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A traditional system-level thermoelectric model requires enormous computing power and time for simulation analysis, especially when multiple optimization algorithms are combined. This paper initially proposed a comprehensive surrogate mode for the accurate modeling, field analysis, and fast optimization of thermoelectric generators. The surrogate model is made up of an artificial neural network (ANN) and a conditional generative adversarial network (cGAN), which can achieve a prediction accuracy of 97 % and a structural similarity (SSIM) of 0.954. Using a twisted tape annular thermoelectric generator (TT-ATEG) as an example, the generalization capability of the comprehensive surrogate model is demonstrated by studying the change of the twisted tape geometry parameters on the output performance and field distribution of the annular thermoelectric generator. The combination of the surrogate model and NSGA-II achieves fast optimization of key parameters under different working conditions, and the computational efficiency is improved by 99.97 %. Meanwhile, the cGAN part can predict the pressure and heat flow fields within 5s to provide intuitive visual feedback. The trained surrogate model requires less computer arithmetic power compared to the traditional multi-physics field simulation software. The successful application of the comprehensive surrogate model in this work provides a new solution idea and an optimization method for system-level TEG. • A comprehensive surrogate mode for performance prediction and optimization. • Surrogate Model Integrates ANN for Data-Based and cGAN for Image-Based Modeling. • Data-based performance computational efficiency improved by 99.97%. • Rapid physical field visualization provided within 5 seconds. [ABSTRACT FROM AUTHOR]

With the development of the energy and carbon markets, it has become a trend for multiple virtual power plants (MVPP) that aggregate distributed resources from different regions to participate in market trading through cooperative operation. In order to study the energy-carbon interactions between VPPs and the supply-demand interactions for MVPP and users, this paper introduces a load aggregator (LA) and establishes a cooperative alliance consisting of MVPP and LA. Firstly, a carbon emission allowance allocation and transaction mechanism for MVPP is proposed based on the modified Shapley value method, which considers the impact of energy interactions within the alliance on the carbon emissions. Then, a cooperation model for the MVPP-LA alliance is established based on Nash bargaining theory, and it is decomposed into two subproblems of the maximal alliance benefits and the optimal benefit distribution. In terms of benefit distribution, this paper modifies the bargaining mechanism based on the contribution of each VPP to the alliance benefit. A dynamic penalty factor-based alternating direction method of multipliers (DP-ADMM) is used to solve the two subproblems in a distributed manner. Finally, the validity of the proposed trading mechanism, allocation method and solution algorithm are verified through several cases. • A contribution-based bargaining model is proposed for a fair benefit distribution. • A modified CEA allocation mechanism for MVPP cooperation is proposed. • A combined carbon trading mechanism is developed. • A DP-ADMM is adopted to solve the cooperation problem. [ABSTRACT FROM AUTHOR]

Integrated energy system plays a crucial role in achieving low carbon emissions while supplying multiple loads. As the energy Internet continues to evolve, integrated energy system, acting as a cell-level prosumer, exhibits intricate coupling characteristics between multiple energy flows and carbon flows. This paper presents a flexible planning method based on a unified energy-carbon coupled operation model. It can realize blank-paper planning under multiple energy-carbon flow constraints, that is, jointly optimize equipment configuration and capacity size from scratch. The proposed method combines clustering algorithms and Latin hypercube sampling to generate the operational scenarios for renewable resources and energy loads, enhancing the representativeness and reliability of planning solutions. The results demonstrate that the proposed method can effectively meet the requirements of electricity, cooling and heat loads under different seasonal scenarios. Furthermore, it elucidates the corresponding carbon emission distribution across distinct energy flows of the planned system. Compared with the conventional fixed equipment connected planning method, the more flexible blank-paper planning method can achieve annual economic cost savings of about 7.5 % and reduce annual carbon emissions by 6.7 %. • A unified multi-energy-carbon flows operating-planning model is constructed. • A blank-paper planning for equipment configuration and capacity size is implemented. • An operating scenarios is selected to improve the reliability of planning solutions. [ABSTRACT FROM AUTHOR]

Against the backdrop of addressing the dual challenges of tightening energy constraints and carbon emission reduction, this paper, building upon the construction of an urban-level Energy Trilemma Index, employs a Difference-in-Differences (DID) model to assess the impact of carbon emission right trading policy (CERTP) on the energy trilemma (ET). Key findings include: Firstly, compared to non-pilot areas, the ET in CERTP pilot areas decreased by 1.86 %; the policy's impact persists and is even greater in the long term, reaching 2.84 %. Secondly, CERTP stimulates green technological innovation, improves energy consumption structures, and alleviates ET levels. Furthermore, the study shows that CERTP, as a market-driven energy-environmental strategy, is further enhanced by the regulatory effects of marketization and government intervention. Lastly, the policy's impact is more pronounced in eastern China, resource-based cities, regions with stringent environmental regulations, and areas with high carbon prices. Based on these findings, the paper proposes a series of policy insights, not only aiding in understanding the causal relationship between CERTP and ET but also providing valuable references for designing carbon trading mechanisms to address ET. Importantly, the conclusions offer beneficial guidance for China's energy structure optimization, ensuring energy security and sustainable development goals. [Display omitted] • The energy trilemma index levels of Chinese cities have been calculated. • The impact of carbon emission right trading policy on China's energy trilemma has been analyzed, with a discussion on the short-term and long-term policy effects. • Two intermediary channels through which carbon emission right trading policy mitigates the energy trilemma have been discussed: green technological innovation and the energy consumption structure. • The positive moderating effects of marketization levels and government intervention have been analyzed. • The inhibitory effect of carbon emission right trading policy on the energy trilemma is more pronounced in regions with high carbon prices, in eastern China, resource-based cities, and areas with strict environmental regulations. [ABSTRACT FROM AUTHOR]

The two-phase impulse turbine (TPIT) can improve the efficiency and economy of various thermal systems, because it can directly convert the internal energy of thermal fluids into the shaft power of the turbine with no needs of additional low-temperature sub-systems. However, current efficiency of reported TPITs is much lower than gas or water turbines, due to unknown characteristics of the two-phase flow in TPITs. This paper comprehensively analyzed the performance characteristics of a TPIT, which was used in the refrigeration system. CFD methods were employed to analyze the two-phase flow based on flashing models and validated with experimental results. Three average methods were used to derive the Euler power and compared with the output power derived by the torque on each blade, while the average method based on the modified factor can evaluate the Euler power accurately. The non-uniform flow caused by the nozzle position was significant, while the liquid film near pressure side and the shroud were also three dimensional and affected by wall effects. The liquid layer on the shroud was related to the negative torque on blade tips. Results presented in this paper are constructive for designing efficient thermal systems without additional energy recovery devices. • Numerical models for simulating R134a two phase impulse turbines were built. • Impact of nozzle position on non-axisymmetric distribution of torque was quantified. • Negative torque near the blade tip was found to be caused by the liquid layer. [ABSTRACT FROM AUTHOR]

Ensuring the optimal performance of Heating, Ventilation, and Air Conditioning (HVAC) systems is paramount for achieving energy efficiency. This paper investigates the application of deep reinforcement learning algorithms in HVAC system control, aiming to identify the most suitable Q-network structure for optimizing HVAC systems and comparing the performance of Deep Q-learning (DQN) and Double Dueling Deep Q-learning (D3QN) algorithms. Initially, this paper evaluates and analyses existing literature to perform a normalization treatment on the state space. Through systematic simulation and rigorous data analysis, the impact of the Q-network structure on the efficacy of the DQN and D3QN algorithms is evaluated, resulting in the proposal of specific values for the Q-network structures within these two algorithms. Subsequently, comparisons are drawn on the optimization effectiveness, stability and reliability of these algorithms across diverse engineering projects. Results highlight the superiority of the D3QN algorithm over the DQN algorithm regarding both optimization effectiveness and stability across all evaluated projects. The proposed efficient Q-network structure comprises two hidden layers, with 64 and 12 neurons respectively in each layer. The findings of this paper are crucial in providing insights for HVAC systems control optimization using reinforcement learning and pave the way for advanced research and applications in the future. • This paper evaluates DRL algorithms for HVAC control, focusing on balancing Q network complexity and generalization. • A two-hidden-layer Q network with a specific neuron combination (64,12) is found optimal for both DQN and D3QN. • D3QN consistently excels over DQN in optimization performance, stability, and reliability across projects. • The advantages of D3QN over DQN amplify with the increased complexity and scale of HVAC systems. [ABSTRACT FROM AUTHOR]

Measuring energy efficiency is important to achieve the goal of sustainable economic development. This paper adopts the Malmquist index to measure the trend of regional total factor energy efficiency. This paper analyses the spatial aggregation characteristics of TFEE and the impact mechanism of industrial transfer and technology diffusion on TFEE convergence from a dynamic perspective. The results of the study show that there is a large gap in TFEE between regions in China, with a spatial characteristic of "high in the south and low in the north". In 2006–2021, the convergence rate of TFEE shows a slowing down trend. Industrial transfer has a negative effect on TFEE. The impact of technology diffusion on TFEE is different in different periods. The impact of researchers, technology market, technology introduction, and import-export on TFEE have significant differences. The convergence rate of the south is higher than that of the north. The impact of industrial transfer and technology diffusion on TFEE in the two parts is different. Policymakers should implement differentiated energy policies based on the characteristics of the industrial structure and technological development of each region. They should actively guide technological innovation, promote industrial diversification, and build a clean and efficient energy system. • TFEE is assessed in China based on the Malmquist index. • China's inter-regional TFEE differences first increased and then decreased. • Analyze the mechanism of industrial transfer and technology spillover on TFEE. • Convergence characteristics of TFEE are heterogeneous between the North and South. [ABSTRACT FROM AUTHOR]

This paper presents a novel distributed multi-energy coupled system that combines solar PV, nuclear power, and energy storage systems to address the power supply challenges in remote regions. Through parameter analysis and multi-objective optimization, the study aims to minimize the need for frequent reactor adjustments during system operation. The key findings indicate that by regulating the rotational speed of the main-compressor, it is possible to meet the power load requirements for a single nuclear power system. The battery capacity and the number of PV panels have a significant impact on the adjustment frequency of the reactor and the power output of the nuclear power system, respectively. The optimal battery capacity is determined to be 183 kWh, while the optimal number of PV panels is 327. These configurations result in an average power output of 20.86 kW for the PV system and 3458.28 kW for nuclear power system. To maximize the utilization of PV energy and minimize reactor adjustments, an energy dispatch strategy is proposed. With the optimal configuration, the adjustment frequency of the reactor decreases to 339 and 276 during the winter and summer months, respectively. Overall, this paper offers a feasible configuration and energy dispatch method for regional power supply. • Power load tracking of nuclear and solar-based distributed system is analyzed. • Off-design performance and regulation performance analysis are carried out. • Configuration optimization of PV panel number and battery capacity is performed. • A dispatch strategy to minimize adjustment frequency of reactor is proposed. [ABSTRACT FROM AUTHOR]

The transition from conventional energy to clean renewable energy is becoming a global trend, and related issues are attracting widespread attention. Given the possible time-varying energy transition process, this paper proposes a dynamic signed network topology approach for empirical analysis, which is then applied to evaluate the transition from conventional energy to clean renewable energy in G7 countries. The research findings indicate a discrepancy between the positive and negative spillover effects of conventional and clean renewable energy consumption in G7 nations. The transition from conventional energy on clean renewable energy involves alternating positive and negative spillover effects. On average, oil exhibits a positive spillover effect on clean renewable energy consumption. However, the spillover effect of natural gas on clean renewable energy consumption presents an asymmetric relationship across the G7 countries. • This paper presents a symbolic network topology approach. • The approach is applied to the case of energy transitions in G7 countries. • The study employs a historical decomposition methodology. • The study examines the dynamic transition from traditional energy sources to clean and renewable energy. • Based on extracted policy implications led transitioning from dirty energy to clean energy dependency. [ABSTRACT FROM AUTHOR]

Waste-to-energy (WTE) is a technology that converts inexhaustible waste into electricity, thus significantly alleviating energy crisis. Integrating hydrogen storage into WTE provides an effective way to store and utilize surplus electricity from WTE on-site (by producing hydrogen). Additionally, integrating power-to-gas (P2G) into WTE provides an effective way to reduce CO 2 emission from WTE on-site (by producing natural gas). To this end, a novel waste-driven renewable energy system (WRES), which is consisted of WTE, hydrogen storage, and P2G, has been proposed. However, the uncertain waste supply and electrical load markedly interfere with WRES operation. Meanwhile, when WTE, hydrogen storage, and P2G belong to different agents, the collaborative benefit from WRES operation should be rationally allocated among agents for system sustainability. This paper endeavors to achieve WRES scheduling and collaborative benefit allocation for WTE, hydrogen storage, and P2G under uncertainties. Firstly, a temporal relevance based distributionally robust optimization model is proposed for WRES scheduling under uncertainties, in which the possible range of the joint distribution for uncertainties is depicted by data covariance matrices involved ambiguity set. Secondly, collaborative benefit is allocated according to WTE, hydrogen storage, and P2G contributions, in which Gauss-Legendre quadrature formula is integrated with Aumann-Shapley value method to reduce calculation complexity. Finally, simulation results show that 1) the proposed scheduling model guarantees the economic and stable operation of WRES under uncertainties. 2) after considering the temporal relevance, WRES operation benefit is 8.35 % higher, which indicates that the proposed scheduling model has superiority in decision conservatism by introducing temporal relevance to remove impractical distributions in ambiguity set. 3) the proposed allocation model rationally distributes the collaborative benefit according to contributions, and presents lower calculation complexity, e.g., the calculation time is 95.52 % lower than the Shapely value method. This paper provides policy insights to promote widespread application and sustainable operation of WRES. • Collaborative scheduling and benefit allocation are studied for WRES. • Temporal relevance based DRO model is proposed for collaborative scheduling. • Aumann-Shapley value method plus Gauss-Legendre quadrature for benefit allocating. • The proposed scheduling model can reduce conservatism and resist uncertainty. • The proposed allocation model can reduce calculation complexity and keep fairness. [ABSTRACT FROM AUTHOR]

This paper uses the bootstrap rolling-window Granger causality test to investigate the relationship between U.S. monetary policy uncertainty and green bond. The method addresses the limitations of ignoring the instability of coefficients and the time-varying relationship between the variables in previous literature. The results find both insignificant and significant relationship in the full period. One explanation of the insignificant relationship is that the changes of investor sentiment influence their judgment regarding to the monetary policy uncertainty and green bond, and thus the significance of the relationship between the variables. Besides, this paper finds the significant inter-relationship is time-varying. Specifically, in the sub-period when the market sentiment is low, the rising monetary policy uncertainty influences green bond price negatively. Meanwhile, the unexpected changes of green bond may also have both positive and negative effects on monetary policy uncertainty in different sub-periods. Thus, this paper proposes that the impact mechanism between the variables is effective when the investors are irrational. Furthermore, the findings may provide valuable implications for investors and governments, including adjusting investment strategy when the monetary policy is unclear, increasing monetary policy transparency, and monitoring speculative activity in green bond market. In addition, it is necessary to emphasize that the validity of the findings is limited to the U.S. and the period when investors' sentiment experience large fluctuations. Finally, future research may consider expand the analysis to the green stock, as it is another important channel for renewable energy investment. • The inter-relationships between monetary policy uncertainty and prices of green bonds are examined. • The rolling-window Granger causality method is applied. • The results find both insignificant and significant relationship. • The rising monetary policy uncertainty may influence green bond prices negatively. • The green bond may have a time-varying impact on monetary policy uncertainty. [ABSTRACT FROM AUTHOR]

To better qualify the ability of nuclear power to contribute to the net-zero emissions transition, the nuclear power sector needs to understand its sensitivity to hydro-climatic and anthropogenic stresses at the local level. While current approaches based on statistical modelling can be used to assess climate change impacts on electricity networks at a global or national level, they are limited when extrapolated to regulatory thresholds at a local level. As part of a series of two papers aimed at assessing the impact of climate change on site-specific nuclear power outages, this paper presents a modelling chain for estimating hydro-climatic-policy scenarios for nuclear power generation. The second one will implement the modelling chain in context of climate change, based on downscaled IPCC scenarios, and applies the method to two contrasted French nuclear power plants. The modelling framework (power outages modelling bias of +7.0 %) appears as a strong basis for investigating the impact of climate change on nuclear power outages in terms of frequency and magnitude. Such work could inform on the evolving sensitivity of the nuclear fleet to support the energy grid, to potentially optimize its management, and to define water-energy nexus levers to better anticipate the effects of climate change. • Transferable methodology to evaluate the power outages for a nuclear power plant under a hydro-climatic policy scenario. • Improved understanding of nuclear plant sensitivity, considering local constraints. • Ability to assess cumulative sensitivity of current or planned nuclear fleet. [ABSTRACT FROM AUTHOR]

The instability of renewable energy generation seriously undermines the safe and stable operation of the power system and contributes to the wastage of power resources. Against this background, this paper proposes a hybrid renewable energy system (HRES) coupled with photovoltaic/wind/battery/hydrogen. By considering the minimization of average annual cost as the upper-level objective and the minimization of power shortage and excess generation as the lower-level objective, a bi-level optimization model is constructed. Based on the scenario simulation, this paper analyzes the optimal configuration of HRES under different technology development levels and different power curtailment rate (PCR) constraints. The results indicate the following: (1) At the current technology level, the high installed proportion of wind power and the low cost-effectiveness of energy storage systems enable the high PCR. (2) Increasing the proportion of photovoltaic power generation and expanding the installed capacity of battery storage and hydrogen storage can effectively reduce the PCR of the power system. (3) As technology advances, the growth in levelized cost of electricity (LCOE) driven by declining PCR constraint will diminish, making it possible to fully utilize renewable energy sources. • A coupled photovoltaic-wind-battery-hydrogen energy system is constructed. • A bi-level capacity configuration optimization model is developed. • The optimal configuration at various technology levels is studied. • The impact of the power curtailment rate constraint is considered. • Developing photovoltaic and energy storage could reduce power curtailment rate. [ABSTRACT FROM AUTHOR]

Desiccant materials have an important impact on the dehumidification performance of desiccant-coated heat exchanger (DCHE). In this paper, a desiccant-coated heat exchanger based on a composite desiccant material of metal organic frameworks (MOFs) and silica gel was proposed. The coating was experimentally prepared and tested for its hygroscopic performance under different binder types (PVP, PVA, and PVB), different binder mass concentrations, and different MOF mass concentrations in the composite adsorbent. Further simulations were performed to investigate the effects of key structural and operational parameters on the performance of DCHE. Experimentally, the optimal hygroscopic performance of the composite adsorbent was obtained at 15 wt% PVP and 20 wt% MOF, which was 51.71 % higher than that of silica coating, and the cost of the composite adsorbent drops 78.04 % by comparing to the pure MOF for laboratory fabrication, and based on the novel economic efficiency index proposed in this paper, the mass concentration of the MOF should not exceed 32.8 wt%. Simulation results showed that under the conditions as follows: 1.2 mm (fin spacing), 25 mm (fin height), 0.28 mm (coating thickness), 25 °C (cooling water temperature), and 0 % (initial moisture content), the corresponding moisture removal capacity (MRC) and of DCHE was 6.740 g/kg, which was 149.63 % higher than that of silica coating. • A composite coating is fabricated using silica/Mil-100(Fe). • The fabrication method and mixture percentage are optimal selected. • Multiple evaluation indexes are introduced to estimate the comprehensive performance. • The effects of structural and operation related parameters are clarified. [ABSTRACT FROM AUTHOR]

Although China's energy consumption is very high, foreign demand has become crucial factor affecting China's energy consumption due to exports. A detailed analysis of the embodied energy intensity of China's exports from the demand side could provide valuable insight into promoting China's energy-saving development. This paper explores disparities in the embodied energy intensity of China's exports to different trading partners and the energy supply structure behind these exports. Furthermore, it explores the driving factors of export embodied energy intensity using multiplicative structural decomposition analysis. The results show that China's export embodied energy intensity to developing countries, such as India, is higher than that of European and American countries; however, a convergence trend has emerged over time. Regarding the driving factors influencing changes in export embodied energy intensity over time, the sectoral energy intensity effect plays a pivotal role in promoting its decline, while the production structure and export structure effects change from inhibition to weak promotion. The adjustment of export structure has significant potential for reducing China's export embodied energy intensity, especially in emerging economies. Finally, this paper proposes policy directions for collaborative opening up, energy saving, and emission reduction goals. [Display omitted] • The sectoral energy intensity effect plays a pivotal role in promoting China's export embodied energy intensity decline. • The export structure effect has significant potential for reducing China's export embodied energy intensity. • The export scale of low energy intensity industries should be expanded. [ABSTRACT FROM AUTHOR]

With the increasing shortage of fossil fuels and the worsening carbon emission problems, ammonia has been attracted widely to alleviate the current challenges faced by internal combustion engines. Igniting direct injection of liquid ammonia by diesel may be a measure to achieve effective ammonia application, while the research on the characteristics of combustion and the unburned ammonia emission is little. Therefore, this paper studies the effects of ambient parameters and injection parameters on the characteristics of diesel ignition and flame propagation and distribution based on a constant volume combustion chamber, through the shadow method. The results indicate that the premise to ensure that ammonia can fully participate in combustion is that the flame can backpropagate to the junction of the two sprays. The current optimal injection strategy is the simultaneous injection of diesel and ammonia combined with a complete ammonia injection before the maintained flame. At a low temperature and high diesel injection pressure, ammonia will participate in combustion after a delay, which may result in a relatively low heat release rate and high unburned ammonia emissions. Overall, the paper can provide important references for professional researchers which is of great significance for the development of diesel/ammonia engines. • A comprehensive study is conducted on the effects of ambient and injection parameters. • The propagation and distribution of diesel/ammonia flame in spray are investigated for the first time. • Near injection time of diesel and ammonia can improve flame development and reduce unburned ammonia and soot emissions. • High diesel injection pressure and low ambient temperature can cause the flame to be unevenly distributed in the spray. • The effective combustion zone is mainly the convergence zone of diesel and ammonia sprays. [ABSTRACT FROM AUTHOR]

The turbulent world situation is an urgent crisis in energy trade and may exacerbate regional energy tensions. This paper measures the links between geopolitical risks (GPR), economic policy uncertainties (EPU) and energy import (EI) from 2005:M3 to 2023:M3. We use the Wavelet-Based quantile-on-quantile (QQ) approach based on China's import statistics for the first time. The results show that both GPR and EPU are important factors affecting EI. We confirm the negative impact of GPR and EPU on EI in the short term, while EI tends to recover in the long run. The result is consistent with the theoretical mechanism that reflects the interrelationship between external uncertainties and EI. As a result, the government should advance diversified energy access options, timely adjustment of energy policies to respond to crises, increase energy reserves and actively undertake energy innovation to reduce external energy dependence. • We measures the links between external uncertainties and energy imports. • This paper uses the Wavelet-Based quantile-on-quantile approach. • External uncertainties are important factors affecting energy import. • Energy import tends to recover in the long run. [ABSTRACT FROM AUTHOR]

Battery state of charge (SOC) estimation is crucial for assessing electric vehicle safety and evaluating the remaining driving range. Owing to the complexity, variability of operating conditions, and the highly nonlinear internal mechanisms of batteries, accurate SOC estimation remains a focal point of current research. Therefore, this paper proposes a random search optimization-based Long Short-Term Memory (RS-LSTM) neural network for precise SOC estimation. The paper firstly uses the CALCE dataset, extracting discharge capacity and discharge energy as critical features from six battery parameters by employing the random forest algorithm. The Look-back, Epoch, Batch size, and Learning rate parameters in the LSTM neural network optimized by random search algorithm. The study result reveals optimal settings (Look back: 45, Epoch: 177, Batch size: 64, Learning rate: 0.0026) achieving superior estimation accuracy, evidenced by mean average error（MAE） and root mean square error（RMSE）of 0.221 % and 0.262 %, respectively. Furthermore, the method's superiority, effectiveness, robustness, and applicability were verified by conducting tests across various estimation methods, various SOC estimation intervals, various temperature conditions, the addition of Gaussian noise, and tests on experimental and real-world vehicle data. The research process demonstrates that the proposed method has superior precision and indicates promising potential for future applications. • The SOC estimation is crucial to ensure the safety of electric vehicle operation. • A novel SOC estimation based on LSTM improved by Random search is proposed. • A innovative parameter downscale through random forest algorithm is implemented. • The accuracy and robustness of the method under various conditions is validated. [ABSTRACT FROM AUTHOR]

In the context of contemporary global warming, transitioning from traditional fossil energy to renewable energy sources emerges as a crucial strategy to reduce carbon emissions and achieve the 7th sustainable development goal (SDG). Tax policy significantly shapes the investment landscape, influencing all factors concerning the transition to renewable energy, such as technological innovation and trade openness. However, no empirical studies have examined the direct and moderating role of taxation on renewable energy investment, mainly due to the scarcity of tax data. Therefore, this paper utilizes the recently released Government Revenue Dataset (2023) to explore the complex link between taxation, technological innovation, trade openness, and renewable energy investment for a sample of the top 37 renewable energy-producing countries during the period (1996–2021). The results of the cross-section ARDL (CS-ARDL) and the pooled mean group ARDL (PMG-ARDL) models indicate that taxation has a negative and significant influence on renewable energy investment across all model specifications, both in the short and long run. Conversely, innovation and trade openness exhibit a positive and significant influence on clean energy investment. Regarding the moderating influence of taxation, the results revealed that tax revenues depress the positive impact exerted by technological innovation and international trade. Furthermore, the fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) models affirm the robustness of the long-run results obtained from CS-ARDL and PMG-ARDL models. The study's findings offer significant insights into how countries engaged in renewable energy production can enhance their taxation framework to leverage trade and innovation to promote renewable energy investment. • The paper examines the roles of taxation, technological innovation, and trade openness in promoting renewable energy investment (REI). • The analysis utilizes the latest Government Revenue Dataset (2023). • Taxation has a negative impact on renewable energy investment (REI). • Technological innovation and trade openness positively influence REI. • Taxation depress the positive effect of technological innovation and international trade on REI. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is a crucial way to achieve sustainable development. Whether the digital economy helps or makes it harder to cut emissions is a matter of debate. This paper proposes a new digital economy index based on the entropy weight method. The two-way fixed effect panel model is used to analyze the data of 30 provinces in China from 2011 to 2021. The results suggest that (1) The digital economy can significantly reduce carbon emissions. (2) Improving energy efficiency and promoting energy structure transformation are two essential mechanisms for carbon reduction in the digital economy. (3) There is heterogeneity in the impact of the digital economy on carbon emissions. The digital economy significantly reduces carbon emissions from coal but does not significantly impact carbon emissions from other sources. Moreover, the emission reduction effect of the digital economy is related to regional energy endowment, intelligent manufacturing level, and industrial agglomeration level. This paper provides new empirical evidence for clarifying the relationship between the digital economy and carbon emissions and provides policy implications for promoting the realization of carbon neutrality goals. • The digital economy can significantly reduce carbon emissions. • Energy efficiency and energy structure are two essential mechanisms. • The emission reduction effects of the digital economy vary by region. • The effect is related to intelligent manufacturing and industrial agglomeration. [ABSTRACT FROM AUTHOR]

The multiproduct pipeline network with multi-source and multi-terminal encompasses multi-point injection and multi-point distribution. Upon reorganization of batches at transfer depots, the intricacies of precise batch tracking become increasingly complex, thereby rendering the optimization of multiproduct pipeline network scheduling particularly arduous. In this paper, we introduce a dynamic index batch numbering method. By delineating the boundaries of intermediate injection points, the issue is bifurcated into two sub-problems: the optimization of batch planning for multiproduct pipeline with single-source and multi-terminal and the optimization of resource allocation at intermediate oil depots. The proposed algorithm is validated on two genuine multiproduct pipeline networks in China, demonstrating its capability to generate feasible solutions within a computational constraint of 18,000 CPU seconds. The findings reveal that this methodology holistically considers the entire pipeline network system, encompassing upstream oil intake, downstream oil supply, and the tank capacities of transfer depots, thereby establishing a coherent link between pipeline scheduling and depot planning. This paper may serve as a reference for the automated formulation of multiproduct pipeline network scheduling strategies. • A novel schedule method for multiproduct pipeline network is proposed. • Propose dynamic index batch numbering method for precise batch tracking. • The effectiveness of the proposed method is validated through solid examples. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity, stochasticity, and high coupling of multi-energy loads, this paper proposes a time-series probabilistic forecasting method based on radial basis function network-based autoregressive with exogenous inputs (RBF-ARX) and Gaussian mixture model (GMM). First, due to the nature of the nonlinear coupling between multi-energy loads, the degree of correlation is quantitatively evaluated by introducing the maximum information coefficient with the optimal feature method. Second, the Fourier transform method is used to categorize the load data into deterministic and stochastic components. For the deterministic component, the RBF-ARX method is used for forecasting, which can realize multiple inputs and multiple outputs. It only requires less training data with faster computation speed than other benchmark methods. For the stochastic component, a GMM with an improved Markov Monte Carlo sampling is used to generate the time-series stochastic component, which can accurately characterize the probabilistic properties. This paper compares the forecasting accuracy and computation time under 4 seasons using the proposed model with benchmark models. Results show that the proposed model can improve forecasting accuracy of multi-energy loads by at least 50 % and the computational efficiency can be improved by at least 30 %, which means a superior performance. • Time-series probabilistic forecasting method with considering multiple coupling factors for multi-energy loads is proposed. • The RBF-ARX model with GMM is first used to forecast the multi-energy load. • The proposed model is superior than the traditional forecasting methods, e.g., LSTM, Bi-LSTM models. [ABSTRACT FROM AUTHOR]

Accurate prediction of the state of health (SOH) of lithium-ion batteries is important for real-time monitoring and safety control of lithium-ion batteries. In this paper, a hybrid kernel least square support vector regression (HKLSSVR) prediction model based on variational modal decomposition (VMD) and improved dung beetle optimization (IDBO) is proposed. First, the original data is decomposed using VMD to reduce the non-smoothness of the data and to reduce the impact of non-smoothness on the prediction performance. The prediction is then carried out using the IDBO-HKLSSVR model, where the parameters in the prediction model are optimized using the IDBO optimization algorithm. Finally, all prediction components are superimposed to obtain the final results. The experimental results show that the coefficients of determination of the SOH of the six batteries predicted by the model are above 0.98388, which are higher than those of the other algorithms, confirming the high accuracy of the model in predicting the SOH of lithium-ion batteries. Meanwhile, compared with the existing prediction methods, the VMD-IDBO-HKLSSVR model proposed in this paper can predict the SOH of lithium-ion batteries more accurately. • The algorithm was validated using both NASA and CALCE datasets. • Improving the dung beetle optimization algorithm using three strategies. • The superiority, robustness and stability of the algorithm are verified. [ABSTRACT FROM AUTHOR]

An efficient thermal regulation strategy is of great significance in ensuring the safe operation of electric vehicles (EVs). However, the commonly used thermal management systems suffer from the problem of not being able to accurately and uniformly control the temperature distribution of lithium-ion batteries (LIBs), which poses great risks to the thermal safety control of batteries. In this paper, an advanced thermal management system for LIBs based on thermoelectric cooler (TEC) was designed to overcome the above problems. First, a temperature regulation mechanism model for LIB was constructed. Then, a novel temperature distribution modeling method for TEC was developed by using spectral method. This modeling process took into account the unsteady heat transfer characteristics, which can achieve high modeling accuracy. Next, a state space model of temperature control was constructed by combining a differential model of LIB with the proposed cooler model. On this basis, a temperature control strategy for LIB using nonlinear model predictive control (NMPC) method was proposed to optimize the cooling process because of its superior processing ability to constraints and nonlinearity. A various of experiments and verifications demonstrated that the presented thermal regulation strategy was effective and feasible. • An advanced thermal regulation system for lithium-ion battery based on thermoelectric cooler is designed. • A novel temperature distribution modeling method for TEC is proposed. • A regulation strategy is developed to control the uniformity of temperature distribution. • The paper provides a good guidance for the application and further study of advanced thermal management system in electric vehicle. [ABSTRACT FROM AUTHOR]

Rooftop photovoltaic (PV) power generation uncertainty is one of the prominent challenges in smart homes. Home Energy Management (HEM) systems are essential for appliance and Energy Storage System (ESS) scheduling in these homes, enabling efficient usage of the installed PV panels' power. In this context, effective solar power scenario generation is crucial for HEM load and ESS scheduling with the objective of electricity bill cost reduction. This paper proposes a two-step approach, where a machine learning technique, Wasserstein Generative Adversarial Networks (WGANs), is used for PV scenario generation. Then, the generated scenarios are used as input for the HEM system scheduler to achieve the goal of cost minimization. The generated solar energy scenarios are considered in a single household case study to test the presented method's effectiveness. The WGAN scenarios are evaluated using different metrics and are compared with the scenarios generated by Monte Carlo simulation. The results prove that WGANs generate realistic solar scenarios, which are then used as input to a Mixed Integer Linear Programming (MILP) problem aiming for electricity bill minimization. A 41.5% bill reduction is achieved in the presented case study after scheduling both the load and ESS, with PV fluctuations taken into account, compared to the case where no scheduling, PV, or ESS are considered. • This paper proposes a novel machine learning method for management in smart grids. • The smart grids include battery energy storage systems. • The model uncertainty is presented for a practical location. • The results of the proposed model are compared considering different test cases. [ABSTRACT FROM AUTHOR]

Thermoelectric generators have struggled with low efficiency, limiting their widespread use. To further improve the performance of the thermoelectric generator, this paper introduces the biconical pin structure into the segmented annular thermoelectric generator, and simultaneously incorporates exergy efficiency and economic cost to evaluate its performance. Experiments are first used to verify the accuracy of the simulation model, and then the effects of the cone angle θ , leg height H , leg length ratio δ and resistance ratio ε are discussed. Finally, correlation analysis is used to obtain the weight of the impact of research parameters on thermoelectric performance, and the optimized results are obtained. The results demonstrate that the output power of the biconical segmented annular thermoelectric generator is 20.23 % higher, the exergy efficiency is 8.55 % higher, and the economic cost is reduced by 21.36 % compared to the traditional segmented annular thermoelectric generator. As the θ and H increase, the thermoelectric performance enhances. The influence of δ on thermoelectric performance is more complicated. When ε is 1.5, the thermoelectric generator has optimal thermoelectric performance. This paper provides a reference for optimizing the segmented annular thermoelectric generator structure and is of paramount importance for enhancing the efficiency of waste heat recovery. • The biconical segmented annular thermoelectric generator is proposed. • Research is conducted using exergy analysis and economic cost analysis. • The influence weight of parameters on performance is explored. • The optimal parameters of thermoelectric generator are obtained. [ABSTRACT FROM AUTHOR]

Since the severity and frequency of extreme events have seriously threatened the stability of fossil energy markets, this paper conducts multilayer dynamic network analysis to demonstrate the evolution of the entire market structure during extreme events. Based on the data of 64 Chinese energy firms ranging from March 2018 to February 2023, this paper examines the sectoral discrepancies, and identifies systemically important companies during different crises. The results indicate that the risk transmission mechanisms among fossil energy markets are different induced by the two crises, namely the COVID-19 pandemic and the Russia–Ukraine conflict. Specifically, the COVID-19 has rendered greater influence on the oil and gas industry, while the Russia–Ukraine conflict has brought instability to the coal industry. Additionally, the heterogeneous effects of these events on individual energy firms are identified, and the results suggest that both small and large firms have played prominent roles in risk transmission. • Extreme risk contagions among fossil energy firms are examined. • The dynamic evolution of market structure is demonstrated by multilayer network. • Topological characteristics are presented from market, sector, and firm levels. • The COVID-19 has rendered significant impacts on the oil and gas sectors. • The Russia–Ukraine conflict has brought instability to the coal sectors. [ABSTRACT FROM AUTHOR]

Hybrid systems for generating electricity from multiple sources are becoming an increasingly popular subject of analysis in science and industry. This paper presents a validated model of a hybrid ORC plant powered by solar and geothermal energy. A key challenge in optimizing the operating parameters over time was the variability of solar conditions, which was the main energy source of the system. The operation of the ORC plant is simulated using a complex model with Multiple Input Multiple Output (MIMO) variables, which is nonlinear. The input variables represent the system's operational parameters, while the output variables describe the plant's performance indicators. The main objective of this paper is to optimize the year-round performance of the ORC installation through different computational techniques. The first approach involves the application of the gradient-based optimization method that is known as sequential quadratic programming (SQP). With the use of SQP, two distinct simulation runs (SQP-N and SQP-Q/N) of the system are performed, each with a specific objective function to be optimized. The second approach is based on reinforcement learning principles and leverages the method known as Deep Deterministic Policy Gradient (DDPG) algorithm. The main advantage of DDPG over SQP is that DDPG does not require knowledge of the model. This improves the algorithm flexibility, making it well-adapted to fluctuating environmental conditions. Overall, three optimization runs (two using SQP, one using DDPG) have been performed, aiming at identifying the optimal year-round control strategy for the installation. The results revealed that under the control of DDPG, the hybrid system has produced the highest amount of electricity (4993.4 MWh), outperforming in this matter SQP-N and SQP-Q/N optimization variants by 16.83 % and 10.49%, respectively. • Thermodynamic model of a hybrid organic Rankine cycle (ORC) system is developed. • Three approaches for optimizing the annual operation of the ORC are presented. • An approach based on the Deep Deterministic Policy Gradient (DDPG) algorithm is proposed. • Application of the DDPG algorithm results in the best annual performance of the ORC. [ABSTRACT FROM AUTHOR]

In the layout optimization of natural gas gathering pipeline network, a lot of work has been done, but few scholars focus on the capacity optimization of gas gathering station (short for station). The structural relationship parameters of dendritic pipeline network and the flow parameters of pipeline network are complicated, which makes it difficult to establish the model, and at the same time, the existence of nonlinear term causes the difficulty of solving the problem. In the current two stage star-dendritic natural gas gathering pipeline network, pipeline sizes and station capacity are not well taken into account for the optimization of pipeline network layout at the same time. In this paper, a consideration station capacity and pipeline sizes: Two-stage star-dendritic network layout optimization model (SCPSTSDLO-Model) is constructed by integrating the factors of site discrete characteristics, station capacity and pipeline sizes, and considering the hydraulic pressure drop conditions of the network, and a solution framework based on the nonlinear processing is proposed, which can obtain the optimization results of optimal affiliation, station type, station site and pipeline sizes at the same time. Five cases are used to verify the validity and accuracy of the model and algorithm, compare the sensitivity analysis and solution algorithm, compare the hydraulic pressure drop conditions, and analyze the application in different forms of pipeline networks. The results show that the SCPSTSDLO-Model and the solution method established in this paper can obtain the optimal solutions under different topologies according to the application requirements, taking into account the capacity of stations and pipeline sizes. • A comprehensive model for pipeline network layout incorporating discrete site characteristics and station capacities. • A concept of intermediate stations is proposed to overcome the difficulties of modeling branched pipeline networks. • The model can obtainpipeline network connection relationship, station capacity, and pipeline specification at the same time. • The adopted method gives faster, more stable and convergent results. [ABSTRACT FROM AUTHOR]

During the driving of the fuel cell vehicles, temperature fluctuations occur in the Proton Exchange Membrane Fuel Cell (PEMFC), whose output efficiency decreases and hydrogen consumption increases. Rule-based (RB) and proportional-integral-derivative (PID) controllers can roughly suppress the temperature fluctuation, but they cannot perform multi-objective optimization, which cannot reduce hydrogen consumption at the same time. To solve this problem, in the first step of this paper, the hydrogen consumption factor and the temperature fluctuation factor in the operating temperature control are introduced to design the DDQN controller. In the second step of this paper, the DDQN controller is trained offline and verified under simulation conditions. The simulation results show that compared with the RB controller and PID controller, the average temperature fluctuation of the DDQN controller is reduced by 27.27 % and 28.50 %, and the hydrogen consumption is saved by 1.78 % and 2.58 %, respectively. The research in this paper is innovative in that it reduces the hydrogen consumption of PEMFC and improves the driving range of fuel cell vehicles from a specific point of view. • Design a DDQN controller to achieve intelligent real-time control of the operating temperature of PEMFC. • It can effectively reduce temperature fluctuations of PEMFC under complex operating conditions. • Simultaneously considering controlling temperature fluctuations and reducing hydrogen consumption. [ABSTRACT FROM AUTHOR]

With the promotion of low-carbon models, the proportion of wind power energy has significantly increased. Accurate wind power forecasting is of great significance for the scheduling of power systems. Previous studies often focused on improving forecasting accuracy, neglecting the issue of model failure (abnormally large forecast error occurring). However, forecasting model failure brings significant misleading information to the scheduling of the power system. To address this issue, this paper firstly analyzes the error distribution of prediction models under various neural networks (CNN, CNN-GRU, DNN, ConvLSTM, ELM, GBDT, AR, TREE and XGBoost) and various loss function (MAE, MSE, MLSE and Log-cosh) combinations. Subsequently, based on the Backward cloud generator and Pearson correlation analysis, the paper confirmed that the forecasting errors mainly come from the volatility of the wind power sequence itself, rather than the types and structures of the models. Finally, the paper uses variation and variance to assist Bi-LSTM in 1-h-ahead early warning of forecasting model failure under two kinds of thresholds, and has achieved excellent reliability and accuracy. The warned models show obvious failure situations, both in terms of single error peaks and cumulative errors. • The paper advocates studying the failure of forecasting models. • Multiple neural networks and loss functions are applied. • The main sources of forecast errors have been identified. • The relationship between original variation and forecast error is analyzed. • A method about early warning for forecast model failure is proposed. [ABSTRACT FROM AUTHOR]

Industrial symbiosis has gained prominence in pursuing sustainable industrial practices, aiming to optimise resource utilisation and reduce environmental impacts. A critical aspect of this endeavour is the efficient management of energy resources within an industrial ecosystem. This paper presents a novel approach to enhance Total Site Heat Integration (TSHI) implementations by employing Blockchain as a facilitator for decentralised energy management. TSHI is an efficient and widely applied method for industrial symbiosis concerning energy flows, which employs the steam mains in site utility systems as the platform for exchanging heat between industrial processes at various temperature levels. It is shown that the synergy of Pinch Analysis and Smart Contract technologies is capable of facilitating energy integration of processes belonging to independent market actors, compared with the currently dominant integration inside a single company. The proposed framework leverages Blockchain as a distributed ledger to enable secure, transparent, and automated energy management across multiple industrial entities in an industrial symbiotic network. The integration of Pinch Analysis principles ensures that the Heat Integration process is optimised to improve the overall energy efficiency. Smart contracts enable automatic negotiation and execution of energy transactions based on predefined rules, minimising the time lag for concluding deals on energy resource exchange and conservation. This paper examines several scenarios to illustrate the implementation of the proposed Blockchain-based TSHI concept within an industrial symbiosis network. It is demonstrated that up to 16 % cost savings are possible by simply enabling transparency via Blockchain. The results could drive innovative development to revolutionise decentralised energy management in a complex industrial ecosystem, especially by synchronising energy exchanges in time. [Display omitted] • Blockchain concept for managing data of site-level industrial plants/prosumers. • Pinch-based smart contract on utility allocation with Total Site Heat Integration. • A cryptocurrency for the blockchain system can be used for utility transactions. • Bidding or selling of heat surpluses/deficits via Pinch-based smart contract. • Information transparency allows tangible cost savings up to 16 %. [ABSTRACT FROM AUTHOR]

Dynamic stall often leads to unsteady load and performance degradation in horizontal axis wind turbines. Therefore, accurate modeling of dynamic stall is crucial. However, due to the large variations of the blade aerodynamic profiles and the complexity of dynamic stall flow, numerical simulation and wind tunnel experiment are costly. On the other hand, widely used semi-empirical models have limited accuracy. Hence, this paper proposes a data-knowledge fusion method that incorporates physical knowledge into a neural network to improve its accuracy and generalization ability. Firstly, the force components of the Leishman–Beddoes model are incorporated into the network. An efficient dynamic stall model for the S809 airfoil is thus established with a small amount of high-precision experimental data. It achieves extrapolation predictions of reduced frequency and angle of attack with only 1/5 of the samples in the database to train. Moreover, to make full use of the accumulated existing airfoil data to assist in modeling other airfoils, the obtained S809 model is fused in the network to predict the aerodynamics of S810 and S814. The average relative error of the prediction cases is nearly 10%. Comprehensively, this paper provides a new paradigm for assessing the dynamic stall of the wind turbine blade. [Display omitted] • Develop a dynamic stall modeling neural network by integrating knowledge. • Create an efficient model that incorporates the force components of the L-B model. • Utilize accumulated aerodynamic data to model the other cross-sections of the blade. • The data fusion strategy reveals the importance of interpretable basis functions. [ABSTRACT FROM AUTHOR]

In view of the weak energy supply stability of the single regional integrated energy system (RIES), the ability to deal with uncertainties is poor. To solve the problem of equilibrium optimization among system economy, robustness and efficiency, this paper interconnects multiple RIES with different resource to form multi-RIES through peer-to-peer energy trading. The control strategy of energy interactive operation involving multiple RIES is optimized. Using the uncertainty confidence set of renewable energy to depict the uncertainty on the source side, a two-stage distributed robust optimal control model for RIES based on data-driven is proposed, and the column and constraint generation (C&CG) algorithm is adopted to significantly improve the solution efficiency. Then, a multi-RIES cooperative game model with uncertain scenarios is proposed, and according to this model, a coupled C&CG-alternating direction multiplier method (ADMM) algorithm is proposed, which realizes the privacy protection of RIES and makes the system have good convergence performance. Finally, the case analysis proves that the strategy proposed in this paper can not only realize energy coordination and complementarity among multiple RIES, but also improve the stability of the system operation and significantly reduce the dependence of the system on the distribution network. • Data-driven RIES two-stage distributed robust optimization model is used to consider the uncertainty of renewable energy. • The multi-RIES cooperative game model based on data-driven two-stage distributed robust optimization is proposed. • A coupled C&CG-ADMM algorithm is proposed. [ABSTRACT FROM AUTHOR]

District heating (DH) systems have been identified as an important component in efforts to mitigate greenhouse gas emissions and achieve climate targets in the near future. Software tools for analyzing these systems use different modeling approaches, making it challenging for practitioners to choose the most suitable tool based on specific use case requirements. This paper systematically reviews available DH network modeling and simulation tools, comparing their modeling approaches, application scope, and functional capabilities. An online survey was conducted among DH tool developers and experts to assess the technical evaluation criteria for these tools. First, the basic concepts and principles of DH networks modeling are discussed. Moreover, it provides an overview of DH network mathematical models, and discusses various numerical methods used for simulation and analysis, while highlighting potential areas for further research and tools development. The aim of this paper is to support academic and industrial users in selecting appropriate modeling approaches, tools, and libraries. • Reviews DH network modeling and simulation tools, comparing their methodologies, application scopes, and capabilities. • Examines of the current state of the art in DH network modeling, comparing different tools' approaches and functionalities. • Includes insights from an online survey of DH tool experts and highlights areas for further research and tool development. • Guides practitioners in selecting appropriate tools for specific DH network use cases. [ABSTRACT FROM AUTHOR]

Under the trend of global carbon neutrality, the integrated energy system with the characteristics of multi-energy scheduling and gradient utilizing will be widely constructed and applied in the future energy market. For the construction of energy systems in emerging building complex, this paper analyzes the load probability characteristics of regional building complex at the planning stage, and conducts an aggregation analysis of its multiple uncertainties, and obtains the conclusion that the load factor at time t obeys the normal distribution. In order to formulate this uncertainty, this paper combines the case study with the scenario analysis method containing scenario generation and reduction to transform the stochastic programming model into several deterministic models and analyses the discrepancies of the optimization results under different objectives and different load variances. The results show that after considering load-side uncertainty, the total system capacity increases by 7 %, the total investment under the minimum investment objective by 4 %, and the carbon emission under the minimum carbon emission objective by 3 %. In addition, the system needs to pay 32.8 % of the total investment increment to obtain 80 % carbon reduction when adopting carbon emissions as the objective. • Mechanism of IES load uncertainty is illustrated. • Scenario analysis is applied to obtain an easy-to-follow MILP model. • The incremental investment due to uncertainty intensity is calculated. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) feature multiple working modes. Thoughtful selection of these modes can optimally balance driving performance, power demands, and energy consumption, thereby enhancing the overall efficiency of the vehicle. This paper presents a soft actor-critic (SAC) approach trained on a multi-modal driving cycle (MDC) for selecting operational modes of electro-hydraulic hybrid electric vehicle (EHHEV). Firstly, characteristic parameters are extracted and clustered for five typical driving cycles through principal component analysis and K-means clustering, creating a multi-modal driving cycle. Secondly, based on the operational characteristics of EHHEV, state variables, action variables, reward functions, learning rates, and other parameters are set for the SAC algorithm, and the EMS framework is built based on the electro-hydraulic hybrid electric power system. Subsequently, the SAC algorithm is trained using the MDC to construct the SAC-MDC EMS. Results demonstrate that compared to EV, RB EMS, and SAC EMS, IREC achieves maximum improvements of 22.38 %, 5.55 % and 0.80 %, respectively. The dynamic performance and the motor load optimization capability are also enhanced. To further validate the practicality and reliability of the SAC-MDC EMS, this paper validates it using actual driving data, revealing that it still exhibits outstanding performance. • A multi-modal driving cycle containing information about multiple driving situations is created. • The SAC algorithm is proposed for switching working modes in electro-hydraulic hybrid electric vehicles. • The output actions of the SAC algorithm are transformed into rule-based control to enhance algorithm interpretability. • The multi-modal driving cycle is applied to the training of the SAC algorithm to improve its adaptability. [ABSTRACT FROM AUTHOR]

Growing energy demand and increasing environmental challenges underscore the importance of precise forecasts for photovoltaic (PV) operations in renewable energy generation systems. At this stage, it is mainstream to combine both temporal and spatial factors to forecast PV power generation. However, there are fewer studies that consider factors at very large spatial scales. This paper proposes Hybrid Dual Stream ProbSparse Self-Attention Network (HDSPAN), a novel spatial–temporal photovoltaic power forecasting network architecture that can solve the above limitations. The model implements an encoder–decoder approach that extracts the required spatial–temporal information through a dual stream distilling mechanism. In addition, the ProbSparse self-attention mechanism is employed to improve model efficiency and reduce repetitive and redundant information processing. The hyperparameters are optimized using Tree-structured Patzen estimator to improve forecasting outcomes. This paper demonstrates the effectiveness of spatial–temporal PV forecasting by using ERA5 reanalyzed PV data as a case study. Our results show that the HDSPAN model achieves a 10% higher forecasting accuracy compared to the baseline models, significantly advancing PV power forecasting. • Introduces a novel method for spatial–temporal photovoltaic power forecasting. • The proposed method employs dual stream distilling mechanism for spatial–temporal data. • The proposed method employs ProbSparse self-attention for data processing. • Analyzes spatial factors with Euler distance for station grouping. • Employs Tree-structured Patzen estimator to enhance hyperparameter tuning. [Display omitted] [ABSTRACT FROM AUTHOR]

Solar photovoltaic thermal system (SPTS) can fully tap solar energy resources to realize thermal and electric supply for users simultaneously, but the volatility and uncertainty of renewable energy and load cause the imbalance of energy supply. This paper proposes a multi-time scale optimal scheduling method for SPTS based on event-triggered model predictive control (ET-MPC) considering battery performance deterioration. Firstly, SPTS is divided into day-ahead and day-in optimization layers from different time scales. Day-ahead scheduling takes the minimization economic cost as the optimization function to obtain pre-scheduling plan, and day-in scheduling adapts rolling optimization scheme based on model predictive control (MPC) to reduce power fluctuations and achieve optimal scheduling adjustment. Moreover, the battery performance decay parameter is introduced into the MPC model and embedded into the optimization problem to prolong the cyclic life deterioration, and an event-triggered mechanism is introduced into MPC to improve the computational efficiency and reduce the communication frequency. Finally, the results show that the proposed double-layer scheduling method improves 4.15 %, 66 % and 13.39 % respectively regarding cost, power fluctuation and battery maintenance, and improve the calculation efficiency of 48.89 % compared with standard MPC method, which indicates that the proposed method has good economy, stability and execution efficiency. • This paper proposed energy scheduling for solar photovoltaic thermal system (SPTS). • Double-layer optimal scheduling is proposed to realize the economy and stability. • Battery service time is prolonged through defining performance index. • An event-triggered mechanism is introduced to MPC to reduce the communication burden. • The proposed method improves performance from different perspectives. [ABSTRACT FROM AUTHOR]

Conventional energy sources, including diesel and petrol, are decreasing day by day, while their reserves are limited. However, it is difficult to find alternative fuels to replace them. Hence, to fulfill the growing energy demand and reduce environmental emissions, it is vital to find a wide variety of oxygenated compounds to conventional diesel fuels that show low emission potential from transport engines. In this research, we investigated diesel engine performance, combustion, and exhaust emissions with the addition of 10 %, 15 %, 25 %, and 30 % of three different oxygenated blends, including Diethylene Glycol Dimethyl ether (DGM), Di- n -Butyl Ether (DBE), and Tripropylene-Glycol Monomethyl ether (TPGM). This paper presents the brake-specific fuel consumption (BSFC), Brake Thermal Efficiency (BTE), Brake Mean Effective Pressure (BMEP), and Brake Specific Energy Consumption (BSEC) for these fuel blends. The generated emissions of carbon dioxide (CO 2), Carbon Monoxide (CO), and nitrogen oxide (NO) were quantified for four different engine speeds of 1500, 1800, 2000, and 2400 rpm for neat (100 %) oxygenated blends. This research also shows the in-cylinder pressure and heat release rate (HRR) of these three oxygenated blends. The outcome of this paper will help future researchers to develop automobiles and vehicles for oxygenated blends with conventional fuels. • Up to 30% DGM, DBE, and TPGM biodiesel provides similar engine performance to diesel. • Variation of BSFC is low for DGM, DBE, and TPGM biodiesel for a wide range of engine speeds. • DGM, DBE, and TPGM oxygenated fuels show lower CO and CO 2 but high NO emissions. • DGM, DBE, and TPGM provide similar in-cylinder pressure and heat release rate (HRR) to diesel. [ABSTRACT FROM AUTHOR]

To advance the field of lithium-ion battery (LIB) research, this paper unveils an accurate modelling of LIB that primarily relies on the equivalent circuit model, backed by the Osprey Optimization Algorithm (OOA). In the modelling stage, both single and double resistance-capacitance models are evaluated to depict the charge dynamics, incorporating the effects of fading, load, and temperature variations. The OOA approach is utilized to minimize integral squared errors between the actual measured and model-predicted battery voltages under constraints imposed by the model design variables. This approach is applied to a commercial 2.6 Ahr Panasonic LIB, with the performance of the OOA-based model being benchmarked against models developed by means of other optimization algorithms for further validation. Moreover, the robustness of the OOA method is assessed under battery uncertainty conditions or model parameter variation. A sensitivity analysis is performed on the battery model by employing a proposed approach that evaluates the impact of varying each parameter of the battery model by ±5 %, in a sequence that ascends and descends from 0 to 5 %. The single resistance-capacitance model is selected for in-depth validations. Notably, the OOA approach excels in estimating parameters for LIB modeling under both normal and abnormal operating conditions. • This paper presents novel Osprey optimization algorithm-based optimal lithium-ion battery model. • The model can be used with electric vehicles and power grid applications. • Practical measurements are implemented to verify the battery model and sensitivity analyses. • The OOA approach is proposed to solve the optimization problem. • The effectiveness of the proposed method is compared with other methods. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) panels' photoelectric conversion efficiency will decrease as dust deposition on their surface. An approach to dust removal on the PV panel's surface by longitudinal high-speed airflow was investigated to increase the output power. In this paper, commercial CFD software was used to numerically simulate the characteristics of dust removal by longitudinal high-speed airflow. The PV panels' output characteristic model under the action of dust removal by high-speed airflow is established by Simulink software, and the influence of high-speed airflow on the output characteristic is studied. Firstly, the dust removal mechanism of the PV panel under high-speed airflow is studied. Secondly, the impact of different tilt angles of the PV panel, dust particle size, airflow velocity, and blowing time on the dust removal effect of the PV panel surface were studied. The optimal airflow velocity and blowing time were obtained according to the airflow velocity and blowing time and their influence on the dust removal rate. Finally, the effect of high-speed airflow dust removal on PV power generation's efficiency and output characteristics is studied. The findings demonstrate that as the tilt angle increases, so does the dust removal rate increases continuously. In addition, the rate at which dust is removed increases with airflow velocity and blowing time. When the tilt angle is 30°, the best airflow velocity of dust removal is 5 m/s, and the best blowing time is 8 s. When the tilt angle is 45°, the best airflow velocity of dust removal is 10 m/s, and the best blowing time is 5 s. With the increase in airflow velocity and blowing time, the output power of PV panels continues to increase. When the airflow velocity is 10 m/s, and the blowing time is 5 s, 7.5 s, 12.5 s, and 15 s, the maximum output power after dust removal is increased by 15.44 %, 23.05 %, 23.38 %, and 23.39 %. This paper's research results can guide the design of the practical engineering application of longitudinal blowing high-speed airflow in the dust removal of PV panels. • A dust removal method based on longitudinal high-speed airflow on the surface of PV panels. • Dust removal under the action of longitudinal high-speed airflow is simulated by CFD. • The effect of high-speed airflow dust removal on the output power is studied. [ABSTRACT FROM AUTHOR]

District heating and cooling systems have been present on the European market for more than a century, constantly evolving into more efficient energy infrastructures important for decarbonization of the heating and cooling sectors. With roughly 17,000 operational district heating systems, serving over 70 million citizens, currently, the final energy consumption attributed to district heating in Europe is estimated at over 450 TWh. As the European district heating and cooling sector varies extensively in every analyzed aspect, there are vast differences between European countries regarding technical preferences, technological development, market structure and regulatory frameworks of these systems. The main purpose of this paper is to provide a comprehensive overview of the present status of the district heating and cooling systems in Europe with insight(s) and suggestions into future trends. In addition, the provided data serves as a valuable baseline for future research. Finally, this paper draws attention to the new socio-demographic approach, observed when analyzing the district heating and cooling systems, that has been recently introduced into the scientific community. This new approach also reveals the opportunity for creating new indicators that will influence the modeling of existing and future district heating and cooling systems. • Provides a review on ongoing trends in European district heating and cooling sector. • Analyses European DHC systems from technological, market, and regulatory aspect. • Provides an overview on commonly used performance indicators and energy models. • Discusses the possibility of creating new DHC indicators. • Discusses the trends in future DHC development. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the automotive industry and the increasing shift towards cleaner energy, electric and hydrogen vehicles have great potential for growth. Therefore, charging stations need to transition from single charging forms to electric-hydrogen coupling charging. This paper focuses on the problem of mismatch between source and load in time and space when operating a Charging-Hydrogenation Composite Station (CHCS) independently. This leads to resource waste in the full-cycle and long-time sequence, making it difficult to achieve economic benefits. The characteristics that differentiate CHCS, such as geographic location, operating equipment, charging demand, and temporal attributes, are taken into account. CHCS in suburban, residential, tourist attractions, and industrial areas are modeled differently. Through improved Nash negotiation game modeling, a peer-to-peer (P2P) energy trading mechanism is established. The CHCS leverages its resource endowment advantages to ensure that each subject and the alliance as a whole can benefit through the coupled electricity-hydrogen energy trading. To ensure privacy in CHCS transactions, a distributed alternating direction method of multipliers (ADMM) algorithm is used to iteratively solve the problem and achieve a fair distribution based on the energy contribution size of each CHCS. Finally, simulation examples are used to verify the validity of the proposed model. The study shows that the electricity-hydrogen energy coupling trading strategy proposed in this paper can realize the synergistic mutual benefit of each CHCS, and in terms of economy, the overall benefit of the alliance has been improved by 7.58 %, and in terms of new energy consumption, the regional PV has gone from a consumption rate of 49.98 % to realize 100 % full consumption, and the wind power consumption rate has increased from 52.38 % to 96.34 %. • The coupled trading and revenue sharing of electric and hydrogen energy among multiple CHCSs. • The differentiation of temporal attributes, spatial geography, equipment types, and load characteristics are considered. • Implications of Nash negotiations and energy sharing on the CHCSs costs and consumption of new energy are investigated. • Distributed ADMM algorithms are used to solve the Nash bargaining problem. [ABSTRACT FROM AUTHOR]