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In the midst of a global energy crisis, fuel poverty has become increasingly prevalent. This study examines the effectiveness of energy efficiency retrofits (EERs) in alleviating fuel poverty in the United Kingdom. Using the England fuel poverty dataset with XGBoost and interpretive analyses like Individual Conditional Expectation (ICE) and SHapley Additive exPlanations (SHAP), the research investigates the impact of EERs on fuel poverty. Findings reveal that while energy efficiency enhancements hold potential for improving conditions for vulnerable households, their effectiveness diminishes beyond a certain threshold. The study identifies solid wall and cavity wall insulation, condensing boilers, well-insulated lofts, and central heating systems as the most effective retrofit measures. However, the optimal selection of retrofit methods varies depending on household and dwelling conditions due to interactive effects. Additionally, comprehensive cost-efficiency analyses show that installing condensing combination boilers in homes without existing boilers and adding cavity wall insulation to houses with uninsulated cavity walls are more cost-efficient options. These insights shed light on the complex relationship between energy efficiency retrofits and fuel poverty, informing the development of targeted strategies to address this pressing issue in the UK and globally. • EERs help reduce fuel poverty, but the benefits diminish beyond a certain point. • Cavity wall insulation emerges as the most cost-effective solution for alleviating fuel poverty. • The retrofit solutions that maximize energy efficiency may not always be the most effective in addressing fuel poverty. • Profiling vulnerable households susceptible to fuel poverty. • Enhance interpretability by converting Shapley values into Odds Ratios. [ABSTRACT FROM AUTHOR]

In recent years, the energy crisis has prompted widespread attention to electric vehicles (EVs). As a core component of electric drive system in EV, motor controller has a significant impact on vehicle energy consumption. In such situation, this paper proposes a minimum loss modulation method for all power factors and reduces current THD by dead-band compensation. Firstly, the mechanism model of the controller is established for loss calculation, and the relationship between the clamping region and the current peak is analyzed based on the traditional modulation methods. Secondly, a discontinuous pulse width modulation considering power factor angle method (CPFA-DPWM) is proposed to ensure that clamping region always falls at the peak current at all power factors. Furthermore, the adoption of current vector method instead of direct sampling for current polarity determination effectively reduces judgment error and avoids false dead-band compensation. The effectiveness of the proposed method is verified by simulation and thermal measurement experiment. Under China Light-duty Vehicle Test Cycle (CLTC), the controller loss of CPFA-DPWM is reduced by 18.61 % compared to SVPWM. • A general distribution law of clamping intervals in DPWM methods is summarized. • A discontinuous modulation method is proposed to reduce the motor controller losses at arbitrary power factors. • In dead band compensation, the current vector method is used to improve the accuracy of the current polarity judgement. • The CPFA-DPWM can reduce the motor controller loss by 18.61 % under CLTC. • MATLAB/Simulink & PLECS and bench test platform were carried out to verify the proposed control strategy. [ABSTRACT FROM AUTHOR]

Large-scale underground oil storage is vital for addressing the energy crisis. Leveraging the insoluble sediment space at the bottoms of salt caverns for oil storage is particularly effective in high-impurity salt mines, enhancing oil storage capacity. The process of extracting oil from the sediment void is essential for utilizing this resource. Three experimental devices were developed to investigate this extraction process. We conducted experiments on oil extraction processes and rates for various oil types, analyzing weight changes and influencing factors. The sediment and water content in the extracted oil were also evaluated. Results indicated that extracting oil from the sediment void is feasible, yielding average recovery rates over 90.0 %. High-viscosity oil at 50 °C exhibited three stages: initial stability, a rapid rise, and final stability. Low-viscosity oil correlated with brine injection rates, displaying a rapid rise, stable phase, and subsequent decline. Petrolatum extraction was easier than diesel extraction, and ground temperature improved recovery rates. Changes in water and sediment content had minimal impact on oil quality. This research provides insights for large-scale underground energy storage. • A novel salt cavern insoluble sediment void oil storage method is proposed. • A series of oil extraction process experiments from the sediment void are conducted. • The extraction of oil quality from the salt cavern sediment void is analyzed. • The average oil recovery ratio from the salt cavern sediment void can reach 90 %. [ABSTRACT FROM AUTHOR]

Deep learning algorithms have transformed day-ahead electricity price predictions in recent years. This phenomenon has implications for price predictability and perceived risks, which in turn could impact investments. This study aims to unravel the impact of deep learning-based price forecasting (DPF) on price predictability and its correlation with price volatility. This has been done by examining 19 bidding zones across 11 European countries from January 2018 to June 2023. This timeframe captures the unique market dynamics influenced by the COVID-19 pandemic and the Global Energy Crisis. This analysis has been enabled by the development of the Day-ahead European Electricity Price Forecasting Kit (Deepforkit), a forecasting tool developed in-house and open-sourced. Notably, Deepforkit offers a running speed at least 5 times faster than a previously suggested benchmark, enabling large-scale and multi-market analyses such as the one conducted in this study. It is quantitatively proven that if DPF is used, price volatility is just moderately correlated to unpredictability, and using it as a proxy for price unpredictability is not reliable. This is attributed to the capacity of DPFs to adjust their predictions in response to evolving market trends and patterns. This finding has an important implication on the investment risk perception and the cost of capital for new investments in the power sector. Moreover, the predictability behavior of prices is compared and contrasted in 3 markets, namely EPEX SPOT, OMIE, and Nord Pool, and across 11 European countries (Belgium, Denmark, Finland, France, Germany, Netherlands, Norway, Portugal, Sweden, Spain, and Switzerland). • Electricity price volatility does not necessarily translate into unpredictability. • Day-ahead European Electricity Price Forecasting Kit (Deepforkit) is proposed. • Deepforkit runs more than 5 times faster than its counterpart model. • Electricity price behavior is investigated during Covid-19 and Global Energy Crisis. • Electricity network connectivity to other networks impacts its price volatility. [ABSTRACT FROM AUTHOR]

As the urgency of the global energy crisis escalates and environmental protection standards become more stringent, the industrial sector is placing increased emphasis on enhancing energy efficiency and advancing vibration control technologies. Within this context, multi-stage centrifugal pumps (MSCPs), pivotal in energy systems, play a crucial role in influencing both system performance and environmental impact. This paper embarked on employing an inverse design method (IDM) to innovate the redesign of MSCP blades, introducing a steady-state index for characterizing the intensity of unsteady pressure fluctuations, thereby streamlining optimization costs. Optimization techniques utilizing an artificial neural network model and a multi-objective evolutionary algorithm were employed to achieve an optimal balance between maximizing energy efficiency and reducing hydro-induced vibrations. The optimized MSCP enhanced not only boosting performance but also a 2.75 % increase in weighted efficiency, significantly widening the high-efficiency operation zone. The improvement in vibration characteristics was manifested by an average reduction of more than 25 % in the amplitude of the primary frequency of pressure pulsations within the double volute, with a maximum reduction reaching 50.9 %. Furthermore, the research delved into the analysis of the internal energy dissipation mechanisms of MSCPs, drawing upon the entropy production theory and the enstrophy concept. This analysis reveals that energy dissipation in the mainstream zones is predominantly affected by shear enstrophy, whereas, in the wall regions, it is primarily governed by fluid velocity. The comprehensive performance enhancement of the optimized model is significantly credited to the refinement of the blade profile. • An innovative method based on inverse design for better efficiency and less vibration in multi-stage pumps is proposed. • A steady-state indicator to characterize unsteady pressure fluctuation intensity is introduced. • Analysis based on entropy production theory and enstrophy concept reveals energy dissipation mechanisms. [ABSTRACT FROM AUTHOR]

The multifaceted challenge of carbon dioxide (CO 2) emissions during building operations, construction, and material production perpetuates energy shortages and climate adversities. Urgent imperatives underscore the need for effective energy reduction and carbon mitigation, driving the building sector toward a paradigm shift in clean, low-carbon evolution. However, the complex multidimensional and nonlinear characteristics of influencing factors of building energy consumption make it difficult in precisely understanding the spatial and temporal relationships between the features in the predictive modeling of buildings. Therefore, a novel Graph Wavenet (GWN) based on the spatio-temporal attention fusion mechanism (STA-GWN) is proposed for spatio-temporal graph prediction modeling of structures, which can capture hidden spatial relationships using an adaptive dependency matrix. To overcome the drawbacks of standard predefined adjacency matrices and their inability to capture faraway dependencies, a spatio-temporal attention fusion mechanism is introduced to extract latent spatio-temporal features dynamically. By combining extracted features, building energy consumption can be predicted more precisely. Moreover, the proposed model becomes more interpretable, making it easier to reveal how spatio-temporal features relate to building energy consumption. Lastly, the proposed method is applied to energy conservation and emission reduction in the building industry. A predictive model for electrical energy consumption during two public datasets with an operational phase of buildings is established to analyze and optimize energy utilization in residential structures that rely on electricity. The experimental results demonstrate that the STA-GWN model surpasses other deep learning methods in terms of prediction accuracy and robustness. Moreover, the proposed model can offer optimal solutions for building energy processes, reducing approximately 201.44 kg (Kg) and 64.08 Kg of CO 2 emissions in the appliances energy prediction dataset and the individual-household-electric-power-consumption (IHEPC) dataset, respectively. • Novel Graph Wavenet based on the spatio-temporal attention fusion mechanism is proposed. • The proposed model surpasses other deep learning methods in terms of the prediction accuracy. • The proposed model reduces 201.44 kg of CO 2 in appliances energy prediction dataset. • The proposed model reduces 64.08 kg of CO 2 in individual-household-electric-power-consumption dataset. [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]

With the acceleration in industrialisation and urbanisation, building a community-integrated energy service system that includes renewable energy is an effective way to resolve energy shortages and reduce environmental pollution. Energy coupling can be realised using technology, such as electricity-to-gas conversion equipment, gas turbines, and gas boilers. And a carbon-trading mechanism can be introduced to build a community-integrated energy service system. First, the composition and characteristics of the community-integrated energy service system are analysed, and an index evaluation model for the community-integrated energy system is constructed considering the four influencing factors of cost, reliability, energy consumption, and environmental protection. The weight assigned to each indicator is determined based on the binary comparison weighting method and the improved criteria importance though intercrieria correlation method, and a multi-criteria compromise ranking evaluation system is established. A community was selected as an example of sensitivity and comparative analysis to verify the correctness and rationality of evaluating a community-integrated energy service system using the multi-criteria compromise ranking evaluation system based on the binary comparison weighting method and the improved criteria importance though intercrieria correlation method. • We evaluate community-integrated energy service system by multi-criteria evaluation. • A carbon trading model with reward and punishment mechanisms is introduced. • Subjective and objective methods were combined to determine indicator weights. • We aim to improve data processing and analysis with appropriate evaluation methods. [ABSTRACT FROM AUTHOR]

Citizens, businesses, and governments faced energy scarcity, skyrocketing costs, and a high degree of unpredictability due to the Russian invasion of Ukraine in 2022. This situation has forced the European Union and national governments to switch to local energy resources and increase energy efficiency more strictly. But has society's attitude changed in the same way? Two separate public opinion surveys and network analyses were carried out to gain an understanding of energy efficiency practices prevalent in Latvia before and after the war erupted. In addition, the energy community simulation tool has been used to quantify the economic indicators under various conditions. The household survey results show that there, in fact, have been relatively few measures adopted to increase energy efficiency among the population during the energy crisis. The developed policy energy community simulation tool shows, that the energy costs in existing multi-family buildings have increased from around 61 EUR/m2 to 83.5 EUR/m2. However, it is still not motivating households to seek solutions toward more efficient energy use and RES generation due to the governmental subsidies to cover steep cost increases. Interventions targeting efficient energy communities through building managers would give higher strength and long-term effects. [ABSTRACT FROM AUTHOR]

Desalination technology is an effective solution for global freshwater crisis, but conventional methods usually consume fossil energy. Solar-driven interfacial evaporation desalination offers a promising solution to address freshwater scarcity and energy crisis. Its key feature is that the solar absorber is at interface between seawater and air above, where localized heating generates vapor. However, there remains crucial challenges in designing evaporators that are simple to prepare, salt-resistant, stable, and highly efficient in evaporation. Janus evaporators in this study demonstrates superior performance compared to other carbon nanotube-based evaporators reported in current literature, particularly in terms of evaporation and salt resistance. In-laboratory experimental results showed the evaporation rate of deionized water was 2.49 kg m−2 h−1 and the energy conversion efficiency was 91 % under 1 kW m−2. Furthermore, evaporators exhibited excellent salt resistance performance, with evaporation rates of 2.0 and 1.9 kg m−2 h−1 in Bohai seawater and 3.5 wt% NaCl solution, respectively. Additionally, salt crystals formed during the evaporation with salinity higher than 15 wt% for continuous 12 h could be self-cleaned in absence of light. In outdoor environments, a simple device based Janus evaporator also exhibited good desalination performance. Overall, this study indicates great potential of Janus evaporators for future sustainable energy applications in solar desalination. • The Janus MWCNTs-SPNG evaporators, prepared through a simple process, are utilized for solar desalination. • This self-floating evaporator exhibits an average broad solar spectrum absorbance of 99.0 %. • Under 1 kW m−2, the evaporation rate and energy conversion efficiency of the evaporators reach a high level. • The evaporators demonstrate an excellent self-cleaning ability when used for desalination of high-salt feed water. [ABSTRACT FROM AUTHOR]

The park-level integrated energy system characterized by electricity heat cooling storage is regarded as a viable solution to energy and environmental crises due to its cleanliness and high efficiency. However, decision-makers encounter challenges in picking the optimum construction scheme. The paper introduces the matter element extension into the investment decision-making of the park-level integrated energy system. To begin, the paper analyzes the composition of the evaluation index system from the basic composition of the park-level integrated energy system, and establishes a standard system for comprehensive performance analysis, including economic, environmental, energy, security, and intelligence. Second, the subjective weight is calculated using the fuzzy analytic hierarchy process, the objective weight is calculated using the entropy weight. The matter element extension is used to evaluate the alternatives' performance. Finally, an industrial park in Jiangsu Province of China is selected as a case to verify. The results show that good comprehensive performance can be obtained by using photovoltaic, wind turbines, electric energy storage, ice storage, and heat pumps. The decision-making results will not be affected by the change in subjective and objective weight and the change in index weight within 20 %, but changes in matter-element classification would. • Intelligent performance criterion is introduced. • The evaluation system includes economy, environment, energy, security, intelligence. • A decision-making framework for the park-level integrated energy system is established. • A variety of sensitivity analyses were performed. • Using renewable energy and energy storage can obtain good comprehensive performance. [ABSTRACT FROM AUTHOR]

Urban integrated energy systems (UIES) have emerged as a promising solution to address the challenges of urban energy supply and consumption. However, UIES are vulnerable to extreme disasters, such as earthquakes, hurricanes, or floods, which can disrupt the energy infrastructure and lead to power outages and energy shortages. This study introduces a novel approach aimed at enhancing the resilience and load recovery capabilities of UIES in the face of extreme events. The proposed approach encompasses several key innovations, including the comprehensive coordination of local energy sources, prioritized restoration of critical loads, and a node-based modeling approach for gas and heat networks. Firstly, by fully integrating electricity, gas, and heat sources, the overall resilience of UIES is significantly improved, ensuring higher levels of load recovery even in the event of energy supply shortages. Secondly, the critical load recovery is prioritized by load classification, and the weights set can fully consider the connection status and load level of nodes to ensure priority provisioning of important node loads. In addition, the node-based modeling approach allows for accurate consideration of the flow of gas and heat in the pipe during modeling of the pipe energy storage. Finally, the case study section conducts simulation experiments with the UIES E33-G20-H6 test system to verify the effectiveness of the proposed approach and to demonstrate its potential in enhancing the post-disaster load recovery capability of UIES. • Proposed a UIES post-disaster load recovery model, effectively coordinating local resources. • Proposed a cross-energy type node weight calculation method. • Introduced a novel node-based pipeline energy storage model. • Defined a resilience evaluation index for UIES load recovery. • Conducted comprehensive simulations of the post-disaster load recovery process for UIES. [ABSTRACT FROM AUTHOR]

Due to the scarcity of fossil fuels and the potential for energy shortages, the use of energy-efficient devices is of utmost importance. There is an increasing trend in the utilization of heat pumps in air conditioning systems (heating/cooling) for both residential and commercial buildings. This article focuses on the analysis of the technical and economic aspects of gas engine heat pumps (GEHP) in various climates throughout Iran. The evaluation of this article revolves around two perspectives: that of the consumers and the government. The implementation of GEHP has been carefully considered from the consumers' perspective to ensure it does not have any negative effects on them. Additionally, from the government's point of view, the available resources are considered, and the supply of electricity and gas, along with their cost and energy consumption, are thoroughly investigated by the Ministries of Energy and Petroleum. The findings indicate that, from the consumers' perspective, the economic efficiency of GEHP can be up to 11 times higher than that of electric heat pumps (EHP). Moreover, from the government's viewpoint, GEHP leads to a reduction of over 92 % in electricity consumption, 5 % in gas consumption, and 92 % in annual expenses for the Ministry of Energy. Furthermore, the implementation of GEHP increases the income of the Ministry of Petroleum by 9.8 times. • Gas engine heat pumps (GEHPs) proposed to address electricity grid imbalance in Iran. • Consumer and government benefits of GEHPs across diverse Iranian regions analyzed. • Results show GEHPs reduce electricity consumption & costs for the Ministry of Energy. • GEHPs decrease gas consumption & boost revenue for the Ministry of Petroleum. [ABSTRACT FROM AUTHOR]

The ejector can create a vacuum environment without electricity and produce no pollutants, making it widely used in various fields. However, during the working process, a large amount of high-pressure air needs to be continuously supplied to maintain the vacuum level. Given the current energy shortages, it is essential to enhance the entrainment capacity and reduce the energy requirements of the vacuum ejector. To achieve this goal, a two-stage vacuum ejector (TSVE) and structure optimization are adopted. While achieving the same vacuum level (80 kPa or 90 kPa), the air consumption ( m ˙ p) and entrainment ratio (Er) of vacuum ejectors with various shapes are compared. Through structural optimization, five high-performance vacuum ejectors (P p < 0.6 MPa, Er > 0.65) are selected. Computational Fluid Dynamics (CFD) is applied to observe the mixing processes in the vacuum ejector. In addition, the optimized vacuum ejector and TSVE are compared to the original one. An experimental system is designed for this purpose. The findings indicate that both methods can enhance the performance of the vacuum ejector. However, the benefits of TSVE are more pronounced at lower vacuum levels, while the advantages of the optimized vacuum ejector become more obvious as the vacuum level increases. • Two methods are proposed to improve the performance of vacuum ejectors. • The advantages of TSVE are more pronounced at lower vacuum degrees. • As the vacuum increases, the optimized vacuum ejector is more effective. • Provide a reference for the design of vacuum ejectors to suit various occasions. [ABSTRACT FROM AUTHOR]

The 2022 energy crisis augmented the European ambition for RES capacity expansion. However, beyond the issue of "how many" renewables, the research question that remains, is which capacity mixes achieve the emissions reduction, renewable integration, and energy autarky targets in a cost-effective way. This paper tries to answer this inquiry supported by two energy models and one decision support model. The analysis focuses on Greece, as an example country which opted for lignite phase out, turning to temporary heavy reliance on natural gas for power generation. The results indicate that even though from the energy and environmental targets' perspective, more wind turbines in the RES capacity mix yield better results, when incorporating the economic point of view, balanced wind and solar mixes enable the achievement of targets in a cost-effective way. The most cost-efficient mixes consist of 50–55% wind and 45–50% solar capacity, with cost per additional percentage of RES share in the generation mix, equal to 40 million €. Nevertheless, in order to materialize them in a timely manner, and limit the short-term dependency on imported gas, permitting simplification, tenders for large-scale projects and incentives for small-scale projects are needed to facilitate accelerated RES deployment. • A modelling framework allowing exploratory RES capacity expansion is presented. • Renewable energy sources and storage capacity mixes are explored. • Balanced wind and solar mixes achieve energy targets economically efficiently. • RES deployment pathways to 2030 based on different perspectives are analyzed. • Further policy action is needed to enable accelerated RES deployment. [ABSTRACT FROM AUTHOR]

The escalating energy and environmental crises underline the imperative for sustainable cities and societies. For effective and real-time energy management, this study proposes an enhanced building energy consumption prediction system. It introduces a novel concept named region-wide occupant energy-use behavior probability and incorporates it into the input system, which better reflects real-time and complex energy-occupant-environment interactions in buildings. In addition, it integrates the squeeze-and-excitation attention mechanism, sparrow search algorithm, and convolutional neural network processes for optimizing data processing and hyperparameter selection. Validation in seven sample buildings demonstrates the proposed prediction system has a better balance between time and accuracy, reducing 36.32% MAPE and 31.20% CV-RMSE on average compared to all other prediction systems, only with 118.354s of extra time consumption increase compared to the least time-consuming method. Furthermore, this study discusses methods for selecting suitable input systems and algorithms based on building type, data collection conditions, accuracy, and time consumption. Finally, the enhanced prediction is applied to forty-five buildings in a university community, yielding a 12.35% MAPE and a 0.1707 CV-RMSE on average, reaffirming its superiority and practicality. • An enhanced energy consumption prediction including improving input and algorithm is proposed. • It introduces occupant energy-use behavior probability as inputs. • It introduces SSA, SE attention, and CNN modules for improving training algorithms. • It discusses the performance comparison of proposed algorithms and other advanced algorithms. • Method for selecting suitable inputs and algorithms in different buildings is presented. [ABSTRACT FROM AUTHOR]

The development of hot dry rocks (HDRs) is of great significance to adjusting energy structure, alleviating energy shortage, reducing pollution, etc. Low-permeability granite is the predominant rock type in deep HDRs, making fractures the primary pathways for fluid circulation and heat extraction. The production of HDRs is significantly influenced by variable fracture conductivity, but current conductivity characterization primarily relies on the elastic deformation of the matrix, neglecting the impact of damage. Accordingly, we propose an experimental method and a supporting apparatus, which is used to unveil the conductivity evolution characteristics resulting from the comprehensive effects of damage and elastic deformation. The experimental results demonstrate that when subjected to confining force squeezing inward, the fracture conductivity experiences varying degrees of decrease compared to its initial state before the experiment. By utilizing the conductivity evolution rate as the evaluation criterion and conducting grey correlation analysis, it has been determined that temperature exerts the most significant influence on the conductivity evolution, followed by injection flow, and lastly, confining pressure. Moreover, rock particle types and production cycles also have different degrees of effect. After considering the comprehensive effects of damage-elastic deformation at the field-scale, the damage has a positive effect on conductivity enhancement. Our study provides a new approach for the characterization of fracture conductivity evolution for deep geothermal projects. • A new method for damage analysis on the conductivity evolution is formed. • A experimental platform for the measurement of conductivity is developed. • Mechanisms of the damage-elastic deformation on conductivity evolution are analyzed. • Effects and priorities of multiple parameters on conductivity evolution are discussed. [ABSTRACT FROM AUTHOR]

This paper investigates the challenges isolated islands face in transitioning from fossil fuel-based electricity generation to renewable energy sources. The Canary Islands serve as a case study, where photovoltaic and wind power are the primary renewables, but their variability requires a deep techno-economic analysis. The island's energy demand is predicted to rise by 100% due to economic growth, electrification and electric vehicles. However, implementing renewable systems encounters obstacles, such as limited suitable sites and protected areas. The study uses Wilks' methodology and Monte Carlo sampling to explore 59 combinations of randomly selected inputs of the uncertain variables, aiming for a 95/95% coverage and confidence level in the results. In most cases, they experience energy shortages, failing to meet electric demand. Even though a new generation mix appears to cover demand under all circumstances, the uncertainty unveils a different reality, leading to an approximate 25% increase in system costs. Surpluses in energy generation, while seemingly positive, can pose challenges. The new system's Levelized Cost of Energy increases from around 14 to 17c€/kWh. These cost increases are contingent upon future performance and the variability of uncertain parameters, leading to excesses ranging from slightly below 25% to over 40%. • Demand coverage scenario designed to meet electric demand through renewable sources. • Uncertainty analysis of a scenario for the complete electrification of the economy. • Best Estimate Plus Uncertainty analysis implementing the Wilks' methodology. • Resizing the system to cover the demand with 95% coverage and confidence levels. • Differences in using deterministic and stochastic methods for electric demand coverage. [ABSTRACT FROM AUTHOR]

The development and utilization of renewable energies and waste heat contribute to alleviating global issues of fossil energy shortage and environmental pollution, and energy consumption structure transition. Organic Rankine cycle (ORC), specifically transcritical ORC (tORC), garners significant attention for its potential in low-grade heat power generation. However, the existence of non-condensable gases (NCGs) caused by an incomplete vacuum process or organic fluid pyrolysis may have negative effects on system performance. Analyzing and quantifying the effects of different NCGs is of great significance for putting forward the relevant solution measures and helps improve the mechanism of pyrolysis products' influence. In this work, a novel thermodynamic model is proposed for tORC systems considering off-design performance to evaluate the effects of NCGs. Five different hydrocarbons including n-butane, n-pentane, i-pentane, cyclopentane, and n-hexane, are selected as working fluid candidates, while four NCGs are chosen as study objects including nitrogen, methane, ethane, and propylene. The established model realizes the function of analyzing and comparing the effects of different NCGs on thermodynamic performances of tORC systems. Results showed that the tORC system using cyclopentane had the best thermodynamic performances after the optimized design process. The case study indicated that an increase in the mass fraction of NCG caused the condensing pressure increase and system's thermodynamic performance degradation. Furthermore, larger variations were exhibited in the net power output when the design condensing temperature was higher. Finally, the comparative study showed that methane had the greatest negative effects on thermodynamic performances of tORC systems, especially for using n-butane. • A novel thermodynamic model is developed for tORC considering NCG influences. • Off-design performances of tORC with HCs are analyzed under different NCG contents. • The influence mechanism of NCG on system parameters and performance is analyzed. • Comparative study is conducted around effects of different NCGs on tORC with HCs. [ABSTRACT FROM AUTHOR]

With the arrival of the first truly global energy crisis, how to precisely forecast the hydroelectric generation becomes a hot spot for allowing governments to obtain more valuable information. A novel forecasting model, Seasonal Fractional Incomplete Gamma Nonlinear Grey Bernoulli Model (SFIGNGBM(1, 1)), is proposed in this paper to precisely forecast the hydroelectric generation in some countries. First, the seasonal raw data are classified into four seasonal groups based on their significant seasonal fluctuations. Second, a novel SFIGNGBM(1, 1) model is established by combining the Bernoulli equation, the fractional-order accumulation operator, and the incomplete gamma function to further optimize partial parameters in the forecasting model and improve the forecasting performance. Third, the Whale Optimized Algorithm (WOA) is employed to optimize the Bernoulli power exponent η , the fractional order parameter r , and the incomplete coefficient h for minimizing the MAPE values and enhancing the fitting precision. Finally, our results present that our proposed model outperforms a set of baseline forecasting models with the smallest three error measure values in all fitting results, and its MAPE values converge before 10 iterations. This indicates that our proposed model has a favorable forecasting performance with fast-convergence for hydroelectric generation in the elected countries. • A novel model is proposed to precisely forecast the hydroelectric generation. • The proposed model uses the seasonal data grouping to enhance the forecasting precision. • The proposed model uses three approaches to further optimize partial parameters. • A meta-heuristic optimization algorithm is used to further enhance the forecasting precision. [ABSTRACT FROM AUTHOR]

Russia's invasion of Ukraine fuelled an energy crisis, which considerably impacted Europe given its heavy reliance on Russian natural gas imports. This study uses an ensemble of four global integrated assessment models, which are further soft-linked to two sectoral models, and explores the synergies and trade-offs among three approaches to living without Russian gas in Europe: (a) replacing with other gas imports, (b) boosting domestic energy production, and (c) reducing demand and accelerating energy efficiency. We find that substituting Russian gas from other trade partners would miss an opportunity to accelerate decarbonisation in end-use sectors while risking further fossil-fuel lock-ins, despite featuring the lowest gas price spikes and potentially reducing heating costs for end-users in the near term. Boosting domestic, primarily renewable, energy production on the other hand would instead require considerable investments, potentially burdening consumers. Energy demand reductions, however, could offer considerable space for further emissions cuts at the lowest power-sector investment costs; nonetheless, an energy efficiency-driven strategy would also risk relocation of energy-intensive industries, an aspect of increasing relevance to EU policymakers. • Three 'corner' strategies to replacing Russian natural gas in the EU are explored. • EU climate targets can still be reached in all three strategies, but costs increase. • Promoting energy efficiency or domestic renewables can boost end-use decarbonisation. • An 'energy efficiency first' response may save millions € in annual electricity costs. • Southern Europe is the most vulnerable to high electricity prices across strategies. [ABSTRACT FROM AUTHOR]

With recent energy and environmental crises, the energy consumption prediction of air conditioning (AC) is essential. Notably, existing general prediction systems use the air temperature, real-time AC usage, time, occupancy, and AC historical energy consumption as inputs and employ feedforward neural networks. However, such systems face certain challenges. First, the input system may be difficult to reflect the real-time interaction among the building environment, occupants, and energy system; Second, the algorithms need to be upgraded to further improve accuracy, speed up convergence, and avoid overfitting. his paper introduces a novel parameter (group occupant behavior probability, GOBP) in the input system; and uses swarm intelligence algorithms with chaotic mapping and adaptive weight adjustment as optimization with LSTM and BP as the basic algorithms. The verification results obtained from five different buildings indicate the following: (1) The novel prediction system improved prediction performance with 20.08%–78.37 % of MAPE and 3.99%–53.28 % of CV-RMSE, however, it will increase 148.70s–1065.11s of calculation time. (2) For accuracy, using GOBP will improve the 0.30%–51.87 % and 0.18%–64.77 % by using swarm intelligence optimization algorithms. (3) This paper discusses the priority of selecting suitable inputs, algorithms, and optimization to combine a whole prediction system. • The definition of a simplified model using the group occupant behavior probability is proposed. • The chaotic mapping and adaptive weight adjustment are used for optimizing the swarm intelligent algorithms. • A reconstituted AC energy consumption prediction system is proposed with novel inputs and improved algorithms. • The reconstructed prediction system demonstrates improved prediction accuracy and data acquisition difficulty\. • The best combination of inputs and algorithms is discussed based on 60 groups of comparative experiments. [ABSTRACT FROM AUTHOR]

Nowadays, the emissions from conventional vehicles significantly contribute to increasingly serious environmental issues. In addition, the energy crisis and the low energy efficiency of conventional vehicles also offer a good opportunity to develop electric vehicles. Hybrid electric vehicles have better fuel economy compared to conventional vehicles, but they are just an interim step in vehicle development and pure electric vehicles are the ultimate goal. Currently, the technologies of hybrid electric vehicles can be found in numerous literature surveys, however there is a lack of published papers to present a comprehensive technical review for pure electric vehicles. In this study, the characteristics and typical models of energy sources of pure electric vehicles are firstly described. Then the existing pure electric vehicle types are depicted and the environmental impacts of the typical pure electric vehicles are evaluated. Moreover, energy management strategies for pure electric vehicles and charging technologies are investigated. The main challenges faced by pure electric vehicles and corresponding solutions are discussed, whilst the latest developments of pure electric vehicles are presented. The awareness of environmental issues and the energy crisis as well as the incentives from the governments of many countries continuously enhance the rapid development of pure electric vehicles. • The main reasons to develop pure electric vehicles (PEVs) are presented. • The characteristics and typical models of energy sources for PEVs are described. • The existing configurations in PEV types up to present are illustrated. • A brief study on energy management strategies employed in PEVs is conducted. • The main challenges for PEVs and general problem-solving methods are discussed. [ABSTRACT FROM AUTHOR]

Hydrocarbons in the form of natural gas can be used in long-term energy production and could strengthen the defossilisation of the energy sector. However, operational reliability will decrease due to gas shortages in integrated energy systems. Correlations exist among the multiple failure patterns such as overloads of the transmission lines, overloads of the combined heat and power generator, and over-limit operations of gas compressors. The energy reliability assessment problem becomes more difficult to resolve for integrated energy systems than for power systems. This paper proposes an analytical method to address the multiple correlated risks that are caused by the energy network interactions in an integrated energy system. Three case results in MATLAB demonstrate the accuracy and computational efficiency of the proposed method. Compared to the Monte Carlo algorithm, which required approximately 550 s to obtain a precise failure probability with 50 thousand samples, the proposed method takes approximately 12.7 s to obtain an exact failure probability. The advantage is that, different from wide-bound theory and narrow-bound theory, the proposed method can offer a certain value rather than two bounds with respect to the failure probability. • The correlations among different failure patterns are established for integrated energy systems. • Theoretical analysis and statistical experiments are presented to estimate IES reliability. • Network constraints are modelled to fully analyse the nonlinearity of the IES failure estimation. • Both the estimation accuracy and efficiency are ensured using the proposed analytical method. [ABSTRACT FROM AUTHOR]

In recent years, the population growth has caused the water and energy shortages. Therefore, the current paper proposes a novel method for day-ahead optimization of integrated water-heat-electricity systems with the aim of minimizing the fuel cost of the desalination, heat treatment and power production units. The technical limits of the power only units, combined power and water generation units, cogenerators of heat and electricity, heat only units, and the seawater purification process are incorporated in optimization problem. In addition, a real-time demand side management strategy is applied on heat, pure water and power loads to reduce the objective cost function as low as possible by shifting a percentage of each demand from peak periods to mid or off peak hours. Moreover, aggregators of pure electric vehicles participate in bulk energy management by charging electricity during off-peak low-price hours and discharging it over the on-peak high-price periods. To validate the feasibility and cost-effective performance of the water-heat-power hub system, a mixed-integer non-linear program is developed by generalized algebraic mathematical modelling system (GAMS) optimizer and solved by branch-and-reduce optimization navigator (BARON) tool. The ramp down and ramp up limits of the thermal power plants, generation capacity of water/heat/power producers, and the load-generation balance criterion are considered as optimization constraints. It is found that $659,573 cost saving is obtained due to participation of PEVs and DRPs in trigeneration process. • Optimal short-term coordination of combined desalination, heating and power systems is presented. • Participation of aggregated PEVs in water-heat-power nexus is optimally scheduled. • Real-time DRPs is implemented on water, heat and electricity loads. • Four cases are studied incorporating PEVs and DRPs in optimal day-ahead co-scheduling of tri-generation grid. [ABSTRACT FROM AUTHOR]

Abstract In this work, the influence of turbulence intensity on knocking characteristics was studied. Different levels of initial swirl ratio inside cylinder were firstly performed to investigate the effect of turbulence intensity on combustion process. The results show that the enhanced turbulence intensity with increasing initial swirl ratio accelerates the spark-ignited flame (main flame) propagation, resulting in a shortage of combustion phasing (or an advance combustion phasing) and a faster flame speed. Under low turbulence intensity, the faster flame propagation can facilitate knocking combustion because of the enhanced compression of SI flame on the improvement of end-gas thermodynamic conditions. However, further increases in turbulence intensity and flame speed suppress the knocking combustion due to the insufficient time for end-gas autoignition occurrence. Further analysis shows that knock intensity mainly depended on the Unburned Mass Fraction (UBMF). Under lower levels of initial swirl ratios, the enhanced compression of SI flame with the increase of turbulence intensity induces an advanced knock onset, which leads to a larger UBMF and heavier knock intensity. However, under higher swirl ratio scenarios, UBMF and knock intensity exhibited an opposite trend because the consumption of end-gas by the fast main flame played a dominating role. Highlights • Effects of swirl flow on flame speed and engine knock were numerically studied. • In-cylinder turbulence intensity increased with the increase of initial swirl ratio. • Knock intensity behaves a non-monotonic correlation with turbulence intensity. • Knock intensity mainly depends on the Unburned Mass Fraction. [ABSTRACT FROM AUTHOR]

What are the spillover effects of the Russia-Ukraine conflict? Are there other effects besides leading to an energy crisis? We analyze the impact of the Russia-Ukraine conflict on Chinese new energy vehicle (NEV) sales, and then on the Chinese automotive energy structure, based on data of Chinese NEV sales from March 2016 to August 2022, using the Chow test and ordinary least squares (OLS). The results show that, first, the outbreak of the Russia-Ukraine conflict caused a structural and abrupt increase in the sales of NEVs in China, which had a positive impact on the automotive energy structure in China. Second, compared to the impact on battery electric vehicle (BEV) sales, plug-in hybrid electric vehicles (PHEVs) are less affected by their ability to run on both oil and electricity. Third, the boosting effect is mainly realized through the change in the relative price of oil and electricity. Lastly, the positive impact further accelerates the transformation of China's automotive energy and transportation sectors and helps China achieve its dual carbon target at an early date. Overall, this study reveals the positive impact of the Russia-Ukraine conflict on China's automotive energy transition, providing policy insights for the development of the NEV industry. • The outbreak of the Russia-Ukraine conflict caused an abrupt increase in the sales of new energy vehicles in China. • The Russia-Ukraine conflict mainly influenced the sales of new energy vehicles through the mechanism of energy prices. • Battery electric vehicles were more affected by their high dependence on electricity. • It further contributed to the achievement of China's dual carbon target and the upgrading of the transportation sector. [ABSTRACT FROM AUTHOR]

The scale benefit exploitation of oil shale is of great significance to alleviate the current world energy shortage. The autothermic pyrolysis in-situ conversion process (ATS) method shows the feasibility and broad prospects of in-situ efficient utilization of oil shale resources (Guo et al., 2022) [29]. This study conducts in-situ pyrolysis of oil shale by ATS method with oxygen content and injection gas flow as experimental parameters, further clarifying the energy efficiency mechanism and influence characteristics of oxygen injection amount on the in-situ autothermic pyrolysis. The results showed that the in-situ self-pyrolysis reaction can be successfully triggered at 300 °C under the injection parameters of 12,16,21% oxygen content and 3,5,7 L/min injection gas flow rate. However, neither too low nor too high oxygen injection amount can achieve a good in-situ conversion effect. Within the range of parameters considered in this study, the optimal combination of oxygen injection parameters for in-situ pyrolysis of oil shale by the ATS method is 16% oxygen content and 5 L/min injection gas flow. The recovery rate of its cracked oil is 67.1%, and its energy efficiency is as high as 3.46, which is 6.78 times the energy efficiency of in-situ high-temperature nitrogen pyrolysis, realizing the benefits of the transformation of oil shale resources. Meanwhile, according to the apparent optical characteristics of the sample after the experiment and the evolution of the material components in each reaction region, the in-situ autothermic pyrolysis has a significant regional fingering feature. It is hoped that this study will provide reliable data support for the large-scale application of the ATS method. • The energy efficiency mechanism and influence characteristics of oxygen injection amount on in-situ autothermic pyrolysis are further clarified. • The optimal combination of oxygen injection parameters for in-situ autothermic pyrolysis is preliminarily determined. • It is revealed that the in-situ autothermic reaction has a significant regional fingering feature. [ABSTRACT FROM AUTHOR]

Power shortage is a development challenge that most developing countries often face, which not only brings negative impacts to firms' growth, but also has an impact on their environmental performance. In this study, a city-level power shortage index in China from 2008 to 2015 was used to investigate how electricity shortages can affect firms' carbon dioxide (CO 2) emission intensity. We also examined the indirect impact of electricity shortages on CO 2 emission intensity through three channels, namely, technological innovation, energy intensity and productivity. Our empirical results indicate that power shortages have a statistically significant positive impact on firms' CO 2 emission intensity. The findings are robust to various robustness checks and endogeneity concern. The results verify the robustness of our proposed three mechanisms and imply that power shortages have an inhibitory effect on China's green and low-carbon transition in addition to their other well-known negative effects. Therefore, policymakers should consider the effect of power shortages on the energy transition in addition to the conventional negative impacts of power shortages on economic activities. • Assessment of the environmental implications arising from power shortages. • Utilizing China's city-level power shortage index and firm-level emission and economic data. • Power shortages contribute to an increase in the carbon emission intensity of firms in China. • Power shortages raise firms' CO 2 intensity by curbing R&D, boosting energy use, and constraining productivity. [ABSTRACT FROM AUTHOR]

Given the evident spatial disparity in China's energy consumption and economic development, achieving Pareto optimization of regional energy allocation efficiency proves to be a formidable challenge. Based on this, this paper ingeniously proposed an extended "labor-capital-energy-environment" resource mismatch mode, using 2004–2018 provincial panel data to measure and analyze the spatiotemporal pattern of China's energy mismatch and efficiency loss, while further investigating influencing factor. The results show that: (1) Although the degree of resource misallocation has decreased, the reduction of the number of provinces with excessive energy allocation and the serious shortage of energy allocation are the only effects of the optimization of energy resource misallocation, which has not significantly improved the situation overall. (2) Energy resource misallocation has grown to be a significant factor in China's losses in green total factor productivity (GTFP) brought on by resource misallocation. In addition, the extent of energy mismatch's loss in GTFP demonstrates the spatial heterogeneity of the "eastern > central > northeast > west" region. (3) Environmental regulation can significantly reduce energy mismatch and it is more successful at addressing the issue of excess energy than inadequate energy allocation. Also, the "rebound effect" of technological progress is the main factor aggravating the shortage of energy allocation. • We innovatively proposed an expanded "labor-capital-energy-environment" HK model to construct a theoretical framework for the impact of factor price distortions on GTFP. • The optimization of energy resource misallocation is only reflected in the serious shortage of energy allocation and the reduction of the number of provinces with excessive energy allocation. • The degree of GTFP loss of energy mismatch shows the regional heterogeneity of "eastern > central > northeast > west" spatially. • Environmental regulation can significantly improve the problems of energy mismatch and surplus. [ABSTRACT FROM AUTHOR]

The Ukraine war, evolving from a geopolitical crisis to an energy crisis, calls attention to understanding the impacts of restarting oil- and coal-fired power plants on carbon dioxide (CO 2) emissions. To fill the remaining research gaps relating to energy structure and CO 2 emissions in Europe, in this study, we systematically investigate the carbon emissions (CE) and their influencing factors, especially those energy-related, from 1995 to 2020. We find that European Union (EU) countries are currently at different stages of the energy transition. The CO 2 emissions in the EU show clear positive spatial dependence. Thus, we deploy the spatial Durbin model to analyze the influences of consuming coal, oil, natural gas, hydropower, solar and wind energy, nuclear power, and other renewable energy sources on CE. It is observed that natural gas is the only fossil fuel source that does not have a significant positive effect on CO 2 emissions. The developments of hydropower, nuclear power, and other renewable energy sources have significant negative impacts on CE. Estimation results of direct and indirect effects clearly show that many energy sources have spillover effects, suggesting that to fulfill future climate targets, joint-effort must be made among the EU member states. • Positive and significant spatial dependence in CO 2 emissions in the EU. • Despite its fossil fuel nature, natural gas is tested to be carbon-neutral. • Increasing consumption of coal and oil positively affects CO 2 emissions. • Renewable and clean energy sources are negatively correlated with CO 2 emissions. • Reducing carbon emissions in the EU requires a joint effort of all the member states. [ABSTRACT FROM AUTHOR]

Abstract Recent years, renewable energy has taken growing penetration in power systems due to the energy shortage and environmental concerns. As a result, system operators encounter increasing difficulties in solving unit commitment optimization. In this paper, a data-driven unit commitment model is proposed to handle the hybrid uncertainties of wind power and future load. First, a non-parameter kernel density method is utilized to represent the above hybrid uncertainties, and a novel bandwidth selection strategy for the above method is then proposed to capture the inherent correlation between uncertainty representation and unit commitment. Second, a Monte Carlo simulation is developed to integrate the hybrid uncertainties into Value-at-Risk to get a comprehensive system reliability measurement. Third, considering that system operators might be interested in the inherent conflict between reliability and economy, minimizing operation costs and maximizing system reliability are taken as two objectives in the model. To get more practical schedules, the transmission line constraint is considered as well when building the mathematical model. Additionally, by integrating the reinforcement learning mechanism, a novel multi-objective particle swarm optimization algorithm is proposed to solve the complicated nonlinear model. Finally, several experiments were performed to demonstrate the effectiveness of this research. Highlights • Non-parameter kernel density method is used to represent hybrid uncertainties. • A novel bandwidth selection strategy is proposed to adjust uncertainty distribution. • A data-driven multi-objective unit commitment model is proposed. • A reinforcement learning-based particle swarm optimization algorithm is developed. • The model and algorithm are effective to solve unit commitment under uncertainties. [ABSTRACT FROM AUTHOR]

The power industry is playing an increasingly important role in the world economy, and insufficient power supply may lead to huge enormous economic loss throughout the world. Then, the problem of power shortage is receiving a great deal of attention from operators and managers in electrical power system. With the merits of fast startup-shutdown, hydropower is regarded as one of the most reliable renewable energy sources to smooth the electricity shortage of power grid. Thus, this paper focuses on the operation of head-sensitive hydropower system considering power shortage aspect. To effectively address this problem, a novel method based on linear programming and successive approximation is proposed, where the initial hydraulic heads of each hydroplants is generated based on the actual working condition, and then the linear programming method is used to solve the fixed-head hydropower optimization problem involving a carefully-designed min-max optimization objective, while the successive approximation strategy is employed to incrementally improve the solution's quality by dynamically updating water heads of all the hydropower plants. The simulations demonstrate that compared with the original load demand, our method can make an average of approximate 20% reduction in electricity shortage of power system, demonstrating its effectiveness and practicability. [ABSTRACT FROM AUTHOR]

In recent years, the energy crisis severally appears due to the enormous human development. The renewable energy is a new source for world energy demand nowadays, especially the wind energy. Generally, wind energy is the most promising source for electric power demand in world because of the availability of the high wind speed around the year in several locations. This work investigates the drag type wind turbines, which have strong potential in small electric power generation demands. The scenario of this work is divided into two sections; the first one is the numerical analysis for the conventional Savonius with fully optimizing for the shape of the blade using a genetic algorithm. This optimization is performed to maximize the power coefficient with the same dimension of the conventional Savonius turbine. The second part is conducted experimentally to measure the performance of the conventional Savonius with the two and three blades; in addition, the optimal blade design (S shape) is tested to compare the performance of this new design with the conventional one or standard semi-circular blade. The results indicated that the captured efficiency of the optimal shape blade has the optimum value with 28% in contrasts to 14 and 10% for the two and three blades respectively. [ABSTRACT FROM AUTHOR]

Innovative technologies and strategies to decarbonize electricity generation, transport, and heat supply sector are key factors to achieve the global climate targets set by international organizations. One of these strategies implies a significant increase of the share of renewable electricity in the energy mix. Given the intermittent behaviour of renewable energy sources (RES), a detailed assessment of future energy scenarios is required to estimate the potential surplus in electricity production. To facilitate the penetration of renewable energy sources up to significant shares, massive long-term electricity storage technologies must be considered. Among these technologies, power-to-gas (PtG) systems may foster the fossil fuels switch by providing storage of surplus renewable electricity in the form of hydrogen or synthetic natural gas. Thus, this energy carrier could be reconverted to electrical power to cover peak demand periods. In this work, a study of the prospective Spanish scenario is presented and the potential of PtG technology is assessed in terms of expected renewable surplus. We found that the annual electricity surplus for 2050 might vary between 1.4 TWh and 13.5 TWh, and the required PtG capacity would be in the range 7.0–19.5 GW, depending on the renewable production pattern and the increment of demand. [ABSTRACT FROM AUTHOR]

The growing energy crisis and severe atmospheric pollution have put much pressure on the Chinese government and resulted in a lot of revolutionary changes. Considering the upcoming new environmental and carbon tax, how would the tax reform affect the power sector; which types and how much of the different power generation technologies would be added are important questions that need to be answered by decision makers. In this study, a deterministic optimization model is proposed for determining the optimal power mix through the introduction of environmental and carbon taxes. A case study of Hebei Province in China is provided to illustrate the effects of these two taxes. The capacity additions of different generation technologies, air pollutants and CO 2 emission amounts, system costs, and regional power security under different tax levels are profoundly examined. The modeling results indicate that such tax policies could significantly improve to the power mix adjustment as well as the quality of the ambient air quality. Higher tax levels would promote the development of renewable power generation. Meanwhile, different degrees of CO 2 and air pollutant emission reduction can be achieved. The modeling results could help the decision makers identify the satisfactory tax levels in the future. [ABSTRACT FROM AUTHOR]

With the rapid economic development, the demand for energy is becoming stronger in recent years. However, due to the lack of sufficient electrical capacity to satisfy the huge load demand, the problem of energy shortage is becoming an increasingly prominent issue throughout the world. With the merits of fast startup and shutdown, hydropower is often preferred to reduce the electricity shortage of power system as much as possible. Hence, this research presents an effective optimization model for the complex variable-head hydropower system operation problem considering electricity shortage objective, and then a novel quadratic programming (QP) method is developed to resolve this model. In the QP algorithm, the domain knowledge is used to estimate the initial operation condition of hydroplants from upstream to downstream, and then the nonlinear generation characteristics of hydroplants is addressed by solving a series of subproblems with dynamically updated water head, and the quality of solution is gradually improved via iterative optimization. The simulations in different scenarios indicate that the QP method can effectively achieve the goal of reducing electricity shortages of power grid, which provides a new perspective to enrich the published knowledge in the field of hydropower operation. [ABSTRACT FROM AUTHOR]

Exploring efficient renewable energy conversion devices is one of the vital subjects to address the current energy crisis. Double-acting free piston Stirling generator is a promising type of Stirling generator with the advantages of high efficiency and wide energy adaptability, so it has a bright future in the energy sector. In this paper, the SAGE software and acoustic-electric coupling model are used in the generator system for exploring the influence of key parameters on performance. The results indicate that the system exhibits higher power output when the unit number is 4 or 5. The piston clearance may induce greater exergy losses in a double-acting configuration, because of the greater pressure difference and the temperature difference on the two sides of the pistons. Reducing dead volume can improve power density and efficiency. Moreover, any parameter inconsistencies between units can affect the output electric powers of all units. Further investigation reveals that this issue can be effectively addressed by adjusting the external resistance and reactance. This work may offer some guidance for the design of double-acting free piston Stirling electric generators. • An acoustic-electric coupling model for the DAFPSG is established. • The system performs better when the unit number is 4 or 5. • The loss induced by the piston clearance may greatly deteriorate the performance. • Performance degradation induced by parameter inconsistency can be partly suppressed. [ABSTRACT FROM AUTHOR]

Not only due to the energy crisis European policymakers are exploring options to substitute natural gas with renewable hydrogen. A condition for the application of hydrogen is a functioning transportation infrastructure. However, the most efficient transport of large hydrogen quantities is still unclear, and deeper analyses are missing. A promising option is converting the existing gas infrastructure. This study presents a novel approach to develop hydrogen networks by applying the Steiner tree algorithm to derive candidates and evaluate their costs. This method uses the existing grid (brownfield) and is compared to a newly built grid (Greenfield). The goal is the technical and economic evaluation and comparison of hydrogen network candidates. The methodology is applied to the German gas grid and demand and supply scenarios covering the industry, heavy-duty transport, power, and heating sector, imports, and domestic production. Five brownfield candidates are compared to a greenfield candidate. The candidates differ by network length and pipeline diameters to consider the transported volume of hydrogen. The economic evaluation concludes that most brownfield candidates' cost is significantly lower than those of the greenfield candidate. The candidates can serve as starting points for flow simulations, and policymakers can estimate the cost based on the results. • Conceptual evaluation method for the conversion of the gas transmission grid in the context of the energy transition. • Application of Steiner Tree Algorithm to derive hydrogen network candidates. • Development of five brownfield and one greenfield candidate based on a case study for Germany. • Starting points of the systematic evaluation of hydrogen with regard to costs and security of supply. [ABSTRACT FROM AUTHOR]

The energy crisis and environmental pollution are the most serious threats to human survival. Currently, many countries and regions have set the goal of carbon neutrality by 2030–2060. Multi-energy systems (MESs), which integrate different energy systems such as electricity, natural gas and district heating networks, have received significant support and development to expedite this process. However, due to the complexity of its structure, the consideration of such a concept necessitates an appropriate model to describe the planning and optimal energy flow throughout the system. Another critical direction for future MES development is the rapid and accurate calculation of MES models. Numerous research institutions and large corporations have concentrated their efforts in this area, which has resulted in the publication of thousands of papers. This article aims to provide readers with a comprehensive and critical overview of the typical MES model and its associated optimisation algorithms to assist them in identifying appropriate models and associated solution methods for their research or engineering problems. We summarise the concept and potential three-layer structure of MESs and introduce their fundamental components in depth. Subsequently, according to different research or application objectives, we divide MES models into three categories and discuss their application scenarios, modelling methods and solution techniques. Additionally, we report six significant obstacles encountered in current real-world MES projects based on our practical engineering experience and propose seven potential development directions for future optimisation of the operating technology. • Providing readers with an overview of the MES model and related algorithms. • Summarizing the concept and potential three-layer structure of MESs. • Dividing MES models into three categories, discussing their modelling and solving. • Reporting six significant obstacles encountered in current MES projects. • Proposing seven potential development directions for future technology. [ABSTRACT FROM AUTHOR]

The recent Russia-Ukraine war has reignited the world's biggest energy crisis in decades. As an important pillar of energy security, system resilience has attracted much attention. Previous research has focused on conception, composition and measurement. Few captured the dynamic features of resilience process of energy systems. In this paper, an input-output nested system dynamics (IO-SD) model is proposed to study the resilience of natural gas systems in a crisis. As a special feature, the model captures both inter-sectoral linkages and dynamic transmission of the crisis. The study highlighted and analyzed the role of emergency measures such as the release of stockpiles and emergency supplies. Later, the resilience performance of China gas system in 2017-crisis was examined in various aspects, e.g., economic loss and efficiency of emergency measures. We find that strategic storage could be beneficial to the resilience of NG systems. But expanding the storage size in isolation is not enough. Two key measures to enhance the resilience of the system are: (1) increasing the flexibility and operational efficiency of emergency facilities, e.g., storage releasing capability and gasification efficiency; and (2) building efficient infrastructure and services, enhancing connectivity of storage infrastructure and supply networks, and removing bottlenecks in regional interconnection. • Resilience plays an increasing important role in securing natural gas security. • The proposed resilience framework considers both restoring and economic performance. • The IO-SD model captures dynamic feature and intersection relations of gas system. • Enhance storage releasing rate can improve China gas system resilience effectively. • Strengthen connectivity of infrastructure can improve system resilience effectively. [ABSTRACT FROM AUTHOR]

The environmental emergency and energy crisis are serious challenges exposed to Europe, which results in a fundamental shift in the linkages regarding fossil fuel, renewable energy and carbon trade markets. This study investigates the spillover effects based on the quantile VAR network. We find that the impact of the extreme market on the connectedness network of underlying assets proves to be more pronounced compared to the standard conditions. The risk spillovers in extreme cases originate primarily from the renewable energy and carbon markets, while fossil fuels account for the most significant contributor under normal circumstances. Hence, investors are supposed to develop dynamic and flexible portfolios to hedge potential risks. Concerning the governments, corresponding initiatives should be implemented to facilitate the continuous evolution of the European Union carbon trading system and the energy transition. These provide a feasible solution to Europe's dual dilemma regarding energy security and the climate emergency. • The spillover effects linkages among fossil fuel, renewable energy and carbon trade markets. • The extreme market on the connectedness network proves to be more pronounced. • The risk spillovers in extreme cases with significant contributor under normal circumstances. • Develop dynamic and flexible portfolios to hedge potential risks. • Facilitate the continuous evolution of the EU carbon trading system and the energy transition. [ABSTRACT FROM AUTHOR]

In this study, an inexact two-stage stochastic fuzzy programming (ITSFP) is developed for regional power generation planning with considering the intermittency and fuzziness of renewable energy power output. ITSFP incorporates interval-parameter programming (IPP), two-stage stochastic programming (TSP), and fuzzy credibility constrained programming (FCCP) within a general optimization framework which can tackle uncertainties expressed as intervals, probability distributions, and fuzzy sets. The developed method is applied to a regional electric power system over a one-day optimization horizon coupled with air pollution control. The power generation schemes, imported electricity, and system cost under various environmental goals and risk preferences are analyzed. The obtained results indicate that the model can provide a linkage between predefined electric power generation schedule and the relevant economic implications, as well as more reasonable decision alternatives for decision makers by loosening system constraints at specified confidence level. Besides, the fuzziness of forecast error corresponding to the variability of renewable energy resources could be effectively reflected. Moreover, the results are useful for addressing the trade-off between system economy and system risk. [ABSTRACT FROM AUTHOR]

By large group decision-making, obtaining large group response plans within a limited amount of time plays a crucial role under emergency of local energy shortage. An energy network dispatch optimization under emergency of local energy shortage has been found to possess broad applications in energy system; however, the existing mechanism did not consider and failed to provide an approach to deal with the large group decision makers taking part in the energy network dispatch optimization. In this paper, the main contribution is that an energy network dispatch optimization under emergency of local energy shortage with web tool for automatic large group decision-making is proposed, in which anytime and anywhere, each expert can login in the system and provide his/her personal preference to the emergency of local energy shortage alternatives, and then the large group response plans to emergency of local energy shortage will be obtained within a limited amount of time. A case study and simulation results are presented that prove that the proposed model is feasible, and achieves better performance of energy network dispatch optimization under emergency of local energy shortage than the method proposed in the literature. [ABSTRACT FROM AUTHOR]

New energy vehicles (NEVs) have become an effective way to solve environmental pollution and energy shortages in transportation. Therefore, China has issued Regulations on Average Fuel Consumption and New Energy Vehicle Credits for Passenger Vehicle Enterprises(dual credit policy), to promote the development of NEVs. This paper establishes a technological innovation decision-making model under the conditions of information asymmetry and information sharing. This model obtains the optimal solution for NEV technology innovation considering the market demand prediction of the manufacturer and supplier. Moreover, this paper provides the conditions for information sharing of demand forecasting for the manufacturer and supplier and designs the incentive mechanism for information sharing. The results show that the dual credit policy positively stimulates the technological innovation of NEVs, but information asymmetry negatively affects the dual credit policy's incentive effect. The supplier is always willing to share information; however, whether the manufacturer shares information depends on the supplier's technology innovation cost coefficient. In addition, implementing the dual credit policy enhances the manufacturer's and supplier's willingness to share information; the higher the credit coefficient of technological innovation and the credit price is, the stronger the manufacturer and supplier's willingness to share information. • Dual credit policy can effectively stimulate to increase NEVs' technology innovation. • Dual credit policy enhances both parties' willingness to share information. • Higher credit price & technology innovation credit coefficient, policy affect more. • Information asymmetry negatively affects the dual credit policy's incentive effect. • Supplier is willing to share information, but manufacturer needs certain conditions. [ABSTRACT FROM AUTHOR]

The global energy shortage is gradually intensifying. A pump is a versatile device with significant power consumption in energy conversion equipment. Of the world's total energy generated, pumps consume about 20%. With the continuous expansion of the application field, the pump's transient working characteristics are increasingly getting important for energy saving and consumption reduction. Due to ignoring the influence of impeller rotational acceleration and fluid inertia, traditional methods have significant errors in predicting transient energy conversion characteristics, which leads to improper design and unreasonable lectotype, resulting in low pump operation efficiency. Improving the prediction accuracy of transient energy characteristics and ultimately improving the pump operation efficiency is considered to be the key solution to reducing energy consumption. Therefore, this research paper proposes a prediction model of transient energy characteristics in a mixed-flow pump. Firstly, the dynamic hydraulic torque, an important parameter of energy growth during the start-up process was calculated according to the momentum moment equation. Secondly, based on the torque energy balance equation and the relative Bernoulli equation, the transient head expression during the start-up process of the mixed-flow pump is derived, and the saddle area correction is carried out. Finally, considering the additional shaft power loss caused by the pump's backflow and clearance leakage flow, a calculation method for the instantaneous energy conversion efficiency of the mixed-flow pump is established. The accuracy of the model is compared with traditional quasi-steady-state calculation results. The results show that the predicted head of the model is in good agreement with the test head, the head estimation error is reduced from 15% to 5%, and the modified head in the saddle area is closer to the test value. In addition, this study also shows the ability of the model to simulate different start-up modes and start-up times. The models presented in this paper help guide the transient hydraulic design and prediction of transient energy characteristics of pumps. • Prediction model for transient energy characteristics of mixed-flow pump is proposed. • TransIent efficiency model including turbulence loss is established. • The head estimation error is reduced from 15% to 5%. • The ability of simulating different start-up modes and start-up times is analyzed. [ABSTRACT FROM AUTHOR]

In order to solve the energy crisis, many countries have actively explored alternative energy and more efficient power cycle systems, among which flexible operation supercritical carbon dioxide (S–CO 2) power cycle system with a compact structure has attracted widespread attention. However, the problem of flow instability in the natural circulation parallel channel system needs to be solved. When the oscillation flow instability occurs, it not only affects the operation efficiency, but also cause local damage for system. In this paper, a new experimental facility was established to study the flow characteristics of S–CO 2 natural circulation with parallel channels. Three types of flow instability phenomena were observed in the experiment: the systematic oscillation occurs when the outlet temperature of the test section reaches the pseudo-critical temperature. The type Ⅰ oscillation of parallel channels occurs in the low-power region, and the type Ⅱ oscillation of parallel channels occurs in the high-power region. The influences of different system pressures and inlet temperatures on steady-state flow are discussed and the damage of flow oscillation is evaluated. A theoretical model of steady state characteristics of natural circulation is established. The experimental results and theoretical model can provide reference for natural circulation system design and risk assessment. • A new theoretical dimensionless model of natural circulation was proposed. • Systematic oscillations and two types of parallel channel oscillations were observed. • The flow instability region is marked by dimensionless numbers. • The hazard assessment of three flow instabilities was carried out. [ABSTRACT FROM AUTHOR]

As an innovation of the Chinese government energy policy, solar photovoltaic poverty alleviation projects (PPAPs) play a positive role in solving energy shortages and poverty reduction. However, there is limited research on energy poverty in China's PPAP areas. This study has proposed a three-dimensional theoretical model for energy poverty, evaluated the energy poverty in the PPAP areas and analyzed its influencing path. The results show that: (1) The implementation of PPAPs has improved energy poverty to various degrees in all three types of solar resource areas; (2) The availability of energy services is the most prominent problem in energy poverty, and the PPAPs improve the availability of energy services the most; (3) Farmers' economic status and education have the greatest impact on alleviating energy poverty, and (4) The PPAPs directly improve the availability of energy services by increasing power generation and stability of the grid, and indirectly affect the cleanliness of energy consumption and energy affordability through farmers per se, thus affecting local energy poverty. Thus, public policies for promoting clean energy and energy utilization technologies (such as solar PV, etc) would help alleviate energy poverty in the rural areas. [Display omitted] • Proposed a three-dimensional theoretical model for energy poverty. • Investigated the determinants of energy poverty in China's PPAP areas. • China's PPAPs have direct and indirect effects on local energy poverty. • Farmers' economic status and education are the most significant factors. [ABSTRACT FROM AUTHOR]

With the aggravation of energy crisis and greenhouse effect, energy transformation is imperative. The problems of renewable energy uncertainties and carbon emission need to be solved urgently. Therefore, to deal with uncertainties and cut down carbon emission in the park-level integrated energy system (IES), this paper proposes a low-carbon robust economic dispatch model considering price-based integrated thermo-electric demand response (DR) and vehicle-to-grid (V2G). Firstly, The V2G model, price-based integrated thermo-electric DR model and carbon trading model are established. Then, considering energy balance constraints of power/natural gas/heat, operation constraints and energy exchange constraints, a deterministic day-ahead scheduling model for low-carbon operation is presented with the goal of maximizing system social welfare. Finally, considering the uncertainties of wind/solar generation and electricity/heat load, a two-stage robust economic dispatch model is proposed. Case studies verify that the deterministic scheduling model could effectively increase system flexibility, while reducing the total production cost by 18.58% and carbon emission by 16.77%. Furthermore, the proposed robust scheduling model sacrifices 12.65% economy to maintain system security against various uncertainties and maintain carbon emission in the worst-case scenario with an acceptable range. • A low-carbon robust economic scheduling model for the park-level IES is proposed. • The proposed model ensures secure and economical operation of IES while maintaining an acceptable range of carbon emission. • V2G technology and price-based integrated thermo-electric DR could improve system flexibility. • Considering carbon trading mechanism, a full carbon utilization cycle is modeled. [ABSTRACT FROM AUTHOR]

In India, power shortage is a major issue for economical growth. According to the data provided by National Load Despatch Centre, peak power shortage in Tamilnadu during the year 2014 varies between 3000 MW and 4000 MW. This power shortage can be reduced by increasing installed capacity of conventional and non-conventional energy sources. But the construction of new generation plants is cost-effective and also power generation is not assured throughout the year. This power shortage can also be minimized by implementing load management in the consumer side. This paper focuses on the reduction of peak demand by the proper operating schedule of major equipments. For this analysis, three rice industries have been considered. The major operating sections in the rice industries are pre-cleaning, soaking, pre-milling and milling. In this proposed work, to reduce the peak demand, the operating time of pre-milling section is rescheduled using the optimization techniques, DE (Differential Evolution), PSO (Particle Swarm Optimization) and ABC (Artificial Bee Colony). The rescheduled results given by DE, PSO and ABC algorithms reduce the peak demand cost of the energy consumed in three rice industries. However, the optimum scheduling obtained by ABC reduces the feeder power flow than the DE and PSO scheduling. [ABSTRACT FROM AUTHOR]