Renewable and Sustainable Energy Reviews最新文献

Nearly 40 % current global annual energy-related CO₂ emissions come from the fossil fuel-dominated power sector. Accurately accounting for carbon emissions in power systems from the consumption-based perspective is crucial for achieving the low-carbon power transition. Consumption-based carbon accounting has emerged as a major research focus, which aids in the implementation of targeted measures such as low-carbon demand response and dispatch. Choosing an appropriate method to account carbon emission needs thorough consideration of characteristics of various methods. There still lacks a systematic review that concludes the essence and application status of these methods, as well as comparing their advantages and disadvantages. To address this gap, a consumption-based carbon accounting framework for power systems is proposed. This framework groups four typical methods into two perspectives: Attributional methods and consequential methods. The principles, calculation approaches, and research application status of these methods are comprehensively summarized in a transparent, integrated and comparative manner, which makes progress in two critical limitations: (i) temporal and spatial granularity, and (ii) consideration of the actual topology and operational constraints of the power grid. As improvements in the transparency and quality of electricity data and expansion of application scenarios, the flexibility and applicability of the framework will continue to improve to achieve the unity of efficiency and fairness. The proposed framework can serve as a valuable guide to conducting research and exploration on low-carbon energy management, policy and regulatory decisions and to inform the development of effective strategies for the low-carbon transition of power systems.

Lithium-based batteries are essential because of their increasing importance across several industries, particularly when it comes to electric vehicles and renewable energy storage. Sustainable batteries throughout their entire life cycle represent a key enabling technology for the zero pollution objectives of the European Green Deal. The EU's (European Union) new regulatory framework for batteries is setting sustainability requirements along the whole battery, including value chains. For a comprehensive assessment of battery technologies, it is necessary to include a life cycle thinking approach into consideration from the beginning.

This review offers a comprehensive study of Environmental Life Cycle Assessment (E-LCA), Life Cycle Costing (LCC), Social Life Cycle Assessment (S-LCA), and Life Cycle Sustainability Assessment (LCSA) methodologies in the context of lithium-based batteries. Notably, the study distinguishes itself by integrating not only environmental considerations but also social and economic dimensions, encapsulating the holistic concept of sustainability. Challenges unique to each assessment method are outlined, including data availability (with 35 % of the reviewed studies having openly accessible inventory data), methodological inconsistencies, uncertainty around future costs and social impacts. Difficulties such as data uncertainty, challenges in cost comparison, and the lack of standardized measures are underscored. The research identifies critical future directions for LCA, including the need for better data quality, adaptation to new technologies, and alignment with Sustainable Development Goals (SDGs). Future research directions are suggested -including the standardization of methodologies, and fostering interdisciplinary collaboration. Overcoming these challenges holds the potential to advance sustainable practices in the battery industry and contribute to a cleaner energy future.

Thermal demand for heating and cooling has been predominantly supplied by fossil fuel combustion in the United States, although low-carbon alternatives are extensively available including geothermal, solar thermal, and waste heat. This study analyzed end-use energy consumption, fuel expenditure, and data center commissioned power data to geospatially characterize the U.S. low-temperature heating and cooling demand at the county level in residential, commercial, manufacturing, agricultural, and data center sectors and understand potential opportunities for geothermal applications. In the analysis, the regional-scale energy consumption data was incorporated with system efficiencies to address actual demand and was then disaggregated with weighting factors to the county level. The results indicated that total low-temperature heating and cooling demand is 16.7 EJ, combining heating demand of 10.8 EJ and cooling demand of 5.9 EJ. Overall, 59.9 % (10 EJ) of the low-temperature heating and cooling demand occurred in the residential sector. The heating and cooling demand visualized in maps represented that the geospatial distribution of heating and cooling demand in the residential and commercial sectors is governed by the number of housing units and climate zone designations, while heating and cooling demand in the manufacturing, agricultural, and data center sectors is dependent on the number and location of facilities. The results also demonstrated that geothermal heat pumps are broadly used in the residential and commercial sectors for heating and cooling in the U.S. Midwest, South, and Northeast regions but are limited in the West, implying great decarbonization potential in the future.

This study comprehensively reviews biomass gasification and its empirical correlations for syngas production. The types of biomass, reactors, gasification agents, and gasification operating conditions were evaluated thoroughly using PRISMA and meta-analysis approach. Decreasing syngas production was observed in air gasification with increasing ER, but increasing syngas was observed in steam gasification with increasing S/B, both at a constant temperature. Biomass properties dictate syngas production in air gasification, whereas the optimum syngas production is effectively driven by the S/B value. The predicted total syngas yield may have the global maximum, the global minimum and the saddle-point optimisation form, depending on the gasification agents and the reactor types. Air gasification of MSW and wood pellets, and steam gasification of wood pellets might be performed using a fluidised-bed or updraft reactor because their mean syngas productions are not significantly different. Steam gasification with sawdust resulted in significantly different syngas production in downdraft, updraft, and fluidised-bed reactors. This study is concluded by providing guidelines for selecting the most appropriate biomass for gasification, the required operating parameters according to the type of gasification agent and the gasifier reactor.

With the development of new energy vehicles, EVs have received ever-increasing research attention as an essential strategic orientation for the world to face climate change and energy issues. EVs have significant energy-saving and emission-reduction advantages, but power battery state estimation accuracy has always been a bottleneck restricting its promotion. Centered on power battery cloud management and control methodology, this work systematically examines the development of battery cloud models, formulates battery life and safety management strategies, and investigates the integration of cloud management technology within advanced electronic and electrical architectures. Firstly, the overall framework of the device–cloud fusion technology is introduced. Secondly, aiming at the complex problem of power battery state estimation, the models and fusion estimation methods of the cloud and vehicle battery models are summarized. Then, the joint estimation method is outlined for the power battery states, including the state of charge and state of health. Finally, a viable cloud-based management solution is elucidated through a comprehensive comparison and analysis of the current battery management technologies’ strengths and limitations. This offers a theoretical framework for advancing power battery cloud management and control technology.

As the energy system transitions to an intelligent smart grid with a mostly renewable energy supply, synthetic energy time series are required to facilitate the development and improvement of methods for smart grid applications. These synthetic energy time series must exhibit characteristics similar to the real energy time series and applicable to specific use cases. Furthermore, evaluation methods must be applied to verify that synthetic energy time series have the desired characteristics. Whilst many methods exist in the literature to generate synthetic energy time series, up until now, no work has focused on analysing and comparing these methods. Therefore, this study provides a structured literature review of generating synthetic energy time series. The review focuses on five aspects: (1) Identifying methods used to generate synthetic energy time series, (2) categorising these methods according to the generation approach taken, (3) analysing the characteristics of these generated synthetic energy time series, (4) identifying the uses cases for which the time series are generated, and (5) considering how the generated synthetic energy time series are evaluated. In total, this study reviews 169 articles focusing on generating synthetic energy time series and identifies several key research gaps leading to multiple open research fields. The most important open research fields include the need for a standardised evaluation, more generation methods for synthetic time series from generation and battery storage systems, and a stronger focus on further use cases.

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Making transport sustainable is one of the grand global challenges. Numerous policies are currently underway, particularly in the European Union, to mitigate greenhouse gas emissions substantially. Germany has set ambitious targets, aiming for a 65% reduction in emissions by 2030 and becoming climate neutral by 2045. Unlike other sectors, Germany and the European Union have struggled to reduce emissions in the transport sector. Thus, the effectiveness of the current policy framework has been questioned. This study presents the outcomes of a comprehensive policy review across multiple sectors with direct or indirect implications for the sustainable transition in the transport sector. For this purpose, 44 policies were identified in the fields of transport, energy, agriculture, bioeconomy, and climate change. A mixed method content analysis approach was used to scrutinize policy documents, examining targets, fuel alternatives, transport modes, and coherence among policies. The findings underscore Germany’s commitment to meet the climate targets through different pathways regulated by national and European policies. Electrification has been the predominant pathway for the decarbonization of the transport sector, followed by renewable fuels, e.g., Power-to-X fuels. Although well-designed plans exist for the electrification of road passenger transport, findings indicate low policy support for renewable fuels, especially for parts of the transport sector not well-suited for electrification, e.g., aviation and maritime.

The aim of this investigation is the development of robust models for the performance prediction and automatic monitoring of large photovoltaic systems, based on historical and real-time electric and thermal data. This issue is increasingly important due to the worldwide diffusion of large photovoltaic systems and their need to identify and predict failures and malfunctions, in order to promptly assess the convenience of maintenance actions. The present model describes the response to irradiance and temperature conditions of both modules and inverters and also it is able to predict shading conditions able to affect the energy yield. The model has been validated against real electric measurements in 6 large PV plants located in southern Italy and it demonstrated to be able to predict the real time power production within a 4.1 % error. Even more importantly, the model and its comparison with subhourly measurements over several years has demonstrated its effectiveness in detecting downtime conditions caused by inverter or string problems. Simulations and measurements revealed that missed energy production due to electrical grid coupling downtime can exceed 50 % on certain days and that the shading conditions (up to 5 % of the daily energy production) can be easily detected and separated from component problems, thus avoiding false alarms. Finally, the analysis of aerial infrared images allowed to further test the model in failure detection capability, assess the relationship between thermal anomalies and underperformance conditions and in predicting the yearly deterioration rate at the PV plants.

In spite of the rising demand for rechargeable batteries and competing advancements in the field, aqueous electrolytes still hold advantage over other types of electrolytes because of their inherent safety, ease of fabrication, feasibility, and environmental friendliness. Addressing the leakage issue of liquid aqueous electrolytes, gel polymer aqueous electrolytes are attracting increasing attention. They have ionic conductivities between liquid and solid electrolytes, are mechanically and thermally stable, have high flexibility and corrosion resistance, and can accommodate volume expansion. However, the transition from synthetic polymers to natural polymers is still in the research phase. This review links biomass materials and aqueous electrolytes of rechargeable batteries by summarizing and analyzing the potential of a wide array of natural polymers (e.g., cellulose, alginate, carrageenan, natural gums, etc.) from various sources (plants, animal, algae). The properties, composites, and electrolyte fabrication techniques of each material are discussed, and future perspective is presented. Results show that the most used fabrication technique is solution casting while the highest ionic conductivity demonstrated is 96.89 mS/cm by a cellulose-based electrolyte. Additionally, natural gums show the highest capacity retentions (100 % even after 3300 cycles). By reviewing a wide range of materials and fabrication techniques, we aim to offer valuable insights into the development of innovative energy storage solutions that are sustainable, safe, and feasible.

Building energy modeling is pivotal in achieving sustainable energy goals throughout a building's design and operation. However, discrepancies often arise between actual energy consumption and predictions made by building energy models due to uncertainties in input parameters. This study addresses this challenge by calibrating building energy modeling with empirical data, focusing on apartment buildings in South Korea. Specifically, it targets public and lease apartments, which is crucial in attaining Zero Energy Building (ZEB) ratings. This study develops an apartment building model based on architectural design and energy use patterns, employing the whole building energy simulation tool, EnergyPlus. Key calibration elements were identified through systematic literature reviews, statistical analysis, architectural drawings, and monitoring data. The calibrated model achieved a prediction error of 2.7 % and a CV(RMSE) of 9.4 %, closely mirroring actual measurements. This was particularly influenced by using realistic occupancy schedules instead of generic ones. The results provide valuable insights for refining the residential building energy modeling process, especially in the context of ZEB goals. The study underscores the importance of accurate data collection and model calibration in bridging the gap between theoretical models and real-world energy consumption.