significant positive impact on the long-term development of the industry. TGC valid period has little effect on industrial development in our study. At the end of the paper, policy implications are offered as references for the government. [ABSTRACT FROM AUTHOR]

• the distributionally robust optimization method is adopted for renewable uncertainty. • an alternating direction method of multipliers algorithm is used for privacy protection. • peer-to-peer transactive energy trading improves the profit of microgrids. • the network transmission cost will encourage the electricity transactions nearby. Distributed renewable energy requires market-based measures to remain competitive as subsidies are phased out. However, the intermittence and volatility of renewable energy power generation lead to great challenges in decision-making. To address the uncertainty issues induced by inaccurate RE forecast, this paper proposed a peer-to-peer transactive energy trading strategy for multiple microgrids based on distributionally robust optimization. First, an uncertainty fuzzy set based on Wasserstein distance is created for the renewable energy prediction errors in each microgrid. Second, a day-ahead microgrids peer-to-peer transactive energy trading model is proposed based on the distributionally robust optimization theory to address the power fluctuation problems of renewable energy. Third, using the dual theory, the proposed nonlinear model is addressed by transforming it into a linear and convex programming problem. Considering the independence of microgrids, a distributed strategy based on the alternating direction method of multipliers is then developed to preserve their privacy. Finally, the case study proves that the method proposed can increase the income of microgrids containing renewable energy through peer-to-peer transactions, protect the privacy of microgrids, and then promote the development of renewable energy. The distributionally robust optimization approach also guarantees the economy and reliability of the transaction results for real-time deployment. [ABSTRACT FROM AUTHOR]

• A multi-VPP energy-sharing model is proposed based on GNB theory. • The network transmission cost encourages the consumption of renewable energy nearby. • The non-convex GNB-based model is divided into two sub-problems by transformation. • A distributed method based on the fast-ADMM is developed. • A contributing factor for profit distribution is applied to endorse renewable energy. The benefits of small-scale renewable energy resources are restricted by a feed-in-tariff strategy that works with distribution grids only. Virtual Power Plant (VPP) aggregates diverse distributed energy resources on the demand side for efficient control, which can improve their revenue and encourage the growth of renewable energy. Based on the general Nash bargaining theory, this paper proposes a multi-VPP energy-sharing mechanism. This approach enables VPPs to increase their revenue by engaging in peer-to-peer energy transactions. First, a general Nash bargaining-based model is proposed for multi-VPPs energy sharing considering peer-to-peer energy transactions. The model is then decomposed into two subproblems: minimizing the total cost of the VPP energy-sharing alliance, and the cost allocation of peer-to-peer energy transactions. Considering the privacy protection of VPPs, the first subproblem is solved by a distributed algorithm based on the alternating direction multiplier method. In the second subproblem, due to the introduction of a bargaining factor that is positively related to each VPP's contribution to energy sharing alliance, the cost allocation better reflects their contribution. The numerical test demonstrates the proposed energy-sharing mechanism, with little computational complexity, delivers a cost distribution result that is comparable to the classical Shapley value while promoting the development of renewable energy resources. [ABSTRACT FROM AUTHOR]

Policies targeting energy efficiency and renewable energy are sometimes viewed as more politically feasible than carbon pricing in pursuit of emission reduction goals. This paper assesses underlying drivers of energy policy development. These factors include economic, social, environmental, and institutional variables. The between estimator for panel data is an appropriate method for our focus on some exogenous variables that vary more across countries than over time. We find that larger oil reserves per capita have a negative relationship with renewable energy policy development. Education and political globalisation have strong positive relationships with both energy-efficiency and renewable-energy policy development. These results suggest that greater participation in global political groups can be an indirect approach toward energy policy development, in cases where direct and immediate policies are hard to implement. OECD countries have higher policy scores by 33 and 25 points in energy efficiency and renewable energy respectively. Blinder-Oaxaca decomposition analysis shows that these higher scores are primarily due to social and political institution predictors rather than economics and physical endowments. • Large-n cross country international sample on continuous policy score measurement. • Political globalisation is positively linked with renewables and energy efficiency policies. • Education is positively linked to energy policy development, especially for efficiency. • Higher OECD countries' policy scores are primarily due to social and political predictors. • Higher incomes are unlikely to lead to automatic improvements in energy policies. [ABSTRACT FROM AUTHOR]

With its strong ties to the European Union, Germany is well-positioned to assist many countries achieve sustainability goals. Therefore, the main objective of this paper is to examine how renewable energy intensity and renewable energy share improve environmental sustainability, the load capacity factor (LCF) in Germany from 1970 to 2018. To this end, the study uses the autoregressive distributed lag model, with the effects of the Kyoto Protocol included as a dummy variable. The study contributes to the literature by considering and testing for the first time the impact of renewables on the LCF in terms of intensity and share for Germany. The empirical results show that (i) economic growth lowers the LCF faster in the early stages of economic development than in later periods; therefore, the environmental Kuznets curve hypothesis is valid in Germany. (ii) A 1% upsurge in renewable energy intensity has no statistically significant effect on the LCF, while a 1% rise in the share of renewables in the total energy mix increases LCF by 0.48%. (iii) A 1% rise in urbanization reduces the LCF by 6.52%. (iv) A 1% augment in human capital enhances the LCF by 3.49%. These outcomes suggest that the German government should use human capital development and increasing the share of renewable energy as policy instruments to achieve the sustainable development goals. [Display omitted] • The EKC hypothesis is valid for Germany. • Renewable energy intensity has no influence on the load capacity factor. • A higher renewable share in the energy mix is associated with an improvement in LCF. • Human capital plays a key role in solving environmental challenges. • The urbanization process has a devastating effect on the environment. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable energy resources on the demand side, peer-to-peer (P2P) energy sharing has emerged as a promising method for consuming the surplus energy produced by energy prosumers. This paper reviews 180 papers and summarizes the recent development of P2P energy trading environment, including the market structure, market mechanism, trading platforms, social sciences. We also discuss the relevant policy based on the review of the state-of-the-art literature, pilot projects and industrial practice. The cooperative and non-cooperative game theory-based models are specifically classified. The optimization algorithms in distributed and centralized structures are studied thoroughly. Deep reinforcement learning and blockchain-based optimization are two vital research directions to simulate the optimization-seeking process in the P2P energy sharing market. The constraints of the system network are also reviewed to consider the physical constraints during energy trading. We present the methodology discussion to classify and compare these optimization methodologies. In this paper, the relevant resource-sharing markets are modeled as extended P2P energy sharing markets. Emission right sharing, negawatt sharing, and energy storage are all carefully reviewed. The coupling of the relevant resource sharing and conventional energy sharing is discussed carefully. We also provide the conclusions containing the prospects and possible questions of P2P energy sharing at the end of this article. It can be concluded from the review result that P2P energy sharing is a promising research direction of the transactive energy market from the perspectives of both academia and industry. [Display omitted] • We list and compare the different types of P2P energy sharing. • We categorize and discuss the methodologies of various optimization mechanisms. • The carbon emission, energy storage and negawatt trading are proposed and discussed. • We discuss the possible questions and future opportunities of the P2P energy sharing. [ABSTRACT FROM AUTHOR]

With the development of distributed energy resources, the peer-to-peer (P2P) transactive energy market has emerged as an effective method for consuming the excess renewable energy on the end-user side. In the current research on transactive markets, distributed energy storages (DESs) have been widely applied in energy trading for transaction flexibility and system management. These small-scale energy storages have achieved a considerable momentum that, if properly harnessed, may unleash their potential for prosumers' utility maximization, reserves for renewable energy uncertainty, and transmission congestion relief. The present work reviews distributed energy storage in the transactive market, classifying and analyzing 120 papers according to their applications, algorithms, and adopted policies. This study first identifies DES functions in wholesale and transactive markets and then provides the mathematical models of DESs in various transactive market types in detail. This paper then focuses on the pricing mechanism for the DESs' contributions in the P2P transactive energy market. A transactive energy market mechanism for accommodating DESs is proposed based on the Vickrey–Clarke–Groves framework to meet incentive compatibility. The energy policies related to the promotion of DESs in electricity markets are listed and compared. Furthermore, this review highlights the possible questions and future development opportunities according to the analyzed studies. With the roles and relationships among aggregators, DES owners, and power grid determined, the benefit of participating P2P transactive market can be further improved. [Display omitted] • We analyze the application of distributed energy storage in electricity markets. • We focus on reviewing distributed energy storages in transactive energy market. • We propose a Vickrey–Clarke–Groves mechanism for accommodating energy storages. • The future development and possible questions are discussed in detail. [ABSTRACT FROM AUTHOR]

Abstract Eco-friendly technologies regarding electricity production are urgent need of sustainable energy development. The renewable energy resources (RERs) have brought green revolution in mitigation of greenhouse gaseous emission resulted from traditional energy resources (TERs). Moreover, the effective utilization of these resources is influenced by pricing schemes which have limitations. Therefore, this paper aims at optimization modeling for dynamic price-based demand response (DR) which includes flexible and inflexible loads along with the effective utilization of RERs i.e. photovoltaics (PVs) and wind turbines (WTs) in a microgrid (MG). The optimization problem regarding profit maximization for loads (flexible and inflexible) is solved via particle swarm optimization (PSO). Two cases are used to evaluate the performance of proposed dynamic pricing scheme. The simulation results have shown that proposed scheme is suitable in term of profit and comfort for flexible and inflexible loads as compared to fixed pricing scheme in both cases. In addition, the dynamic pricing scheme is exemplified as plug and play devices because of its easy implementation in present market structure without any modification. Highlights • The dynamic price-based demand response which includes flexible and inflexible loads. • The effective utilization of renewable energy resources in a microgrid. • The optimization problem is solved by particle swarm optimization. • Two cases are used to evaluate the performance of proposed dynamic pricing scheme. • This scheme is suitable regarding profit and comfort for flexible and inflexible loads. [ABSTRACT FROM AUTHOR]

Abstract The Association of South East Asian Nations (ASEAN) is a diverse region characterized by rapid economic growth, demographic change and urbanization. The dominance of energy supply from non-renewable resources in the region means that the increasing energy demand has implications for energy security, as well as adverse local and global environmental effects. Climatic conditions in the region are favorable for renewable energy (RE) resources, especially but not only wind and solar technologies. ASEAN countries differ strongly in terms of their national policy frameworks and progress in renewable energy development; the overall target of 23% renewables by 2025 is very ambitious under current policy frameworks. This paper identifies a gap between these national policies and local governance, especially in urban areas, which requires attention to ensure future target fulfilment. By employing a new combined correlation and decomposition approach at country and city levels, we investigate the determinants of RE expansion and explore the trend drivers in ASEAN countries from 1995 to 2013. An Impact Matrix is developed to position and interpret the relative push (e.g. policy) and pull (e.g. market) impacts on RE development, and to derive policy recommendations for countries and sectors. The results highlight that urban areas should be the focus of RE policy and governance in addition to rural areas. The tremendous impact of economic growth creates a great impetus for renewable energy development, but urbanization is the second pull for renewable energy extensions. Since the two effects are located in the first quadrant of the Impact Matrix, if strategists affect these factors, they will create the most powerful incentive for renewable energy growth. This confirms that if the strategic aim is to promote renewable energy market development, through policy and governance measures, the focus should be on urban areas, non-electricity sectors and the demand side. [ABSTRACT FROM AUTHOR]

• Failure patterns of damaged radial gate caused by fluid-induced vibration are summarized. • Underlying failure mechanisms of radial gate are analyzed. • Methods in response prediction of fluid-induced vibration in radial gate are discussed. • Characteristics and challenges in hydrodynamic loads exerted on radial gate are presented. • Further research directions in revealing internal failure mechanisms of radial gate are provided. Radial gate is the key factor to control water discharging in flood prevention and sustainable hydropower energy generation. Unfortunately, fluid-induced vibration (FIV) problem always occurs owing to the fluctuation load of flow water exerted on the gate, causing vibration failure of radial gate and threatening the safety of hydropower system. The rapid development of renewable energy system renders the gate's vibration issue more urgent, as the gate need to be operated in small gate openings. In recent years, many works had been conducted on this subject. However, the underlying failure mechanisms of radial gate remain unclear, besides, there is still loss knowledge on the dynamic safety evaluation criterion of radial gate considering the FIV issues in the specification. To address this gap, this paper comprehensively reviewed the recent advancements and challenges in hydroelastic vibration failure of radial gate from four aspects: failure patterns, failure mechanisms, vibration responses and hydrodynamic characteristics. The vibration failure characteristics of radial gate are summarized from the typical gate failure cases recorded in definitive literature. The research of the FIV issue of radial gate is mainly concentrated into the stage of dynamic response prediction in practical engineering, the existing shortcoming and challenge are analyzed. The hydrodynamic characteristics exerted on gate are analyzed, meanwhile the existing challenges are also provided. Four reported FIV mechanisms of radial gate are presented, the challenges in revealing the fluid–structure interaction (FSI) process and the underlying failure mechanism are discussed. Moreover, the further research directions on the vibration failure problem of radial gate are provided. The perspectives provided in this work may be insightful for addressing the knowledge gap in this classical vibration problem. [ABSTRACT FROM AUTHOR]

• The analysis framework of a digital business model of PV fishery is constructed. • Multiple value propositions in the digital business model of PV fishery to create value for stakeholders. • The digital business model of PV fishery improves the operational efficiency of PV power generation. • The digital PV fishery increases the diversification of revenue sources of PV agricultural company. A business model that combines solar photovoltaic and fishery plays a role to expand distributed renewable energy and improve rural economy development. However, the business model of solar photovoltaic fishery is encountering growing difficulties with revenues decreasing due to changes of policy such as withdrawal of photovoltaic subsidies and land control. The spreading of digitization in photovoltaic industry and rural areas provides new opportunities for innovation of traditional solar photovoltaic fishery. This paper focuses on multiple value propositions to discuss a digital business model based of solar photovoltaic fishery on a case with a local Chinese photovoltaic agricultural company. The study results show that the digital business model of solar photovoltaic fishery improves the operational efficiency of solar photovoltaic power generation, economic benefits of aquaculture, and the diversification of revenue sources of solar photovoltaic agricultural company and leasing company. The transformation of business model from traditional solar photovoltaic power generation and fishery business to digital service platforms can not only accelerate expansion of distributed renewable energy and be potential deployed globally but also assist photovoltaic agricultural company to effectively respond to policy change. Finally, this paper proposes that supportive policy may focus on advocating system coupling to expedite the development of distributed renewable energies based on digital technology, and digital photovoltaic combined with rural energy market and financial, which will advance development of renewable energy electrification and promote rural economic growth. [ABSTRACT FROM AUTHOR]

• Site suitability assessments are at the heart of renewable energy planning. • A collaborative approach enables developing a fit for purpose decision-support tool. • Stakeholder-driven site suitability criteria supports transparent decisions. • The tool supports informed discussions and fosters consensus on deployment areas. • Data and technological limitations signal action areas for advancing practice. Competing land-use demands associated with planning for renewables is resulting in growing social and political challenges. Spatial decision-support tools are being developed and applied to tackle some of these challenges by facilitating systematic identification of suitable development sites based on technical, environmental and socio-economic criteria. Nevertheless, decision-support tools are often developed with limited input from stakeholders affecting participative and accountable planning and decision-making. This paper aims to address this shortcoming and foster greater transparency in the development of support tools for renewable energy planning, and thus in siting decisions. It present and discusses a stakeholder-driven approach to the co-creation of an online decision-support tool that attends to planning policy, technical considerations, environmental protection and social concerns influencing renewable energy planning decisions in Ireland. This bottom-up approach, easily transferable to other jurisdictions, fosters proactive expert input, awareness raising and greater comprehensibility and acceptance of siting decisions. It also enables developing an interface that responds to users' needs. The paper contributes to advancing renewable energy planning practice through effective and meaningful stakeholder engagement that supports transparent and accountable decisions from the outset. It also highlights ongoing data and technological shortcomings affecting the integration and operationalisation of stakeholders demands and needs, which signal priority areas of action to further advance the development of renewable energy decision-support tools. [ABSTRACT FROM AUTHOR]

With the widespread development of renewable energy sources, collective self-consumption (CSC) has been increasingly considered over recent years to sell the surplus of generated power to nearby consumers. However, appropriate energy allocation rules must be designed to efficiently share the energy among consumers. This paper aims to study two kinds of repartition keys for a CSC operation with multiple producers in the framework of an industrial multi-energy microgrid located in France. The default key considered in French regulations is compared to an optimized key based on the maximization of the income for the producers, in such a way as to maximize profitability by allocating the energy according to the consumers' electricity purchase price. The simulation results show that a CSC with an optimized key decreases the total electricity bill by 11.7%. For some producers, a reduction in the payback period between 6 months and 5 years is observed, compared to the case without CSC. A sensitivity analysis on economic assumptions is also proposed. The results show that the grid use tariff exemption is a key lever and makes it possible to find a more profitable selling price for producers and consumers, and to significantly reduce the payback period for investors. • A collective self-consumption (CSC) operation for an industrial microgrid is studied. • Two kinds of energy allocation rules are proposed and compared. • An optimization problem allocates the energy to increase the producers' income. • The CSC allows electricity bill savings and improves the profitability. • The results are sensitive to the CSC tariff and a grid use tariff exemption. [ABSTRACT FROM AUTHOR]

Abstract: The main aim of this paper is to review of the current state of energy systems in the Western Balkans countries. The paper gives a review of the individual parameters relating to energy development, defines the current state of the achieved level of sustainable energy development as a whole and defines possible strategic directions for energy development in the region. The paper also examines the possibility for implementation of the adopted EU target of 20% RES energy production. The main characteristics in the region are high energy consumption, high values of carbon emission, fossil fuels import dependency, constant production and electric energy supply, and high potential but minimal RES energy production. Further development of regulation framework and market liberalization is are basic preconditions of demanding energy reform in all countries. Increasing energy the second energy development priority. The third long term strategic goal is increasing the amount of the energy obtained from hydro and transition to clean coal technologies. Intensifying RES production is currently the fourth strategic priority. The analysis shows that production of energy from wind is priority for the region, but currently it is constrained by too great investment requirements for developing countries. [Copyright &y& Elsevier]

The Renewable Fuel Standard (RFS) requires production of cellulosic biofuel, such as ethanol produced from switchgrass. However, the bioenergy industry faces a well-established “chicken and egg” conundrum where biorefineries cannot be built until adequate farmers' participation in cellulosic biomass production is ensured; on the other hand, farmers will not commit to growing cellulosic biomass until a market is established. We assume that individual farmers in a future biofuel market are boundedly rational, and will endure the risk of growing switchgrass if they are likely to receive a better payoff in the future. After the number of switchgrass farmers reaches a certain threshold, sufficient biomass can be procured to build an economically viable biorefinery. If a biorefinery is built in a region, the reductions of logistics costs and economies of scale lead to the realization of a public good, and all farmers can be benefited from it; otherwise, the efforts of early switchgrass adopters could be unsuccessful. In this paper, an appropriate biorefinery capacity and the corresponding incentives provided to the farmers are determined by balancing the impact and the risk of the public good. Our incentive model is more efficient than a program that incentivizes all switchgrass growers equally. [ABSTRACT FROM AUTHOR]

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

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

• DSM mechanism in SMG is introduced where cost and PAR constraints are minimized. • Integrates PID-MSH∞ model for optimal regulation of frequency and voltage. • Introduces a new Modified Cat Swarm Algorithm for carrying out optimization. • Least value was attained by developed approach for utility emission cost. The amplified use of power applications by customers is a rising concern in the energy field every day, which leads to an imbalanced ratio of supply and demand. The integration of renewable energy sources and increasing greenhouse gas emission leads to power demand and affects the climate. Shortage of power supply is the major issue in remote areas, to rectify this issue microgrids have become the best solution. Demand side management (DSM) is a very important means for avoiding major deficits from the supply side and improving energy efficiency (EE). The development in the management of energy mainly focuses on minimizing the entire cost of electricity with no limitation in the utilization part and instead, it chooses to lessen the power utilization at peak hours. This paper intends to introduce a sophisticated DSM and controlling scheme for an Efficient Energy Management system (EMS) in Smart Microgrids (SMG). Initially, an optimal cost-efficient EMS operation is established depending on Modified Cat Swarm Algorithm (M-CSA) model and is augmented via time-of-use pricing (ToU) theory. Subsequently, the SMG frequency and voltage are optimally controlled via an improved PID-based Mixed Sensitivity H-infinity (PID-MSH∞) model while functioning in islanded mode. Thus, the developed approach exploits the massive IoT (Internet of Things) aptitudes for ensuring a secure and economic operation of SMG. The energy cost value of the proposed method is 8138.9 which is 33.67%, 37.94%, 35.42%, and 32.27% superior than the traditional approaches such as CSA, IDE, GA and JA. Thus, several analyses are used to demonstrate the created approach's superiority. [ABSTRACT FROM AUTHOR]

Unsustainable and uncontrolled development of urbanization, as well as the expansion and increase of environmental challenges, has caused extensive activities in recent years to reduce environmental pollution and efficient use of energy resources, worldwide. One of the appropriate solutions to reduce carbon dioxide (CO 2) emission and the optimal use of energy sources is the construction of an industrial energy park (IEP). Given the pivotal role of energy parks in different energy networks, accurate techno-economic-environmental assessment of energy parks, along with the promoted energy conversion facilities and flexibility options has become a challenging task. To address this open issue, this paper focuses on the coordinated operation of IEPs with a variety of thermal, electrical, and cooling loads to satisfy economic and environmental benefits. The proposed combined cooling, heat, and power (CCHP)-based energy park is equipped with multiple energy conversion facilities, such as absorption and electric chiller, power-to-heat facility, multi-energy storage, as well as demand response (DR) program as the flexibility option to supply energy demands, while has energy exchanging (power and heat exchanged) with the corresponding markets. The main objectives of this paper are to minimize the total operation cost of the energy park and decrease CO 2 emission rates. The coordinated model is exposed to the fluctuation of wind power, electrical and heat loads, and power prices, so the hybrid stochastic/information gap decision theory (IGDT)-based robust approach is used to handle them. The technical implications of using the proposed strategy are: (1) Creating a robust platform for industrial park operators to actively participate in different energy markets and take advantage of existing economic opportunities to meet the required energy demands at the lowest cost; and (2) Encouraging industrial park operators to participate in DR programs to reduce operating costs, increase energy efficiency, and contribute to the development of energy network plans. The proposed stochastic-IGDT multi-objective model is examined on the sample park and numerical results are discussed for different cases. Results reveal that under the proposed coordinated approach, total operation cost and emission pollution are decreased up to 3.1%, and 2.2%, respectively. • Evaluating the operation of the CCHP-based IEP under coordinated strategy • Providing a hybrid IGDT/stochastic approach for operation of the IEP • Developing economic-environmental scheduling for IEP under hybrid approach • Integrating multi-carrier storage, DR, boiler, CHP, chiller, P2H, and RES in IEP [ABSTRACT FROM AUTHOR]

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

The spatial distribution of biomass resources is not uniform. At present, the zoning of biomass energy development suitability is primarily based on resource potential, whereas energy demand is ignored as an important factor affecting the degree of local energy self-sufficiency. Under the constraints of high transportation costs, solar energy with low geographical constraints can be a supplementary energy source for biomass despite its instability. Based on the energy utilization potential of solar energy and biomass resources, this paper evaluates the energy surplus factor (ratio of renewable resource potential to energy demand) of each city in the study area, takes meeting the specific energy surplus factor as the construction prerequisites of biomass combined heat and power (CHP) plants, calculates the biomass resource utilization rate and energy surplus factor in the study area under different conditions, and provides zoning suggestions and data reference for the unified planning of biomass CHP plants. The minimum energy surplus factor that is suitable for the development of biomass energy is set as the construction threshold of biomass CHP plants (The construction threshold is the minimum condition for the construction of biomass CHP plants. Only areas where the energy surplus factor reaches the preset construction threshold are allowed to build biomass CHP plants). The results show that the construction threshold is negatively correlated with the utilization rate of biomass resources. When the construction threshold is less than 1.4, the utilization rate of biomass resources decreases rapidly with the increase of the construction threshold; when the construction threshold is greater than 1.4, the utilization rate of biomass resources decreases slowly and tends to be flat. It is suggested that biomass resources should be developed in different regions and in stages, and priority should be given to developing areas with an energy surplus factor greater than 1.4. Areas with an energy surplus factor of 0.3–1.4 are considered secondary development areas, and areas with an energy surplus factor of less than 0.3 are not recommended for development; these can meet 31% and 59% of the total energy demand and reduce 50319380 tons and 180524400 tons carbon emissions so that the limited construction cost can meet higher energy demand and biomass resource utilization. • Priority development areas are identified based on solar and biomass potential. • Energy surplus factor is calculated to determine the development suitability. • Strategies for zoning and phased planning of biomass resources are proposed. [ABSTRACT FROM AUTHOR]

As a response to climate change and the desire to gain independence from imported fossil fuels, there is a pressure to increase the proportion of electricity from renewable sources which is one of the reasons why electricity grids are currently being turned into Smart Grids. In this paper, we focus on private consumers’ acceptance of having Smart Grid technology installed in their home. We analyse acceptance in a combined framework of the Technology Acceptance Model and the Norm Activation Model. We propose that individuals are only likely to accept Smart Grid technology if they assess usefulness in terms of a positive impact for society and the environment. Therefore, we expect that Smart Grid technology acceptance can be better explained when the well-known technology acceptance parameters included in the Technology Acceptance Model are supplemented by moral norms as suggested by the Norm Activation Model. We tested this proposition by means of an online survey of Danish ( N = 323), Norwegian ( N = 303) and Swiss ( N = 324) private consumers. The study confirms that adding personal norms to the independent variables of the Technology Acceptance Model leads to a significant increase in the explained variance in consumer acceptance of Smart Grid technology in all three countries. [ABSTRACT FROM AUTHOR]

Redevelopment of brownfields has gained significant attention in recent years due to political, social and financial reasons. A special field of such redevelopment is, when the investor aims to reutilise the property for energy generation purposes. To make such investments feasible, extensive analysis of the conditions of the brownfield and the potential of different energy sources has to be performed. Present paper introduces a multi-objective examination and evaluation method, which provides a basis for the business analysis of an energy purpose redevelopment project. A case study is also presented to illustrate the process; the site of a gas power plant is examined, and the key findings concerning the conditions and the potential utilisation of the site are listed. The authors have prepared a recommendation to the investor, detailing the advantages and disadvantages of several different power generation technologies, and also proposing ideas for converting part of the existing infrastructure to reinterpret the operation. Technologies that might draw the interest for further examination and/or are feasible enough to perform an in-detail business analysis have been selected as well. The results of the study have confirmed that the proposed multi-objective method can be applied to arbitrary brownfield sites, while a further extension of the analysis criteria is possible if environmental aspects are also necessary to be handled. [ABSTRACT FROM AUTHOR]

The support of the renewable energy sources (RES) is an important endeavor that might help to reduce the impacts of global warming and prevent the depletion of world's energy resources. However, it calls for the application of special government-funded plans envisaged for its support and stimulation. Political uncertainties and ineffectiveness of the state support often tend to increase the risks associated with the RES projects and, as a result, hinder their successful development. Hence, the assessment of the soundness of the current government-sponsored policies on renewable energy development becomes a very relevant and important task. Our paper presents the results of a theoretical analysis of the main types of state support measures for renewable energy in Russia. Furthermore, it employs an energy-specific logit-model that allows to assess the external and internal factors having an impact on default of the Russian RES projects. Moreover, the paper studies the risk dynamics at all stages of the life cycle of RES projects and evaluates the effectiveness of state measures to support the RES sector. In addition, we carry out the assessment of the "real" efficiency of state support mechanisms without "zero-risk" projects. Our results might be used for improving the existing approach to risk assessment in the RES market, as well as for the development of a concept of competition between traditional and renewable energy at the global energy market. Image 1 • Energy-specific logit-model was developed to assess the risk of default of project. • The model was employed for assessing factors of default of Russian RES projects. • The risk dynamics at all stages of the life cycle of RES projects was tracked. • The effectiveness of state measures to support the RES sector was evaluated. [ABSTRACT FROM AUTHOR]

This paper shows that interventions based on social norms and on increasing the visibility of people's decisions to others ("decision observability") present promising pathways of generating public support for renewable energy development. In a laboratory experiment (n = 300), we show that social norms and decision observability increase support for renewable energy, even at a financial cost to oneself: When exposed to pro-environmental social norms, participants donated 35% more money to an existing renewable energy initiative than participants in the control condition (Cohen's d = 0.35). Participants whose decisions were observable to others donated 23% more compared to control (d = 0.23). And participants exposed to both treatments (their decisions being observed by others and learning about norms) donated 69% more compared to control (d = 0.67). In addition, our treatments had a positive effect on participants' post-decisional emotions of happiness and pride, which partly alleviates existing concerns about possible adverse side-effect of social influence interventions. Suggestions for policy makers and for future research in this area are presented. [ABSTRACT FROM AUTHOR]

The compatibility of renewable energy consumption and economic growth is the key to both the development of renewable energy, and to sustainable economic development. This paper explores the influence mechanism of renewable energy and economic growth from the two dimensions of natural resources and institutional environment. Resource dependence and anticorruption regulations are mainly selected as the intermediary variables. The hypothesis is tested by using the panel data (from 2002 to 2018) of 104 selected countries and regions, and by conducting regression estimation. On the whole, this study finds that a positive relationship exists between renewable energy and economic growth. The group discussion results show that, for high-income countries, the relationship between renewable energy and the economy is inconsistent under different thresholds. Where, the role of renewable energy in promoting the economy is significant. For the middle-income group, the relationship between renewable energy and economic growth is not the same in the resource dependence and anticorruption regulation models. However, for the low-income groups, no matter which model is used, a U-shaped relationship emerges between renewable energy and economic growth. [Display omitted] • Examine the relationships between economic and renewable energy for 114 countries. • Resource dependence and corruption regulations are important intermediary factors. • The impact of renewable energy on economic varies across income groups. • The U-shaped relationship is supported in the high income group. [ABSTRACT FROM AUTHOR]

[Display omitted] • Classifications and strategies of NZEBs were reviewed. • Multi-criteria analysis supports NZEB designing. • Renewable energy supply options for NZEBs are summarized. • Cost-benefit analysis and life cycle assessment needed for criterion evaluation. Decarbonizing the building sector is extremely important to mitigating climate change as the sector contributes 40% of the overall energy consumption and 36% of the total greenhouse gas emissions in the world. Net-zero energy buildings are one of the promising decarbonization attempts due to their potential of decreasing the use of energy and increasing the total share of renewable energy. To achieve a net-zero energy building, it is necessary to decrease the energy demand by applying efficiency enhancement measures and using renewable energy sources. Net-zero energy buildings can be classified into four models (Net-Zero Site Energy buildings, Net-Zero Emissions buildings, Net-Zero Source Energy buildings, and Net-Zero Cost Energy buildings). A variety of technical, financial, and environmental factors should be considered during the decision-making process of net-zero energy building development, justifying the use of multi-criteria decision analysis methods for the design of net-zero energy buildings. This paper also discussed the contributions of renewable energy generation (hydropower, wind energy, solar, heat pumps, and bioenergy) to the development of net-zero energy buildings and reviewed its role in tackling the decarbonization challenge. Cost-benefit analysis and life cycle assessment of net-zero energy building designs and their challenges were reviewed to shape the priorities of future development. It is important to develop a universal decision instrument for optimum design and operation of net-zero energy buildings. [ABSTRACT FROM AUTHOR]

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

• A metaheuristic-based model is proposed to optimally size a hydrogen-based microgrid. • The microgrid system is equipped with an innovative hydrogen refuelling station. • The performances of eight metaheuristics are studied in terms of accuracy and speed. • The moth-flame optimizer is found to outperform the popular algorithms in this area. • The system's levelized costs of electricity and hydrogen are $0.09/kWh and $4.61/kg. There has been growing interest in the development of sustainable energy systems using the potential of renewable energy sources. However, due to the intermittent nature of renewable energy sources, they must be accompanied by appropriate storage devices to be optimally integrated into so-called smart micro-grid systems. The optimal design problem of sustainable micro-grids is associated with several nonlinearities and non-convexities, and therefore is not amenable to exact methods of optimization. Accordingly, this paper proposes a metaheuristic-based approach for optimizing the size and typology of the components of an off-grid hydrogen-based micro-grid, backed up with super-capacitors and stationary fuel cell systems, in the presence of a hydrogen refuelling station. The paper also compares the performance of six recent metaheuristics. The simulations are carried out for the climatic conditions of the Feilding area, New Zealand using MATLAB. Based on the comparative results, the moth-flame optimization algorithm is found to result in a significant reduction in the total net present cost of the system in comparison with other investigated algorithms. Notably, it outperforms the genetic algorithm and the particle swarm optimization in terms of solution quality by ~2.1% and ~3.2% (equating to cost savings of $123,910 and $188,129 from the target system). The results also demonstrate both the technical feasibility and cost-effectiveness of the proposed stand-alone micro-grid architecture. Particularly, the levelized costs of electricity and hydrogen of the conceptualized system are found to be $0.09/kWh and $4.61/kg, respectively, which are well below the current tariffs in New Zealand. [ABSTRACT FROM AUTHOR]

• A framework for retrofitting existing houses to NZEBs is proposed. • The framework includes renewable system design, building envelope improvement and energy system retrofit. • The framework practicability and reliability were demonstrated in a real building retrofit. • Energy consumptions before and after retrofit were presented and compared. Nearly zero energy building is a promising solution for tackling the conflict between the reducing fossil fuel reserves and the ever-increasing energy demand. Initiatives and goals for nearly zero energy buildings have been announced in different countries, as well as a series of strategies and incentives for advocating renewable energy applications. Within this context, homeowners' energy awareness has been strengthened and more and more of them are willing to invest on energy efficiency improvement and renewable energy application. However, there is no clear method or procedure for common practitioners to follow in the practice of energy retrofit of existing buildings, more specifically existing houses. This paper fills this pressing research gap by developing a framework for retrofitting existing houses to nearly zero energy buildings. The developed framework is then implemented on a real house in Beijing to demonstrate its reliability and practicability. The innovations of this study lie in the full consideration of the actual conditions of existing houses as well as the practicability of the framework, and for the first time, the detailed energy performance measurements of a real-life house from its original inefficient condition to after being retrofitted to a nearly zero energy building are presented. The framework can be easily extended to applications of other renewables and serve as a guideline for the wide common practitioners in their real building renovation practices. [ABSTRACT FROM AUTHOR]

Abstract: China has set ambitious goals to increase the use of renewable energy. Developing renewables in rural areas is also one of the most important energy strategies. This paper examines rural social acceptance of renewable energy deployment taking Shandong as a case study via a field questionnaire survey. Theory of planned behavior is adopted to establish an analytical framework, and a logit model is used to examine possible determinants of local social acceptance. The results show that rural residents are generally supportive renewable energy development given its positive impacts on environment. A stated willingness to pay more for renewable electricity is taken as a variable representing an individual’s behavioral intention. The probability of occurrence of positive intention is found to increase with household income, individual knowledge level and belief about costs of renewable energy use but decrease with individual age. Residents with higher level of income are more likely to be willing to pay more for green electricity, so are the younger people. Enhancive knowledge and understanding about renewable energy (for instance, the cost) would be conducive to win public acceptance of renewable energy deployment. [Copyright &y& Elsevier]

Abstract: Thermal energy storage (TES) is widely recognized as a means to integrate renewable energies into the electricity production mix on the generation side, but its applicability to the demand side is also possible. In recent decades, TES systems have demonstrated a capability to shift electrical loads from high-peak to off-peak hours, so they have the potential to become a powerful instrument in demand-side management programs (DSM). Thermal storage is a technology that ensures energy security, efficiency and environmental quality. Of particular interest are applications where TES systems help manage the mismatch between availability of renewable electricity and the demand for electricity in buildings where hot water, heating and cooling are delivered by heat pumps and air conditioning for example. Thus this paper demonstrates the state of the art of present applications of thermal storage for demand-side management. A particular focus of this work is the attention paid to the characteristics of DSM and their relationship to different thermal storage systems. If TES effectiveness for the abovementioned applications is demonstrated, TES devices have a small percentage of the potential market. Therefore challenges and guidelines for a development plan are suggested. [Copyright &y& Elsevier]

Abstract: Renewable energy sources (RES) are being increasingly considered as key elements in future development strategies on a national, regional and local level. This paper discusses the methodological approach to local energy planning proposed by the authors and identifies the most important drivers for its application at the regional level. This approach is different to the previous ‘top-down’ or strategic assessments as seen many times in the past in Croatia and is expected to herald a new future for the local community ownership and ‘buy-in’ to renewable energy, resulting in the greater probability of individual project success where local stakeholder sympathy is retained. The implementation of the developed approach is demonstrated on Karlovac County (western Croatia), which has considerable potential for the utilisation of different RES. The main outcomes are described: approved Regional strategy for utilisation of renewable energy sources in the Karlovac County; number of individual projects identified, supported and launched; and a Regional energy agency created as the implementing body for future renewable energy projects. [Copyright &y& Elsevier]