Under the strategic goal of “peak carbon dioxide emissions and carbon neutrality” in China, industries with high energy consumption and high pollution, such as iron and steel plants, are facing great pressure of energy conservation and emission reduction, and are in urgent need of green and low-carbon transformation. In this paper, 46 iron and steel plants in Hebei province are taken as examples. GIS spatial analysis and environmental emission list method are used to build a comprehensive evaluation model of rooftop photovoltaic, and to calculate the technical potential, energy saving and emission reduction benefits and economic feasibility of deploying rooftop photovoltaic in iron and steel plants. Finally, carbon trading mechanism is introduced to analyze its impact on the carbon trading market. It is found that 46 iron and steel plants save 216,700 tons of standard coal, reduce 144,700 tons of CO2 emissions and reduce 1,500 tons of SO2, NOX, PM and other air pollutants every year. The economic benefit of power generation self-use mode is greater than that of grid-fed mode, with an average return on investment of 140% and a payback period of 5.5 years. The results verify that rooftop photovoltaic in iron and steel plants has dual benefits of energy saving and emission reduction and economy, and this data can provide a feasible path for iron and steel plants to use photovoltaic for green and low-carbon transformation.
The development of photovoltaic industry can effectively alleviate the energy crisis and environmental pollution. The deployment of photovoltaic power stations along the high-speed railway is a new mode combining photovoltaic new energy with infrastructure. This paper constructs a comprehensive decision-making framework for the site selection of PV power station along high-speed railway combining the subjective method and the objective method. A scientific and reasonable evaluation index system comprehensively considering multiple factors is constructed in this framework. Analytic hierarchy process and Entropy weight method are combined to determine the weight of every index, which combines the expert knowledge and data information effectively, relatively reducing systematic error and random error. Grey relational analysis is used to choose scheme among several alternatives, which greatly deal with the strong grey correlations among indexes. The framework established in this paper is used to select PV power stations along the Beijing-Shanghai high-speed railway, which also verifies the effectiveness of the framework.
With the development of data centers for high heat flux, the energy consumption of cooling systems has gradually increased. Introducing an optimal thermal management method for water operation parameters is important for energy saving, but there is a lack of research on this topic. This study focuses on the optimal match for internal and external cooling water operation parameters based on the demand for low energy consumption using TRNSYS simulation software. The relationships among the primary cooling water flow rate, the secondary cooling water flow rate, the cabinet inlet water temperature, and the water supply temperatures of the chiller are explored based on a 4.8 kW data server water cooling system equipped with an air-cooled chiller and a fin-type heat sink, and assuming a safe chip temperature of 70 ℃ and an environmental temperature of 20 ℃. Finally, the optimal performance of the internal and external water cycles were obtained by minimizing the cooling power consumption. The fitting curve of the optimal parameters is provided to improve the design of a low-energy consumption system. In addition, a high chiller water supply temperature is recommended; when the water supply temperature increases from 6 ℃ to 16 ℃, the total power consumption can be reduced by 14%.
Social distancing (SD) is one of the main policies in response to coronavirus disease 2019 (COVID-19). People spend more time indoors due to this policy. However, such a behavior change can vary in different social groups due to their socioeconomic conditions. This study examines the relationship among SD policy, socioeconomic factors, and building electricity energy use before–after the COVID-19 outbreak in Seoul to reveal the impacts of the city SD policy on residential daily behaviors. Using a panel model, the study found that among the three SD levels in Seoul, SD levels 2 and 2.5 had a significantly positive effect on building electricity energy use, whereas SD level 1 had no significant influence. This result is in line with the observation that people have more at-home activities and more residential electricity energy uses when a high level of SD restrictions was announced. The findings with the interaction term variables provide a deep understanding of how the SD policy changed the electricity energy use patterns of different social groups, where the unequal impact of SD policy on residential behavior can be inferred. Particularly, expensive apartments had more electricity energy use increase, and apartments with more elderly people tended to have less increase when SD level 2 was applied compared with the period without SD policy, suggesting that high income changed their daily behavior more greatly, whereas the elderly had the opposite response. This study provides new evidence from the perspective of building energy to inform policymakers on how the SD policy affects the residential daily behaviors and building energy use for different social groups. Such information sets the basis for a more comprehensive evaluation of the current SD policy and proposals of future post-COVID-19 recovery policy.
This paper investigates the novel integration of heat transfer devices into a mashrabiya device to improve indoor thermal comfort conditions in buildings in hot climates. The benchmark case building model was validated using detailed wind tunnel data based on particle image velocimetry (PIV) and Computational Fluid Dynamics (CFD) modelling results. Good agreement was observed between the modelling results and previous works data. Then, three configurations were evaluated: a) base case, b) single row of heat transfer devices, and c) double rows of heat transfer devices combined with the mashrabiya. The results of the building with mashrabiya indicated that the slats’ inclination plays a vital role in the airflow distribution in the room, and this was evident with tilting the slats angle to +30° or -30°, as the airflow became more directed and sharper towards the ceiling or the floor. Also, as compared to the benchmark case, the mashrabiya contributed to increasing the airflow rate into the room. Overall, the addition of heat transfer devices decreased the temperature by up to 7.5°C (18.8%).
Overheating in buildings is a growing challenge in the context of climate change and global warming. Many researchers are focusing on developing different passive strategies to minimize overheating and cooling electrical consumption in buildings. Thermal mass provides thermal energy storage, which could be utilized to store extra heat during hot summers to avoid overheating. To fulfil the cyclical behaviour of the thermal mass, it must be discharged to store heat again and follow this charging-discharging process on a daily basis to modulate overheating. Night ventilation performs the discharging phenomenon to maximize the effect of the thermal mass. Shading devices prevent the penetration of solar radiation into the building in summer. The aim of this study was to evaluate the effect of thermal mass and night ventilation to modulate overheating in the cold climates in Beijing, China. A model of the BESTEST ASHRAE Standard 140 Case 600FF was used to perform full-year dynamic building simulations with Energyplus at different levels of thermal mass. The results allow optimizing the thermal mass configuration according to each climatic condition and in accordance with the performance of night ventilation and shading devices availability. The results confirm the important role played of night ventilation and shading devices to modulate overheating with the potential to reduce maximum temperatures up to 20% by using heavyweight thermal mass compared to lightweight. The results of this study will help to develop the decision support systems to inform the implementation of thermal mass into regional and local building regulations.
This study proposed a regional low-carbon development pathway evaluation model under the carbon peak and carbon neutrality (CPCN) targets. First, regional economic and social development goals, carbon reduction targets and scenarios is set in a top-down manner. Secondly, it designs the development scenarios and goals for different sectors including industry, building, transportation and energy supply. Then, it carries out the integration, evaluation and iteration of these bottom-up solutions until consistent with the set goals. Finally, an empirical study of Sichuan Province using the model is conducted. The results show that (1) under the CPCN scenario, Sichuan’s carbon emissions will peak at 253.1 million tons in 2028 and drop to 44.8 million tons in 2057, and carbon neutrality of energy consumption can be achieved there; (2) carbon peak will be achieved in different sectors in the order of industry (2022), transportation (2030) and construction (2035) and among all energy varieties in the sequence of coal (2015), oil (2025) and gas (2040) in a coordinated, stepwise and well-organized manner; (4) under the carbon neutrality target, Sichuan’s electrification level will rise from 34.7% in 2015 to 75.0% in 2057, and the share of non-fossil power on the supply side will rise from 86.1% in 2015 to 95.7% in 2057.
The influence of urban contextual form in the studies on the relationship between built form and building energy has been attracting increasing attention. However, most emerging studies on this idea adopted form indicators on the basis of urban planning conventions and researchers’ expertise to measure the urban contextual form. This indicator approach often suffers from confounding effects in research and lacks a clear connection to urban energy policy. With the understanding that urban form is often self-organized and self-adapted as a complex system, this study takes a typology approach to measure urban contextual form. Particularly, the local climate zone (LCZ) is used as the typology framework, which has been developed in the urban climatology field to establish standard urban form typologies on the basis of thermal performance. The LCZ typologies connect the individual buildings to their contextual areas through mutual shading and microclimate pathways, which are among the major mechanisms behind the relationship between built form and building energy use. With the LCZ approach, this study investigates how urban contextual forms as typologies influence building energy use in Seoul. The research is divided into two steps. In the first step, an LCZ map for Seoul is created using a GIS-based LCZ mapping method. In the second step, the linear mixed model is employed to examine how individual building characteristics and urban contextual form defined by LCZ classes influence annual building energy use in Seoul in 2018. Results show that LCZ classes significantly influence building energy use both directly and indirectly. The findings suggest a promising application of the LCZ framework in urban energy policies, in addition to its application in microclimate-oriented urban management.
Worldwide global warming has become a crisis for all human beings. As the largest carbon emitter in the world, China has committed to reducing its carbon emissions to net-zerobefore2060.Considering the fact that the energy system is the largest emission source in China, reducing carbon emissions from burning fossil fuels has significant meaning for this country. This paper establishes an hourly low-carbon energy system planning model, which jointly considers load curve and renewable energy output characteristics by using mixed-integer linear programming method. Also, the model creatively introduces the function of seasonal regulation of hydropower stations into the planning model of the low-carbon energy system. Sichuan Province, one of the largest suppliers of renewable energy in China, is selected as a case study. The case study discusses the possibility of realizing a carbon-neutral power energy system in Sichuan Province in 2060 under two scenarios, lower demand and higher demand. Under the scenario of lower load estimation, that is, when the annual load is634.8 TWh, Sichuan can reduce carbon emissions to zero at a reasonable annual cost of 11.6 billion CNY. The high-load scenario assumes that all potential renewable energy has been explored in the province. This scenario can generate 878.4 TWh of clean electricity every year, with an annualized cost of 34.3 billion CNY.
In this paper, the maximum charging or discharging current that the lithium-ion battery can withstand within safe voltage constraints, i.e., the peak current is researched. The equivalent circuit model is employed to describe battery dynamic. Three model parameter identifying methods are discussed to evaluate its influence on the peak current calculation. Results show that the parameter accuracy of both the offline and online methods is far lower than that of the optimization method. Moreover, the artificial intelligence is adopted to accelerate the prediction of peak current. The mean absolute percentage error of prediction results is only 0.373% and 1.447% for charging and discharging process, indicating its validity and practicability.