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Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems. • A hybrid technique for predict fault in standalone PV to improve the performance of PV • The proposed method is the joint execution of ANN and GEO • The major contribution of this work is to improve the performance of PV systems • The use of GEO provides an efficient optimization technique for ANN training • The proposed method is more accurate and effective than existing techniques [ABSTRACT FROM AUTHOR]

This article presents a state-of-the-art review of the status, development, and prospects of DC-based microgrids. In recent years, researchers' focus has shifted to DC-based microgrids as a better and more feasible solution for meeting local loads at the consumer level while complementing a given power system's reliability, stability, and controllability. DC microgrid has an advantage in terms of compatibility with renewable energy systems (RESs), energy storage, modern electrical appliances, high efficiency, and reliability. However, the integration of different distributed generations has complicated the control of bus voltage and current. Therefore, several efforts have been made in the research community to further explore efficient control techniques for a reliable and stable DC microgrid. In spite of the numerous review papers published on DC microgrid control, so far, not any has given sufficient emphasis on the power flow analysis methods used in various DC microgrid topologies and the hybrid control topologies discussed in this review. In general, this paper presents a meticulous explanation of DC microgrid architecture; power flow analysis; control strategies with comparative analysis; challenges with recommendations; as well as classical and intelligent-based energy management strategy (EMS). Finally, suggestion for further research is presented. This review paper will go a long way in helping readers to understand the present state of development on DC microgrid control. [ABSTRACT FROM AUTHOR]

This paper deals with a dynamical analysis of a supercapacitor based multilevel inverter. In Fact, applications for multi-level inverters based on supercapacitors include uninterruptible power supplies (UPS), grid-tied renewable energy systems, electric vehicles, and other power conversion systems where high-power quality and rapid energy transfer are important. These systems are the subject of ongoing research and development in the field of power electronics and energy storage, as they have the potential to enhance the performance and efficiency of various electrical and electronic applications. However, If the input voltage sources are fixed, this can limit the flexibility of the inverter, especially in terms of regulating the output voltage and adapting to load variations. This study is focused on the dynamic analyses which is crucial for designing, optimizing, and verifying the performance of supercapacitor-based multi-level inverters in various applications. Hence, the behaviour of the system was studied on a resistive load, material calculations showing the functioning of the circuit as well as simulations were performed for the positive phase of the sinusoid at the terminals of the charge. [ABSTRACT FROM AUTHOR]

This paper has sorted out the general logic of the impact of COVID-19 on energy consumption. In the short term, the epidemic has forced governments to adopt different levels of lockdown measures. The total electricity consumption decreased and the share of renewable energy has increased. Based on the International Energy Agency's (IEA) data on electricity production and consumption, this paper analyzes the long-term effects: The COVID-19 pandemic does affect overall electricity demand and market structure in the short term, but in the long term, the impact of the pandemic is detrimental to the proportion of electricity generated from renewable sources. Increased, especially in hard-hit countries. The impact on countries with different levels of development is different, and countries with higher levels of economic development are better able to resist such exogenous shocks. The pandemic is still ongoing, how to steadily promote sustainable development? In this paper we suggest that to cope with climate change, to accomplish energy transformation and upgrading or even sustainable development, we should not only rely on reducing human activities, but give more support to countries with low development levels in realizing sustainable development. [ABSTRACT FROM AUTHOR]

• Classified sales channels for different quality renewable energy. • Dynamic assessment indicators for the renewable energy's power deviation. • Power curve optimization strategy considers the self-owned battery. • Accurately and timely transmit the peak shaving pressure. • Guarantee the independent decision-making power of new energy stations. By the end of 2023, the installed capacity of renewable energy (RE) in China accounted for 36.0% of the total installed capacity, while the installed capacity of conventional thermal power units had decreased from 64.0% in 2016 to 47.6% in 2023. RE accounts for a higher proportion, leading to a shortage of peak shaving resources (PSR). As a result, the current RE quota system becomes unsustainable. In this paper, RE stations can classify their electricity into high-quality and low-quality parts based on the accuracy of their power curves. High-quality RE will be fully purchased and under stricter assessment, while low-quality RE will compete for clearing in the intraday spot market. Firstly, the article estimates the degree of PSR shortages in different periods based on technical parameters of thermal units, RE forecast information, and load forecast information. Consequently, the PSR supply capacity (PSR supply capacity) can be divided into PSR shortage status and sufficient status. Secondly, a clearing method for low-quality RE in the spot market is designed. The grid can set different PSR supply capacitys for different time periods according to the degree of PSR shortage. The assessment indicator values vary under different PSR supply capacitys. Lastly, based on the classification sales channels, this paper proposes a power curve optimization strategy that considers self-owned battery energy storage (BES). Case studies show that the proposed classification sales channels can effectively decrease the degree of PSR shortage while taking into account the benefits of RE station. The designed PSR supply capacity dividing method can accurately reflect the degree of PSR shortage in real-time, and the proposed power curve optimization strategy can further enhance the economic benefits of RE station. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

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

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

• Pioneer Spain: first country to include in grid code the synthetic inertia capability. • Synthetic inertial response of a PV inverter studied based on the Spanish grid code. • Analyzed the role of power plant controller in the dynamic response of the facility. • PV system able to contribute to frequency control providing an inertial response. As the large-scale integration of renewable energy grows, inertia in power systems is reduced, which causes the rate of change of frequency to increase and worsens the frequency nadir during disturbances. There is, therefore, an urgent need to accelerate the development of international standards and regulations that require solar PV systems and other renewable installations to also contribute to stability in the network by emulating inertia. Spain is a pioneer in this field: it is currently the only country that includes the synthetic inertia technical requirement in its grid code. In this context, the present paper analyzes the synthetic inertial response of a PV system model that forms part of an actual PV power plant in Spain under two scenarios, according to its grid code: i) when measurements are conducted at the power conversion PV system level and hence only its control algorithms are executed; and ii) when measurements are conducted at the power plant level and the power plant controller also influences the response of the entire installation. Among its aims, this paper seeks to thoroughly assess the response of an actual PV power conversion system that has the capability of actively contributing to frequency regulation in a network and to critically analyze the suitability of having a power plant controller when the PV facility contributes to power-frequency control by means of an inertial response. The results reveal, at the power conversion PV system level, that the inertia emulation module significantly reduces the response times of the installation to changes in frequency -by more than 68% in the worst case under frequency increases, and by 73% under frequency decreases, thus contributing to more rapid power-frequency regulation in the network. However, at the power plant level, the power plant controller disturbs the PV power conversion system's behavior and causes the opposite effect: response times increase -up to more than 7% in the most unfavorable case when the inertia module is enabled. [ABSTRACT FROM AUTHOR]

In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [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]

• 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]

In recent years, the conventional power system is becoming a hybrid power system with increments in the interconnection of Renewable Energy (RE) sources, High Voltage Direct Current (HVDC), and Energy Storage (ES). At the same time, the power system network is also becoming more flexible in managing demand and supply variability. These factors posed a challenge in achieving frequency stability when there are abrupt changes in the power system such as load increment/decrement, loss of generators, and faults. Due to this, load frequency control (LFC) has shifted from traditional controllers to hybrid controllers and from traditional optimization methods to hybrid metaheuristic optimization to achieve better stability in hybrid power systems. This paper provides a comprehensive review of load frequency control (LFC) and power system flexibility. Other frequency stability mechanisms that support further LFC i.e. application of frequency nadir and rate of change of frequency (RoCoF), control in wind turbines, and demand response are also presented. This review also highlights the research gaps and potential future research direction of LFC for multi-area hybrid power systems. [ABSTRACT FROM AUTHOR]

Compressed air energy storage (CAES) systems offer significant potential as large-scale physical energy storage technologies. Given the increasing global emphasis on carbon reduction strategies and the rapid growth of renewable energy sources, CAES has garnered considerable attention. However, the optimal design of CAES systems presents challenges due to the intricate coupling of multiple parameters, complex physical processes, and highly variable operating conditions. This paper comprehensively reviews three key aspects related to CAES optimal design. Firstly, it examines system analysis and optimization methodologies. Secondly, it investigates the off-design characteristics of CAES systems. Lastly, it explores system design methodologies specifically addressing off-design operations. Notably, the corresponding-point methodology (CPM) is proposed as a means to provide clearer guidance for system improvements. Future research should aim to combine CPM with traditional methods to establish more efficient and intuitive system optimization approaches. Furthermore, the exploration of active off-design regulation strategies, including the integration of various adjustment methods and multistage combined regulation for CAES systems experiencing wide load fluctuations, requires further investigation. Novel CAES configurations that enable efficient off-design conditions should also be explored, despite the inherent complexities associated with regulation and control. Finally, a coupled design methodology based on off-design operation data and probabilistic-load factor analysis is presented. This paper provides valuable insights and guidance for the optimal design and study of CAES systems. • CPM combined with traditional methods is needed to improve CAES round-trip efficiency • Not high temperature/pressure but "corresponding" is key factor to high round-trip efficiency • Multiple regulations and new system structure are needed for wide-load operation • Design methodology addressing complex off-design operations needs further improvement [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]

The increasing penetration of renewable energy sources (RES) into modern power systems may reflect the problem of frequency stabilization. Therefore, this paper proposes a new combined Fuzzy Fractional-Order Proportional-Integral (FOPI) and Tilt-Integral-Derivative (TID) controller to improve the frequency response of a hybrid power system. The proposed controller combines the advantages of intelligent Fuzzy FOPI and conventional TID controllers, resulting in more effective and robust load frequency control. In this study, the parameters of the proposed Fuzzy FOPI + TID combination are optimized using a novel metaheuristic algorithm, namely Wild Horse Optimizer (WHO). The case study system is a two-area conventional power system integrated with different RES including photovoltaic (PV) and wind generation as well as distributed electric vehicles (EVs) between the two areas. The effectiveness of the proposed controller is tested under various scenarios such as step load perturbation, random load variation, wind speed fluctuation, solar irradiance change and sensitivity analysis. In addition, the disturbance of wave energy oscillation is applied in Area 2 to evaluate the robustness of the proposed controller. For each scenario, the proposed Fuzzy FOPI + TID controller is compared with the conventional PID, single TID, and individual Fuzzy FOPI controllers using the proposed WHO algorithm and other optimization algorithms presented in the previous literature. The results show that the proposed FOPI + TID controller is superior to other controllers in terms of integral square error, peak overshoot, maximum undershoot and settling time for all scenarios. Furthermore, the presence of EVs helps in improving the frequency and tie-line power deviations. Finally, the time domain simulations are implemented using Matlab/Simulink. [ABSTRACT FROM AUTHOR]

Renewable energy has demonstrated a significantly greater increase in its share of total energy consumption than carbon emissions in recent decades. This rise in renewable energy consumption did not effectively mitigate climate change, as evidenced by the accelerated rate of climate change. This paper aims to determine the cause of this failure by examining the total effect centrality and mediating effects of renewable energy in economic systems in the short run and long run. The total effect centrality and mediating effects of renewable energy illustrate the evolution of its overall impact on the major variables of an economic system and its role in mediating the indirect effects of these variables on carbon emissions, respectively. The study employs a recently developed network analysis that is a pioneer in the estimation of the time-varying total effect centrality and time-varying mediating effects of renewable energy, while also accounting for its cross-associations among all variables in a network. The study derives three conclusions by examining the three countries with the highest carbon emissions in their respective regions: the United States in North America, China in Asia, and Germany in Europe. First, renewable energy has become an essential element of economic systems, having a substantial total effect centrality on macroeconomic systems and, by way of its mediating effects, substantially mitigating the indirect effects of major macroeconomic variables on carbon emissions in the short run. Second, the total effect centrality and mediating effects of renewable energy diminish in the long run, indicating that its capacity to effectively mitigate climate change remains constrained in structural terms. Third, the long-term decline in total effect centrality rises by 0.3, 0.7, and 0.4 for the United States, Germany, and China, respectively. In the same nations, the long-term decline of mediating effects rises by 0.2, 0.4, and 0.2, respectively. The study provides pertinent policy recommendations on how to augment the total effect centrality and mediating effects of renewable energy in economic systems. [Display omitted] • Explains why renewable energy failed in curbing the accelaration of climate change. • Introduces the 'total effect centrality' of renewable energy in economic systems. • Illustrates the time-varying mediation role of renewable energy in economic systems. [ABSTRACT FROM AUTHOR]

To accomplish the desired reduction in carbon emissions, society must improve the integration of renewable generation and accelerate the adoption of technologies like peer-to-peer energy trading. This necessity is particularly evident in the agriculture sector, specifically for energy-intensive forms of farming, such as dairy farming. Nevertheless, the integration of renewable energy sources and peer-to-peer energy trading presents notable challenges. Effective control strategies are essential for dairy farms to engage in peer-to-peer energy trading markets. This paper introduces the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator-extended (MAPDES), which leverages multi-agent systems to facilitate the participation of dairy farms in peer-to-peer markets. The energy trading strategy yields a 43% reduction in electricity costs and a 42% decrease in peak demand, while also increasing energy sales by 59% compared to the baseline scenario, which does not include peer-to-peer energy trading or renewable energy sources. This model has been tested across various farm community sizes, renewable energy combinations, and geographical locations to determine the ideal farm community configuration and assess the model's applicability across different settings. • Developing a Multi-Agent System for Peer-to-Peer Energy Trading in Dairy Farming. • Analyzing case studies of dynamic renewable energy and farm sizes/locations for optimal setup. • Evaluating P2P dairy farm energy trading using case studies from Finland and Ireland. • Evaluating the algorithm's practical use in dairy farming by assessing its efficiency and real-time capabilities. [ABSTRACT FROM AUTHOR]

Constructing an accurate and reliable solar photovoltaic (PV) power forecasting system is crucial for smart grid management and dispatch. However, due to the intermittent, non-stationary and random nature of solar energy, current methods cannot effectively capture the dynamic change patterns of PV data, resulting in a forecasting accuracy that cannot satisfy the requirement for stable operation of smart grids. To address this issue, in this paper, a new solar PV power forecasting system is proposed based on a new adaptive mode decomposition method that combines machine learning models. First an adaptive mode decomposition method is proposed for adaptively eliminating high-frequency information of PV data, mitigating the impact of high noise on the forecasting performance. Then the powerful mapping ability of machine learning model is utilized to construct a solar PV power generation combination forecasting system. The system adopts a multi-objective optimization strategy to determine the optimal weights of the combination model, which reduces the volatility of the forecasting results and enhances the stability of the forecasting system. Experimental results demonstrate that the proposed system achieves the best overall performance with online manner on 10 state-of-the-art baselines. Furthermore, the evaluation analysis by Diebold–Mariano test, improvement ratio and sensitivity analysis further show that the stability and accuracy of the proposed system outperforms the comparative baselines. [ABSTRACT FROM AUTHOR]

The major problems in the power system network of the South Asian Association for Regional Cooperation (SAARC) region include the utilization of substantial fossil fuels, which leads to significant emissions of CO 2. Moreover, due to the depletion of fossil fuels, it is very difficult to manage the demand requirement of the SAARC regions, which leads to significant power shortage issues. These problems would be resolved through the integration of the substantial amount of renewable energy resources (RERs) that are available in excess in the SAARC regions into the power system networks. This will provide a futuristic roadmap to a sustainable and green energy environment in the SAARC region. In this regard, the SAARC region has devised a new plan to transform its smart grid infrastructure into the super smart grid (SSG), which would connect the SAARC countries using the high voltage transmission lines utilizing each country's substantial RERs. To accelerate this ecofriendly effort, the key objective of this review study is to evaluate the potential of the SAARC SSG for demand management and emissions reduction. This study provides a comprehensive overview of SSG technology, covering its technical background, management, power flow improvement techniques, power stability techniques, simulation models, and future steps for development in the SAARC region. First, the regional conflicts, energy deficiency, and potential of the SAARC region have been discussed. Second, a potential architecture of the SAARC SSG has been simulated. Third, a novel Markov chain modeling has been presented for demand management in the SAARC regions. The results and discussions section presents findings from simulations and models. The paper also discusses CO 2 reduction techniques, including methods for capturing, storing, and utilizing carbon in biofuel. The study also discusses futuristic optimization techniques in SSG, including blockchain, federated learning, reinforcement learning, the metaverse, digital twin technology, artificial intelligence, and cloud computing. The SSG challenges and limitations section summarizes the challenges and limitations faced by SSG. Finally, a roadmap to a sustainable and green energy environment for the SAARC power grids has been identified to support the implementation of the futuristic SAARC SSG. The results exhibited that there is a good potential for successful demand management and emissions reduction through the SAARC SSG. [ABSTRACT FROM AUTHOR]

• A multi-microgrid energy real-time optimal control scheduling strategy is proposed. • Energy storage devices can actively participate in optimal energy scheduling. • Improved resilience and flexibility of energy dispatch for multiple microgrid. • Significantly reduce the number of microgrid connections to the distribution grid. • Maximising the full uptake of renewable energy in multi-microgrid systems. With the increasing prominence of the energy crisis and global climate issues, it has become an important cornerstone of the energy revolution to improve the utilization rate of renewable energy and gradually reduce the use of fossil energy. Nonetheless, the erratic fluctuations and instability of renewable energy sources such as wind and solar energy have presented certain obstacles to the effective utilization of renewable energy. In order to maximize the utilization of renewable energy, enhance its utilization efficiency, and reduce the carbon emission of power supply, this paper first proposes a real-time collaborative optimal energy scheduling and dispatching control model for multi-microgrids. Subsequently, it proposes a real-time optimal control and dispatching strategy for multi-microgrid energy based on storage collaborative. This model considers the energy storage device as an energy management controller, enabling it to participate in the energy collaborative dispatch of multi-microgrid. This enables the energy storage device in the microgrid not only to actively participate in the optimal scheduling of energy, but also to make the energy in the storage device be reasonably distributed in the multi-microgrid system, to maximize the full absorption of renewable energy in the multi-microgrid, thereby reducing the dependence of the microgrid on the distribution network, and significantly improving the elasticity and reliability of the multi-microgrid system. Finally, the feasibility and effectiveness of the proposed optimal control scheduling strategy are verified by simulation. [ABSTRACT FROM AUTHOR]

In recent years, dual-atom catalysts (DACs) have emerged as a significant area of interest, extending beyond the scope of single-atom catalysts. The DACs can offer enhanced metal loading and a wider variety of active centers by integrating two metal atoms. The advancement of atomically dispersed catalysts has led to broader adaptability and superior catalytic efficiency for sustainable energy conversion reactions, showcasing great potential in electrocatalysis. However, it is still facing challenges and uncertainties in thoroughly understanding their mechanisms and identifying precisely and universally applicable synthesis methods. In this review, we firstly provide a comprehensive introduction of the synthesis method of DACs, including pyrolysis, atomic layer deposition, and impregnation. Then, we introduce their potential applications in several key energy conversion reactions such as hydrogen evolution reaction, which can achieve an impressive overpotential of only 10 mV to generate a current density of 10 mA cm−2 with IrRu DACs, and oxygen evolution reaction. Through a comprehensive discussion of the synthesis, application, and catalytic mechanism of DACs, this paper aims to understand their structure-performance relationships and the deep catalytic mechanisms, hoping to provide new insights for catalytic science in the field of renewable energy. We anticipate that incorporating DACs into our analysis will pave new pathways for tackling energy and environmental challenges, thereby steering catalytic science towards a future of enhanced efficiency, stability, and sustainability. [Display omitted] • Advanced synthesis techniques for dual-atom catalysts are summarized systematically. • Dual-atom catalysts for wide-ranging electrocatalytic applications are highlighted. • Innovative strategies for enhancing the performance of dual-atom catalysts are discussed. • Future research directions for dual-atom catalysts are analyzed and outlined. [ABSTRACT FROM AUTHOR]

Under the background of carbon peak and carbon neutrality, this work proposes a novel municipal solid waste (MSW) oxygen-fuel combustion system driven by electrolytic oxygen production from renewable energy. Then, the corresponding mathematical model is established, which includes the supply sub-model related to the renewable energy generation process and water electrolysis process, the storage sub-model with battery and oxygen tank, and the demand sub-model of the oxygen-fuel combustion process, featuring a mixed integer linear programming one. To describe in-out characteristics of the MSW oxygen-fuel combustion system, two methods with pre-defined parameters and simulation models, are adopted respectively in this paper. Taking the minimum total cost of the system as an optimization target, the configuration scheme and operating strategy of the oxygen-fuel combustion system can be obtained. An example of an MSW plant coupled with a photovoltaic (PV) system is adopted as a case study to verify the effectiveness of the proposed methods. Results show that the design schemes can be determined by two proposed methods, but the one with simulation models superiors to describe the in-out performance of the MSW oxygen-fuel combustion system. What's more, the effects of the intrinsic intermittence and fluctuation of renewable energy on the MSW oxygen-fuel combustion process are investigated comprehensively, by analyzing the operation scheme of the proposed system. Finally, the environmental impacts of the MSW oxygen-fuel combustion system driven by electrolytic oxygen production from renewable energy are evaluated. A scheme diagram of the oxygen-fuel combustion system of municipal solid waste driven by electrolytic oxygen production from renewable energy. [Display omitted] [ABSTRACT FROM AUTHOR]

The integration of 5G base station (5G BS) clusters and edge data services introduces novel digital loads (NDLs) into the distribution system (DS), significantly impacting the interactive coordination of 5G-DS. This paper proposes an expansion planning model of 5G and DS considering source-network-load-storage coordination. Here, renewable energy resources (RESs), 5G BS clusters, edge data centers (EDCs), and DS source-network-load-storage are coordinately planned. The dynamic service access of 5G BS cluster are formulated to capture the coupled nature and inherent flexibility of 5G services. Considering the flexible processing of EDCs, a detailed categorization is conducted based on data service type, leveraging the synergies between cloud and edge computing to achieve flexible data load distribution. The original expansion planning model is reformulated as a mixed integer linear programming (MILP) problem to facilitate its computational solution. Simulation results from case studies on Portugal 54 node network over individual expansion planning and 5G static-access are performed to validate the effective and superior performances of the proposed method. Simulations results show that the system cost can be reduced by at most 15% with a higher operational flexibility. [Display omitted] • An expansion planning model of 5G and DS considering source-network-load-storage coordination is proposed. • NDLs from 5G BS clusters and EDCs are introduced to accommodate the RESs. • Dynamic access services of 5G BS clusters are formulated to leverage their flexibility. • Synergies of cloud and edge computing in EDCs are captured to facilitate source-network-load- storage coordination. [ABSTRACT FROM AUTHOR]

Solar thermal energy is one of the important sources of renewable energy. The solar thermal energy gives clean energy which can be utilized for industrial applications. Industrial heaters which require heat energy for many industrial uses can be attained using these solar thermal absorbers. The solar thermal absorber is designed using a layer design that consists of material compositions of Titanium (Ti) and Stannic selenide (SnSe 2) for ground and substrate sections respectively. A graphene layer which has the good properties to be used in solar absorbers is added between the Aluminum and SnSe 2 material. A thin graphene section is inserted above the SnSe 2 substrate and below the Ti resonator. To catch the good absorption in the current design, the best four wavelengths (micrometer) have been attached such as 0.22, 0.8, 2.46, and 2.85. This design's contribution can be studied in 4-atm regimes (from UV, V to NIR, and MIR). According to the wavelength and bandwidth ranges, the current absorption explores more than 97% (97.4%) in between 2.4 and 3 μm (600 nm) bandwidth and exceeds 95% (95.2%) from 0.2 to 1 μm (800 nm) respectively. To the overall range of design, it extracts 90.3% for the 2800 nm band between 0.2 and 3 wavelength regime. The flow of the presentation of the proposed paper is (i) Introduction (ii) Design and Parameters, and (iii) Results and Discussions. The explored absorber can be effective in water heating and swimming pool heating for homes. Moreover, it can also be applied in large industries as a solar thermal absorber for heating applications. [ABSTRACT FROM AUTHOR]

• Description of a multi-objective problem that connects two energy and water systems. • Proposing an enviro-economic model to minimize water extraction and operating cost. • Integrating the electrical, thermal, water, and hydrogen energy systems. • Proposing a day-ahead decision-making for optimal management of networked EHs. • The profit of cooperation is divided among EHs based on their contributions. This paper proposes a cooperative multiple-objective approach for the sustainable economic scheduling of networked energy systems to simultaneously optimize the cost of energy and groundwater extraction from underground sources. The suggested strategy enables networked energy hubs to exchange electricity for greater financial gain. Various energy hubs collaborate to maximize gain that is fairly allocated among the hubs to guarantee the coalition's economic stability, according to the collaborative game theory. The coalition achieves its best gain through collaboration and payoff, which is equally and regularly divided among the hubs, according to the Shapley value. The suggested model takes into account a networked system with hubs connected to various types of energy, including electrical and natural gas, renewable energy sources, electrical, and thermal energy storages, combined heat and power units, and gas boilers, cooling, hydrogen, and water systems to meet demands. The numerical results show that the suggested cooperative approach reduces the cost function of the system by 12.71%. The self-discharging rate of battery storage devices can increase the cost function by $ 7.65. Also, the sensitivity analysis on the battery storage sizes demonstrated that they can reduce the total cost of the system by $ 15.65. [ABSTRACT FROM AUTHOR]

• Power sources operation for wind-solar-thermal power system with nonconvex models. • Combination of the federated reinforcement learning (FRL) with the model-based method. • Grid-connected renewable energy and thermal power at a bus are aggregated as a VPP. • Private operation with limited but effective information exchange. • Customized FRL algorithm with efficient agent training and privacy preservation. To schedule power sources operated by different entities in a short-time scale considering nonconvex generation cost and deep peak regulation (DPR) service constraints, this paper proposes an FRL-based multiple power sources coordination framework in wind-solar-thermal power network. In the studied power transmission network (TN), renewable energy sources and thermal power units connected to the same bus are aggregated as a wind-solar-thermal virtual power plant (WSTVPP). The transmission system operator (TSO) sends dispatch instructions to each WSTVPP by optimal power flow program, and allocates the cost of DPR service in TN. Based on the dispatch instruction, the internal power sources of each WSTVPP are scheduled by its local center control agent to achieve local economic operation while maximizing the overall DPR service revenue for the WSTVPP from the auxiliary service market. The multiple WSTVPPs operation is modeled as a partially observable Markov decision process, and solved by a designed FRL algorithm. The FRL algorithm employs a global neural network (NN) model for coordination, heterogeneous local NN models and data to efficiently train each WSTVPP control agent with individual objectives for handling multiple power sources scheduling in TN while preserving local privacy. Numerical studies validate the effectiveness of the proposed framework for handling the short-time scale power sources operation with nonconvex constraints. [ABSTRACT FROM AUTHOR]

A hybrid integrated energy system (HIES) optimal scheduling model is developed by considering energy storage (ES), solar thermal (ST), wind turbine (WT), demand response (DR), and carbon capture and storage (CCS) to minimize the economic cost and pollutant gas emission; A multi-population dung beetle optimizer based on Q-learning (MODBO-QL) is designed to solve the HIES optimal scheduling model. In MODBO-QL, three subpopulations are constructed first: Global search subpopulation (GSS), Local search subpopulation (LSS), and Balanced search subpopulation (BSS). Second, the search and evolution strategies corresponding to the population characteristics are given. The GSS and LSS perform global search and local search, respectively. At the same time, the BSS is responsible for randomly exploring or exploiting the solution to increase the diversity of the population. Finally, the number of populations is dynamically and adaptively adjusted using Q-learning to enhance information exchange between different sub-populations and improve solution performance. An optimal dispatch experiment was conducted using three typical seasonal load data and renewable energy forecast data in a region of China. The experimental results show that compared with the original system, the three typical quarters' average economic costs of the dispatch results obtained by the method in this paper are reduced by 8.7%, 20.99%, and 3.97%, and carbon emissions are reduced by 617.35t, 284t, and 19.5t, respectively; MODBO-QL performs better compared to MOPSO, MOWOA, and MODBO. [ABSTRACT FROM AUTHOR]

The electrical system performance can be improved considerably by controlling the reactive power flow in the system. The reactive power control can be achieved by optimal reactive power dispatch (ORPD) problem solution and optimal integration of the FACTS devices. With high penetration of renewable energy sources (RESs) and the load growth, the ORPD solution became a challenging and a complex task due to the stochastic nature of the RERs and the load growth. In this regard, the aim of this paper is to solve the stochastic optimal reactive power dispatch (SORPD) with optimal inclusion of PV units, wind turbines and the unified power flow controller (UPFC) under uncertainties of the load growth and the generated powers. An Adaptive Beluga Whale Optimization (ABWO) is proposed for solving the SORPD which is based on the Fitness-Distance Balance Selection (FDBS) strategy and the territorial solitary males' strategy of the Mountain Gazelle Optimizer. The proposed ABWO is tested on IEEE 30-bus system and a comparison with other optimization techniques for solving the ordinary ORPD is presented for validating the proposed ABWO. The obtained results reveal that the TEPL is reduced from 5.3168 MW to 3.97985 MW with optimal integration of the RERs and UPFC. Likewise, the TEVD is reduced from 0.1794p.u. to 0.10689p.u. and the TVSI is decreased from 0.1289p.u. to 0.0476p.u. [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]

Abstract The use of renewable energy resources, especially wind power, is receiving strong attention from governments and private institutions, since it is considered one of the best and most competitive alternative energy sources in the current energy transition that many countries around the world are adopting. Wind power also plays an important role by reducing greenhouse gas emissions and thus attenuating global warming. Another contribution of wind power generation is that it allows countries to diversify their energy mix, which is especially important in countries where hydropower is a large component. The expansion of wind power generation requires a robust understanding of its variability and thus how to reduce uncertainties associated with wind power output. Technical approaches such as simulation and forecasting provide better information to support the decision-making process. This paper provides an overview of how the analysis of wind speed/energy has evolved over the last 30 years for decision-making processes. For this, we employed an innovative and reproducible literature review approach called Systematic Literature Network Analysis (SLNA). The SLNA was performed considering 145 selected articles from peer-reviewed journals and through them it was possible to identify the most representative approaches and future trends. Through this analysis, we identified that in the past 10 years, studies have focused on the use of Measure-Correlate-Predict (MCP) models, first using linear models and then improving them by applying density or kernel functions, as well as studies with alternative techniques, like neural networks or other hybrid models. An important finding is that most of the methods aim to assess wind power generation potential of target sites, and, in recent years the most used approaches are MCP and artificial neural network methods. Graphical abstract Image 1005 Highlights • This paper provides an overview of how the analysis of wind energy has evolved over the last years. • We employed an innovative and reproducible literature review approach called Systematic Literature Network Analysis. • From 145 articles was possible to identify the most representative approaches and future trends. • Developing countries will reach the same level of investment in the field of wind energy. [ABSTRACT FROM AUTHOR]

Abstract This paper provides a comprehensive review of 48 studies on the Life Cycle Assessment (LCA) of biogas production from 2006 to 2018 (13 years). The scope of the study, type of feedstock and impact categories used in the reviewed studies were discussed. Although various studies have been carried out to assess the efficiency and potential of wastes for biogas production, however, little attention has been paid particularly in the developing countries to conduct comprehensive analysis related to the environmental performance of biogas generation based on the LCA methodology. In Malaysia, the utilization of palm oil mill effluent (POME) as the source of energy has recently been expanded due to its abundant production at the palm oil mills. Therefore, this paper also highlights and discusses the feasibility of LCA approach on biogas production from POME as well as the opportunities and challenges from the Malaysian perspective. Highlights • Utilization of biogas production from organic waste to provide a clean energy. • Environmental performance of biogas as a renewable energy using an LCA methodology. • The trends of LCA approach in biogas production was reviewed worldwide. • The opportunities of biogas production from POME in Malaysian was discussed. • The feasibility of LCA approach on biogas production in Malaysian was discussed. [ABSTRACT FROM AUTHOR]

Highlights • Conduct comprehensive literature review of U.S. microgrid development in the recent decade. • Discuss U.S. progress on microgrid policies, demonstration projects, control methods, and software tools. • Summarize key successful experience of U.S. microgrid development. Abstract Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool’s core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries. [ABSTRACT FROM AUTHOR]

Highlights • Procurement auctions ignoring grid development may lead to sub-optimal deployment. • Presented auction integrates dynamic transmission planning & renewables procurement. • Full integration of both modelling frameworks reduces investor rent-seeking. • A more efficient deployment path is demonstrated under this policy framework. Abstract Competitive renewable energy procurement auctions are becoming increasingly prevalent. In a pay-as-bid auction, investors bid the price support required and receive that price if successful. Bidding strategy may be influenced by factors external to the auction, such as transmission expansion planning decisions. This may increase costs. In this paper, we show that integrating a pay-as-bid auction with transmission expansion planning may allow for closer total system cost minimisation over many time periods. This paper develops an auction mechanism and associated modelling framework to carry this out. The contributions of this framework are verified using a numerical example. Our results show that ignoring generation costs in transmission expansion planning can have economic consequences, while traditional pay-as-bid auctions can benefit from incorporating features associated with transmission expansion planning, such as multi-period optimisation. Full integration of both modelling frameworks can lead to efficiency improvements, both in terms of reduced investor rent-seeking and a more efficient deployment path. [ABSTRACT FROM AUTHOR]

Highlights • Power-to-gas can provide energy storage for renewable energy storage. • PtG can play different roles in different energy systems. • PtG can support the transition of energy system toward sustainability. • PtG can be an important source of renewable gases such as hydrogen, methane or SNG. Abstract This paper presents an overview of power-to-gas opportunities. Power-to-gas technology is gaining more and more popularity. It can provide large and long-term storage for increasing share of renewable sources in the energy system. In this paper, we would like to review the role of the power-to-gas system in different energy system architectures. Authors have analyzed the literature in the context of the problems, which could be solved by power-to-gas technology. Additionally, different assessment such as techno-economic, Life Cycle Assessment and Multi-criteria decision analysis for power-to-gas were revised. [ABSTRACT FROM AUTHOR]

Small islands are fragile and dependent territories in many sectors, especially energy. Hence, renewable energy microgrids (MGs) can offer an opportunity for environmentally sustainable energy supply but also a driver for new development strategies in these territories. This paper aims to analyse the role that MGs can play in insular spaces for the achievement of more sustainable communities. Initially, MGs played a crucial role in ensuring energy supply in spaces characterised by a difficult supply. However, with technological improvements in both generation and storage, as well as institutional and legislative changes, MGs can play a fundamental role in achieving more socially cohesive and environmentally sustainable spaces beyond their role as energy suppliers. We discuss three main topics after a meta-analysis of 18 MGs, both on-grid and off-grid, located in insular spaces: 1) the characteristics of MGs built in insular territories in the last 25 years; 2) MGs influence islanders' well-being and lifestyle; and 3) how MGs can encourage responsible consumption and promote postgrowth strategies. Thus, this paper explores the role that MGs have played in the past and present and what role they could play in the future of small island spaces. It might be concluded that MGs are a key factor in the development of more sustainable societies. However, they must be accompanied by changes in our economies, societies and lifestyles in a direction other than the pursuit of economic growth traditionally understood as a limitless increase in consumption and production. [ABSTRACT FROM AUTHOR]

The deployment of renewable technologies as part of climate mitigation strategies have provoked a range of responses from various actors, bringing public acceptance to the forefront of energy debates. A key example is the reaction of communities when renewable projects are proposed in their local areas. This paper analyses the effect that community acceptance has had on planning applications for onshore wind and solar farms in Great Britain between 1990 and 2017. It does this by compiling a set of indicators for community acceptance and testing their association with planning outcomes using binomial logistic regression. It identifies 12 variables with statistically significant effects: 4 for onshore wind, 4 for solar farms, and 4 spanning both. For both technologies, the visibility of a project, its installed capacity, the social deprivation of the area, and the year of the application are significant. The paper draws conclusions from these results for community acceptance and energy justice, and discusses the implications for energy decision-making. [ABSTRACT FROM AUTHOR]

Transitioning electricity generation sectors to renewable energy is a necessary element of climate change mitigation but a challenging one with years of technical and social lock-in creating considerable inertia and a pace of change insufficient to meet the task. Transition management has emerged as a promising approach to proactively manage and accelerate transitions in sectors like energy and water and has the potential to do so in manner that would help secure energy justice. However, the approach has been criticised for ignoring the political dynamics of transitions, risking procedural and recognition injustice for traditional energy production regions while increasing opposition to a transition. This paper uses a qualitative case study approach to understand how a transition to renewable energy generation could be achieved in a way that secures energy justice for traditional energy production regions like those of Gladstone in the Australian state of Queensland. The paper augments the transition management approach with the “just transitions” concept, which has emerged from labour movements as a means to mitigate negative impacts on workers and communities in traditional energy production regions, to create a “Just Transition Management” framework that is applied as a diagnostic tool to the case study. While results suggest a transition is imminent within Queensland, a lack of: consistent, supportive federal policy; long-term visions, and a clear process leaves it at risk of being captured by the powerful incumbent resources sector utilising an “environment vs. jobs” narrative. Yet, the results also suggest that adopting a just transition management approach has the potential to successfully resist the influence of the resources sector, by providing cheap, secure renewable energy and supporting affected workers into roles in the renewable energy sector using a reflexive, representative network governance approach. Data analysis reveals how the application of just transition principles has already engaged communities and unions that were previously sceptical of renewable energy. Overall, this study suggests that the just transition management framework assists in identifying the political barriers to transitions and energy justice more broadly, while also providing a management approach which creates powerful niche actor-networks to counter the narratives and influence of the incumbent resource sector. Thus it could support successful transitions that achieve the distributional, recognition and procedural justice needed for energy justice globally. [ABSTRACT FROM AUTHOR]

The high variability and uncertainty introduced into modern electrical distribution systems due to decentralized renewable energy generators requires new solutions for grid management and power quality assurance. One of these possible solutions includes grid integrated energy storage. The appropriate size and placement of decentralized storage is highly dependent on purpose of the battery system and expected operational strategy. However, battery operational strategies are difficult to simulate simultaneously during a sizing and placement planning calculation. The motivation of this paper is to propose an algorithm that is capable of integrating sizing, placement and operational strategies of batteries into an Optimal Power Flow (OPF) distribution grid planning tool. The choice of the OPF approach permits to account for grid constraints which is more adapted for grid-connected storage devices compared to other approaches in the state of the art that are based only on an email balance analysis. This paper presents an alternating current (AC) multi-temporal OPF algorithm that uses a convex relaxation of the power flow equations to guarantee exact and optimal solutions with high algorithmic performance. The algorithm is unique and innovative due to the fact that it combines the simultaneous optimization of placement and sizing of storage devices taking into account load curves, photovoltaic (PV) production profiles, and distribution grid power quality constraints. The choice to invest in battery capacity is highly sensitive to the price of battery systems. The investment in battery systems solely for reducing losses an operational costs was proven not to be cost effective, however when battery systems are allowed to buy and sell electricity based on variable market prices they become cost effective. The assumptions used for this study shows that current battery system prices are too high to be cost effective even when allowing battery system market participation. [ABSTRACT FROM AUTHOR]

Development of electric mobility and sustainable energy result in new technologies such as contactless electric vehicle charging and roadway energy harvesting methods, but also self-healing asphalt roads. By combining these technologies a new concept of Future Sustainable Roads for Electric Mobility is created and presented in the paper. This paper bridges the gap created by these unilateral technology developments using a multi-disciplinary approach including placing cautions when necessary and suggesting viable alternatives for optimal utilization of these energy transfer and conversion techniques. Through theoretical analysis, simulations, and tests on lab-scale experimental prototypes, the impact of our proposal is showcased. Thermal and loss models are developed for self-healing asphalt. Also, integration study of solar roads and contactless charging is performed. Applying the insight gained from the results, it is discussed how some challenges also pave a way towards interesting opportunities, for instance, infrastructure sharing for material use optimization and efficient mosaic integration. Finally, an economic viability case study is presented for a future Dutch highway with such newly emerging components. [ABSTRACT FROM AUTHOR]

During its operation, a photovoltaic (PV) panel is exposed to various and in general, stochastic thermal and wind conditions, which have a significant influence on the PV panel operating temperature. The increased PV panel operating temperatures normally have an unfavorable effect on the PV panel performance and lifetime, thus various cooling methods were proposed in order to increase the photovoltaic energy conversion efficiency. A reasonable step before the consideration of a specific cooling strategy is to obtain a PV panel performance analysis with respect to its surroundings (specific geographical micro location) for various operating conditions. This paper presents such a study, which was conducted through an experimentally validated numerical model. Numerous CFD simulations regarding heat and fluid flow around a monocrystalline PV panel coupled with a heat flow simulation inside the PV panel elements were conducted. The aim of the paper was to investigate an effective heat transfer field and the variations in the PV panel temperatures with respect to various wind velocities and heat source conditions. A detailed study regarding pressure, viscous and body (buoyancy) forces as well as their effects on flow separation was also conducted. The study also investigated the changes in the effective heat transfer near the PV panel. The gained results in this study turned out to be useful for the consideration of a specific cooling strategy for PV panels, which can ultimately lead to an increase in PV panel efficiency and lifetime. Additionally, the conducted study could be used to improve the delivered electricity prediction from the PV system at a selected location defined by specific wind velocities and solar insolation distributions. [ABSTRACT FROM AUTHOR]

The rapid development of distributed generation in different forms and capacities is transforming the conventional planning of distribution networks. Despite the benefits offered by renewable distributed generation technologies, several economic and technical challenges can result from the inappropriate integration of distributed generation in existing distribution networks. Therefore, the optimal planning of distributed generation is of paramount importance to ensure that the performance of distribution network can meet the expected power quality, voltage stability, power loss reduction, reliability and profitability. In this paper, we firstly discuss several conventional and metaheuristic methodologies to address the optimal distributed generation planning problem. Metaheuristic algorithms are often used as they offer more flexibility, particularly for multi-objective planning problems without the pursuit of globally optimized solution. Analytical techniques are considered suitable for modeling power system mechanisms and validating numerical methods. Then, this paper conducts a comprehensive review and critical discussion of state-of-the-art analytical techniques for optimal planning of renewable distributed generation. The analytical techniques are discussed in detail in six categories, i.e. exact loss formula, loss sensitivity factor, branch current loss formula, branch power flow loss formula, equivalent current injection and phasor feeder current injection. In addition, a comparative analysis of analytical techniques is presented to show their suitability for distributed generation planning in terms of various optimization criteria. Finally, we present conclusive remarks along with a set of recommendations and future challenges for optimal planning of distributed generation in modern power distribution networks. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of distributed energy management in multi-area integrated energy systems (MA-IES) using a multi-agent deep reinforcement learning approach. The MA-IES consists of interconnected electric and thermal networks, incorporating renewable energy sources and heat conversion systems. The objective is to optimize the operation of the system while minimizing operational costs and maximizing renewable energy utilization. We propose a distributed energy management strategy that makes hierarchical decisions on intra-area heat energy and inter-area electric energy. The strategy is based on a multi-agent deep reinforcement learning framework, where each agent represents a component or unit in the MA-IES. We formulate the problem as a Markov Decision Process and employ Q-learning with experience replay and double networks to train the agents. The proposed strategy is evaluated using a simulation of a four-area MA-IES. The results demonstrate significant improvements in energy management compared to traditional methods, with higher renewable energy utilization and lower operational costs. Specifically, the strategy achieves 100% utilization of wind power, and decreases operational costs by 5.53%. Furthermore, it leverages the generalization capabilities of reinforcement learning to respond in real-time to uncertainties in demand and wind power output. The results highlight the advantages of the proposed strategy, making it a promising solution for optimizing the operation of multi-area integrated energy systems. • Hierarchical framework prioritizes heat-electric energy balance, facilitates joint optimization, enhances system performance. • MADRL-based approach manages dynamic loads, uncertainties, renewables in distributed energy systems. • Proposed method optimizes energy sources, demand, grid constraints in multi-area integration. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources (RES) significantly increases, their inherent uncertainty has brought about a paradigm shift in distribution system planning and operation. The existing planning methodologies usually consider the uncertainty during the investment and day-ahead stages while neglecting the interactive power fluctuations during the intraday stage, resulting in planning schemes with a risk of inadequate regional stability. In light of this, this paper proposes a novel adaptive robust distribution system planning approach that integrates local uncertainty management (LUM) to provide a comprehensive and refined consideration of uncertainty. First, we introduce the concept of LUM that can effectively address the deviations between predicted and actual power of RES and load. Second, we propose the adaptive robust planning model for the distribution system, in which the investment and day-ahead operation are considered in the first stage, while LUM is considered in the second stage to maintain the local stability of interactive power. The proposed model can actively manage uncertainties of RES and load demand, reaching the optimal balance between the investment cost, operational cost, and LUM cost. Finally, by employing the column-and-constraint generation (C&CG), the adaptive robust planning model is decoupled into a master min problem and a slaver max-min problem, after which the max-min problem is converted into a single mixed-integer linear programming (MILP) problem through the strong duality theory and big-M method. The effectiveness of the proposed model is demonstrated using a real-world distribution system located in Fujian Province, China. The simulation results indicate that the proposed model achieves a quantitative decrease of approximately 10% in total cost compared to deterministic planning. Sensitivity analysis is also conducted to reveal the trade-off between economic cost and robustness level. • Local uncertainty management (LUM) is incorporated into distribution system planning. • A three-stage framework is developed for the planning problem incorporating LUM. • An adaptive robust distribution system planning model incorporating LUM is proposed. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of coordinating renewable and network investments in a context of unstable subsidies. It contributes to the literature by developing a three-stage analytical model to address the network operator investment strategies being either proactive or reactive and considering the sensitivity of investments to future renewable incentives, being uncertain and variable. A dynamic stochastic modeling is used to formalize actors' choices. The study's major results show that proactive operator may cause a loss of welfare compared to reactive operator. This depends on network investment costs and network investments delays, the intensity of support measures of renewable energy and the maturity of renewable technologies. While network operator needs to be proactive, especially if moderate new network investments are expected, our results demonstrate that there exists a threshold level of network investment beyond it being reactive is economically preferable. An assessment of some countries cases confirms our theoretical predictions. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2) and hydrogen (H 2) present substantial feedstock potential for advancing sustainability in the chemical industry. In this paper, a new integrated system is proposed consisting of four subsystems, including an ICT-OEC six-step Cu–Cl cycle, a CO 2 capture using monoethanolamine (MEA), an ammonia production unit, and a methanation unit. The simulation of these subsystems was carried out using Aspen Plus software. Based on literature indicating a 3-step Cu–Cl cycle producing 125 TPD of hydrogen, comparative simulations of a similar-capacity six-step Cu–Cl cycle were conducted for the current analysis. All related values correspond to it. Converting CO 2 into methane and nitrogen into ammonia holds great promise in reaching the net-zero target and fostering sustainability. A comprehensive cradle-to-gate Life Cycle Assessment (LCA) investigates the environmental impacts of ammonia and methane production using green hydrogen and CO 2. Significantly, the utilization of renewable energy sources such as nuclear, wind, and hydropower in the CO 2 refinery process generates a net negative Global Warming Potential (GWP). It was noted that the GWP value of the electrical grid could increase significantly, reaching up to 370,000 kg CO 2 eq., particularly when producing 23,277.2 kg/h of ammonia and 2141.71 kg/h of methane at a hydrogen consumption rate of 5211 kg/h (125 metric tons of H 2 per day). The significant environmental impact of the CO 2 refinery process is notably influenced by the choice of electrical grid mix as the energy source. However, in the case of photovoltaics as an energy source, the value of 0.517 kg sb eq, surpasses that of the electrical grid mix. However, nuclear energy emerges as a particularly low-GWP impact, with values of −4190 and −4140 CO 2 kg eq. for the 3-step and 6-step Cu–Cl cycles, respectively. The findings suggest that the CO 2 refinery process generates a carbon credit in both scenarios. Based on the findings of this study, policymakers have a basis for developing effective strategies to address and alleviate environmental impacts. [Display omitted] [ABSTRACT FROM AUTHOR]

Bangladesh has encountered several challenges, including issues like overpopulation, energy shortage, and global warming for the last few decades. Addressing the increasing energy demand has become a crucial concern because of rapid increase in population and lack of growth in the economic. The electricity production of a country heavily relies on fossil fuels, particularly natural gas, which makes up approximately 54% of the current installed capacity. Bangladesh is an agrarian nation and biomass stands out as a source of renewable energy having significant potential to mitigate the demand for heat and electricity. Biomass resources in the country mainly comprise animal dung, agricultural crop residuals, solid waste from municipalities and forest residues. The existing biomass resources contribute to producing a high amount of energy and content of around 1574.16 PJ of energy equivalent to 437.28 TWh of electricity in which agricultural residues, animal manure, municipal solid waste, and forest residues impart around 852.32 PJ, 399.04 PJ, 112.16 PJ, and 210.64 PJ of energy respectively. This paper explores the extent, possibilities, and technologies associated with biomass energy conversion. Additionally, the study delves into the several biomass projects that the government, as well as non-governmental groups, are working on for environmental sustainability along with plans, challenges, and methods that are being utilized to encourage biomass technologies across Bangladesh. [Display omitted] • The biomass potentiality in Bangladesh for heat and electricity generation is emphasized. • The energy scenario and necessity of alternative energy sources in Bangladesh are discussed. • The available energy conversion technologies from biomass in Bangladesh are presented. • The existing biomass resource content of around 1574.16 PJ (437.28 TWh of electricity) of energy. • The economic aspects and challenges of biomass conversion into energy are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a novel research work on the potential benefits of using, a unique and sustainable energy source - the human-power. The paper discusses the benefits and the practicality of using human-power as a domestic energy source to power electrical and electronic devices and explores the benefit of its use in a more diverse perspective for possible change in behaviour and energy savings for improved sustainability in the society. The economic and sustainability gains of using Human-Powered Products (HPPs) are investigated. Two studies have been conducted, the first is a study which involves assessing the environmental impact of a human-powered products via Life Cycle Assessment (LCA); and the second is a case study called Home User Study (HUS), where the technology is experimentally tested. The human-powered system is used to explore the real benefits and life-cycle analysis towards assessing the sustainability of such products. The Home User Study (HUS) has been conducted to explore the in-situ use of human-powered systems using a new prototype that has been built and deployed. The study has used a monitoring device to measure the outcome of the interaction between the user and human-powered products. The results interestingly have revealed various other forms of benefits beyond renewable energy that could add extra motives for using human-powered products, such as health benefits. The paper also puts an emphasis on the opportunity of using Human-Powered Products which can be perceived as a great viable solution against counter measuring social issues such as physical inactivity and increasing sedentary behaviour. The results of the theoretical study indicate that Human-Powered Products (HPP) do not offer significant financial savings. The results of the HUS indicate that using human-powered products for health prospective outweigh the benefit gained from the prospective of energy savings and renewable energy. Based on the findings of this study, the paper suggests an approach that shows how the implementation of Human-powered systems could provide the opportunity to sustainable energy generation and energy savings, but in all cases they can be retained through a motivational drive in improving the health benefit via encouraging physical activities. [ABSTRACT FROM AUTHOR]

As a renewable energy source, wind power has received growing attentions, but mostly been utilized in wind power farms or high-rise buildings where the wind with high speed is available. Few studies focus on the micro-wind utilization in low-rise buildings due to the bottleneck that the wind speed cannot meet the minimal speed requirement of small wind turbines. This paper aims to identify the optimal building design which could enable the harvesting of the maximal micro-wind power around low-rise residential buildings. Based on the comparison among different building shapes and the computational fluid dynamics (CFD) analysis via software Phoenics , this paper identified that the building shape of “composite prism” could enable the harvesting of the most micro-wind power. The identified building shape was then tested in a simulated environment of a residential community in Pingtan Island, China. The local wind conditions, long-term community planning, and the requirement of comfort level were all considered in the simulation model. The result shows that the potential of utilizing wind energy in low-rise residential buildings is huge by adopting the proposed building shape of “composite prism”. The finding has significant implications for renewable energy utilization in built environment. [ABSTRACT FROM AUTHOR]

Forest-based biomass has a significant role to play in the development of a renewable U.S. energy portfolio, especially in the context of mandated cellulosic biofuel production requirements. Biorefinery development has the potential to create economic, environmental, and social benefits. In Maine, co-location opportunities for biorefineries and existing pulp and paper manufacturing facilities could strengthen industry and mill communities. Understanding general public and local perceptions of bioenergy industries and products may play a key role in their establishment, success, and sustainability. Our aim was to compare the general Maine public and mill towns in their support for forest-based biorefineries, and we surveyed: (1) Statewide residents, and (2) Mill Town residents from 10 communities with major pulp and paper facilities. We found differences between local and state support for biorefineries across all response categories. Mill Town respondents were significantly more supportive of biorefinery projects in their communities, and overall the potential positive impacts of biorefineries were rated higher than the potential negative impacts. Both groups preferred facility siting at pulp mills no longer in operation, and Mill Town respondents expressed less concern about potential negative impacts than Statewide respondents. Our findings provide important insight into forest-based biorefinery issues important to communities. We encourage policy makers, industry leaders, and researchers to use our results as a foundation to actively engage statewide and local communities future biorefinery policies and siting proposals. [ABSTRACT FROM AUTHOR]

High step-up, high efficiency, low cost DC/DC converters have operated as an interface to make use of the renewable energy system generated power. In order to obtain desired output voltage, the DC/AC voltage conversion to AC mains voltage is an important consideration mainly achieved through inverters. Taking into acoount the performance of the non-isolated high step-up DC/DC converters for the renewable energy systems, the substantial amount of topologies studied in past years are the non-isolated high step-up DC/DC converters. Based on proposed and generalized configurations, the non-isolated high step-up DC/DC converters are classified into several categories and reviewed in this paper. So, to clarify the distinguishing solutions, the key features; topological variations, merits and demerits of these converters are discussed and compared. This review work aims to give a well-informed and a well-detailed general framework about these converters and facilitates to derive the new well topologies in the future. [ABSTRACT FROM AUTHOR]