Advances in Applied Energy最新文献

The development of reliable and longevous infrastructures and structural components is the key for the implementation of a hydrogen economy that is currently enjoying unprecedented political and research momentum due to the globally strong demand for clean energy. This is, however, strongly impeded by the risk and concerns of hydrogen embrittlement (or hydrogen-induced degradation in mechanical properties) that generally exists in almost all metallic materials. Structural components and materials operated in the hydrogen production-transport-storage-usage chain can be subjected to a very wide range of temperature, environmental and loading scenarios, which will essentially trigger different hydrogen embrittlement responses and even different embrittling mechanisms. It is thus important to have a systematic assessment and discussion of hydrogen embrittlement behavior of different materials at different testing conditions, which is the focus of the presented review. Here we cover the typical materials (mainly metallic materials) that have been used or planned to be used in the fields of hydrogen energy. We first briefly summarize the current understanding of fundamental hydrogen embrittlement mechanisms in metallic materials and the research progress in recent years. Then we analyze and discuss the hydrogen -induced damage phenomenon in typical materials used in the field of high-pressure hydrogen transport and storage. In addition to room-temperature hydrogen embrittlement behavior, the hydrogen embrittlement phenomenon of some alloys at elevated and cryogenic temperatures is also reviewed, with the aim to provide some guidelines of material selection and design in developing fields such as hydrogen gas turbines and long-flight-duration hydrogen powered aircraft. Finally, the current challenges in the study of hydrogen embrittlement are identified and discussed to guide future research efforts.

This study examines the impact of low-income assistance and electrification programs on a disadvantaged community in Southern California. An urban building energy model is paired with an AC power flow and electric distribution system degradation model to evaluate how the cost of energy, carbon emissions, and pollutant emissions change after applying building weatherization, energy efficiency, and electrification measures to the community. Results show that traditional weatherization and energy efficiency measures (upgrading lighting and appliances, improving insulation to current building code standards) are the most cost-effective, reducing the cost of energy and carbon emissions by 10–20 % for the current community. Heat pump water heaters offer a 40 % average reduction in carbon emissions and almost 50 % decrease in criteria pollutant emissions, but at a cost increase of 17–22 %. Appliance electrification also reduces carbon emissions 5–10 % but increases cost by 7 % to 25 %. For reducing carbon, government programs that support building electrification are most cost-effective when they combine switching from natural gas to electricity with high efficiency system. Electrifying hot water and appliances effectively reduces emissions but must be paired with improved low-income assistance programs to prevent increased energy burden for low-income families. The urban building energy model and electrical distribution simulations used in this study can be replicated in other low-income communities.

To achieve the European Union's goal of climate neutrality by 2050, negative emissions may be required to compensate for emissions exceeding allocated carbon budgets. Therefore, carbon removal technologies such as bioenergy with carbon capture (BECCS) and direct air capture (DAC) may need to play a pivotal role in the power system. To design carbon removal strategies, more insights are needed into the impact of sustainable biomass availability and the feasibility of carbon capture and storage (CCS), including the expensive and energy-intensive DAC on achieving net-zero and net-negative targets. Therefore, in this study the European power system in 2050 is modelled at an hourly resolution in the cost-minimization PLEXOS modelling platform. Three climate-neutral scenarios with targets of 0, -1, and -3.9 Mt CO₂/year (which agree with varying levels of climate justice) are assessed for different biomass levels, and CCS availability. Findings under baseline assumptions reveal that in a climate-neutral power system with biomass and CCS options, it is cost-effective to complement variable renewable energy with a mix of combined cycle natural gas turbines (CCNGT) for flexibility and BECCS as base load to compensate for the CO₂ emissions from natural gas and additional carbon removal in the net-negative scenarios. The role of these technologies becomes more prominent, with -3.9 GtCO₂/year target. Limited biomass availability necessitates additional 0.4–4 GtCO₂/year DAC, 10–50 GW CCNGT with CCS, and 10–50 GW nuclear. Excluding biomass doubles system costs and increases reliance on nuclear energy up to 300 TWh/year. The absence of CCS increases costs by 78%, emphasizing significant investments in bioenergy, nuclear power, hydrogen storage, and biogas. Sensitivity analysis and limitations of the study are fully discussed.

The burgeoning proliferation of integrated energy systems has fostered an unprecedented degree of coupling among various energy streams, thereby elevating the necessity for unified multi-energy forecasting (MEF). Prior approaches predominantly relied on independent predictions for heterogeneous load demands, overlooking the synergy embedded within the dataset. The two principal challenges in MEF are extracting the intricate coupling correlations among diverse loads and accurately capturing the inherent uncertainties associated with each type of load. This study proposes an attentive quantile regression temporal convolutional network (QTCN) as a probabilistic framework for MEF, featuring an end-to-end predictor for the probabilistic intervals of electrical, thermal, and cooling loads. This study leverages an attention layer to extract correlations between diverse loads. Subsequently, a QTCN is implemented to retain the temporal characteristics of load data and gauge the uncertainties and temporal correlations of each load type. The multi-task learning framework is deployed to facilitate simultaneous regression of various quantiles, thereby expediting the training progression of the forecasting model. The proposed model is validated using realistic load data and meteorological data from the Arizona State University metabolic system and National Oceanic and Atmospheric Administration respectively, and the results indicate superior performance and greater economic benefits compared to the baselines in existing literature.

Marine renewable energy is gaining prominence as a crucial component of the energy supply in coastal cities due to proximity and minimal land requirements. The synergistic potential of integrating floating photovoltaics with offshore wind turbines presents an encouraging avenue for boosting power production, amplifying spatial energy generation density, and mitigating seasonal output fluctuations. While the global promise of offshore wind-photovoltaic hybrid systems is evident, a definitive understanding of their potential remains elusive. Here, we evaluate the resource potential of the hybrid systems under geographical constraints, offering insights into sustainable and efficient offshore energy solutions. We compile a database with 11,198 offshore wind turbine locations from Sentinel-1 imagery and technical parameters from commercial project details. Our analysis reveals an underutilization of spatial resources within existing offshore wind farms, yielding a modest 26 kWh per square meter. Furthermore, employing realistic climate-driven system simulations, we find an impressive potential photovoltaic generation of 1372 ± 18 TWh annually, over seven times higher than the current offshore wind capacity. Notably, floating photovoltaics demonstrated remarkable efficiency, matching wind turbine output with a mere 17 % of the wind farm area and achieving an average 76 % increase in power generation at equivalent investment costs. Additionally, the hybrid wind and photovoltaic systems exhibit monthly-scale complementarity, reflected by a Pearson correlation coefficient of -0.78, providing a consistent and reliable power supply. These findings support the notion that hybrid offshore renewable energy could revolutionize the renewable energy industry, optimize energy structures, and contribute to a sustainable future for coastal cities.

The global energy sector is now transitioning its structure towards carbon neutrality aided by renewable resource use. Despite its immense potential, solar energy contributes minimally to the global energy mix due to its intermittent nature and challenges with power demand fluctuations. Increased use of distributed solar sources alters market dynamics, necessitating conventional power plants to ramp up output during lower renewable energy production times and manage oversupply risks. Concentrated solar power (CSP) can contribute to grid decarbonization, but its high levelized cost of electricity (LCOE) impedes widespread adoption. This study proposes hybridizing CSP and photovoltaic (PV) technologies, aiming to leverage their synergy to maximize economic benefits. We develop a comprehensive process design framework that utilizes a robust multi-objective optimization (MOO) approach, which factors in techno-economic and environmental objectives while accounting for model uncertainty from resource prices and life cycle assessment indicators. Optimization results reveal that in Ivanpah, California, hybrid CSP + PV can reduce 41 % of LCOE and limit environmental impacts compared to standalone CSP plants. This robust framework also identifies design trends, such as a constant dependence on the PV field, and a trade-off between the installed area of the solar concentrators and the backup boiler operation. The optimal unit sizes, less susceptible to future market fluctuations and potential changes in the global warming potential (GWP) of technologies, contribute significantly to robust and sustainable energy planning decisions.

Globally, the power sector must undergo a profound transition to achieve the decarbonization development targets. Various roadmaps are implemented, but only from a macro perspective, lacking the consideration of the electricity market rules. In this paper, we develop and present a market-driven generation and transmission expansion planning (MGTEP) model considering the effectiveness of the electricity market. Specifically, generation and transmission companies incorporate hourly market trading and annual capacity investment into strategic decisions to maximize their profits, with the supply function equilibrium model to analyze bidding behaviors. An equivalent quadratic programming formulation is deployed to solve the trilevel MGTEP model. Meanwhile, the MGTEP model is coupled with decarbonization policies to support the state and federal government in assessing energy transition strategies. We implement the MGTEP model with carbon emission allowance and carbon tax policies for the southern China electricity market to achieve carbon peaking by 2030. Carbon emission allowance adopts an intensity-based cap based on generation companies' historical output. The case study results show that 50 % carbon emission allowance or 400 CNY/t carbon tax is required but with several drawbacks, including unsatisfactory decarbonization effect, excessive economic sacrifice, etc. Finally, the case study is extended to dual-track policies with different combinations of policies. An optimal combination is 70 % carbon emission allowance and 160 CNY/t carbon tax. In this case, the power sector's carbon dioxide emissions and electricity prices in the southern China electricity market would increase to 554.6 Mt and 864.34 CNY/MWh in 2030, respectively, along with a carbon price of 850 CNY/t.

With the aim of reducing carbon emissions and seeking independence from Russian gas in the wake of the conflict in Ukraine, the use of hydrogen in the European Union is expected to rise in the future. In this regard, hydrogen transport via pipeline will become increasingly crucial, either through the utilization of existing natural gas infrastructure or the construction of new dedicated hydrogen pipelines. This study investigates the effects of hydrogen blending in existing pipelines on the European energy system by the year 2050, by introducing hydrogen blending sensitivities to the Global Energy System Model (GENeSYS-MOD). Results indicate that hydrogen demand in Europe is inelastic and limited by its high costs and specific use cases, with hydrogen production increasing by 0.17% for 100%-blending allowed compared to no blending allowed. The availability of hydrogen blending has been found to impact regional hydrogen production and trade, with countries that can utilize existing natural gas pipelines, such as Norway, experiencing an increase in hydrogen and synthetic gas exports from 44.0 TWh up to 105.9 TWh in 2050, as the proportion of blending increases. Although the influence of blending on the overall production and consumption of hydrogen in Europe is minimal, the impacts on the location of production and dependence on imports must be thoroughly evaluated in future planning efforts.

The optimal scheduling of home energy systems is influenced by the benefits of different stakeholders, with the hierarchical nature of user's needs being particularly significant. However, previous studies have largely neglected these factors. To bridge the research gaps, a many-objective optimal dispatch framework for home energy systems, which was inspired by Maslow's hierarchy of needs, was proposed. In the framework, user's needs for the optimal dispatch of home energy systems were categorized into various hierarchies referring to the Maslow's theory, which were fulfilled in a specific sequence during the scheduling optimization. In addition to the user's needs, the benefits of grid operators and policymakers were considered in the developed many-objective nonlinear optimal model, which includes six objective functions that capture the interests of end-users, grid operators, and policymakers. Simulation results obtained across the home energy systems with various configurations verified the effectiveness of the proposed framework. Results indicate that user's needs can be fully satisfied and a tradeoff among the benefits of end-users, grid operators, and policymakers was achieved. For various home energy systems, the optimal scheduling demonstrated reductions of 22.33 %-81.05 % in daily operation costs, 14.39 %-25.68 % in CO₂ emissions, and 15.58 %-17.49 % in peak-valley differences, associated with increment of 5.37 %-15.51 % in self-consumption rate and 8.91 %-27.29 % in self-sufficiency rate, compared with the benchmark. The proposed framework provides valuable guidance for the optimal scheduling of various home energy systems in practical applications.

While cool coatings have recently received much attention for building applications, their impact on building energy consumption strongly depends upon climatic conditions. Herein we evaluate the energy, cost, carbon, and interior comfort impact of cool coatings applied to a residential multifamily building across 32 climate zones in the United States by applying advanced cool coating properties to established building energy models. The model not only considers promising cool coating properties based upon recent experiments but also an ideal cool coating. Our calculations show that the ideal cool coating can achieve annual cooling energy savings of up to 6.64 kWh/m² (Phoenix, AZ), annual net utility cost savings up to $1.16/m² (Brawley, CA), and net annual carbon emission savings up to 7.7 % (Phoenix, AZ). We also estimate the change in interior temperature for buildings without space cooling systems and show that cool coatings make buildings in the warmest climate zones in the U.S. without space cooling more comfortable by 30 % to 50 % on a cooling degree days basis. Using analysis of variance, we examine the statistical relationships between building performance metrics and climatic parameters. The presented methodology enables evaluation of cool coating application to buildings in various climate zones across the world.