Advances in Applied Energy最新文献

The accurate quantification and assessment of available renewable energy resources has emerged as a research topic with high relevance to policymakers and industry. Motivated by the need for a contemporary review on the methodologies and practices prevalent in wind resource assessments, we employ a systematic analysis of 195 articles that describe large-scale wind assessments. Our review reveals significant heterogeneity in global and continental-scale potentials and geographical bias of research towards the Northern Hemisphere, despite electrification needs in regions like Africa and Latin America. A fraction of the literature attempts to explicitly include social and political barriers to wind power development, thereby defining ‘feasible’ potentials. We delve into advancements in this domain, focusing on innovative methodologies that encapsulate the viewpoints of subject experts and stakeholders in the assessment process. Our analysis underscores pressing challenges relating to data sharing and scientific reproducibility, with our findings revealing a mere 10 % of studies that offer openly available data for download. This highlights a pervasive insufficiency in the reproducibility of wind assessments. Additionally, we tackle notable hurdles concerning wind data and meteorological characterization, including an over-reliance on single-source wind data and a deficit in adequately characterizing temporal wind variability. Relatedly, we uncover a highly heterogenous approach to turbine siting and characterizing wake-related losses. These methods are frequently simplistic, potentially leading to an overestimation of wind potentials by assuming an overly optimistic capacity density. In each of these domains, we discuss the state of the art for modern wind resource assessments, propose best practices, and pinpoint crucial areas warranting future research.

Building electrification with distributed energy resources (DERs) is a promising strategy to decarbonize the building sector. Considering the inter-dependencies between operations control and systems design, integrating technology operations control optimization with DERs investment optimization can cost-effectively enhance such building decarbonization opportunities. This study proposes a multi-timescale integrated optimization framework to simultaneously optimize the design and control of DERs and electrification technologies for buildings. A novel building operational performance prediction model based on deep learning is developed to approximate and replace the computationally expensive control optimization. This helps resolve the challenging, computationally intractable multi-timescale integrated design and control optimization problem. Applying the proposed framework to a residential building, our results demonstrate its effectiveness in cost-efficient carbon emissions reduction. With integrated design and control optimization for DERs and electric building energy systems, the proposed framework reduces operational carbon emissions by 80% and total costs by 2.7% compared to a base case, which uses typical conventional building energy systems without DERs and control/design optimization. Separate optimization of operations control and system design cannot achieve such performance. Further scenario analyses indicate that as power grids become cleaner, the reliance on DERs can be alleviated but remain important in building carbon emission reduction under 2050 power grid scenario. Overall, as our results demonstrate, it is possible to reduce building operational carbon emissions simultaneously with net electrical load: compared to the base case, the proposed framework helps reduce the carbon emission by 80% while driving down the net electrical load from 44.1 to 19.3 kWh/m²/year.

Renewable fuels can help to reduce carbon emissions from transportation. To inform planning decisions, this paper estimates carbon abatement costs of replacing fossil fuels with renewable hydrogen, ammonia, or Fischer–Tropsch e-fuel in Norwegian freight transport across long-haul trucking, short-sea shipping, and medium-haul aviation. We do this by applying a holistic cost model of renewable fuel value chains. We compare abatement costs across transport sectors and analyze how policy interventions along the value chains – such as carbon pricing, subsidies, and de-risking policies – impact carbon abatement costs. We estimate abatement costs of 793–1,598 €/tCO₂ in 2020 and -11–675 €/tCO₂ in 2050, depending on the electricity source, transport sector, and type of fuel. A 1 €/kg reduction in the cost of hydrogen - e.g. through a subsidy - lowers present-day carbon abatement cost by 95 €/tCO₂ for hydrogen-powered trucking, 133 €/tCO₂ for e-fuel-powered shipping, and 143 €/tCO₂ for e-fuel-powered aviation. We further show that reductions in the weighted average cost of capital materially decrease abatement cost, particularly for renewable hydrogen due to its relative capital intensity.

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.