Advances in Applied Energy最新文献

The urban energy infrastructure is facing a rising number of challenges due to climate change and rapid urbanization. In particular, the link between urban morphology and energy systems has become increasingly crucial as cities continue to expand and become more densely populated. Achieving climate neutrality adds another layer of complexity, highlighting the need to address this relationship to develop effective strategies for sustainable urban energy infrastructure. The occurrence of extreme climate events can also trigger cascading failures in the system components, leading to long-lasting blackouts. This review paper thoroughly explores the challenges of incorporating urban morphology into energy system models through a comprehensive literature review and proposes a new framework to enhance the resilience of interconnected systems. The review emphasizes the need for integrated models to provide deeper insights into urban energy systems design and operation and addresses the cascading failures, interconnectivity, and compound impacts of climate change and urbanization on energy systems. It also explores emerging challenges and opportunities, including the requirement for high-quality data, utilization of big data, and integration of advanced technologies like artificial intelligence and machine learning in urban energy systems. The proposed framework integrates urban morphology classification, mesoscale and microscale climate data, and a design and operation process to consider the influence of urban morphology, climate variability, and extreme events. Given the prevalence of extreme climate events and the need for climate-resilient strategies, the study underscores the significance of improving energy system models to accommodate future climate variations while recognizing the interconnectivity within urban infrastructure.

Electrochemical energy storage technologies hold great significance in the progression of renewable energy. Within this specific field, flow batteries have emerged as a crucial component, with Zinc–Nickel single flow batteries attracting attention due to their cost-effectiveness, safety, stability, and high energy density. This comprehensive review aims to thoroughly evaluate the key concerns and obstacles associated with this type of battery, including polarization loss, hydrogen evolution reaction, and dendrite growth, among others. Additionally, the study highlights ongoing research endeavors focused on addressing these concerns, such as optimizing battery operating conditions and developing new electrodes. Furthermore, recent advancements in experimental processes and multi-scale numerical simulations of Zinc–Nickel single flow batteries, facilitated by the visual literature analysis software VOSviewer, are also explored. The primary objective of this review is to acquire a comprehensive understanding of the electrochemical reaction and internal mass transfer mechanism of Zinc–Nickel single flow batteries, while also anticipating future research directions and prospects.

Industrial demand response will become increasingly important in power grids with high shares of variable renewables, yet the existing knowledge on how the industrial electricity demand and flexibility will change with the decarbonization of chemical processes is limited. Here we develop a mixed-integer linear optimization model, which we use to compare the cost and flexibility of the most relevant decarbonization options for the combined chlor-alkali electrolysis (CAE) and vinyl chloride monomer (VCM) production process. We combine product and energy storage to enable the full flexibility potential of the decarbonized process. Our results show that flexible operation of the CAE process is deemed technically possible but limited by internal process dependencies due to decarbonization of the VCM production. Combining energy and product storage for demand response enables up to 4% operational cost reduction by shifting loads during peak price hours. High overcapacity of PEM electrolyzers is required to release the full flexibility potential in the hydrogen based decarbonization option, while the less flexible direct electrification option shows a potential for OPEX reduction. Full decarbonization of the combined CAE and VCM process without increasing operational cost significantly appears difficult. Our study emphasizes demand response through product and energy storages as a viable pathway for minimizing the added cost, and also enables a significant reduction of electric demand in high-price hours.

Transportation electrification, involving large-scale integration of electric vehicles (EV) and fast charging stations (FCS), plays a critical role for global energy transition and decarbonization. In this context, coordination of EV routing and charging activities through suitably designed price signals constitutes an imperative step in secure and economic operation of the coupled power-transportation networks (CPTN). This work examines the non-cooperative pricing competition between self-interested EV charging service providers (CSP), taken into account the complex interactions between CSPs' pricing strategies, EV users' decisions and the operation of CPTN. The modeling of CPTN environment captures the prominent type of uncertainties stemming from the gasoline vehicle and EV origin-destination travel demands and their cost elasticity, EV initial state-of-charge and renewable energy sources (RES). An enhanced multi-agent proximal policy optimization method is developed to solve the pricing game, which incorporates an attention mechanism to selectively incorporate agents' representative information to mitigate the environmental non-stationarity without raising dimensionality challenge, while safeguarding the commercial confidentiality of CSP agents. To foster more efficient learning coordination in the highly uncertain CPTN environment, a sequential update scheme is also developed to achieve monotonic policy improvement for CSP agents. Case studies on an illustrative and a large-scale test system reveal that the proposed method facilitates sufficient competition among CSP agents and corroborates the core benefits in terms of reduced charging costs for EV users, enhancement of RES absorption and cost efficiency of the power distribution network. Results also validate the excellent generalization capability of the proposed method in coping with CPTN uncertainties. Finally, the rationale of the proposed attention mechanism is validated and the superior computational performance is highlighted against the state-of-the-art methods.

Buildings play a crucial role in global electricity consumption, but their function is evolving. Rather than merely consuming energy, buildings have the potential to become energy producers through participating in flexibility services, which involve demand response and distributed energy supplies. However, the new technological and societal challenges that arise from temporal and spatial changes on both supply and demand sides make building services increasingly complex. This paper presents an opportunity for flexibility services offered by building energy systems via power-to-heat technology and discusses four key aspects: quantitative indicators based on thermal inertia, model predictive control for building flexibility, flexible system optimization for smart buildings, and applications of flexible services. Thermal inertia is a crucial factor that transcends technical constraints and serves as a bridge between the demand and supply sides. Demand-side response and data-driven cogeneration under model predictive control are essential for managing building flexibility. In addition, flexible system optimization is achieved through the combination of demand-side trading and disturbed system optimization. Applications of flexible services represent a combination of demand-side trading and disturbed system optimization in the fields of engineering and sociology. Finally, the paper explores the challenges, as well as the potential and models of building flexibility services technologies, including features that can facilitate automated operational decision-making on both the demand and supply sides.

Lithium is a critical material for the energy transition, but conventional procurement methods have significant environmental impacts. In this study, we utilize regional energy system optimizations to investigate the techno-economic potential of the low-carbon alternative of direct lithium extraction in deep geothermal plants. We show that geothermal plants will become cost-competitive in conjunction with lithium extraction, even under unfavorable conditions and partially displace photovoltaics, wind power, and storage from future renewable energy systems. Our analysis indicates that the deployment of 33 deep geothermal plants in municipalities in the Upper Rhine Graben area in Germany could provide enough lithium to produce about 1.2 million electric vehicle battery packs per year, equivalent to 70% of today`s annual electric vehicle registrations in the European Union. As this number represents only a small fraction of the techno-economic potential in Germany, this lithium extraction process could offer significant environmental benefits. High potential for mass application also exists in other countries, such as the United States, United Kingdom, France, and Italy, highlighting the importance of further research and development of this technology.

Renewable energy forecasting is crucial for integrating variable energy sources into the grid. It allows power systems to address the intermittency of the energy supply at different spatiotemporal scales. To anticipate the future impact of cloud displacements on the energy generated by solar facilities, conventional modeling methods rely on numerical weather prediction or physical models, which have difficulties in assimilating cloud information and learning systematic biases. Augmenting computer vision with machine learning overcomes some of these limitations by fusing real-time cloud cover observations with surface measurements acquired from multiple sources. This Review summarizes recent progress in solar forecasting from multisensor Earth observations with a focus on deep learning, which provides the necessary theoretical framework to develop architectures capable of extracting relevant information from data generated by ground-level sky cameras, satellites, weather stations, and sensor networks. Overall, machine learning has the potential to significantly improve the accuracy and robustness of solar energy meteorology; however, more research is necessary to realize this potential and address its limitations.

Lithium-ion batteries have become the best choice for battery energy storage systems and electric vehicles due to their excellent electrical performances and important contributions to achieving the carbon-neutral goal. With the large-scale application, safety accidents are increasingly caused by lithium-ion batteries. As the core component for battery energy storage systems and electric vehicles, lithium-ion batteries account for about 60% of vehicular failures and have the characteristics of the rapid spread of failure, short escape time, and easy initiation of fires, so the safety improvement of lithium-ion batteries is urgent. This study analyses the causes and mechanisms of lithium-ion batteries failures from design, production, and application, investigates its failure features and warning algorithms for thermal runaway, and the concept of long-medium-short graded warning is proposed based on the battery failure mechanism and its evolution to provide a basis for failure warning. As lithium-ion batteries fires are difficult to completely avoid, the characteristics of lithium-ion batteries fires are explored to improve battery structure and develop fire extinguishing agents and methods for fire prevention and suppression. Improving the safety of batteries is a systematic project, and at a time when there has been no breakthrough in the chemical system, improvements, such as build a practical graded warning system, are needed in all aspects of design, production, use and disposal to improve battery safety and minimize the risk of failure.

The residential sector is the third-largest energy consumer and emitter globally and as such is at the forefront of the energy transition and net-zero emissions pathway. To accelerate the pace of decarbonization of residential buildings, this study is the first to present a bottom-up assessment framework integrated with the decomposing structural decomposition method to evaluate the emission patterns and decarbonization process of residential building operations in 56 countries spanning 12 regions worldwide from 2000 to 2020. The results show that (1) the operational carbon intensity of global residential buildings has maintained an annual decline of 1.2% over the past two decades, and energy intensity and average household size have been key to this decarbonization; (2) end uses have held an increasingly important role in decarbonizing global residential buildings (-46.3 kgs of carbon dioxide per household per year), with the largest contributors being appliances(38.3%), followed by space heating (21.2%) and lighting (12.6%); and (3) although the total decarbonization of global residential buildings was 7.1 gigatons of carbon dioxide and achieved a decarbonization efficiency of 9.4% per yr during this time period, regional decarbonization inequality and uneven distribution remained quite large, especially in emerging economy regions. Moreover, the uncertainty and robustness of the assessment framework are also tested, and adaptive high decarbonization strategies are further proposed for global residential buildings. Overall, this study reviews and compares global and regional performances and motivations for decarbonization to support national decarbonization efforts to reach net-zero emissions and advance the global residential building sector toward a carbon-free century.