2020 4th International Conference on Green Energy and Applications (ICGEA)最新文献

ProjectBAGWIS:AModular,DiffuserAugmentedMicro-WindTurbineSystem DiffuserAugmentedMicro-WindTurbineSystem ProjectBAGWIS:模块化

2020 4th International Conference on Green Energy and Applications (ICGEA) Pub Date : 2020-03-01 DOI: 10.1109/icgea49367.2020.239710

Jeremy C.De Leon, F. Cruz

引用次数: 0

Comparison of Energy Efficiency and Carbon Dioxide Emissions of Gas Turbine Cogeneration with Steam Absorption Chillers versus Conventional Chilled Water Generation 采用蒸汽吸收式制冷机的燃气轮机热电联产与传统冷冻水发电的能源效率和二氧化碳排放比较

2020 4th International Conference on Green Energy and Applications (ICGEA) Pub Date : 2020-03-01 DOI: 10.1109/ICGEA49367.2020.239703

A. Nordin, S. Kamal, D. A. Salim, Danny Tam Hong Khai, M. Yusof

{"title":"Comparison of Energy Efficiency and Carbon Dioxide Emissions of Gas Turbine Cogeneration with Steam Absorption Chillers versus Conventional Chilled Water Generation","authors":"A. Nordin, S. Kamal, D. A. Salim, Danny Tam Hong Khai, M. Yusof","doi":"10.1109/ICGEA49367.2020.239703","DOIUrl":"https://doi.org/10.1109/ICGEA49367.2020.239703","url":null,"abstract":"Malaysia government committed to reduce greenhouse gas emission by 45{%} by 2030. This target was set with 35{%} on unconditional basis and 10{%} on conditional basis upon receipt of climate finance funding, technology transfer and capacity building developed countries. This paper demonstrate how cogeneration district cooling power plant helped to mitigate greenhouse gas emission by comparing fuel efficiency and carbon dioxide emissions of the cogeneration district cooling plant and a conventional electrical chiller plant which electricity was imported from the power grid. The GT Pro simulation is used to estimate the efficiency and the carbon dioxide emissions from the power plant. Results and analyses indicate that the cogeneration district cooling plant consumed 30.47{%} less primary energy and emit 44.43{%} less of carbon dioxide as compared to the conventional power plant. Hence, the adoption of cogeneration district cooling plant will definitely help the nation goal to reduce greenhouse emission intensity by 35{%} to 45{%} by 2030.","PeriodicalId":140641,"journal":{"name":"2020 4th International Conference on Green Energy and Applications (ICGEA)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132661209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling a Solar Module and Tracing Maximum Power Point Under Variant Ambient Conditions 不同环境条件下太阳能组件建模与最大功率点跟踪

2020 4th International Conference on Green Energy and Applications (ICGEA) Pub Date : 2020-03-01 DOI: 10.1109/ICGEA49367.2020.239715

A. Shukla, Jasdeep Kour

引用次数: 3

ICGEA 2020 Table of Contents ICGEA 2020目录

2020 4th International Conference on Green Energy and Applications (ICGEA) Pub Date : 2020-03-01 DOI: 10.1109/icgea49367.2020.9077565

Ruixin Yang, R. Xiong, W. Shen, Y. Hayashi, Yuto Yamamoto, Kosuke Kobayashi

{"title":"ICGEA 2020 Table of Contents","authors":"Ruixin Yang, R. Xiong, W. Shen, Y. Hayashi, Yuto Yamamoto, Kosuke Kobayashi","doi":"10.1109/icgea49367.2020.9077565","DOIUrl":"https://doi.org/10.1109/icgea49367.2020.9077565","url":null,"abstract":"Experimental Study on External Short Circuit and Overcharge of Lithium-ion Battery Packs for Electric Vehicles...................................................................................................................................................................... 1 Ruixin Yang, Rui Xiong , Weixiang Shen A Probabilistic Load Flow Method based on Improved Point Estimate and Maximum Entropy.................7 Qingyan Wang, Yuanyuan Sun, Kaiqiang Cheng, Yahui Li, Peng An Optimal Grid Reconfiguration Algorithm for Improving System Resilience under ExtremeWeather Events ...................................................................................................................................................................................12 Victor Widiputra, Jaesung Jung Identification of Power Demand Characteristics for Each Area and Energy Saving Control Selection Method ................................................................................................................................................................... 17 Toshihiro Mega, Masatada Kawatsu, Yusuke Fujiwara, Noriyuki Kushiro Evaluation and Visualization of kW / kWh Cost of a District Heating and Cooling System for Electricity Adjustment...............................................................................................................................................................22","PeriodicalId":140641,"journal":{"name":"2020 4th International Conference on Green Energy and Applications (ICGEA)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127176801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Operator-oriented Peer-to-peer Energy Trading among Residential Customers in South Korea 韩国住宅用户中面向运营商的点对点能源交易

2020 4th International Conference on Green Energy and Applications (ICGEA) Pub Date : 2020-03-01 DOI: 10.1109/ICGEA49367.2020.241504

Kangioon Heo, Jaesung Jung

引用次数: 0

Stochastic Distribution Expansion Planning with Wind Power Generation and Electric Vehicles Considering Carbon Emissions 考虑碳排放的风力发电和电动汽车随机分布扩展规划

2020 4th International Conference on Green Energy and Applications (ICGEA) Pub Date : 2020-03-01 DOI: 10.1109/ICGEA49367.2020.239694

Vivienne Hui Fan, K. Meng, J. Qiu, Zhaoyang Dong

{"title":"Stochastic Distribution Expansion Planning with Wind Power Generation and Electric Vehicles Considering Carbon Emissions","authors":"Vivienne Hui Fan, K. Meng, J. Qiu, Zhaoyang Dong","doi":"10.1109/ICGEA49367.2020.239694","DOIUrl":"https://doi.org/10.1109/ICGEA49367.2020.239694","url":null,"abstract":"Conventional power distribution system is evolving with the growth of distributed generation and electric vehicle integration. The methods of this multidisciplinary planning under uncertainty have not yet been closely examined. In this work, we propose a framework for distribution network expansion planning considering the stochastic nature of DGs, charging stations associated with carbon impact. The proposed model aims to minimize the overall investment cost, the operation and maintenance cost, energy losses and carbon emissions by optimizing alternative feeder routes, the reinforcement of existing substations or new constructions, and the deployment of DGs and charging stations. A multiobjective mixed-integer nonlinear programme is formulated and recast as a two-stage stochastic problem based on analytical probabilistic approach. The model is solved with Tchebycheff decomposition method based evolutionary algorithm. The proposed approach is examined with a modified case-54 distribution and node-25 transportation system. Sensitivity analysis proves carbon emissions can influence the overall investment cost up to 21%. System cost and energy loss has the potential of 1.5% reduction by integrating wind generators. Numerical results obtained effectively demonstrate the capability and feasibility of proposed method.","PeriodicalId":140641,"journal":{"name":"2020 4th International Conference on Green Energy and Applications (ICGEA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131242556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Research on the Influence of Different Types of Distributed Power Supply on Distribution Network Voltage 不同类型分布式电源对配电网电压影响的研究

2020 4th International Conference on Green Energy and Applications (ICGEA) Pub Date : 2020-03-01 DOI: 10.1109/ICGEA49367.2020.239700

Xiao Ying, Chang Xiao, Zhang Shifeng, S. Yuxin, Zhao Jun, Liu Liqun

引用次数: 0

[ICGEA 2020 Title Page] [ICGEA 2020标题页]

2020 4th International Conference on Green Energy and Applications (ICGEA) Pub Date : 2020-03-01 DOI: 10.1109/icgea49367.2020.9077573

引用次数: 0

Evaluation and Visualization of kW / kWh Cost of a District Heating and Cooling System for Electricity Adjustment 区域供热供冷电力调节系统千瓦/千瓦时成本评估与可视化

2020 4th International Conference on Green Energy and Applications (ICGEA) Pub Date : 2020-03-01 DOI: 10.1109/ICGEA49367.2020.239687

K. Tomita, Y. Hayashi, Y. Iino, Yuto Yamamoto, Kosuke Kobayashi

{"title":"Evaluation and Visualization of kW / kWh Cost of a District Heating and Cooling System for Electricity Adjustment","authors":"K. Tomita, Y. Hayashi, Y. Iino, Yuto Yamamoto, Kosuke Kobayashi","doi":"10.1109/ICGEA49367.2020.239687","DOIUrl":"https://doi.org/10.1109/ICGEA49367.2020.239687","url":null,"abstract":"A District Heating and Cooling system (DHC) with Combined Heat and Power (CHP) is able to not only satisfy regional heat and cold demand but also generate and control electricity by CHP. Therefore, a DHC with CHP has potential as control reserve for electricity adjustment (EA). In Japan, an EA auction was established to secure control reserve efficiently. In our previous research, potential of EA by a DHC for this auction has already evaluated. Furthermore, it was found that kW and kWh costs of a DHC for EA mainly depend on the heat and cold demand. Operators of DHCs need to comprehend the exact costs for EA when they bid on the auction. Therefore, fluctuations in costs for EA due to changes in heat and cold demand have been a critical issue for operators in decision making for the bid. However, costs for EA of a DHC with comprehensive assumption of various heat and cold demands has not been evaluated. Therefore, in this paper, the kW and kWh costs of an existing DHC are quantified and visualized as a multidimensional function based on the conditions of heat and cold demands. In addition, the factors behind those results are discussed. As a result, this research made it possible to predict the costs for EA of the DHC with heat and cold demand forecasts, and the operator could use it as auxiliary information in decision making for the bid.","PeriodicalId":140641,"journal":{"name":"2020 4th International Conference on Green Energy and Applications (ICGEA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127545017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal Grid Reconfiguration Algorithm for Improving System Resilience under Extreme Weather Events 极端天气条件下提高系统弹性的网格重构优化算法

2020 4th International Conference on Green Energy and Applications (ICGEA) Pub Date : 2020-03-01 DOI: 10.1109/ICGEA49367.2020.239685

Victor Widiputra, Jaesung Jung

{"title":"Optimal Grid Reconfiguration Algorithm for Improving System Resilience under Extreme Weather Events","authors":"Victor Widiputra, Jaesung Jung","doi":"10.1109/ICGEA49367.2020.239685","DOIUrl":"https://doi.org/10.1109/ICGEA49367.2020.239685","url":null,"abstract":"Due to global warming, the number of extreme weather events has increased in the last ten years. Consequently, the number of power system blackouts has also increased in this period. The reliability index is incapable of analyzing the power system behavior during these events because it does not account for extreme weather events for its calculation. Therefore, the resilience index is proposed for measuring the system functionality during extreme weather events. To increase the resilience value of the system, its functionality during such events must be increased. One way to achieve this is through the reconfiguration of the power system, to ensure that the parts of the power system which do not experience failure remain operational even during the extreme weather events. This paper proposes an algorithm to determine the optimal reconfiguration of the power system to increase the grid resilience. First, it applies the actual condition of the system during the extreme weather events. Then, the algorithm finds the islanded buses in the power system using bus injection to bus current (BIBC) matrix. Finally, the algorithm utilizes a genetic algorithm to find the optimal reconfiguration for the system. The results show that the reconfiguration strategy can be utilized to increase the system resilience under similar extreme weather events.","PeriodicalId":140641,"journal":{"name":"2020 4th International Conference on Green Energy and Applications (ICGEA)","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123722107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3