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Renewable energies (RE) are rapidly reforming the power sector because they have become more economically competitive, thereby enabling nations to overcome high dependence on energy imports (i.e. reducing energy security concerns), as well as reducing their negative environmental footprints. Therefore, both the public and private sectors need to uncover the future technological trajectory of renewable energy. As part of an entity-linking approach, this paper examines REs developments from 2000 to 2021 and explores high-potential topics by analyzing hot topics during the period to identify evolutionary trends of relevant research interests (using published research papers). The results are analyzed and classified to determine how and when RE development issues "burst" into the public eye. The article tells the story of RE development from the past, the present, and the likely future hot topics for research. We analyze the results and cluster them into five categories: (1) energy sources, (2) sustainable development, (3) technological developments, (4) supply chains, and (5) energy management systems.se Findings reveal signals about the potential future energy market shifts and how REs evolve and contribute to the market. Signals are converted to policies and future market interpretations using an expert-based process. Analysis and methodology outcomes reveal that digital technologies have become an important factor in market changes. The findings of the study highlight how sustainable development actions and digital technologies will likely transform RE supply and management. [ABSTRACT FROM AUTHOR]

Malaysia is one of the leading producers and exporters of oil palm, and the industry contributes significantly to the growth of the national economy. Nonetheless, the consequential oil palm biomass (OPB) with a potential yield of ∼97 million tonnes annually presents both challenges and opportunities, demanding innovative waste management solutions. This paper explores the feasibility of converting OPB into valuable commodities by employing concentrated solar-driven pyrolysis. A detailed overview of the biomass produced from oil palm plantations and mills is provided, along with a discussion of the industry stakeholders and associated biomass management challenges. The pyrolysis of OPB is explored, emphasizing benefits, potential barriers, and techniques. The focus then shifts to Malaysia's solar potential, which ranges from 1470 to 1900 kWh/m2 annually. The potentials of solar pyrolysis are explored, encompassing discussions on solar concentrator types, reactors, and the distribution of solar and OPB resources throughout Malaysia. The paper then recommends Tawau in Sabah, as an ideal location for OPB solar pyrolysis, due to its solar availability (up to 1873 kWh/m2), potential OPB resources from 19 mills and 241 plantations, and supportive infrastructure. The paper proposes solar-driven pyrolysis as a sustainable solution for Malaysia's OPB management, offers insights for policy initiatives and technological innovations in optimizing OPB and solar energy utilization. • Analyzes oil palm biomass potential for solar pyrolysis in Malaysia. • Explores solar resources in Malaysia for solar-driven pyrolysis. • Examines solar concentrators and reactors for pyrolysis. • Identifies potential city, as the optimal location for solar pyrolysis. • Discusses commercialization challenges of solar-driven pyrolysis. [ABSTRACT FROM AUTHOR]

This paper develops a framework for a financial, economic, and stakeholder analysis of a residential rooftop solar net-metering program. The empirical focus of the paper is the net-metering program in Ontario, Canada, but the methodology is applicable to evaluating other public programs. The results highlight that without the Federal Government's subsidy for the initial investment cost, net-metered solar systems are not financially viable for representative households. Moreover, the stakeholder analysis reveals that for each additional net-metered system installed in Ontario, non-net-metered households experience financial losses of six times the benefits to the net-metered households. The net losses to the Federal Government of Canada and the Canadian economy are five and nine times the benefit to the net-metered households, respectively. The only stakeholder who benefits marginally is the Government of Ontario. In terms of environmental benefits, our estimate of the cost of greenhouse gas abatement by residential net-metered solar is 325 CAD per ton of CO 2 , which is significantly higher than the current (65 CAD in 2023) and future (170 CAD by 2030) national carbon price set by the Government of Canada. [ABSTRACT FROM AUTHOR]

• Review paper on the application of energy tunnels to harness renewable energy. • The analytical, experimental and numerical studies are summarised. • Thermal and thermo-mechanical behaviour and influential factors are discussed. • Challenges and recommendations for future research are identified. The thermal activation of underground tunnels, also known as energy tunnels, has shown significant potential to harness geothermal and aerothermal energy as a low-carbon and economical solution for space heating and cooling, water heating and road de-icing. The heat and load transfer mechanisms in energy tunnels are, however, far less understood compared to other energy geostructures, due to their complex interaction with the ground and tunnel environment. This paper aims to provide a comprehensive overview of the current state of knowledge on the thermal and thermo-mechanical performance of energy tunnels based on recent analytical models, field tests, model-scale experiments, and numerical studies. Results suggest that a thermal yield ranging from 5 to 6 W/m2 is attainable for each degree Celsius difference between the fluid inlet temperature and the ground temperature in the presence of groundwater flow and tunnel airflow. In the reviewed studies, the thermally induced tunnel structural responses appear to be insignificant compared to those caused by tunnel excavation. However, the long-term thermally induced ground deformation in clay deposits can reach up to 15 mm, which may become a crucial issue and therefore needs to be examined in geotechnical design. As a new area of research, there is a lack of long-term experimental investigations to solidify the current knowledge of the thermal, structural and geotechnical performance of the system. Further research is also required to develop an integrated analysis and design method considering the techno-economic aspects and explore innovative business partnership models. This work can serve as a useful resource for researchers, engineers, and policymakers seeking to further develop and promote the adoption of this technology in infrastructure projects. [ABSTRACT FROM AUTHOR]

Ever since 2002, when the first Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES) was held in Dubrovnik, the SDEWES conferences series has been providing a forum for world-wide scientists and those interested in learning about the sustainability of development, to present research progress and to discuss the state of the art, the future directions and priorities in the various areas of sustainable development. One of the main issues of the coming decades is to improve efficiencies by integrating various energy systems, using excess from one, as resource in another in the correct moment. Integrating electricity, heating, cooling, transport, water, buildings, waste, wastewater, industry, forestry and agriculture systems will be pivotal towards sustainable development. SDEWES has maintained high publishing standard as 2408 SDEWES papers were published in special issues of leading scientific journals including "Energy - The International Journal". In 2022, the 17th SDEWES Conference was held during November 6–10 in Paphos, Cyprus, the 5th SEE Conference took place in May 22–26, 2022 in Vlorë, Albania, the 3rd Latin American Conference was held through a virtual platform during July 24–28, 2022. The Energy journal has continued its cooperation with SDEWES launching a special issue dedicated to the 2022 Conferences. The special issue includes papers which traditionally cover a range of energy issues - higher renewables penetration and various technologies and fuels assessments at energy supply side, as well as, energy efficiency in various sectors, buildings, district heating, electric vehicles and demand modelling at energy demand side. [ABSTRACT FROM AUTHOR]

To cope with the uncertainty of electric vehicles(EVs) and renewable energy in VPP, this paper proposes a two-stage robust optimal scheduling method for VPP considering the controllability of electric vehicle group (EVG). Highlights of the work in this paper are presented below: • The study found that the controlled capacity of EVG varies significantly, and the resulting operational benefits obtained by VPP vary significantly. It is recommended that operators should minimise the management of EVG in Category III status and increase the dispatch of EVs in Category I and II status in order to improve the controlled capacity of EVG and obtain more operational benefits. • Increasing the ESS capacity within a certain range can increase the revenue of the VPP, but the optimal capacity of the ESS should be configured is not the same when the controlled capacity of EVG varies. Further analysis reveals that when the total number of EVs and the nominal parameters of the system are constant, the VPP system shown in the paper can find the optimal ESS capacity for obtaining stable gains regardless of the number of EVs of each type. • When the forecast deviation coefficient is large, the day-ahead dispatching scheme for VPP developed by the two-stage robust optimization method has the highest total benefit and the strongest ability to resist fluctuations in WPP and PV output compared to other methods, with obvious advantages. To cope with the uncertainty of electric vehicles and renewable energy in virtual power plant(VPP), this paper proposes a two-stage robust optimal scheduling method for VPP considering the controllability of electric vehicle group (EVG). Firstly, the calculation method of the controlled capacity of EVG is given based on Minkowski's addition idea. Then a two-stage robust optimization model of min-max-min structure considering the controlled capability of EVG is developed, which involves the operational constraints and coordinated control of EVG, energy storage systems, convention gas turbine units, and distributed power sources. Finally, the model is solved based on the column and constraint generation(C&CG) algorithm. The results show that fully exploiting the controlled capabilities of EVG can help VPP optimize scheduling schemes and improve the economics of operation. [ABSTRACT FROM AUTHOR]

• The paper investigates the degree of social acceptance of biogas. • The determinants of citizens' perception of new biomass plants. • Two waves of surveys has handed out in order to assess an empirical strategy. • The paper provides some insights on the social acceptability of biogas. • Results assessed the biogas acceptability. The degree of social acceptance of biogas as a renewable green energy source is still somewhat disregarded. Although many initiatives have focused on the construction of new biogas plants around the world, local protests on the construction of new plants often arise. This study aims to analyse the determinants of citizens' perceptions regarding the construction of new biomass plants in their neighbouring areas. In particular, the focus is on prior knowledge of the production process of biogas as well as on other individual characteristics. The investigation is based on two repeated surveys conducted among the population living in two Italian areas where the construction of new large biogas plants were planned (the provinces of Oristano and Andria). The first survey analyses the main variables correlated with the degree of biogas acceptability particularly the biogas knowledge. The second survey (differentiated in two waves) focuses on the role of participatory processes and information campaigns undertaken by policy makers and environmental associations to increase the social acceptance of communities regarding the construction of new biogas plants. [ABSTRACT FROM AUTHOR]

• The paper explores Bayesian bootstrap in probabilistic photovoltaic power forecasting. • Bayesian bootstrap is applied to three probabilistic forecasting models. • The optimal quantile is extracted from the sample bootstrap distribution. • Bayesian bootstrap is compared with traditional bootstrap and with other benchmarks. • Numerical experiments validate the Bayesian bootstrap performance on real data. Modern distribution systems are characterized by increasing penetration of photovoltaic generation systems. Due to the uncertain nature of the solar primary source, photovoltaic power forecasting models must be developed in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to any energy management system, and they are recommended to enable a wider range of decision making and optimization strategies. Real-time probabilistic photovoltaic power forecasting is performed in this paper by using an approach based on Bayesian bootstrap. Particularly, the Bayesian bootstrap is applied to three probabilistic forecasting models (i.e., linear quantile regression, gradient boosting regression tree and quantile regression neural network) to provide sample bootstrap distributions of the predictive quantiles of photovoltaic power. The heterogeneous nature of the selected models allows evaluating the performance of the Bayesian bootstrap within different forecasting frameworks. Several benchmarks and error indices and scores are used to assess the performance of Bayesian bootstrap in probabilistic photovoltaic power forecasting. Tests carried out on two actual photovoltaic power datasets for probabilistic forecasting demonstrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

• This paper proposes a benchmark for multivariate probabilistic solar forecasting. • The proposed benchmark generalizes the complete-history persistence ensemble. • Thorough evaluation using verification tools from the statistical atmospheric sciences. • The benchmark generates consistent trajectories in terms of energy and variogram scores. It is well-known that decision-making processes benefit from the inclusion of uncertainty. Such optimization problems typically extend over a control horizon and could span multiple locations or regions. In addition to uncertainty, these optimization problems require as input a trajectory of scalar values that exhibits the correct spatial and temporal dependencies. Probabilistic forecasts quantify the uncertainty by means of quantiles, predictive distributions or ensembles for a forecast horizon and a site or a region separately, and therefore generally lack spatial and temporal dependencies. One solution is to use a copula to model the spatial or temporal dependencies, which, in combination with the probabilistic forecasts, can be used to issue correlated trajectory forecasts. However, there is currently no benchmark model available to compare multivariate probabilistic solar forecasts with. This paper proposes a multivariate probabilistic ensemble (MuPEn) benchmark model and shows that it generalizes the complete-history persistence ensemble (CH-PeEn) to the multivariate case. The proposed benchmark model requires a forecast issue time and a forecast horizon to construct a multivariate empirical distribution of historical clear-sky index measurements from which a multivariate ensemble forecast can be sampled. Similar to CH-PeEn, the proposed benchmark model generates forecasts that are generally calibrated and consistent in terms of energy score and variogram score. [ABSTRACT FROM AUTHOR]

The present review paper aims to shed lights on the concept of fully green energy system which includes both the source of energy and the storage system. The objective is to propose an energy label "Green to Green" (G2G) that identifies systems involving simultaneously green source and green storage, as an efficient solution to achieve a significant reduction in the dangerous level of pollution that most countries have reached today. Green sources include mainly renewable energy systems such as solar, wind, geothermal and wave energy systems. In its turns green storage includes pumped hydroelectric, flywheel, hydrogen and compressed air. Moreover, and as a case investigation on G2G concept, the paper reviews the main solar-hydrogen coupling systems, that are categorized within four categories parabolic trough-hydrogen, solar tower-hydrogen, photovoltaic-hydrogen and solar chimney-hydrogen. • Green to Green (G2G) is presented as a label to promote pollution-free systems. • Green storage includes Hydrogen, flywheel, compressed air and pumped hydro. • As a case study on (G2G) concept, the paper highlights solar – hydrogen coupling. [ABSTRACT FROM AUTHOR]

This paper aims to quantify the relation between real GDP, CO2 emissions, renewable and nonrenewable energy consumption, tourism development and labor force for France and Germany as these two countries are pioneer countries pushing Paris agreement within and outside the European Union. The time spans have been used for the period of 1995–2015 according to data availability. In the presence of cross-sectional dependency, several first and second-generation unit root tests confirm the unit root problem while Pedroni and Westernlund confirm the cointegration. Results reveal an inverted U-shape relation between CO2 emissions and real GDP in long run confirming the validity of environmental Kuznets curve for the group of France and Germany. Results declare that renewable energy significantly reduces carbon emissions while nonrenewable energy consumption adds to carbon emissions. Tourism sector was found helpful in carbon reduction that appeals to attract more tourists for the development of sustainable tourism industry by taking energy conservation and emissions reduction in relevant industries in the full consideration. • This paper explores the determinants of carbon emissions for France and Germany. • An inverted U-shape relation was found between carbon emissions and real GDP in long run. • Results reveal renewable energy helps in carbon emissions reduction. • Nonrenewable energy consumption was adding in CO2 emissions. • Tourism sector was found supportive in carbon reduction. [ABSTRACT FROM AUTHOR]

The development of information and communication technology (ICT), a subset of many functions such as visual communication, can positively affect renewable energy development in different countries. In this paper, focusing on China as the world's largest emitter of greenhouse gases, the relationship between visual communication and the deployment of renewable energy is investigated using the dynamic ARDL (autoregressive distributed lag) method and annual data from 1990 to 2021. Regarding the empirical estimations, a 1% increase in green finance market size leads to an improvement of sustainable energy deployment by 0.01%. In addition, the ICT development index has a significant and positive effect only in the long term. Furthermore, there is evidence of uni-directional causality from ICT to renewable energy deployment in the medium and long term. The recommended practical policies by the research are developing green financing tools, green economic growth policies, and creating virtual portals for financial interactions. • There is uni-directional causality from visual communication to renewable deployment. • ICT development index has a positive effect in the long term. • Effect of GDP on the development of renewable energy consumption is negative. [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]

Many subarctic communities rely entirely on fossil fuels for their energy needs. Solar-assisted ground-coupled heat pumps (SAGCHP) can be a solution to integrate renewable energy sources into their energy portfolios. However, it is currently unknown how such systems could operate in the context of the high North (cold ground, extreme mismatch between insolation and heating need, electricity from diesel). The objective of the paper is to develop a detailed model of a SAGCHP heating a house in Nunavik (Quebec, Canada) in order to gain a better understanding of its potential and limitations. The solar assistance is provided by PVs. Simulations with and without electric storage (batteries) were run. A complex tradeoff between four different modes of operation was obtained depending on the conditions of the system at each time step. For the test case, results show that the ground experiences a weak long-term thermal depletion partly compensated solar energy, but that a significant portion of the PV power production is preferably used by the compressor or stored in batteries rather than stored in the ground as heat. Over ten years, the SAGCHP system reduced fuel consumption respectively by 38.2% (without a battery) and 59.1% (with a battery). [ABSTRACT FROM AUTHOR]

This paper analyzes the dynamics of CO2 emissions over the last decade for a large group of 25 European countries, an issue that is at the center of the ecological transition project involving various commitments (COP21, COP26, G20 meetings, etc.). To this end, our model measures the repercussions of energy price shocks (oil, gas, coal) for carbon emissions, as well as changes in industrial production and sustainable development in the context of the coronavirus pandemic. Using annual data for 23 EU countries, together with Russia and the UK, our findings show that CO2 emissions reacted significantly to oil and coal price shocks and vary with industrial production cycles. We quantified this reaction while computing the related elasticities. Further, while a significant reduction in CO2 emissions was observed during the COVID-19 pandemic, the stepping up of investment in sustainable development and renewable energy consumption also had a negative impact on CO2 emissions. This suggests that the key driver to reducing the risk of climate change and lowering high carbon dioxide (CO2) emissions involves significant commitment to sustainable development. [ABSTRACT FROM AUTHOR]

The production of hydrogen is still a major challenge, due to the high costs and often also environmental burdens it generates. It is possible to produce hydrogen in emission-free way: e.g. using a process of electrolysis powered by renewable energy. The paper presents the concept of a research, experimental stand for the storage of renewable energy in the form of hydrogen chemical energy with the measurement methodology. The research involves the use of proton exchange membrane electrolysis technology, which is characterized by high efficiency and flexibility of energy extraction for the process of electrolysis from renewable sources. The system consist of PV panel, PEM electrolyzer, battery, programmable logic controller system and optional a wind turbine. Preliminary experimental tests results have shown that the electrolyzer can produce in average 158.1 cc/min of hydrogen with the average efficiency 69.87%. • Zero-emission hydrogen production is possible. • Hydrogen production using a proton exchange membrane (PEM) electrolyzer for Polish conditions. • Proposal for experimental studies on hydrogen production. [ABSTRACT FROM AUTHOR]

This paper investigates the validity of the environmental Kuznets curve (EKC) hypothesis for the G7 group of countries through the ecological footprint and its components (namely built-up land, carbon, cropland, fishing grounds, forest land, and grazing land). Most previous contributions rely on CO 2 emissions as a measure of environmental damage, whereas using disaggregated ecological footprint allows us to consider resource consumption and waste generation compared to nature's resource generation and waste absorption. The empirical application is based on a panel data analysis for G7 countries from 1980 to 2018. The results indicate that the EKC hypothesis is confirmed for the ecological footprint overall, but the impact of GDP growth is unequal across its components. Long-term elasticities point to renewable energy generation significantly improving environmental sustainability. Globalization also decreases pollution indicators, while economic complexity seems to contribute to environmental degradation. [ABSTRACT FROM AUTHOR]

The primary purpose of this paper is to evaluate the impacts of green finance on green energy deployment in China's tourism industry. To this end, the annual data over 1992–2021 through the ARDL (Autoregressive Distributed Lag) approach for the case of China are analyzed. The findings confirm that the issued green bonds positively impact the green efficiency of China's tourism industry in the long term. In contrast, the coefficient of issued green bonds is insignificant for the short-term approach. In addition, in the short and long term, the consumer price index also has a significant adverse effect on the green efficiency of China's tourism industry. The recommended practical policies are improving executive regulations and transparent market information, implementing green fiscal policies, green construction in hotels and tourist accommodations, green globalization, operational phases of innovation, adaptation, and stabilization (IAS), and eco-tourism regionalism. • Green bonds have a positive impact on the green efficiency of China's tourism. • Consumer price index has a negative effect on green efficiency of China's tourism. • Economic globalization index has a significant coefficient on green efficiency. [ABSTRACT FROM AUTHOR]

The lack of partnerships between biomass generating producers is one of the main barriers to efficient biogas production. Partnerships can help producers properly dispose of waste, assist in the supply of biodigesters, allow the union of properties with different potential biomass generators into clusters, and optimize operating costs. In this sense, new measures must be adopted with regard to the establishment of strategic partnerships between producers. Therefore, in this paper, we propose a new clustering algorithm, BiogasCluster , to decide the optimal number of clusters for generating a renewable energy community. To test the proposed algorithm's applicability, we implemented the algorithm in R language. Experiments with real databases showed that the BiogasCluster proved to be efficient in generating stable clusters for all considered scenarios. It was also found that the filters and criteria used in the proposed algorithm have not been used in previous studies. This study provides important guidance to creating strategic partnerships within bioenergy generation in addition to obtaining a preliminary assessment of the possibility of establishing clusters of rural properties with the aim of achieving energy security and adding value to their businesses. [ABSTRACT FROM AUTHOR]

Renewable energy attracts increasing attention from both industry and academia under the context of carbon neutrality. For wind and solar energy, the strong dependence on natural processes results in the imbalance between energy production and real demands. Energy storage technologies, e.g., Compressed Air Energy Storage (CAES), are promising solutions to increase the renewable energy penetration. However, the CAES system is a multi-component structure with multiple energy forms involved in the process subject to high temperature and high-pressure working conditions. The CAES system is a complex process flowsheet consisting of charging and discharging process. The process should be optimized to achieve the best thermodynamic and economic performance. Under the optimal design conditions, it might lead to severe consequences once a failure occurs, e.g., harm to humans, the environment, and assets. Limited attention and scarce available information have been paid to the CAES system risk management yet. Hence, this paper applies the System-Theoretic Process Analysis (STPA), which is a top-down method based on system theory, to identify the CAES system safety hazards. The results are expected to provide a preliminary guideline for practitioners regarding the safety and reliability of the CAES system. As a result, a more reliable CAES system can contribute to a more flexible energy system with more efficient and economic utilization of fluctuating renewable energy. [ABSTRACT FROM AUTHOR]

The combination of biomass and solar energy in the combined cooling heating and power (CCHP) system is conducive to reducing carbon dioxide emissions of the system. To improve the system's economic performance under the punishment of carbon tax, this paper optimizes the system from system design and operation strategies. A hybrid renewable energy CCHP system with shaft power distribution structure of an internal combustion engine (ICE) is proposed. By optimizing the shaft power distribution of ICE, the direct drive heat pump is realized, and the efficiency of the shaft power of ICE is improved. A double-layer optimization method considering the long and short-term benefits is proposed based on the system, fully improving the system economy in design and operation. The case study shows that the CCHP system reduces the annual cost by 40.37% and the CO 2 emission by 88.93% compared with the separate production system. Compared with the non-shaft power distribution system, the maximum daily operation cost is saved by 557.11CNY. Compared with the hourly optimization method, the proposed optimization method based on total daily benefit reduces the annual cost by 7.73% and CO 2 emission by 35.87%, and the utilization rate of the energy storage device is higher. [ABSTRACT FROM AUTHOR]

Climate change and scarcity of energy sources have put developing countries in a situation that demands emergency future planning for matured renewable technologies in order to continue sustainable progress and provide clean and cheap energy to masses. This paper provides a comprehensive structure of site suitability for utility scale solar PV and wind energy projects considering criteria critical to each technology while only using criteria with absolute necessity for varying scenarios incorporating Multi Criteria Decision Analysis (MCDA) along with Geographic Information System (GIS). A novel approach is also considered for far-off areas potentially deprived of electricity access for small scale solar PV implementation. A total of eight sub-criteria were considered in site selection process regarding wind and solar energy, analyzed using Analytical Hierarchy Process (AHP) in order to rank criteria depending on their importance to selection process. Resulting methodology was applied to case study of Pakistan, a developing country with growing climate change emergency and ongoing power crisis. End results showed 25.28% area suitable for utility scale solar PV farms, 5.93% area suitable for utility scale wind farms and 40.02% suitable area for small scale solar PV farms. [ABSTRACT FROM AUTHOR]

Sector coupling will play a key role in the future energy system to realise greenhouse gas emission reductions. A major factor will be green hydrogen based on renewable energies to defossilise consumption sectors. Related business models of power to gas are not yet implemented on the market. However, given the urgency of the change, this is essential. This paper investigates hydrogen business models under current market conditions of high power prices, no existing market for green hydrogen and the given regulatory framework with no levies for green hydrogen production in the German market. For this purpose, an open-source business model evaluation tool for sector coupling, which enables a simple and generic evaluation of sector coupling business models including production and possible transportation infrastructure, is developed and applied. Furthermore, the impact of changes of the input parameters like power prices and the influence of regulatory changes on profitability are assessed. The results show that X-to-power business cases can be already profitable due to high power prices on the wholesale market. However, power-to-X business models like hydrogen production still have negative net present values and the net present value is worsened when infrastructure for hydrogen transportation is considered. Key parameters for the negative result are investment costs and low hydrogen prices. Nevertheless, it must be considered that higher hydrogen prices have a negative impact on the X-to-power business model. To allow for profitable business cases, the market conditions need to be adjusted to ensure sufficiently high prices for green hydrogen. Furthermore, subsidies on investment or operational and maintenance costs can support the integration of power-to-X into the market. Transportation infrastructure has a significant impact on profitability. Given these facts, it is necessary to create the required framework conditions to ensure the realisation of sector coupling. • Presentation of an open-source approach to assessing sector coupling business models. • Case study on hydrogen including renewables, transportation infrastructure, and regulatory and market conditions in Germany. • No market-based introduction of power-to-X business models without subsidies. • X-to-power business models profitable due to high power prices. • Power-to-X and X-to-Power jeopardise each other as they require opposing hydrogen and electricity prices. [ABSTRACT FROM AUTHOR]

Hydrogen production by the electrolysis of water using renewable energy is a key component of the energy transition toward carbon dioxide emission reduction. Therefore, electrolyzers are in increased demand and have to meet higher technical requirements. The most important component of an electrolyzer is the type of cell. The proton-exchange membrane (PEM) cell is the one that offers the most promise. Cell components and their materials are subject to volatile development conditions. In particular, various production technologies come to mind for producing assembled cells the so-called membrane electrode assembly (MEA). Currently, only small quantities are produced. To meet demand, production must be scaled up quickly. Nevertheless, high requirements are placed on the quality of the cells, since high-priced raw materials are used for PEM cells. Therefore, this paper presents a data-driven approach to assessing the appropriate production technology. First, general and specific requirements of a cell production scale-up are derived. Later, characteristics of suitable cell production technologies are extracted. By using a digital factory model, suitable for shop floor data, the basis for the conception and validation of possible production concepts is laid. [ABSTRACT FROM AUTHOR]

Recent developments show that not only material supply chains but also energy supply systems can be hit by strong disruptive forces. This brings new challenges and highlights the importance of a resilient energy supply to the manufacturing industry. These challenges can be solved by implementation of technical solutions and a change of energy supply system designs. However, manifold options available to a company require a methodological approach to rapidly identify the potentially most suitable variants under individual circumstances for a specific factory. This paper introduces a simplified methodology for the first planning steps in the transition towards a resilient energy supply system of production sites. For this purpose, relevant resilience factors are identified and selected for energy supply systems of a factory. With a structured procedure, different technical system combinations are created and evaluated in respect to implementation effort and their potential impact on the resilience factors. Simultaneously, different disruptive scenarios are ranked based on likelihood and expected severity. The impact of these combinations on the energy load profile of the factory is evaluated and the results are presented based on the suitability of each energy supply system alternative in respect to the different disruptive scenarios. This provides information on the potentially most favorable options to the relevant stakeholders for further implementation. To demonstrate the applicability of the developed methodology, a case study on a battery cell factory is presented. The production of batteries is a fast-growing industry with a high relevance for the transition towards a sustainable economy while the battery production itself requires high amounts of electricity and heat at different temperature levels. Alternative supply scenarios besides the unidirectional consumption of fossil fuels are needed in in terms of sustainability and to increase resilience. [ABSTRACT FROM AUTHOR]

[Display omitted] • The empirical result revealed validation of the pollution Halo Hypothesis. • GDP and its square form an inverted U-shaped curve, which supports EKC hypothesis. • Energy structure correlated positively with ecological footprints. • Renewable energy correlated negatively with ecological footprints. • GDP and ecological footprints have a bidirectional causal relationship. • FDI and ecological footprints have a unidirectional causal relationship. The nexus between economic growth and the ecological footprint has been a recurring focus investigation for a long time. European countries are speeding up their environmental efforts to improve air quality, and their commitments for implementing the Paris Climate Agreement's 2015 treaty action plans. Thus, this paper investigates the environmental Kuznets Curve and the pollutant halo hypothesis for the time period of 1990–2020 for 16 European countries. The empirical analysis uses cross-sectionally augmented autoregressive distributed lag modelling, which accounts for time dynamics, cross-sectional dependence, and cross-sectional heterogeneity. The Augmented Mean Group and Dumitrescu and Hurlin panel causality tests were also employed in this study. The empirical result revealed validation of the pollution Halo Hypothesis, foreign direct investment and ecological footprints have a negative correlation. GDP and its square form an inverted U-shaped curve, which supports the environmental Kuznets Curve hypothesis. When it came to ecological footprints, renewable energy had a negative correlation, whereas energy structure had a positive one. Nonetheless, GDP and ecological footprints have a bidirectional causal relationship. Furthermore, the panel non-causality test revealed a bidirectional causality relationship between ecological footprints and GDP, but a unidirectional causality relationship is discovered from foreign direct investment, renewable energy, energy structure and human capital with ecological footprint. According to the findings of the research, a number of policy recommendations for the analysed bloc are provided, including the implementation of resource conservation as well as the establishment of sustainable energy policies. [ABSTRACT FROM AUTHOR]

Global energy markets are experiencing an unprecedented confluence of events that are driving up gas prices and bringing energy security to the forefront, adding further complexity to already uncertain prospects following global economic recovery from COVID-19. This article examines the factors that have led to the current surge in natural gas prices and the role of green investment in enhancing energy security and accelerating the transition pace for a sustainable world. It also discusses the potential short- and long-term implications of ongoing spikes in energy prices on global energy transition. The analysis highlights that while renewables could provide a promising solution to enhance energy security, the current energy crisis has revealed that the current scale of renewable energy investments is not ready to address ongoing energy needs, forcing countries to re-unlock further hydrocarbon fuel sources such as coal, putting global energy transition on hold. The paper concludes with policy recommendations that enable unlocking clean energy investments amid a strained geopolitical context. • We focus on how to balance climate mitigation and energy security goals. • We examine the role of green finance and investments in accelerating the energy transition. • We also discuss the potential short- and long-term implications of ongoing energy spikes. • The current scale of green energy investments is not ready to address ongoing energy needs. • We conclude with policy recommendations that enable unlocking clean energy developments. [ABSTRACT FROM AUTHOR]

In recent years, photovoltaic energy production has experienced significant progress, being integrated into the grid through large-scale distributed systems. The intermittent nature of solar irradiance coupled with the presence of photovoltaic failures causes fluctuations that could compromise the quality and stability of electrical grid. This paper presents a novel photovoltaic power smoothing method in a combination with moving averages and ramp rate to reduce fluctuations with hybrid storage systems (supercapacitors/batteries), the main novelty involves optimizing the number of charging/discharging cycles under PV failures. To achieve this goal, a photovoltaic failure detection method is proposed that uses machine learning to process big data by monitoring the behavior of photovoltaic. The experiments have been done under controlled conditions in the microgrid laboratory of the University of Cuenca. The results show the reduction of the supercapacitor operation with respect to other power smoothing methods. Moreover, the monitoring system is capable of detecting a failure in photovoltaic systems with a root mean squared error of 0.66 and the computational effort is reduced using the new smoothing technique. In this sense, the initial execution time is 4 times lower compared to the moving average method. [Display omitted] [ABSTRACT FROM AUTHOR]

This study analyzes the impact of green finance and renewable energy on energy efficiency in E−7 economies. We analyzed the nonlinear and asymmetric effects of green finance and renewable energy on energy efficiency. We collected data from 1985 to 2017 and applied the NARDL and 2SLS methods. The results reveal that the differences in green finance development in the E−7 region are as follows: China (0.61), Brazil (0.55), India (0.53), Indonesia (0.49), Mexico (0.37), and Russia (0.39). In 2019, the Gini coefficient was the largest in Russia (0.57), followed by Turkey. Based on empirical findings, the largest barrier to green energy efficiency is insufficient private and governmental investment in the energy sector to improve access to power, to increase energy security and promote economic growth in an environmentally sustainable manner. The findings point to a promising but vulnerable future for renewable energy and energy efficiency deployment in E−7 countries. This paper emphasizes that green financing and renewable energy policy limitations must be addressed to realize the funding potential of energy efficiency in E−7 countries. • We have analyzed the barrieres for renewable energy and energy efficiency. • Non-linear and asymmetric effects is observed on energy efficiency. • We have applied NARDL model to examine the relationship among variables. [ABSTRACT FROM AUTHOR]

[Display omitted] • Areas with the potential of Brackish water resources were identified. • To identify the type of water sources, the soil type investigation method was used. • The energy required for RO water desalination devices is supplied by sunlight. • The potential of solar power plant in Iran was calculated to generate electricity. • Four factors were used to identify suitable areas. Iran has 985,798 square kilometers of desert areas, equivalent to 59.8 percent of the country's total area. This factor causes the creation of salt marshes and, accordingly, the salinization of surface and groundwater. For the first time in this paper, an indirect method is presented to identify areas of Brackish water sources with 3000–10000 mg/L TDS in the entire country of Iran and simultaneously identify the area's potential in terms of the solar energy to provide energy for desalination by reverse osmosis devices. Other types of sources of water, such as seawater, which has more salinity than Brackish water (about 35000 mg/liter) are not investigated in this research, because the primary purpose of this article is to use water that exists in the form of ponds, wells, or surface water that water cannot be used for residential or agricultural purposes, at the same time, this water is not seawater. Simultaneously 16 data layers that include economic, environmental, technical, and geological parameters have been considered.The proposed methodology provides a structured decision aid, which can be used in other countries to identify the mentioned areas and produce fresh water using reverse osmosis devices. Geographic Information Systems (GIS) are used to generate layers of data and to apply the elimination criteria and constraints. Multiple Criteria Decision Analysis (MCDA) is then used to rank and sort the grids via the identified evaluation criteria. This research can also have two other applications. The first is for professionals who want to identify areas with saline water potential, and the second is for organizations that want to launch reverse osmosis desalination plants that work with solar renewable energy. Technically, there are about 160 cities in Iran that have an area of about 144440685120.498077 square meters, have saline water sources, and simultaneously have the potential to use solar energy as one of the renewable energy sources to power reverse osmosis desalination devices. Most of these areas are located in the east of Iran and these areas have high water stress. In this research, the city of Khansar with an area of 95917147.16 square meters, which is located in the province of Isfahan and in the central part of Iran, has surface and underground saline water resources that both technically and operationally, has the highest potential. [ABSTRACT FROM AUTHOR]

This paper investigates the connectedness among the climate change index, green financial assets, renewable energy markets, and geopolitical risk index from June 1, 2012 to June 13, 2022, using Quantile Vector Autoregressive (QVAR) and wavelet coherence (WC). The Total connectedness index (TCI) varies as long as the highest TCI originates in the upper quantile. We also note that the higher TCI decreases after the second wave of COVID-19 and increases during the first 100 days of the Russia-Ukraine conflict. Moreover, the results show that Geopolitical risk (GPR) is a net transmitter of the climate change index during the Russian invasion of Ukraine. The green bond and clean energy markets are negatively connected to the GPR at extreme 10 th and 90 th quantiles. The wavelet coherence confirms the QVAR results that the climate change market can be a safe haven against GPR during the Russian invasion. The climate change index, green financial assets, and clean energy are strong influencers in the financial markets and are vital to international peace, reducing geopolitical risk. The study reports a few novel conclusions and implications from a sustainable development perspective. • Connectedness among climate change index, green financial assets, renewable energy markets. • Daily data was used from June 1, 2012 to June 13, 2022. • Role of geopolitical risk for green financial assets and renewable energy market. • Climate change index, green financial assets, and clean energy are strong influencers in the financial markets. [ABSTRACT FROM AUTHOR]

Within the last years but especially since the beginning of 2022 there is a large increase in the demand of renewable energy. Currently, so-called green hydrogen is one of the most promising energy carriers for renewable energy. To meet the demand for green hydrogen, installation of production technologies like water electrolyzer is necessary. In order to play a role within the hydrogen economy, companies have to be able to design their hydrogen production capacities for a spot-on time-to-market. This leads to difficult ramp-up decisions and trade-offs between costs, technological maturity, and market success. As the phases of research and development influences a significant part of the costs (especially within production) after reaching time-to-market, they determine the later cost structure of the company, the possibilities to gain a competitive advantage or to survive on the market. Analyzing literature with regard to wind and photovoltaic industry shows several factors influencing success. This paper identifies and discusses these critical success factors, transfers their relevance onto the current hydrogen economy, and points out critical aspects for market entry decisions. With respect to a globalized world, in which dependencies from single countries can lead to unwanted constraints, the results become even more important. [ABSTRACT FROM AUTHOR]

At present, energy represents a key element in the social and economic development of a territory. This is particularly true for fragile territories that are not interconnected, such as small island states. As is true for many of the Small Island Developing States (SIDS) worldwide, the energy situation of the Comoros is substantially based on fossil fuel imports. The socioeconomic development of this archipelago is overwhelmingly dependent on energy security from sustainability, availability and affordability perspectives. As a forward-looking response to energy vulnerability, the deployment of renewables to diversify the generation of electricity appears to be an essential prerequisite for guaranteeing a sustainable future. Nevertheless, despite a high potential for renewable energy, only 3.8% of the electricity supply in the Comoros is provided by hydropower. This paper provides a comprehensive overview of the energy situation throughout the Comoros and focuses on renewable energy opportunities to facilitate the supply of green power. This study ultimately shows that renewable energies are rarely exploited despite the powerful potential of different resources. Finally, this paper attempts to provide recommendations for the Comoros to pursue a more sustainable future. • Analysis of the current energy situation of Comoros. • Defining renewable potential and ongoing actions. • Analysis of the specific barriers and policy to the development of RES in the Comoros. [ABSTRACT FROM AUTHOR]

The transition from fossil fuels to renewable energy sources is critical to reduce future emissions and mitigate the consequences hereof. Yet, the expansion of renewable energy, especially the highly fluctuating production of wind energy, poses economic challenges to the existing energy system in Denmark. This paper investigates the economic feasibility of integrating a 250 kW, 500 kW, 750 kW and 1 MW water electrolysis system in the existing Danish energy market to exploit excessive off- and onshore wind energy for hydrogen production used as fuel for transportation purposes. In 2018, Danish wind turbines produced excess energy during 1238 h, which poses a capacity constraint as the electrolysis systems are limited to only produce hydrogen for 14% of the total available annual hours. This paper concludes that the net present value of each investment is negative as the fixed and variable production costs exceeds the generated revenues and it is therefore not economical feasible to invest in an electrolysis system with the purpose of only operating whenever excess off- and onshore wind energy is available. • Wind turbines produced excess electrical energy during 1238 h in 2018. • The target sale price of hydrogen from manufacturer is €5.7 kg/H 2. • The lowest achievable hydrogen sales price is estimated to €9.17 kg/H 2. • Hydrogen produced from excess electrical wind energy alone is not cost-competitive. [ABSTRACT FROM AUTHOR]

The paper investigates the role of renewable energy and alternative and nuclear energy in mitigating CO 2 emissions. Trade openness is added to consider its effect on the environment, as it appears to be a crucial factor in interregional cooperation and development. We adopt a sample of nine signatories to the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) from 1971 to 2014. Various time-series econometric methods are utilized including two long-run estimators - fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) - and a Granger-causality test. Several noteworthy findings are achieved from this paper. First , the inverse U-shaped relationship is found in six countries: Australia, Canada, Chile, New Zealand, Peru and Vietnam. The U-shaped relationship is found in Japan and Malaysia whereas no evidence is observed in Mexico. Second , the adoption of either renewable energy or alternative and nuclear energy would mitigate CO 2 emissions and trade openness plays an important role in facilitating this effect. Third , the directions of Granger causality among the variables of interests including consumption of renewable energy; consumption of alternative and nuclear energy, trade openness and CO 2 emissions, do vary across countries and between the short term and the long term. • Inverse U-shaped relationship in Australia, Canada, Chile, New Zealand, Peru, Vietnam. • U-shaped relationship in Japan and Malaysia and no evidence in Mexico. • Consumption of renewable energy, alternative & nuclear energy reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

Short-term forecasting of electricity generation from variable renewable energy sources is not an end in itself but should provide some net benefit to its user. In the case of electricity trading, which is in the focus of this paper, the benefit can be quantified in terms of an improved economic outcome. Although some effort has been made to evaluate and to improve the profitability of electricity forecasts, the understanding of the underlying effects has remained incomplete so far. In this paper, we develop a more comprehensive theoretical framework of the connection between the statistical and the economic properties of day-ahead electricity forecasts. We find that, apart from the accuracy and the bias, which have already been extensively researched, the correlation between the forecast errors and the market price spread determines the economic implications - a phenomenon which we refer to as 'correlation effect'. Our analysis is completed by a case study on solar electricity forecasting in Germany which illustrates the relevance and the limits of both our theoretical framework and the correlation effect. • The economics of short-term electricity forecasts are studied analytically and empirically. • The value and the risk of forecast-based electricity trading are evaluated. • We find that the economics depend on the correlation of forecast errors with prices. • Hence, the most accurate forecast is not necessarily the most accurate. [ABSTRACT FROM AUTHOR]

With a varied landscape, Wales is resource rich in terms of wind and water and a suitable location to develop many different forms of sustainable energy. Whilst farm businesses face increasing challenges in terms of economic stability and traditional production methods, this paper considers the role of renewable energy production as a form of diversification. The study adopts mixed methods as a means of undertaking an in-depth investigation into the role of renewable energy generation in supporting agribusinesses in Wales. Initially a questionnaire obtained 118 responses from farmers in Wales. Subsequently, 15 follow-up semi-structured interviews with farmers were conducted to further investigate the issues from the initial questionnaire. The theoretical contribution of this paper is a segmentation of farmer businesses which allows for distinctions to be made of different attitudes to off-farm income and the adoption of renewable energy sources. Five farm types were identified, varying in relation to farm characteristics, attitudes to diversification, access to renewable energy and resource allocation. These farm types highlight the need for specific policies towards facilitating the increase in renewable energy along with sustaining farming incomes. Furthermore the research provides valuable information to the farming industry on opportunities in renewable energy production, particularly for farmers and farm businesses who are considering diversification strategies. • Farmers face increasingly challenging times and rely on diversification activities to supplement their farm activities. • Renewable energy production represents an opportunity for diversification in rural spaces, such as Wales. • Renewable energy production is a significant investment, but this could reduce costs and generate income on the farm. • Welsh farm businesses are reactive to technology adoption, therefore support is essential in facilitating adoption. • Farmers could play a leading role in supporting rural community projects to develop renewable energy production. [ABSTRACT FROM AUTHOR]

Renewable energy cooperatives (RECs) in Turkey have received considerable attention in recent years and the total number of cooperatives is 46 in 2020. With increased environmental awareness and energy demand, RECs aim the local and renewable resources to be used by the people of the region by creating an example venture model through cooperatives. The growth in the number of cooperatives has resulted largely from Turkish energy policy and arrangement of Renewable Energies Act. This study is aimed to identify energy cooperatives, current situations, advantages, characterizing the implementation barriers RECs face and external factors. For this purpose, an online questionnaire was carried out with REC members and questionnaires were sent to 468 members of 20 cooperatives via e-mail, and approximately 80% return was obtained, 16 management board members were interviewed with categorized questions, and RECs websites were examined, as well. This paper presents an overview of This paper presents an overview of Turkish energy cooperatives in terms of organization, financing, membership, technology, policy, internal and external barriers drawing on collected data. How to create a better understanding of energy cooperatives and contribute to the Turkish energy policy were discussed. RECs could be an example venture model to provide the local and renewable resources to be used by the people of the region. Also, RECs can be an important building block of the energy transition in Turkey where consumed energy mainly imported from abroad and if RECs are to continue to play an important role in the energy transition, policy makers are expected to support their growth. These findings may be a guide for further researches for Turkey. • RECs can be an important building block of the energy transition in Turkey. • Differences in the development of RECs exist mainly regarding the finance. • The main external factor in the development of RECs is policy. • The amount of electricity produced is the most important indicator of success of RECs. [ABSTRACT FROM AUTHOR]

Power systems now face new issues due to incorporating electric vehicles (EVs) and renewable energy resources (RERs). This paper proposes a novel Enhanced Coati Optimization Algorithm (ECOA) for obtaining the optimal solution of the probabilistic optimal power flow (POPF) problems. The ECOA is a metaheuristic optimization algorithm that is robust and efficient for solving complex problems. It is used to tackle the OPF problem, which considers the stochastic characteristics of RERs. Moreover, EVs are included in the presented power systems in this paper. The novel approach is tested and verified on the IEEE-57 and IEEE-118 networks. The effectiveness of the proposed method is demonstrated by making a comparison with other metaheuristic-based methods. To obtain a practical study, real data of wind speed, solar irradiance, and electric vehicles profile are incorporated in the dynamic analyses. The simulation results show that the ECOA is robust and efficient for solving the OPF problem. It can also improve the performance of power systems with RESs and EVs. The findings of this research demonstrate that the suggested approach is promising for power system optimization problems, including RERs and EVs. • This paper presents a novel optimal energy management of smart grids. • The model contains renewables and electric vehicles uncertainties. • A practical study is presented including renewables and EVs real data. • A novel enhanced metaheuristic algorithm is proposed to solve the problem. • The effectiveness of the proposed method is compared with other methods [ABSTRACT FROM AUTHOR]

In the context of the increasing popularity of distributed energy, this paper proposes a home energy management system method considering the working state of flexible load to deal with the problem of mismatch between home energy supply and demand. Specifically, in this paper, the flexible load in the system is studied, and the model of flexible load and its working state are established. On this basis, combined with the real-time electricity price of the grid, the model of home energy management system is constructed to minimize the electricity purchase cost and improve the utilization rate of renewable energy, the genetic algorithm is used to solve it, and the influence of time step, flexible load ratio and whether to use energy storage equipment on the optimization degree of the model are analyzed. The research results show that, while meeting the requirements of human comfort and electricity demand, the daily electricity purchase cost of the system with flexible load scheduling is 22.5% lower than that of the system without flexible load scheduling, and the utilization rate of renewable energy is increased by 16.5%. In terms of model optimization, shortening the prediction time step, increasing the proportion of flexible load and using energy storage equipment can further reduce the electricity purchase cost. The case study confirms the effectiveness of the proposed home energy management system model, which can provide an efficient optimal scheduling scheme for household energy saving. • The optimal scheduling model for home energy management system of rural dwellings with flexible loads is proposed. • Influence of the proportion of flexible load and different time step sizes on the scheduling model are studied. • The operation cost by 26.62% of battery integration strategy based on load scheduling is further reduced. [ABSTRACT FROM AUTHOR]

Accurately predicting wind and photovoltaic power is one of the keys to improving the economy of wind-solar complementary power generation system, reducing scheduling costs and no-load losses, and ensuring grid stability. However, the natural properties of energy result in complex fluctuations in their corresponding power sequences, making accurate predictions difficult. Therefore, this paper proposes an intelligent prediction system that combines decomposition algorithms and deep learning for ultra-short-term prediction of wind and photovoltaic power. First, an improved decomposition algorithm is proposed, based on fuzzy entropy's property that its value increases with the increase of sequence uncertainty, particle swarm optimization (PSO) is used to search for the optimal parameter combinations of variational modal decomposition (VMD), so that it can automatically adjust the parameters for energy data with different characteristics to reduce the human error. Then, a convolutional neural network (CNN) architecture that balances operational efficiency and prediction performance is constructed, and the hyperparameters of the CNN are optimized using the wild horse optimization algorithm (WHO) to improve the stability and accuracy of the prediction model. In this paper, real data from wind power plants and photovoltaic power plants in China are used as experimental objects, and experiments are carried out in three aspects, namely, benchmark model selection, decomposition algorithm comparison and combined model comparison. The results show that selecting CNN as the benchmark model is a good choice; the improved VMD has better decomposition performance than other state-of-the-art decomposition algorithms. The system proposed in this paper is highly generalizable and adaptive, and its prediction performance and accuracy greatly outperform that of other comparative models, with prediction accuracies improved by 72% and 79%, respectively, compared to a single CNN model. • The hyperparameters of VMD are determined by using PSO based on fuzzy entropy. • Optimize convolutional neural network using the wild horse optimization algorithm. • The intelligent prediction system can accurately predict wind power and PV power. • Experiments based on power data from actual wind farms and PV plants. • A deep learning prediction method applied to wind and solar complementary systems. [ABSTRACT FROM AUTHOR]

With the increase in renewable energy penetration and the widespread use of electric vehicles, the uncertainty of controllable resources adversely affects the economic scheduling of smart buildings. Meanwhile, the smart building can take advantage of its flexibility through multiple markets mechanism. Therefore, this paper proposes an optimal scheduling model for the smart commercial building, including electric vehicle charging, which partakes in the day-ahead energy reserve markets. Firstly, the behavior of electric vehicles is obtained through Monte Carlo simulation. This paper categorizes electric vehicles according to their behavioral characteristics and proposes corresponding scheduling strategies. Then, the joint energy and reserve markets mechanism is introduced, and the robust adaptive optimization (ARO) method is employed to deal with uncertainties. The proposed model is a three-level and two-stage model, which can be solved by the column and constraints generation algorithm. Finally, the economy and feasibility of the proposed model are validated through a case study. Results show that the ARO method saves 3%–4% of expenses more than the pure robust optimization method, and the proposed joint markets mechanism can increase profits by 1%–4%. • A three-level optimal model of SCBEMS is proposed through ARO method to reduce its conservativeness. • The joint energy and reserve markets are introduced to coordinate smart commercial buildings' flexibility. • The travel behaviors of EVs are processed by Monte Carlo simulation and different scheduling strategies are applied. [ABSTRACT FROM AUTHOR]

Carbon emissions from the combustion of fossil fuels continue to be a significant barrier in the fight against global climate change. Carbon taxes are an energy policy tool for substantially carbon dioxide (CO 2) emissions reduction. This paper proposes a new strategy to promote the use of renewable energy by enterprises in industrial parks against the backdrop of a carbon tax. Using an evolutionary game model based on life cycle assessment (LCA), this study investigates the impact of dynamic carbon tax mechanisms, fines, and subsidies on the energy choices of industrial park enterprises. This paper's primary contribution is the incorporation of LCA into the evolutionary game model, which facilitates accurate calculation of the carbon tax parameter value. According to the study's findings, the carbon tax rate increases the propensity of enterprises to acquire a high percentage of renewable energy. While there is a negative relationship between fines and the propensity of enterprises to adopt a low proportion of renewable energy, an increase in subsidies decreases the proportion of enterprises that purchase a high proportion of renewable energy. This paper concludes by providing a theoretical foundation for policymakers to construct precise carbon tax schemes and promote renewable energy consumption among industrial park enterprises while simultaneously reducing carbon emissions. [Display omitted] • Carbon tax & energy decisions: life cycle assessment & evolutionary game theory. • Life cycle assessment for enterprise carbon taxes under multiple energy options. • Carbon tax mechanisms, fines, and subsidies influence enterprises energy decisions. • Excessive subsidies discourage renewable energy use, but fines can. [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]

As a large number of renewable energy resources are connected to power systems, the operation, planning, and optimization of power systems have been becoming more and more complex. Power flow calculation, unit commitment, economic dispatch, energy pricing, and power system planning are essentially computation problems. A lot of computing resources are required for these problems, which are non-trivial, especially for large-scale power systems with the high penetration of renewable energy. Traditionally, the calculation and optimization of power systems are completed by classical computers based on the classical computing theory and the von Neumann architecture. However, with Moore's law getting closer and closer to the limit, the importance of quantum computing has become increasingly prominent. Quantum computing has been applied to some fields to a certain extent, yet the applications of quantum computing in power systems are rare. As the power industry is the foundation of the national economy, introducing quantum computing into the power system has far-reaching and crucial significance, such as improving the penetration of renewable energy, enhancing the computing efficiency, and helping in achieving the goal of net zero and climate neutrality by 2050. This paper first introduces the core concepts, essential ideas and theories of quantum computing, and then reviews the existing literature on the applications of quantum computing in power systems, and puts forward our critical thinking about the applications of quantum computing in power systems. In brief, this paper is dedicated to a tutorial on quantum computing targeting power system professionals and a review of its applications in power systems. The main contributions of this paper are: (1) introduce quantum computing into the field of power engineering in a thoroughly detailed way and delineate the analysis methodologies of quantum circuits systematically without losing mathematical rigor; (2) based on Dirac's notation, the related formulae are derived meticulously with sophisticated schematic diagrams; (3) elaborate and derive some critical quantum algorithms in depth, which play an important role in the applications of quantum computing in power systems; (4) critically summarize and comment on the existing literature on the applications of quantum computing in power systems; (5) the future applications and challenges of quantum computing in power systems are prospected and remarked. • Introduce quantum computing into the field of power engineering in a thoroughly detailed way and delineate the analysis methodologies of quantum circuits systematically without losing mathematical rigor. • Based on Dirac's notation, the related formulae are derived meticulously with sophisticated schematic diagrams. • Elaborate and derive some important quantum algorithms in depth, which play an important role in the applications of quantum computing in power systems. • Critically summarize and comment on the existing literature on the applications of quantum computing in power systems. • The future applications and challenges of quantum computing in power systems are prospected and remarked. [ABSTRACT FROM AUTHOR]

As the demand for renewable energy continues to rise, the significance of electrocatalysis research for hydrogen production has also increased. To explore the developmental trends in this field, a scientometric-assisted review was conducted using clustering analysis of keywords from 43,564 papers published between 2013 and 2022. The results of the study demonstrate a globally diverse research landscape, with contributions from Asia, Europe, North America, and the Middle East. China emerges as the most prolific contributor, with consistent growth in research productivity over the past decade. The USA, South Korea, India, and Germany were among the top five leading countries in electrocatalysis research. Singapore, USA, Australia, Saudi Arabia, and Germany lead research impact, measured by total citations per total paper. China has the highest collaboration in electrocatalysis research, with collaborations with the USA, Australia, Singapore, Saudi Arabia, Japan, the UK, Germany, Canada, Korea, and India. Importantly, the study reveals six clusters of electrocatalysis research, with different author keywords, each covering a range of topics and materials. The implications of each of the six clusters in electrocatalysis are examined critically. This study offers valuable insights for researchers, policymakers, and funding organizations in making critical decisions by identifying the main themes and trends in hydrogen evolution through electrocatalysis research. • To explore the developmental trends in this field, a scientometric-assisted review was conducted on renewable energy • The results of the study demonstrate a globally diverse research landscape. • This study offers valuable insights in hydrogen evolution through electrocatalysis research. [ABSTRACT FROM AUTHOR]

This paper deals with extremes and resource assessment of wind and waves in central Mediterranean Sea. The extreme value analysis is performed following Equivalent Storm Models (ESMs) approach, commonly employed in the context of significant wave height return values estimation. In this work, it is extended to wind storm events thanks to some analogies between wave and wind processes at short and long-term scale. The Equivalent Exponential Storm (EES) Model is selected for this study because of its realistic description of actual events time evolution. Energy estimations are based on both real storm data and EES model. The input data used for this analysis comes from the re-analysis of atmospheric and wave conditions performed by MeteOcean group at DICCA of Genoa University and cover the period from January 1979 to December 2020. Sixteen points have been selected including the south and west coast of the Italian peninsula, the Sicily and Sardinia Islands. The study is focused on return values estimation and energy assessment, mainly related on the storm concept; it aims to elucidate analogies and/or differences of wind and wave at a given site as well as how the two resources are distributed along the examined locations, identifying the potential working time over the year and which are the storm events providing the prevalent energetic contribution to harvestable energy. Results show that the location characterized by the severest wind storm and wave storm events do not coincide and not always the location where the severest storm occur is also the most energetic site. Further, the variability of return values with different levels of return period for wind speeds is lower than for significant wave heights: this aspect is motivated by a shape parameter of Weibull distribution that is systematically higher for the wind speed with respect to the significant wave height. The extremes of significant wave height are more variable from one location to another than those of wind speed ones and the same behavior is exhibited by energy resources, elucidating that the wind resource in the investigated sites is more homogeneous. • The paper deals with wind and wave extremes and resources assessment in central Mediterranean Sea. • A storm based approach is proposed, including the Equivalent Exponential Storm (EES) model. • The EES model is new in the context of wind extremes and energy related estimations. • An atlas for site selection and preliminary design of wind and wave energy devices is provided. [ABSTRACT FROM AUTHOR]

This paper provides an extensive review of learning-based short-term forecasting models for smart grid applications. In addition to this, the paper also explores forecasting models including physical, statistical, hybrid, and uncertainty analysis models for wind speed forecasting. The learning-based models are classified into three broad categories, namely classical machine learning, advanced machine learning, and probabilistic learning. In this work, 41 different models are employed to forecast the wind speed. Dataset for this case study is collected from the site of Jodhpur, India. Dataset have 8759 sample with five features i.e., wind speed, pressure, humidity, temperature, and dew point. This forecast also includes the seasonal effects. Model accuracy has been tested considering single and multiple features in the input data. A comparative analysis of the performance of these 41 learning-based models is conducted based on coefficient of regression and error indices. It is observed that the performance of these models varies with the variability in the season. On the basis of the evaluation of these models, future recommendations are also framed out. These recommendations target of energy storage planning, energy market and policymakers, and reliability and reserve sizing direction. These recommendations can be utilized by authorities for effective planning and coordination of power. [Display omitted] • A comprehensive review of various wind speed forecasting models is presented. • A case study comprising 41 machine learning models is conducted. • Comparative analysis of forecasting models is conducted, including seasonal effects. [ABSTRACT FROM AUTHOR]

Energy extraction from renewable sources like hydrokinetic energy from the flowing water of rivers and canals is one of the affordable and clean energy generation technique. The current work is carried out towards performance enhancement of the Darrieus hydrokinetic turbine for open water channel utilization application. The investigation has been done using the flat and concave shape of blocking plate. Both blocking plates have been analyzed for different sizes as well as location at the upstream side of the turbine. The obtained results are reported after considering appropriate velocity correction. The blocking plates with projected width of 76 mm, 89 mm, 101 mm, 127 mm, 152 mm, and 203 mm are selected for the investigation. It is observed that 101 mm (Width Ratio , WR =0.36) with concave blocking plate provides maximum C p of 0.245 at 0.99 TSR. Also, the optimum region is found between 0.143 and 0.286 Distance Ratio (DR) from the top corner of the acrylic plate. The concave blocking plate enhance the performance more compared to flat blocking plate. The conclusion is made that, the performance with flat blocking plate with optimized width and location improve the power coefficient by 10.92% and that of concave blocking plate by 18.45%. • Energy extraction using Darrieus hydrokinetic turbine is one of the affordable and clean energy generation techniques. • The performance of the Darrieus water turbine has been compared with a bare rotor and with using two different types of blocking plates. • The concave and flat blocking plates have been experimentally analyzed, and its effect has been explored in the paper. • The different sizes of concave and flat blocking plates were placed at the upstream side of the Darrieus turbine to enhance the performance. • The paper also includes the optimum region of the optimized shape concave and flat blocking plates placed at the upstream location of the rotor. [ABSTRACT FROM AUTHOR]