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The highly porous and binder-free flexible paper electrodes can enhance the specific capacitance of symmetric supercapacitors (SCs) due to their large surface and effective ion diffusion pathways. We synthesized the exfoliated graphite (ExG) by the thermal exfoliation method of chemically treated graphite flakes and compressed it into a paper-like thin sheet (binder-free) of ∼0.15 mm thickness. The coin cell SCs with copper (Cu) and stainless steel (SS) as current collectors have been fabricated for the electrochemical measurement. The cyclic voltammetry and galvanostatic charge/discharge measurements are investigated at various scan rates and current densities. The SCs with Cu foil as a current collector perform better than SS-based SCs. The Cu current collector-based SCs showed a specific capacitance of 37.08 mF cm−2, whereas it was ∼29.98 mF cm−2 for SS-based SCs at a 0.01 V s−1 scan rate across a 0–0.6 V potential window. Approximately no degradation in charge storage capacity for more than 15 000 cycles at 0.1 V s−1 shows the ultra-stability of the flexible ExG-based binder-free electrodes. A digital watch is powered using the fabricated pouch cell supercapacitor with copper-based current collectors to show the potential of SCs. [ABSTRACT FROM AUTHOR]

Arid areas account for more than 20% of Earth's surface. Many of these arid areas suffer from harsh climates where energy consumption is high and production coexists with high hydrological stresses. In such areas, the production of water is directly linked to the production of electricity, bringing the Energy–Water nexus to a higher level [Heggy et al., Sustainable Energy-Water-Environment Nexus in Deserts (Springer, 2022)]. Under the current energy transition from a fossil-based economy to a renewable energy-based system, arid areas are seen as potential reservoirs for electricity production due to their high solar potential, thereby helping to achieve global decarbonation targets. This energy production transition will strongly affect the Energy–Water nexus as it will contribute to the decoupling of electricity from water production. The International Conference on Sustainable Energy–Water–Environment Nexus 2019 (ICSEWEN19) conference was the first to address the Sustainable Energy–Water Nexus in Deserts, bridging the gap between research and industry and highlighting the research capabilities relevant to Qatar, the region, and internationally. The Invited Papers from the International Conference on Sustainability, Energy, Water, and Environment Nexus in Desert Climates Special Collection in Journal of Renewable and Sustainable Energy presents articles covering the main elements of the conference with respect to solar photovoltaic implementation in desert environments as a key element to succeed in this global energy transition. [ABSTRACT FROM AUTHOR]

A walk in type semicylindrical solar tunnel dryer for drying 1500 handmade papers per batch has been commissioned at M/s Cellulosic Waste Recycling Education Project, Vidhya Bhawan Society, Udaipur, Rajasthan, India. A solar tunnel dryer with 122.95 m2 solar collector area was sufficient for drying handmade paper from 53.85% to 9.96% moisture content (dry basis) in 4–5 h. In this paper the results are shown in terms of the variation of air temperature, solar flux density, relative humidity, air flow rate, moisture content, etc., which indicate that the performance of solar tunnel dryer was quite satisfactory. The performance of the dryer for drying handmade papers has been analyzed through no load and full load test. Technoeconomic analysis of solar tunnel dryers was also carried out by using different economic indicators. [ABSTRACT FROM AUTHOR]

The Korean Government continues to support the photovoltaic (PV) sector to develop high-efficiency, high-reliability PV technologies. However, considering this R&D investment the level of Korean PV technology is low. In this study, PV-related SCI (Science Citation Index) papers issued from 2009 to 2012 are investigated to understand the trends of the technology and its research network. SCI papers on dyesensitized solar cells and organic/polymer solar cells are more common than papers on other PV related technologies. A qualitative comparative analysis is also conducted into the impact factor of the journals. Finally, social network analysis is used to investigate indicators, such as density and centrality, to help policy makers direct future R&D investment more effectively. The results reveal that USA and European countries such as Germany, the UK, and Spain published the most influential papers, while Asian countries such as China, Korea, and Japan stood out in terms of quantity. Countries and institutes with more diverse collaborations tend to publish more papers, and the network of countries is becoming stronger and more diverse, while institutes are tending to collaborate more in exclusive subgroups. Korean PV-related research is relatively high in terms of quantity and collaboration, but more effort is necessary to improve its quality and network range. [ABSTRACT FROM AUTHOR]

There are numerous sources of mechanical energy in our environment, such as ultrasonic waves, body movement, and irregular air flow/vibration. Here, we present a simple, cost-effective approach for fabricating a flexible nanogenerator and apply it to harvest energy from environmental mechanical vibrations. The nanogenerator was based on ZnO nanorods grown on common paper substrate using a lowtemperature hydrothermal method. Piezoelectric currents were measured by attaching the nanogenerator on the surface of a cantilever and a wind-up drum, respectively. At the same time, the vibrations of the cantilever and wind-up drum could also be characterized by the corresponding output signals. This is a practical and versatile technology with the potential for converting a variety of environment energy into electric energy, and also with the application for pre-warning of emergency, such as earthquake and burgling. [ABSTRACT FROM AUTHOR]

Information about several papers discussed at PhotoVoltaic Technical Conference 2016 that was held in South France on May 9-11, 2016 is presented. It discusses the uses of PhotoVoltaic (PV) materials and advance processes to innovative applications on transparent glazing PV, solar roads and solar veils. The article also presents the contributions of PV as a sustainable source of energy and the development of suitable transparent conducting materials in fabrication process.

An introduction is presented in which the editor discusses articles in the issue on topics including photovoltaics (PV), silicon-based solar cells and electricity generation.

The article presents the author's opinion on five selected technical reports which were discussed at the Photovoltaic Technical Conference held in Provence, France in 2011. These papers reportedly tackles low cost photovoltaic solutions to address the grid parity objectives crucial for competitive electricity produced by renewable energy sources. She comments that the solutions presented are key factors in the success of future photovoltaic technologies for consumer production of electricity.

An introduction to the journal is presented regarding the selected papers from the 7th International Green Energy Conference and the 1st Dalian National Laboratory Conference on Clean Energy (IGEC-DCCE) held at the Dalian Institute of Chemical Physics in Dalian, China on May 28-30, 2012.

A fair and efficient renewable energy quota allocation scheme is essential for China to implement the Renewable Portfolio Standards policy. Therefore, based on the principles of fairness and efficiency, this paper comprehensively considers the differences among provinces and then proposes and adopts an improved zero-sum gains data envelopment analysis method to reallocate quotas. Furthermore, for verifying the superiority of the proposed method, this paper establishes a composite index based on the Gini coefficient and the Theil index to compare the rationality of the distribution results. Finally, this paper discusses the relevant advice for the development of renewable energy. The results validate that the proposed method is superior to the traditional method. Additionally, according to the final quota distribution scheme, there are fewer renewable energy quotas in the northern provinces and more in the southern provinces. The quota of most provinces is lower than 50 × 109 kWh, while Guangdong, Sichuan, Yunnan, Jiangsu, and Hunan are the five provinces with the most renewable energy quota of over 100 × 109 kWh. In general, this study provides a more rational renewable energy quota redistribution approach, which will help the government to establish an efficient and fair mechanism of renewable energy quota allocation. [ABSTRACT FROM AUTHOR]

With the development of electric vehicles (EVs), a large number of electric vehicle charging stations (CSs) have been rapidly rolled out to meet the charging demand of EVs. However, high construction costs and long payback periods motivate investigations to improve the profits of CSs. Considering the profit improvement of CSs and carbon emission reductions, this paper first proposes a carbon revenue model for CSs to participate in the carbon trading market. A charging price strategy is proposed to share the carbon revenue with EV users to reduce the charging cost of users, increase the charging income of CSs, and reduce carbon emissions. By describing the EV users' response to the charging price based on the fuzzy theory, this paper establishes the charging behavior model of EV users and solves the profit optimization of the dynamic charging price model by particle swarm optimization algorithm. Finally, the results of the simulation case demonstrate the effectiveness of the proposed strategy. A sensitivity analysis of various grid power purchase prices illustrates the difference between the fixed and dynamic charging price methods. The dynamic charging price method is superior, and under the power purchase price of 0.5 yuan/kWh, it can lower EV user charging costs by 16.25%, improve CS profits by 30.09%, and reduce carbon emissions by 74.68%. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including a cell concept based on silicon heterojunctions, a solution for high efficiency modules, and the use of microwave photoconductivity in metal thin films.

The global energy market has significantly developed in recent years; proof of this is the creation and promotion of smart grids and technical advances in energy commercialization and transmission. Specifically in the Brazilian context, with the recent modernization of the electricity sector, energy trading prices, previously published on a weekly frequency, are now available on an hourly domain. In this context, the definition and forecasting of prices become increasingly important factors for the economic and financial viability of energy projects. In this scenario of changes in the local regulatory framework, there is a lack of publications based on the new hourly prices in Brazil. This paper presents, in a pioneering way, the Dynamic Time Scan Forecasting (DTSF) method for forecasting hourly energy prices in Brazil. This method searches for similarity patterns in time series and, in previous investigations, showed competitive advantages concerning established forecasting methods. This research aims to test the accuracy of the DTSF method against classical statistical models and machine learning. We used the short-term prices of electricity in Brazil, made available by the Electric Energy Commercialization Chamber. The new DTSF model showed the best predictive performance compared to both the statistical and machine learning models. The DTSF performance was superior considering the evaluation metrics utilized in this paper. We verified that the predictions made by the DTSF showed less variability compared to the other models. Finally, we noticed that there is not an ideal model for all predictive 24 steps ahead forecasts, but there are better models at certain times of the day. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is difficult to accurately detect the position and magnitude of pitch angle deviation (PAD) fault of wind turbine (WT) in operation at the same time, this paper proposes a novel multi-parameter synergy mechanism based on a double threshold judgment method combined with power spectral density analysis. First, the influence of different PADs of a single blade on the time domain information of WT parameters under different turbulent wind speeds is discussed. Second, according to the changing rule of three blade parameters caused by the single blade PAD, the double threshold judgment method is introduced to determine the faulty blade and the direction of PAD. Third, this paper put forward the concept of characteristic frequency power ratio. Based on the characteristic frequency of the power spectrum, a method is proposed to determine the magnitude of PAD. Finally, a complete set of fault detection and identification flow for single blade PAD was established. This detection strategy is more accurate and faster than the traditional methods and does not need to add additional data acquisition equipment in the wind power generation system. [ABSTRACT FROM AUTHOR]

The article discusses the role and responsibilities of scientific journals, specifically the Journal of Renewable and Sustainable Energy (JRSE), in mitigating the impacts of climate change. The author reflects on their 5-year term as the Editor-in-Chief of JRSE and highlights the accomplishments of the editorial and production teams. They discuss the importance of curating content for a renewable energy journal and the need for scientific accuracy and ethical responsibility. The article also mentions the efforts to broaden the scope of the journal to include emerging technologies such as solar and wind power. The author emphasizes the selectivity and high standards of the journal, as well as its contribution to increasing research quality and expediting the review process. They acknowledge the special responsibility of energy journals in recognizing and mitigating the negative effects of climate change. The article concludes by expressing optimism for the future of JRSE and thanking the editorial board, staff, and supporters for their contributions. [Extracted from the article]

The optimization of the layout wind farms to reduce the wake effect and increase the output power of wind farms has been a challenge for the wind energy science community. To solve this problem, we propose a method of wake forced recovery based on a small horizontal axis wind turbine (SHAWT). In this paper, the effects of key parameters such as relative distance (L), relative height (H), and number of SHAWT on the output power and flow characteristics of a vertically staggered wind farm (VSWF) are carefully investigated. The results show that this method of forced wake recovery has a positive impact on the output power of the overall VSWF. Meanwhile, we found that the effect of SHAWT on VSWF increased with an increase in L. When L = 6D, the downstream wind turbine output increased by 10.93%. When the H value is larger than its optimal value, continuing to increase H will reduce the output power of the downstream wind turbine. In addition, by studying the number of SHAWTs, we found that increasing the number of SHAWTs is detrimental to the wake recovery of the upstream turbines. On this basis, this paper also explores the applicability of SHAWT under real wind farm boundary conditions (wind shear, turbulence intensity, and rotational speed). [ABSTRACT FROM AUTHOR]

The need for solar photovoltaic (PV) power forecasting arises due to rapid fluctuations in solar PV output. This variation can cause an imbalance between the demand and generation in a microgrid. This paper attempts to analyze the impact of inaccuracy in predicting PV power generation on the frequency of a standalone microgrid system. For this purpose, two different forecasting models have been studied for two different types of weather conditions, characterized by varying degrees of cloud cover. Moreover, when addressing frequency fluctuations, restoring them to the nominal value becomes imperative, and this is where secondary controllers play a crucial role. This paper further evaluates and compares the performance of three proportional-integrative derivative (PID) controllers for smoothing frequency control. For the considered combinations of two forecasting models and three secondary controllers, the standard deviation (SD) of frequency is the lowest for the long short-term memory (LSTM) forecaster and Particle Swarm Optimization (PSO) – PID (PSO-PID) controller. The corresponding reduction of SD, after replacing persistence: PSO-PID with LSTM: PSO-PID, in combination with |clear: cloudy| day is |28.43%: 32.12%| for overshoot and |11.87%: 18.36%| for undershoot frequency deviation. [ABSTRACT FROM AUTHOR]

Enhancing the aerodynamic performance of airfoils is the key to optimizing the energy harvesting efficiency of rotating machinery such as wind turbines. Motivated by the bowl-shaped outline of the dolphin's fluke during the propulsion process, this paper proposes a local indentation method that generates a concave region on the pressure surface of the airfoil. The NACA 0018 airfoil is selected as the reference airfoil, and two types of treatments are applied near the trailing edge point: rigid deformation and flexible deformation. Based on the grid quantity independence and experimental results validation, the results demonstrate that compared with the original airfoil, the local indentation method can modify the pressure distribution of the indentation section itself and optimize the airfoil's overall aerodynamic performance. The lift coefficient of the whole airfoil increases gradually with the rise in the indentation depth and reaches a stable value eventually. Quantitative results reveal that when the indentation depth D = 0.020c, the lift coefficient of the whole airfoil can increase by up to 26.27%; when the indentation depth D = 0.010c, the airfoil's lift-to-drag ratio reaches the maximum, which is 16.39% higher than that of the original airfoil. When replacing the rigid indentation section with a flexible medium, the fluid flowing over the pressure surface interacts with the flexible medium. The method of local indentation proposed in this paper can provide valuable reference for optimizing the aerodynamic profile of airfoils and improving the energy harvesting efficiency of wind turbines. [ABSTRACT FROM AUTHOR]

An introduction is presented in which editor discusses various reports within the issue, which were presented during the Asia-Pacific Forum on Renewable Energy 2012, including waste oil conversion to diesel-like fuel, impacts of ground source heat pumps, and wind turbines.

To better address climate change and environmental pollution, it is of great practical significance to comprehensively evaluate the impact of low-carbon energy transition policies on carbon emission reduction. This paper conducts a policy quantitative evaluation of low-carbon energy transition policies from three dimensions: policy intensity, policy objective, and policy instrument. On this basis, the ridge regression model is used to analyze the carbon emission reduction effect of policies. The results show that (1) changes in policy quantities mainly cause the evolution of total policy effectiveness. The government's emphasis on low-carbon energy transition has fluctuated. (2) The two policy objectives of optimizing the use of fossil energy and developing a low-carbon concept are less synergistic with other policy objectives and neither has a significant impact on carbon reduction. (3) The government prefers to adopt command control policy instruments, but their actual carbon emission reduction effects are inferior to that of economic incentive policy instruments. These findings can provide a decision-making basis for the government to further formulate and optimize policies. [ABSTRACT FROM AUTHOR]

With the massive access to distributed energy resources, an increasing number of users have transformed into prosumers with the functions of producing, storing, and consuming electric energy. Peer-to-peer (P2P) energy trading, as a new way to allow direct energy transactions between prosumers, is becoming increasingly widespread. How to determine the trading strategy of prosumers participating in P2P energy trading while the strategy can satisfy multiple optimization objectives simultaneously is a crucial problem to be solved. To this end, this paper introduces the demand response mechanism and applies the dissatisfaction function to represent the electricity consumption of prosumers. The mid-market rate price is adopted to attract more prosumers to participate in P2P energy trading. The P2P energy trading process among multiple prosumers in the community is constructed as a Markov decision process. We design the method of deep reinforcement learning (DRL) to solve the optimal trading policy of prosumers. DRL, by engaging in continual interactions with the environment, autonomously learns the optimal strategies. Additionally, the deep deterministic policy gradient algorithm is well-suited for handling the continuous and intricate decision problems that arise in the P2P energy trading market. Through the judicious construction of a reinforcement learning environment, this paper achieves multi-objective collaborative optimization. Simulation results show that our proposed algorithm and model reduce costs by 16.5%, compared to the transaction between prosumers and grid, and can effectively decrease the dependence of prosumers on the main grid. [ABSTRACT FROM AUTHOR]

Charge-while-driving technology is a promising application in the future. A routing approach suitable for hybrid stationary and dynamic wireless charging networks is thus worth investigating. This paper aims to determine the optimal path with minimum generalized travel cost as well as provides charging policy recommendations for electric vehicle (EV) users. A hybrid charging network, including charging stations and wireless lanes, is constructed first. The generalized travel cost is then investigated to help EV users understand the complicated cost components. A dynamic programming algorithm is developed as the solution measure. Numerical experiments show that a higher level of wireless charging lane penetration can significantly reduce generalized travel costs, especially implicit costs such as travel time cost or stopping cost. EVs are more likely to prefer wireless charging modes when the value of the user's time and the cost of stopping is high. The methodology proposed in this study not only provides services to EV owners, such as navigation, but is also a useful tool for administrations wishing to direct incentives to facilitate the transition to more sustainable energy sources, as it quantifies the benefits of wireless charging for different network attributes. [ABSTRACT FROM AUTHOR]

The fluctuation of renewable energy resources and the uncertainty of demand-side loads affect the accuracy of the configuration of energy storage (ES) in microgrids. High peak-to-valley differences on the load side also affect the stable operation of the microgrid. To improve the accuracy of capacity configuration of ES and the stability of microgrids, this study proposes a capacity configuration optimization model of ES for the microgrid, considering source–load prediction uncertainty and demand response (DR). First, a microgrid, including electric vehicles, is constructed. Second, the uncertainty of renewable energy resources and electric demand is handled by Monte Carlo scenario generation techniques and K-means-based scenario reduction techniques. Then, a DR model combining price-based demand response and incentive-based demand response is constructed to achieve a better match between electricity demand and supply. Finally, the results of the ES capacity configuration are determined with the objective of minimizing the total daily cost of the microgrid. The simulation results show that the optimal configuration of ES capacity and DR promotes renewable energy consumption and achieves peak shaving and valley filling, which reduces the total daily cost of the microgrid by 22%. Meanwhile, the DR model proposed in this paper has the best optimization results compared with a single type of the DR model. It is verified through comparative analysis that under a certain proportion of flexible loads, the total daily cost of the microgrid is the lowest when the time-shiftable loads and the curtailable loads are both 10%. [ABSTRACT FROM AUTHOR]

Nuclear-renewable integrated energy systems (IES) consist of a variety of energy generation and conversion technologies and can be used to meet heterogeneous end uses (e.g., electricity, heat, and cooling demands). In addition to supply-demand balance, end-use heat demands usually require heat supply of certain temperature ranges. The effective and efficient utilization of heat produced within an IES is, therefore, a critical challenge. This paper examines design options of an IES that includes heating processes of multiple temperature grades. We investigate a cascaded design configuration, where the remaining residual heat after high-grade heating processes [e.g., hydrogen production through high-temperature steam electrolysis (HTSE)] is recovered to meet the low-grade heating needs [e.g., district heating (DH)]. Additionally, a thermal energy storage system is integrated into the DH system to address the imbalance between heat supply and demand. This paper primarily focuses on the design and modeling of the proposed system and evaluates its operation with a 24-h transient process simulation using a DH demand profile with hourly resolution. The results indicate that the residual heat from the HTSE exhaust is insufficient for the DH demand, and additional topping heat directly from the reactor process steam is needed. Furthermore, the inclusion of thermal energy storage within the DH system provides the necessary balance between thermal generation and demand, thereby ensuring a consistent rated temperature of the DH supply water. This approach helps minimize the control actions needed on the reactor side. [ABSTRACT FROM AUTHOR]

The conventional full-scale wind turbine blade testing method is unable to study the detailed structural response of blades' key subcomponents under complex loads. Therefore, developing an efficient and reliable blade subcomponent testing method is of great significance for investigating the structural characteristics of blades. This paper develops an innovative blade subcomponent test method framework, which is capable of studying the structural response under combined bending and torsion load conditions. A high-fidelity finite element model corresponding to the framework that considers nonlinear factors is established. This study further investigated the influence of boundary conditions on the results. The results showed that the test method proposed in this paper is reasonable, and the experiment based on this method is successfully conducted. The contact nonlinearity and size effects will affect the accuracy of the test results, which can be avoided by adjusting the size of the clamp. [ABSTRACT FROM AUTHOR]

Wind energy plays a crucial role as a clean energy source in the electricity system. The unpredictability of wind power makes it more challenging to put into use in comparison to thermal power generation. Accurate wind power prediction algorithms are of great importance for allocation and deployment of wind power. In this paper, a novel time-series forecasting model, SCINet, is used for short-term wind power forecasting and achieves high forecasting accuracy. Furthermore, the addition of reversible instance normalization (RevIN) to SCINet effectively alleviates the shift problem that arises in time series forecasting tasks. This enhancement further improves the model's forecasting ability. Finally, this paper uses knowledge distillation to get a small model that could speed up the computing and save memory resources. The source code is available at https://github.com/raspnew/WPF.git. [ABSTRACT FROM AUTHOR]

Wind turbine layout design has an important impact on the energy production and economic benefits of wind farms. The wind resource grid data include the realistic wind distributions of the wind farm. Combined with the Jensen wake model, it can be used to calculate the net production considering the wake effect of turbines. Based on the wind resource grid data and taking net energy production as the objective function, this paper proposes a random search algorithm for wind turbine layout optimization. The algorithm couples the random function with multiple optimization parameters and optimizes the wind turbine layout by considering restriction conditions of area and minimum turbine spacings. According to the results of the case study in an actual wind farm, the optimization processes using the proposed algorithm have high calculation efficiency and stability. The sensitivity analysis of parameters indicates that the effect of optimization calculation can be effectively improved by appropriately increasing the turbine coordinate searching range or the number of random operations within one single search. [ABSTRACT FROM AUTHOR]

As the global demand for energy continues to increase, floating photovoltaic (FPV) power is gaining more attention as a promising clean energy source. This paper summarizes the unique advantages of FPV, such as its freedom from land restrictions, higher energy output, and potential integration with other forms of energy. However, FPV also faces challenges due to complex and harsh environmental conditions such as wind, waves, corrosion, and biological fouling. Therefore, the integrated mechanism design and the use of lightweight hydrophobic materials are crucial for ensuring the safety and stability of FPV systems under such conditions. This article also highlights certain key design points and optimization techniques that can improve the structural reliability of FPV systems. With continuous technological advancements and the accumulation of experience, the production efficiency of FPV has improved, leading to the expansion of FPV projects and a reduction in investment costs. As a result, the development of FPV has been accelerating globally, with numerous high-capacity projects being constructed. Building upon the previous literature reviews, this paper provides a concise review of the latest FPV case studies, innovative technologies, challenges in marine environments, economic costs, and market prospects from various perspectives. The primary objective is to encourage further research and application in the field of FPV. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind turbine power is an important means to ensure the stable operation of wind turbines. The traditional wind speed-power curve only shows the relationship between wind turbine power and wind speed, without considering other influencing factors, such as rotor speed and pitch angle, and has certain limitations. Therefore, based on the supervisory control and data acquisition data of a wind farm, this paper proposes a hybrid multivariate prediction model that combines polynomial regression and random forest (RF) to predict wind turbine output power. First, to reduce the difficulty of expert analysis, the maximum information coefficient is used to analyze the correlation between data and select features. Wind speed, rotor speed, pitch angle, and wind direction are considered important and chosen for power prediction modeling. Then the relationship between multiple variables and output power is established through polynomial regression. Finally, the new polynomial features and output power are used to train the RF model to predict the output power. The experimental results show that the predicted wind speed and power diagram shows a band-like distribution, which matches closely with the real one; compared with multiple models such as a single RF model, a polynomial regression model, and a feedforward artificial neural network model, the polynomial-RF model has the highest prediction accuracy. The mean absolute percentage error (MAPE) of the polynomial-RF model prediction is 0.06, and the root mean square error (RMSE) is 114. Compared with the single RF model, the MAPE predicted by the polynomial-RF model is reduced by 14%, and the RMSE is reduced by 6%. It can be seen that the polynomial-RF model proposed in this paper has a good application prospect in the prediction of wind turbine output power. [ABSTRACT FROM AUTHOR]

A preface to selected papers from the International Conference on Solar Energy Photovoltaic held in Bhubaneswar, India published in the May 2013 issue of the "Journal of Renewable and Sustainable Energy" is presented.

China's fast growing economy is inducing various technology dynamics. Much attention and resources are being paid to new energy technologies in China. These eight selected papers are a collection of academic studies on modeling and policy analysis of the diffusion of new energy technologies in China. These studies were all presented during the First International Workshop on 'Diffusion of New Energy Technologies in China,' held from 25-26 March 2011, in Shanghai, China. [ABSTRACT FROM AUTHOR]

The article focuses on the renewable technologies, which were discussed in the three papers presented during the 10th International Congress of the Mexican Society of Hydrogen "Renewable Energies," held from September 27 to October 1, 2010 in Toluca, Mexico. It says that a large solar-derived energy source is represented by biomass. It adds that large amounts of carbon dioxide is produced by the industrial conversion of methane into hydrogen.

In the rural areas of Northwest China, the utilization of clean and renewable energy is deemed a crucial measure for reducing building energy consumption and environmental pollutant emissions. This paper focuses on constructing a simulation platform for a solar-assisted air source heat pump heating system. A rural residential building in Yongshou County, Shaanxi Province, serves as an illustrative example. A novel flexible temperature control method with a feedback controller in sub-area and period is proposed in this paper, alongside the selection of three distinct objective functions aimed at optimizing the heating system. The simulation results indicate an average temperature of 17.0 °C throughout the heating cycle, with a peak temperature of 18.7 °C. Moreover, the solar fraction is measured at 25.11%, underscoring the significance of collector area and heat storage tank volume as primary factors in system design. The results also demonstrate that across various optimization objectives, the life cycle cost optimization scheme yields greater economic benefits, while the target building unit heating cost optimization scheme boasts the shortest static payback period and lowest unit heating cost. Conversely, the solar fraction optimization scheme stands out for its superior environmental benefits. These findings offer valuable insights for the design of heating systems tailored to diverse objectives. [ABSTRACT FROM AUTHOR]

In the context of demand response (DR), formulating rational electricity pricing (EP) and electricity pricing subsidy (EPS) strategies is crucial for the power grid when dealing with a high electricity user (EU), particularly an electrolytic aluminum enterprise (EAE) in an industrial park (IP). In addition, it is difficult to assess the response effectiveness of EU. This paper proposes a method to assess demand response willingness (DRW) by introducing indicators such as demand response economy and demand response potential, while taking into account carbon emission deviation. Then, the EPS is formulated based on the result of the DRW assessment. Second, this paper establishes a two-layer electricity supplier (ES)-EAE game model, in which the ES operates as the leader and EAE operates as the follower. The model takes into account the fluctuation and deviation of loads, constructs utility functions for both the leader and follower, selects dynamic EP scenarios at different time scales, and employs a large-scale global optimization particle swarm algorithm based on cooperative evolution for solving. Finally, the model's effectiveness is validated under three electricity pricing strategies: peak-valley pricing, critical peak pricing (CPP), and real-time pricing (RTP). According to the result of simulations, under the RTP strategy, the DRW of EAE has increased by 12.5% compared to the CPP strategy, and the DR load has increased by 82%. Additionally, there has been some reduction in costs of electricity consumption. This indicates that the ES can effectively guide the EU to reduce peak loads through EP, and the EU can also achieve a reasonable reduction in electricity costs. [ABSTRACT FROM AUTHOR]

Renewable energy sources like wind and solar have increased demand for surplus power capacity. The demand is primarily fueled by the growing impact of forecasting errors associated with these intermittent energy sources. Implementing advanced control methods for automatic generation control (AGC) is essential to integrate wind and solar power with conventional generation sources to balance the power system and reduce reliance on traditional reserves. Therefore, this paper comprehensively overviews solar and wind energy integration in the AGC framework to provide optimal grid ancillary services. Initially, the paper presents an overview of the basic equations used to integrate reserve power from the photovoltaic (PV) system by employing the de-loading strategy. Subsequently, a comprehensive review is conducted on integrating the PV system in AGC strategies to provide grid ancillary services. The study also analyzes the contribution of wind power in AGC services using relevant equations and past practices. The paper presents a real-time dynamic control strategy to optimize the dispatch of the AGC unit by integrating the operating reserves from wind energy systems in conjunction with thermal power systems. The study simulates an 8-bus, 5-machine model using the Dig-SILENT Power Factory. The findings reveal that utilizing operating reserves from wind power can significantly reduce large-scale forecasting errors in massively renewable energy resources (RES) integrated power systems, thereby ensuring the necessary system operational security and reducing the reliance on traditional generating units. [ABSTRACT FROM AUTHOR]

The electricity grid with high-penetration renewable energy sources has urged us to seek means to solve the mismatching between electricity supply and demand. Energy storage technology could accomplish the energy conversion process between different periods to achieve the efficient and stable utilization of renewable energy sources. In this paper, a hybrid energy storage system based on compressed air energy storage and reversible solid oxidation fuel cell (rSOC) is proposed. During the charging process, the rSOC operates in electrolysis cell (EC) mode to achieve the energy storage by converting the compression heat to chemical fuels. During the discharging process, the cell operates in fuel cell mode for electricity production and the gas turbine is conducted to recover the waste heat from cell. To evaluate the comprehensive performance of the proposed system, the energy, exergy, and exergoeconomic studies are conducted in this paper. Under the design condition, the results indicated that the proposed system is capable of generating 300.36 kW of electricity and 106.28 kW of heating; in the meantime, the energy efficiency, exergy efficiency, and total cost per unit exergy of product are 73.80%, 55.70%, and 216.78 $/MWh, respectively. The parametric analysis indicates that the increase in pressure ratio of air compressor, steam utilization factor of rSOC stack under EC mode and current density of the rSOC stack under EC mode reduce exergy efficiency and total cost per unit exergy of product simultaneously, while the increment of operating pressure of rSOC stack under FC mode enhances the exergy efficiency and decreases total cost per unit exergy of product. The multi-objective optimization is carried out to improve the comprehensive performance of the proposed system, and the results expressed that the best optimal solution has the exergy efficiency and total cost per unit exergy of product of 65.85% and 187.05 $/MWh, respectively. Compared to the basic operating condition, the improvement of the proposed system has led to the maximum enhancement of 20.32% in exergy efficiency and 18.60% in total cost per unit exergy of product. [ABSTRACT FROM AUTHOR]

In order to overcome the limitations of traditional clean energy utilization methods, this paper proposed an innovative technical solution for a combined heating system that cleverly integrated solar, wind, and geothermal energy to achieve complementarity and synergized among them, thereby ensuring stable and efficient energy utilization. First, a comprehensive mathematical model was developed for the entire heating system, encompassing solar thermal subsystem, geothermal subsystem, wind power generation subsystem, and a second-stage reheating subsystem. Subsequently, Ebsilon simulation software was utilized to cleverly couple these subsystems together, with corresponding boundary conditions set to ensure the overall efficiency and stability of the system. Based on meteorological data and geothermal resource parameters from a typical heating season in Zhengzhou, Henan Province, China, this paper thoroughly analyzed the variations in key performance indicators such as the photothermal conversion efficiency of solar thermal subsystem and the heating capacity of geothermal subsystem. This provided valuable insight for optimizing the design of heating system. The results indicated that during the daylight hours of the heating season, both the photothermal conversion efficiency and the heat supply from the solar thermal subsystem exhibited an increasing trend as solar radiation increased. Among them, the photothermal conversion efficiency peaked at 76.013%, while the maximum heat supply output reached 40.311 kW. When solar direct radiation was relatively weak, the system primarily relied on the heat release process of the thermal storage tank to maintain heating, with a minimum heat supply of 27.268 kW. During nighttime hours of the heating season, the geothermal subsystem dominated the heating process, with a maximum heat supply of 125.556 kW. Additionally, for every 5 °C increased in geothermal water temperature, the heat supply from the geothermal subsystem increased by an average of 6.553 kW, demonstrating excellent heating response performance. Therefore, the integrated clean heating system that combines solar, geothermal, and wind energy not only significantly improves the utilization efficiency of clean energy but also enhances the heating stability of the integrated clean energy coupling system to a certain extent. The clean heating technical solution proposed in the paper had a theoretical total heating capacity of 19 680 kW during the heating season. When converted, this equates to a substitution of 6.9 tons of standard coal, resulting in a reduction of carbon dioxide emissions by 17.94 tons. This demonstrates the considerable cleanliness and environmental benefits of the proposed heating system. This study provides a valuable reference for the engineering application of renewable energy in the field of clean heating. [ABSTRACT FROM AUTHOR]

Hybrid compressed air energy storage (H-CAES) system can effectively reduce the heat loss in the compression process, which is one of the important methods to solve the problem of renewable energy volatility. Based on the H-CAES system that combines adiabatic compression and isothermal compression, this paper proposes a liquid piston compressor arrangement to adapt to the input power fluctuation and proposes a power allocation calculation method to solve the adiabatic compression and isothermal compression in the H-CAES system, with an emphasis on analyzing the impact of the participation of renewable energy sources on the system performance. In addition, the H-CAES system is placed under wind-solar-storage conditions, and scheduling strategies judged by time-sharing electricity price are proposed for different scenarios to explore the actual operating effects of the H-CAES system. The results show that the arrangement of liquid piston compressors from large tanks with fewer groups to small tanks with more groups can better adapt to the power change while maintaining a better isothermal compression effect. On the basis of using the power allocation calculation method proposed in this paper, it is found that higher compressor outlet pressure and lower storage pressure can improve the system efficiency and economic benefits. The system is able to achieve 59.71% efficiency and 0.2261 annual return on investment at the compressor outlet pressure of 4 MPa. Finally, it is demonstrated that the combined operation of H-CAES and wind energy can serve to increase the operating income of the power plant, and a maximum of 8909.236 yuan in daily electricity generation revenue can be achieved. [ABSTRACT FROM AUTHOR]

Offshore photovoltaic power stations (OPVPS) greatly help solve energy problems and land resource scarcity. A crucial phase of the OPVPS project lifecycle is site selection. To select the optimal location of OPVPS with many difficulties such as the uncertainty of the environment, the compensating relationships among criteria, and the different attributes of the alternatives, this paper proposed a fuzzy multi-criteria decision-making framework based on Pythagorean fuzzy Elimination et Choix Traduisant la Realité-III (ELECTRE-III) method. First, the comprehensive criteria system for siting OPVPS was constructed, which included veto and evaluation criteria. Second, the Pythagorean fuzzy set was used to express the uncertain evaluation of experts. Third, considering the actual situation that experts had different experiences and abilities, this paper proposed a novel expert weighting method. Fourth, entropy weighting method, best–worst method, and combination weighting of game theory were introduced to calculate the criteria weights. Fifth, considering the compensation between criteria, ELECTRE-III was used for ranking. Finally, to verify the applicability and robustness of the proposed framework, a China case study was conducted; the results showed that Haizhou Bay is the best alternative. [ABSTRACT FROM AUTHOR]

Recently, the renewable energy supply chain has gained momentum as a vital component for clean energy, contributing to a sustainable future for the world. The development of the renewable energy supply chain is directly linked to circular economy targets and practices. This paper presents a systematic literature review of studies that analyze the roles, functions, and application objectives of the circular economy in the renewable energy supply chain. The aim of this review is to investigate key elements of renewable energy supply chains in the circular economy, shedding light on the state of research on the progress of sustainability in renewable energy supply chains, and guiding future research. Mainly, the systematic literature review demonstrates its findings by identifying and mapping the collated literature to reviews and applied studies that support and drive the renewable energy supply chain under the circular economy. This review paper is handled with a systematically descriptive, bibliometric, and content analysis hybrid approach, framed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology on the research topic. Findings show that circular economy applications have gained momentum in the renewable energy supply chain; nonetheless, some deficiencies persist. Redesign-based applications and design for recycling concepts encompass a significant gap in the renewable energy supply chain. Moreover, the current literature lacks outcomes on the applicability of circular economy 10R targets and the interrelationship and scalability of sustainable development goals in the renewable energy supply chain. [ABSTRACT FROM AUTHOR]

Recently, the large-scale integration of power electronic-based renewable energy power plants has changed the operation and response mechanism of the power system, resulting in several emerging oscillation issues that have seriously been threatening the system's stability. It helps us to recognize the similarities and differences among the triggering causes and formation mechanisms of oscillation scenarios. Following several typical oscillation events in the real world and the timescale decomposition method, this paper comprehensively reviews the wide-bandwidth oscillation study from the aspects of the analysis methods, possible cause, mechanism, and mitigation solution. The paper provides a perspective to classify the oscillations in the modern power systems on the basis of the oscillation frequency and the main oscillation module. This classification framework involves not only emerging oscillations in the power system with large-scale renewable energy sources integration but also includes typical oscillations in traditional power systems. It also systematically presents the relative relationship, development process, and inner influence between emerging oscillations and typical oscillations. Based on this review, the future research is suggested to focus on the relationship between different analytical methods or oscillation mechanisms, as well as the stability risk assessment of hybrid alternating current and direct current power systems. [ABSTRACT FROM AUTHOR]

In response to the shortcomings of existing image detection algorithms in the early damage detection of wind turbine blades, such as insufficient applicability and unsatisfactory detection results, this paper proposes an improved DINO (DETR with improved denoizing anchor boxes for end-to-end object detection) model for wind turbine blade damage detection called WTB-DINO. The improvement strategy of the DINO model is obtained by collecting and analyzing unmanned aerial vehicle (UAV) daily inspection image data in wind farms. First, the lightweight design of DINO's feature extraction backbone is implemented to meet the requirement of fast and effective video inspection by drones. Based on this, the Focus down-sampling and enhanced channel attention mechanism are incorporated into the model to enhance the feature extraction ability of the Backbone for damaged areas according to the characteristics of wind turbine blade images. Second, a parallel encoder structure is built, and a multi-head attention mechanism is used to model the relationship between samples for each type of damage with uneven distribution in the dataset to improve the feature modeling effect of the model for less-sample damage categories. Experimental results show that the WTB-DINO model achieves a detection precision and recall rate of up to 93.2% and 93.6% for wind turbine blade damage, respectively, while maintaining a high frame rate of 27 frames per second. Therefore, the proposed WTB-DINO model can accurately and in real-time classify and locate damaged areas in wind turbine blade images obtained by UAVs. [ABSTRACT FROM AUTHOR]

Over the past decade, significant progress in solar forecasting has been made. Nevertheless, there are concerns about duplication, long-term value, and reproducibility; this is referred to as the "solar forecasting bubble." There is an urgent need to reconcile and improve the current solar forecasting research practice. This discussion paper proposes a so-called "ROPES" guideline that describes the desirable characteristics of future solar forecasting studies. In short, ROPES stands for reproducible, operational, probabilistic and/or physically based, ensemble, and skill. This set of characteristics is intended to facilitate comparison, comprehension, and communication within the solar forecasting field and speed up its development. Compliance with ROPES is evaluated on 79 solar forecasting references published during 2017 January to 2018 July in 6 Elsevier energy journals. Although most current papers fall short of complying with ROPES, evidence suggests that a consensus is forming. [ABSTRACT FROM AUTHOR]

In accordance with the topographic features and other relevant factors, data affecting wind power generation in engineering practice may be difficult to obtain, and three short-term wind power forecasting methods are proposed based on incomplete data. Most wind power forecasting studies are based on wind speed data, but wind power forecasting needs more influential factors in addition to wind speed data, and it is difficult to obtain these data in actual production and life. Therefore, this paper proposes the wind power forecasting under the condition of incomplete data. First, this paper makes theoretical analysis on multi-state space Markov chain wind power forecasting, backpropagation (BP) neural network wind power forecasting, and genetic algorithm (GA)-BP neural network wind power forecasting, and the corresponding wind power forecasting models are constructed. Second, with the actually measured data of a wind farm in the Heilongjiang province as the research object, the historical data are preprocessed first and then imported into three wind power forecasting models for simulation, and the curves of relative error and absolute error of total wind power forecasting in this area are obtained. Finally, the simulation results of three methods based on incomplete data are analyzed and evaluated. The results show that under the condition of incomplete data, the maximum error of the GA-BP neural network wind power forecasting model improved by the genetic algorithm is reduced from 6.8% to 1.6%, and the forecasting accuracy is greatly improved. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the author discusses various reports within the issue on topics including low-cost photovoltaic solutions, enhancing photovoltaic efficiency, and critical grid parity needed for competitive electricity.

The superiorities of renewable energy, such as wind and solar energy, have promoted the development of microgrids (MGs) and multi-microgrids (MMGs). However, how to coordinate the scheduling and transactions of MMGs with multi-timescale is still an important issue. This paper presents a scheduling and trading strategy of MMGs with two time-scales: day-ahead and intra-day. In the day-ahead scheduling stage, a MMG system with peer-to-peer connection is considered. Based on the idea of distributed updating parameters and adaptive selecting values in Alternating Direction Method of Multipliers (ADMM), an accelerated ADMM algorithm named improved adaptive accelerated ADMM (IAA-ADMM) is proposed, which is modeled and solved in a distributed manner. In the intra-day scheduling stage, based on the day-ahead scheduling, this paper utilizes stochastic model predictive control (SMPC) to optimize the intra-day model, which helps address the uncertainties of wind, solar, and load forecasting. The effectiveness of the proposed approach is validated using numerical examples. The results show that the IAA-ADMM provides higher stability and faster convergence and facilitates easier implementation. The SMPC shows higher economic performance and has a higher application potential. [ABSTRACT FROM AUTHOR]

In order to achieve sustainable development goals, China has further increased its goal of reducing carbon intensity and has made digitalization an important support for sustainable development. However, the impact of digitalization on carbon intensity reduction is still unclear. In this context, this paper first evaluates the digitalization level of 30 provincial regions in China and then constructs a spatial Durbin model for two stages, 2012–2015 and 2016–2019, so as to explore the spatial spillover effects of carbon intensity in different stages and the important roles of digital infrastructure and digital inputs in carbon intensity reduction. The main findings are as follows: (1) the current digitization level of each province in China is widely disparate, with the region showing a high level in the east and a low level in the west; (2) carbon intensity reduction has a significant spatial spillover effect, as shown by a 1% reduction in local carbon intensity and a 0.21% reduction in neighboring regions; and (3) digitalization has a more significant positive impact on the reduction in carbon intensity in stage 2. The research results are strong demonstration that digitalization drives sustainable development. [ABSTRACT FROM AUTHOR]

The research on latent heat storage technology is beneficial for the large-scale popularization and application of energy storage technology. In order to solve the difficulties in the latent thermal energy storage (LTES) technology, numerical studies of the solid–liquid phase-change process of LTES units using the enthalpy-porosity method have become research hotspots. Among them, the importance of studying the mushy-zone parameter has been neglected. In this paper, a two-dimensional numerical model is created based on the enthalpy-porosity method to investigate the effects of different mushy-zone parameters within a wide range [104–108 kg/(m3·s)] on the melting and solidification processes of the spherical phase change material capsule. A comprehensive examination of the fluid flow and heat transfer characteristics during two phase-change processes is conducted. Meanwhile, the morphology of the solid–liquid phase boundary and the evolution of the mushy zone under different mushy-zone parameters are discussed in detail. It is worth mentioning that a new analytical perspective is innovated by proposing the "eccentricity phenomenon," and the eccentricity law is further explored. The results show that the influence mechanisms of the mushy-zone parameters on the melting and solidification processes differ greatly. This paper emphasizes the significance of exploring the mushy zone and provides adequate guidance for future simulation studies to determine the mushy-zone parameter. [ABSTRACT FROM AUTHOR]

A renewable portfolio standard is implemented to promote the development of renewable energy at a minimum cost through tradable green certificate market mechanism. Formulating a scientific and feasible renewable energy quota allocation scheme helps RPS function smoothly and optimize resource allocation. This paper proposed a bi-level programming model combined with entropy weight method to allocate renewable portfolio standard quotas with provincial heterogeneity and stakeholders' behavior, and an optimized quota allocation scheme among China's 30 provinces in 2020 was obtained. By comparing with the government's issued scheme, the following were the results under the optimized scheme: (1) Quotas in most provinces have increased, and the responsibility for renewable electricity generation is shared with the provinces with developed economy and well-constructed transmission facilities, where electricity producers can meet the quotas by purchasing tradable green certificate. (2) Quota allocation has positive effects on energy, economy, and environment. Specifically, the non-hydro renewable electricity generation increased by 43.8%, the non-hydro renewable electricity producers' profit increased by 18.4%, and the environmental pollution cost reduced by 27.9%. (3) Quota allocation equity measured by the environmental Gini coefficient increased by 14.3%. Based on these findings, some policy implications related to quota allocation and renewable portfolio standard's institutional arrangement have been put forward. [ABSTRACT FROM AUTHOR]

The marine environment is experiencing significant impacts due to increased shipping traffic. The maritime industry must develop a low-carbon shipping strategy to comply with the increasingly strict emission regulations. This paper comprehensively reviews various decarbonization technologies, including navigation systems, hull design configuration, propulsion and power systems, and alternative fuels. By comparing a wide range of technologies in terms of their emission reduction potential and economic feasibility, this paper is intended to provide a full picture of alternative methods for future green shipping. Alternative fuels and hybrid power systems are found to have high potential for reducing carbon emissions and enhancing sustainability. The type of ship, its design configurations, and operation parameters affect the performance of optimal weather routing systems. With the current maritime policy and technological development, the transition from traditional marine fuel to liquefied natural gas can act as a temporary solution and provide significant decarbonization for maritime transportation. The emission reduction potential can be further enhanced with alternative fuels combined with hybrid power systems with high control flexibility. [ABSTRACT FROM AUTHOR]