Source-Load Coordinated Optimal Scheduling Considering the High Energy Load of Electrofused Magnesium and Wind Power Uncertainty

In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional units obviously can not solve the new energy as the main body of the scheduling problem.To enhance the systemscheduling ability,based on the participation of thermal power units,incorporate the high energy-carrying load of electro-melting magnesiuminto the regulation object,and consider the effects on the wind unpredictability of the power.Firstly,the operating characteristics of high energy load and wind power are analyzed,and the principle of the participation of electrofusedmagnesiumhigh energy-carrying loads in the elimination of obstructedwind power is studied.Second,a two-layer optimization model is suggested,with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation.In the upper model,the high energy-carrying load regulates the blocked wind power,and in the lower model,the second-order cone approximation algorithm is used to solve the optimizationmodelwithwind power uncertainty,so that a two-layer optimizationmodel that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established.Finally,the model is solved using Gurobi,and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment,lower system operation costs,increase the accuracy of day-ahead scheduling,and lower the final product error of the thermal electricity unit.

Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts.

Unit GDP Energy Consumption in 2006:Moving from Increase to Decline

On July 12, Xie Fuzhan, Commissioner of National Bureau of Statistics of China attended the press conference held by State Council Information Office, and delivered the report on China＇s national unit GDP energy consumption in 2006, presenting with major indicators of unit GDP energy consumption both at national and regional levels in the year 2006.

Analysis and forecast of residential building energy consumption in Chongqing on carbon emissions

Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analysis of the energy consumption of residential buildings in Chongqing,China,on the impact of carbon emission factors. Three impacts are analyzed,namely per capita residential housing area,domestic water consumption and the rate of air conditioner ownership per 100 urban households. The gray prediction model established using the Chongqing carbon emission-residential building energy consumption forecast model is sufficiently accurate to achieve a measure of feasibility and applicability.

Research on Energy Consumption of Traditional Natural Villages in Transition: A Case Study in Zhejiang Province

Traditional agriculture is in the direction of increasing integration of the primary industry, secondary industry, and tertiary industry in Zhejiang province. A survey was undertaken on energy consumption of traditional natural villages by taking Anji Ligeng village for an example. This paper firstly studied rural buildings, rural family structure, occupants’ activity and the usage of household appliances in the form of a questionnaire. Then, the household energy resource structure and energy consumption structure were analyzed and compared with other surveys. The results show that, the electric energy consumption was 6 kWh/(m2•a), which was far less than urban residential household. In rural household energy resource structure, the proportion of non-commercial energy resource was higher than commercial energy resource. Firewood accounted for 83%, electricity for 12%, LPG for 3% and solar energy for 2%. In building energy consumption structure, cooking and hot water took up 33%, appliances 31%, lighting 20%, heating 12%, cooling 4%. In all influential factors, frequently used area, number of air conditioner per household and building function were obviously correlated with energy consumption;income, building shape factor and window to wall area ratio had no correlation with energy consumption in the low energy consumption area.

Investigating spatio-temporal characteristics and influencing factors for green energy consumption in China

The green transformation of energy consumption is beneficial for promoting green development in China.This study constructed a green energy consumption evaluation index system and measured the green energy consumption levels in 30 provinces of China from 2000 to 2019 using the fuzzy comprehensive evaluation method.This study further employed the spatial Durbin model to examine influencing factors and spillover effects of green energy consumption.The results showed that,temporally,China’s green energy consumption levels had a fluctuating upward trend.While,spatially,the overall levels of green energy consumption in China showed apparent characteristics of“high in the west and low in the east”.In terms of influencing factors,environmental regulations played an important role in promoting green energy consumption in the region,while economic development,opening up,and industrial structure had considerably inhibiting effects.Additionally,economic development,opening up,and industrial structure of neighboring regions showed marked positive spillover effects,while urbanization level and technological innovation showed substantial negative spillover effects.The regional heterogeneity test results showed that environmental regulation and industrial structure rationalization were the important factors for promoting green energy consumption in the eastern region,environmental regulation played an important driving role in the central region,and opening to the outside world and technological innovation helped improve the level of green energy consumption in the western region.

Analysis of Energy Consumption and Energy Conservation Measures for RFCCU at Shengli Petrochemical Company

This article introduces a string of energy conservation measures adopted over the past sev- eral years by the RFCC unit at Shengli Petrochemical Complex, including the optimization of feedstock properties, the adoption of high-efficiency atomizing nozzles, the revamp of CO boiler, the atomization by means of dry gas, the post-burning of flue gas as well as the application of frequency converting machines and pumps, resulting in ideal effects. The energy consumption of the RFCC unit was gradually decreased to 2984.25 MJ/t from the original level of 3716.99 MJ/t. After comparing basic energy con- sumption values with actual consumption values, the authors have set forward measures for further energy conservation, such as the recovery of low-temperature excess heat contained in oil/gas streams exiting from the fractionation tower top, addition of the fourth cyclone, delivery of hot oil slurry, and heat tracing with hot water.

Energy Factor and Mathematical Modeling for Power DC/DC Converters

Mathematical modelling for power DC/DC converters is a historical problem accompanying DC/DC conversion technology since 1940’s. The traditional mathematical modelling is not available for complex structure converters since the differential equation order increases very high. We have to search other way to establish mathematical modelling for power DC/DC converters.We have theoretically defined a new concept-Energy Factor （EF） in this paper and researched the relations between EF and the mathematical modelling for power DC/DC converters. EF is a new concept in power DC/DC conversion technology, which thoroughly differs from the traditional concepts such as power factor （PF）, power transfer efficiency （η）, total harmonic distortion （THD） and ripple factor （RF）. EF and the subsequential EFV (and EFVD) can illustrate the system stability, reference response and interference recovery. This investigation is very helpful for system design and DC/DC converters characteristics foreseeing. Two DC/DC converters: Buck converter and Super-Lift Luo-Converter as the samples are analysed in this paper to demonstrate the applications of EF, EFV (and EFVD), PE, SE, VE （and VED）, time constant τ and damping time constant τd.

Influential Factors on Deicing Performance of Electrically Conductive Concrete Pavement

The deicing experiment of carbon fiber reinforced electrically conductive concrete （CFRC） slab was conducted in laboratory at first, then the deicing process of CFRC parement was analyzed by means of finite element method （FEM）. At last, based on the energy conservation law and the computing restdts of finite element method, the influential factors including the setting of electric heating layer, environmental temperature, the thickness of ice, material parameters, and deicing power on deicing performance and energy consumption were discussed.

Impact of COVID-19 on the energy consumption of commercial buildings:A case study in Singapore

Numerous studies have demonstrated that commercial activities have significantly reduced during COVID-19,while there are few studies disclosing the consequent impacts on the energy consumption of commercial build-ings.This study explores the changes in energy consumption of different types of commercial buildings in Sin-gapore under the impact of the pandemic,using commercial building energy performance data from 2017 to 2020(n=540).The sampled buildings include 93 hotel buildings,303 office buildings,106 retail buildings,and 38 mixed developments.The analysis mainly used linear regression and paired sample t-test.The results showed that relative to 2019,the mean energy use intensity(EUI)of sampled commercial buildings decreased by 56.77 kWh/m^(2)in the pandemic year(2020),a plunge of 19.9%.The extent to which the EUI of each type of commercial building is affected by the pandemic is found as:mixed development>retail>office>hotel.The study also identi-fied the factors that significantly influenced the EUI of commercial buildings before and during the pandemic.The results of the study complement existing knowledge about the factors influencing energy consumption in com-mercial buildings by considering the impact of the pandemic and furthermore contribute to the improvement of energy management in commercial buildings by providing directions for building energy efficiency approaches.

Influence of urban morphological factors on building energy consumption combined with photovoltaic potential: A case study of residential blocks in central China

Studies on urban energy have been growing in interest,and past research has mostly been focused on studies of urban solar potential or urban building energy consumption independently.However,holistic research on the combination of urban building energy consumption and solar potential at the urban block-scale is required in order to minimize energy use and maximize solar power generation simultaneously.The aim of this study is to comprehensively evaluate the impact of urban morphological factors on photovoltaic(PV)potential and building energy consumption.Firstly,58 residential blocks were classified into 6 categories by k-means clustering.Secondly,3 energy performance factors,which include the energy use intensity(EUI),the energy use intensity combined with PV potential(EUI-PV),and photovoltaic substitution rate(PSR)were calculated for these blocks.The study found that the EUI of the Small Length&High Height blocks was the lowest at around 30 kWh/(m^(2)·y),while the EUI-PV of the Small Length&Low Height blocks was the lowest at around 4.45 kWh/(m^(2)·y),and their PSR was the highest at 87%.Regression modelling was carried out,and the study concluded that the EUI of residential blocks was mainly affected by shape factor,building density and floor area ratio,while EUI-PV and PSR were mainly affected by height and sky view factor.In this study,the results and developed methodology are helpful to provide recommendations and strategies for sustainable planning of residential blocks in central China.

An Assessment for Energy Management in Retail Stores

Retail stores are responsible for large energy consumption, which requires more intensified action to improve energy efficiency. Effective energy management can improve energy efficiency in retail stores. However, it is a challenge to implement energy management in retail stores due to different stakeholders’ roles and diverse store features. Literally, technical and management aspects of energy management have received much attention in research. However, limited studies systemically investigate internal and external factors and stakeholders’ involvement in the energy management of retail buildings. With multi-cases in the Philippines, this paper examines the energy profiles in retail stores and develops an assessment for energy management in retail stores. The assessment includes store features, internal and external stakeholders, climate, electricity price and grid condition, energy consumption, and management. The assessment can assist retail stores to develop their energy management plans with their store profile.

电化学水处理技术降低循环水硬度的实验研究

使用电化学法处理某焦化企业实际循环水的实验,探究了循环水的硬度和碱度去除率随处理时间的变化情况。分析了电流密度、水力停留时间、极板间距等实验操作参数以及商用阻垢剂对电解效果和能耗的影响。结果表明:硬度和碱度去除率随处理时间呈现先上升后平稳的趋势,且碱度去除率总是高于硬度去除率;电流密度为70 A/m^(2)、水力停留时间为6 min、极板间距为3 cm时电解效果最佳,硬度和碱度去除率可达27%以上,单位能耗低至13 kWh/kg;商用阻垢剂对电解效果具有阻碍作用。

“双碳”目标下旅游碳排放影响因素变化规律研究

以旅游碳排放为研究对象,以南京为起点,通过设计四种不同的旅游线路,重点分析交通方式、住宿形式、旅游活动内容等不同旅游环节对碳排放的影响变化规律。研究结果表明,与近郊游相比,省际游交通碳排放最高可达近郊游的18.9倍和73.5倍。在同样选择快捷酒店的情况下,省际游住宿碳排放量是近郊游的8.9倍和13.3倍。随着旅游距离的增加,交通方式碳排放占比逐渐增大。近郊游交通方式碳排放占比为25.4%,而省际游三亚交通碳排放占比高达53.4%,超过了旅游活动所产生的碳排放占比。1次近郊游产生的碳排放所需要的碳汇森林面积约168.8 m2,而省际游青岛和海南三亚所产生的碳排放需要的碳汇森林面积高达1423.77 m2和2948.74 m2,省际游所需碳汇森林面积是近郊游的8.43倍和17.47倍。研究结论对旅游政策的制定、节假日规划以及旅游线路规划具有很好的指导和借鉴作用。

单位面积能耗与人均能耗综合对标方法研究与案例分析

为了公共机构能耗的合理对标,以国家和江苏省能耗定额标准及合理用能指南中指标规定类型为对象,分析了单项指标(单位面积能耗)对标方法和双项指标(单位面积能耗和人均能耗)对标方法,得到了指标判定区间及区域表达式,对比分析两种方法。提出单位面积能耗和人均能耗综合对标方法,推导指标定额限值数学表达及区域表达式。以《公共机构能耗定额及计算方法》中小学定额为例,推导出单位面积能耗和人均能耗综合对标限值及区域表达式。以南京某小学的单位面积能耗和人均能耗指标为例,进行3种方法的对标,分析对标结果的区别,为公共机构能耗指标的对标提供参考。

基于位置和能耗启发的改进蚁群算法路径规划

为了解决经典蚁群优化算法应用于移动机器人路径规划中存在综合寻优能力差、收敛速度慢和复杂环境中算法鲁棒性不强等问题,提出了一种基于位置和能耗启发的改进蚁群优化算法。综合考虑机器人行进路径长度、行进路径坡度和转弯带来的能耗问题,提出综合能耗启发因子;考虑路径起点与终点之间,直线距离最短,提出到起止点直线距离启发因子,引导蚂蚁往起止点直线附近路径靠近;提出到终点距离启发因子,引导蚂蚁往目标点方向行进。设计了综合三种启发因子的启发函数,优化状态转移计算方式。此外,通过引入动态信息素挥发因子、改进信息素增量、设计信息素限制等优化信息素更新策略。多种环境多次仿真实验结果对比分析表明,改进算法在寻优路径长度、路径高度均方差、综合性能等方面具有更加优秀的表现。

水冷冷水机组热回收的节能分析

本文首先介绍了夏热冬暖地区生活热水系统常用的三种热水供给方案,并对其所用的热源机组进行能效分析及能耗分析,把空调制冷与生活热水作为一个整体,具体计算了三种方案热源机组的单位制热量耗电指标,这一指标的计算结果表明了回收水冷冷水机组的冷凝余热用于生活热水系统节能效果明显,且在此基础上推导出不同环境温度下运行经济性最佳的水冷冷水机组热回收温度,进而通过工程实例广东佛山某酒店项目的生活热水系统热源机组年能耗计算佐证之。本文量化分析了水冷冷水机组热回收的节能效果以及热回收温度对节能效果的影响,以期为水冷冷水机组热回收的应用及运行提供有益的参考和指导。

湖北省能源消费碳排放及其影响因素分析

低碳发展已成为区域高质量发展的趋势,湖北省作为首批低碳试点省份,是区域碳减排的“领头羊”。该文分析了2000-2021年湖北省能源消费碳排放变化情况和2007-2017年湖北省各市州碳排放空间分布特征,并利用STIRPAT模型分析影响湖北省碳排放的主要因素。结果表明:①2000-2021年湖北省碳排放经历了快速增长、缓慢增长、波动变化3个阶段,碳足迹强度呈下降趋势;②2007-2017年湖北省各市州碳排放呈上升趋势、碳足迹强度呈下降趋势,但碳排放区域差异较大,其中武汉市最高,神农架林区最低;③人口规模和能源消费结构是影响湖北省碳排放的主要因素,其他因素按影响程度从大到小依次为城镇化率、经济产出、技术水平、产业结构、政府支出,其中产业结构是唯一对湖北省碳排放有抑制作用的因素。

原油集输系统能耗影响因素的综合判定模型

随着油田生产能源需求的不断增长,原油集输系统在能源供应链中所起的作用日渐显著,其能耗所占比重在整体油田生产中也日益增加,而系统能耗又受到多种因素的制约,如何科学合理地分析集输系统能耗影响因素成为油田面临的重要课题。同时,部分因素之间还会存在交叉影响而造成信息重叠,这给准确把握系统能耗主要影响因素带来较大难度。结合集输系统能耗构成特点和生产实际运行状况,综合考虑产液量、含水率、油品黏度等影响集输系统能耗的客观因素,采用改进熵权法、主成分分析法以及多元逐步回归分析法对各项因素的影响程度进行定量描述,根据权重大小进行排序,并将3种方法结合进行综合判定,确定含水率和产液量为影响油田集输系统能耗的主要影响因素,最后针对主要因素变量对系统的相关制约原因进一步深入探究,并提出系统具体降耗措施,以期为原油集输系统节能提效提供理论参考。

农居建筑围护结构改造节能敏感性分析:以内蒙古河套地区为例

建筑节能问题是全社会普遍关注的焦点,农村建筑高能耗现状依然严峻。既有建筑节能多关注单一分类要素对能耗的量化影响,而不同组合要素对建筑热负荷影响的总体趋势、显著性和主次关系人们并不清楚。以内蒙河套地区农居为研究对象,通过正交实验、能耗模拟和多元回归方法,研究围护结构因素对建筑热性能的影响规律。结果显示:内蒙古河套地区建筑空间结构因素的主次顺序分别为阳光间进深>保温层厚度>透明围护结构>外墙结构>建筑朝向>窗墙比;当阳光间进深在1 m,保温70 mm,窗墙比为0.5时建筑热负荷最小;优化改造后,呼和浩特、包头、巴彦淖尔和鄂尔多斯的节能率分别为67.5%、69.2%、72.8%和76.5%,为分析空间围护结构因素量化规律和参数优选提供决策依据。