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Contribution to the Economic and Optimal Planning of Multi-GED and a FACTS in a Distribution Network by Genetics Algorithms 遗传算法对配电网多栅极规划和事实规划的经济贡献
IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2020-06-20 DOI: 10.11648/j.epes.20200902.11
Arouna Oloulade, A. M. Imano, F. Fifatin, Mahamoud Tanimomon, Akouèmaho Richard Dansou, Ramanou Badarou, A. Vianou
The distribution networks are more and more heavily loaded due to economic growth, industrial development and housing. The operation of these networks under these conditions generates voltage instabilities and excessive power losses. The present work consisted in the optimal integration of multi-GED (Decentralized Energy Generators) (Photovoltaic (PV), Fuel Cell (FC or PAC) and Wind Generator (WG)) and FACTS (SVC) in a Medium Voltage distribution’s departure of the Beninese Electrical Energy Company (SBEE), with a view to improve its technical performances. The diagnostic study of the Ouidah 122-nodes test network, before optimization, revealed that the active and reactive losses are 457.34588 kW and 625.41503 kVAr respectively. This network has high voltage instability with a minimum voltage of 0.80455 p.u. and a minimum VSI of 0.41897 p.u. The optimization of the size and positioning of GED and FACTS was based on the Non-dominated Sorting Genetic Algoritm II (NSGA II). After optimization with the NSGA II, a comparative study of the different combinations between the three GEDs and the SVC, made it possible to choose that of the placement of a 121 kW Wind Generator at node 75, a PV of 131 kW at node 51, a system of Fuel Cell (FC, PAC in french) of 700 kW at node 34, and an SVC of 2.126 MVAr at node 94 of the network. This positioning enabled a reduction of 65.11% in active losses and 65.12% in reactive losses. The voltage profile and the voltage stability are clearly improved, with a minimum voltage of 0.96993 p.u. and a minimum VSI of 0.88505 p.u. The initial investment for this project is seven hundred and seven million three hundred and fifty-two thousand three hundred and fifty-eight point seven CFA francs (707,352,358.7 CFA francs). The technical and economic evaluation shows that the payback period is approximately 4 years 6 months and 14 days. The relevant results obtained show that the method used is efficient and effective, and can be applied to other MV departures of the SBEE.
由于经济增长、工业发展和住房问题,配电网的负荷越来越大。在这种情况下,这些网络的运行会产生电压不稳定和过大的功率损耗。目前的工作包括在贝宁电力公司(SBEE)出发的中压配电中对多分散式能源发电机(光伏(PV),燃料电池(FC或PAC)和风力发电机(WG)和FACTS (SVC)进行优化集成,以提高其技术性能。通过对Ouidah 122节点测试网络的诊断研究,优化前的有功损耗为457.34588 kW,无功损耗为625.41503 kVAr。这个网络具有较高的电压不稳定的最低电压0.80455动力装置和最低VSI 0.41897 p.u。优化的大小和定位GED和事实是基于Non-dominated排序遗传使单机II (NSGA II)。与NSGA II,优化后的比较研究三个GED和SVC之间的不同组合,使人们有可能选择的一个121千瓦风力发电机节点的放置75,一个131千瓦的PV节点51岁,第34节点的燃料电池(FC,法语为PAC)系统功率为700千瓦,第94节点的SVC功率为2.126 MVAr。这种定位使有功损耗减少65.11%,无功损耗减少65.12%。电压分布和电压稳定性得到明显改善,最低电压为0.96993 p.u.,最低VSI为0.88505 p.u.。本项目初始投资为七亿七千三百五十二万三千五百五十八分七非洲法郎(707,352,358.7非洲法郎)。技术经济评价表明,投资回收期约为4年6个月14天。相关结果表明,该方法是有效的,可以应用于SBEE的其他MV偏离。
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引用次数: 0
Driving Forces Analysis of Power Consumption in Beijing Based on LMDI Decomposition Method and LEAP Model 基于LMDI分解和LEAP模型的北京市电力消费驱动力分析
IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2020-05-15 DOI: 10.11648/J.EPES.20200901.12
Dong Jun, Palidan Ainiwaer, Liu Yao
With increasing pressure on resources and environment, sustainable development is becoming more and more important. As the largest energy consumer in the world, China needs to take measures to achieve energy transformation more urgently both from supply and demand side, which is of great significance for sustainable development and achieving carbon emissions target. In recent years, the capital city Beijing has also made great efforts to promote the replacement of electric energy in residential heating, manufacturing, transportation, power supply and consumption. In order to explore driving forces of total power consumption in Beijing`s final demand sectors, this paper decomposes the factors into industrial electricity substitution effect, industrial energy intensity effect, industrial structure effect, economic scale effect, population structure effect, residential electricity substitution effect, residential energy intensity effect and population size effect based on the logarithmic mean Divisia index (LMDI) decomposition method. The decomposition results show that the industrial electricity substitution effect made the largest contribution to increase power consumption in Beijing’s final energy consumption sector, followed by economic scale effect, residential energy intensity effect, population scale effect and residential electricity substitution effect, and other`s effect does the opposite. Finally, seven different scenarios are set up to forecast the future power consumption of Beijing`s final sectors based on the long-term energy alternative planning model (LEAP), which reveals the impact of energy efficiency improvement and electricity substitution polices on electricity consumption in Beijing`s final energy consumption sectors.
随着资源环境压力的增大,可持续发展显得越来越重要。中国作为世界上最大的能源消费国,更迫切需要从供给侧和需求侧采取措施实现能源转型,这对可持续发展和实现碳排放目标具有重要意义。近年来,首都北京在居民供暖、制造业、交通运输、电力供应和消费等方面也大力推动电能替代。为了探索北京市最终需求部门总用电量的驱动因素,本文基于对数平均分度指数(LMDI)分解方法,将影响因素分解为工业电力替代效应、工业能源强度效应、产业结构效应、经济规模效应、人口结构效应、居民电力替代效应、居民能源强度效应和人口规模效应。分解结果表明,工业电力替代效应对北京市最终能源消费部门电力消费增长的贡献最大,其次是经济规模效应、居民能源强度效应、人口规模效应和居民电力替代效应,其他效应则相反。最后,基于长期能源替代规划模型(LEAP),设置了7种不同情景,对北京市最终能源消费部门的未来用电量进行了预测,揭示了能效提升和电力替代政策对北京市最终能源消费部门用电量的影响。
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引用次数: 1
Energy Audit and Management of a Tannery Company: A Case Study of Kano State 一家制革公司的能源审计与管理:以卡诺州为例
IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2020-03-24 DOI: 10.11648/J.EPES.20200901.11
Jamilu Ya’u Muhammad, S. Alhassan, I. Abdulmajeed, N. H. Waziri, Najib x Hassan Waziri, Faru Faruk Tukur
Energy audit is considered as one of the comprehensive methods in checking the energy usage and wastage in facilities/buildings. This paper presents the results of the energy audit conducted to investigate the energy consumption pattern of tannery company from its record of fuel expenditure and electricity bills for a period of 5 years (2012 - 2016). Also, the use of energy models system, Energy Quick Energy Simulation Tool (eQUEST) to evaluate the consumption of the energy end users and performance of the company. Results shows peak electricity demand during the hot months from April to August due to high cooling or significant Air condition requirement. 2.37% of electricity consumed was contributed by the burning of AGO in the diesel power generators showing very less contribution over that of National grid 97.63%. The annual average consumption demand of electricity and diesel (kWh equivalent) of the company were 118960.72 kWh and 2881.17 kWh respectively. The energy modeling and simulation results shows that the sum total of the total monthly energy consumption by the end users is 138164 kWh representing the total average value of the annual energy use in air-conditioning (space cooling) was 27%, ventilation fan 2%, factory machineries 39%, heat rejection is 4%, pump and auxiliary is 2% and area lighting 26%. Also, the total monthly peak demand by space cooling was 22372.2 kWh, ventilation fan 1376 kWh, factory machineries 14294kWh heat rejection is 4461 kWh, pump and auxiliary is 1343 kWh and area light 11023 kWh respectively having a sum total monthly peak demand by the end users to be 44969.2 kWh. This represent energy use in air-conditioning (space cooling) was 41%, ventilation fan 3%, 26% factory machineries, heat rejection is 8%, pump and auxiliary is 2% and 20% area light of the annual peak demand. The Energy Used Index (average annual electricity use per tones of leather) was found to be 717.38kWh/tones of leather/Annum.
能源审计被认为是检查设施/建筑物的能源使用和浪费的综合方法之一。本文介绍了能源审计的结果,该审计旨在调查制革公司5年(2012年至2016年)的燃料支出和电费记录中的能源消耗模式。此外,使用能源模型系统,能源快速能源仿真工具(eQUEST)来评估最终用户的能源消耗和公司的绩效。结果显示,4月至8月的炎热月份,由于制冷或空调需求较高,电力需求达到峰值。柴油发电机组燃烧AGO占总耗电量的2.37%,远低于国家电网的97.63%。公司年平均用电需求为118960.72千瓦时,年平均用电需求为2881.17千瓦时。能源建模和仿真结果表明,最终用户每月总能耗为138164千瓦时,其中空调(空间冷却)年能耗的总平均值为27%,通风机为2%,工厂机械为39%,散热为4%,泵及辅助为2%,区域照明为26%。此外,空间制冷的月总峰值需求为22372.2 kWh,通风机为1376 kWh,工厂机械为14294kWh,排热为4461 kWh,泵及辅助为1343 kWh,区域灯为11023 kWh,最终用户的月总峰值需求为44969.2 kWh。这代表空调(空间冷却)的能源使用量为41%,通风机为3%,工厂机械为26%,散热为8%,泵和辅助为2%,区域照明为20%的年度高峰需求。能源使用指数(每吨皮革的年平均用电量)为717.38千瓦时/吨皮革/年。
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引用次数: 1
DESIGN OF FEEDBACK LINEARIZATION CONTROLLER FOR STATCOM FOR VAR COMPENSATION IN AN INTERCONNECTED SYSTEM 互联系统无功补偿状态控制器的反馈线性化设计
IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2020-01-01 DOI: 10.2316/j.2020.203-0227
S. Parvathy, K. Thampatty, T. Nambiar
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引用次数: 0
ROBUST CONTROL STRATEGIES DEDICATED TO ELECTRIC VEHICLE MOTORIZATION 用于电动汽车机动化的鲁棒控制策略
IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2020-01-01 DOI: 10.2316/j.2020.203-0089
S. Tounsi, S. H. Abdallah
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引用次数: 2
WIRELESS POWER TRANSFER SYSTEM FOR SCORBOT ER-4U ROBOTIC ARM scorbot er-4u机械臂无线电力传输系统
IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2020-01-01 DOI: 10.2316/j.2020.203-0044
Samta Shastri, Yusuf Parvez, N. Chauhan
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引用次数: 2
A PLANNING STRATEGY FOR REACTIVE POWER IN POWER TRANSMISSION NETWORK USING SOFT COMPUTING TECHNIQUES 基于软计算技术的输电网无功功率规划策略
IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2020-01-01 DOI: 10.2316/j.2020.203-0214
B. Bhattacharyya, Nihar Karmakar
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引用次数: 10
VECTOR CONTROL BASED REGENERATIVE BRAKING FOR INDUCTION MOTOR DRIVEN BATTERY ELECTRIC VEHICLES 基于矢量控制的感应电机驱动电池电动汽车再生制动
IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2020-01-01 DOI: 10.2316/j.2020.203-0122
V. Sudhakaran, Varsha A. Shah, M. Lokhande
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引用次数: 1
MODELLING OF A DOUBLE-INPUT BIDIRECTIONAL DC–DC CONVERTER FOR HESS AND UNIFIED CONTROLLER DESIGN FOR DC MICROGRID APPLICATIONS Hess双输入双向dc - dc变换器建模及直流微电网统一控制器设计
IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2020-01-01 DOI: 10.2316/j.2020.203-0196
P. Srinivas, Udaya Bhasker Manthati
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引用次数: 2
ASSESSING THE IMPACT OF RENEWABLE PURCHASE OBLIGATION ON INDIAN POWER SECTOR 评估可再生能源购买义务对印度电力部门的影响
IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2020-01-01 DOI: 10.2316/j.2020.203-0252
Prateek Mundra, A. Arya, S. Gawre
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引用次数: 0
期刊
International Journal of Power and Energy Systems
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