Table Of ContentAdvances in Intelligent Systems and Computing 1403
Zhengbing Hu
Bo Wang
Sergey Petoukhov
Matthew He Editors
Advances
in Artificial
Systems
for Power
Engineering
Advances in Intelligent Systems and Computing
Volume 1403
Series Editor
Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences,
Warsaw, Poland
Advisory Editors
Nikhil R. Pal, Indian Statistical Institute, Kolkata, India
Rafael Bello Perez, Faculty of Mathematics, Physics and Computing,
Universidad Central de Las Villas, Santa Clara, Cuba
Emilio S. Corchado, University of Salamanca, Salamanca, Spain
Hani Hagras, School of Computer Science and Electronic Engineering,
University of Essex, Colchester, UK
László T. Kóczy, Department of Automation, Széchenyi István University,
Gyor, Hungary
Vladik Kreinovich, Department of Computer Science, University of Texas
at El Paso, El Paso, TX, USA
Chin-Teng Lin, Department of Electrical Engineering, National Chiao
Tung University, Hsinchu, Taiwan
Jie Lu, Faculty of Engineering and Information Technology,
University of Technology Sydney, Sydney, NSW, Australia
Patricia Melin, Graduate Program of Computer Science, Tijuana Institute
of Technology, Tijuana, Mexico
NadiaNedjah,DepartmentofElectronicsEngineering,UniversityofRiodeJaneiro,
Rio de Janeiro, Brazil
Ngoc Thanh Nguyen , Faculty of Computer Science and Management,
Wrocław University of Technology, Wrocław, Poland
Jun Wang, Department of Mechanical and Automation Engineering,
The Chinese University of Hong Kong, Shatin, Hong Kong
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Zhengbing Hu Bo Wang
(cid:129) (cid:129)
Sergey Petoukhov Matthew He
(cid:129)
Editors
fi
Advances in Arti cial
Systems for Power
Engineering
123
Editors
Zhengbing Hu BoWang
Schoolof Educational Schoolof Electrical andAutomation
Information Technology WuhanUniversity
Central China NormalUniversity Wuhan, China
Wuhan, Hubei, China
MatthewHe
Sergey Petoukhov HalmosCollege ofNatural Sciences
MechanicalEngineering Nova SoutheasternUniversity
Russian Academy of Sciences Plantation,FL,USA
Moscow,Russia
ISSN 2194-5357 ISSN 2194-5365 (electronic)
Advances in Intelligent Systems andComputing
ISBN978-3-030-80530-2 ISBN978-3-030-80531-9 (eBook)
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Contents
Optimal Dispatch of Combined Cooling, Heating and Power
Microgrid with Advanced Adiabatic Compressed Air
Energy Storage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Manshang Wang, Jiayu Bai, Bosheng Chen, Laijun Chen, and Wei Wei
Chatbots as a Tool to Optimize the Educational Process . . . . . . . . . . . . 15
Nataliya Mutovkina
Intelligent System of Computer Aided Processes Planning. . . . . . . . . . . 26
Georgy B. Evgenev
Micro-level Modeling of Traffic Flows Through Signalized
Crossroads of an Arbitrary Structure . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Andrey M. Valuev
Polypolar Coordination by the Multifocal Lemniscates . . . . . . . . . . . . . 50
T. Rakcheeva
Non-intrusive Load Identification Decision Method Based
on Time Signatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Zhengqi Tian, Zengkai Ouyang, Meimei Duan, Guofang Xia,
Zhong Zheng, Xiaoxing Mu, and Chao Zhou
Research on Non-intrusive Load Identification Method Based
on Support Vector Machine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Yixuan Huang, Qifeng Huang, Hanmiao Cheng, Kaijie Fang,
Xiaoquan Lu, and Tianchang Liu
Intelligent Detection of Electricity Stealing by Replacing Instrument
Transformer Based on Daily Load Date Mining . . . . . . . . . . . . . . . . . . 87
Gaojun Xu, Xin Zhang, Li Sun, Weimin He, Shuangshuang Zhao,
Weijiang Wu, and Jian He
v
vi Contents
Fuzzy Management of Teacher-Student Interaction in Distance
Learning Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
N. Yu. Mutovkina
Evaluation Method of Distributed Renewable Energy Access
to Distribution Network Based on Variable Weight Theory. . . . . . . . . . 108
Peng Wang, Chengliang Zhu, Hongjian Wang, Sen Wang,
and Hengrui Ma
Research on Realization Technology of Arc Grounding Fault
on Distribution Network on Field Test Data . . . . . . . . . . . . . . . . . . . . . 122
Xiaoyong Yu, Lifang Wu, Weixiang Huang, Shaonan Chen,
and Liqun Yin
Research on Reactive Power Compensation Configuration of Wind
Farm Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Junfang Wang, Caifu Chen, Shuang Zhang, Zheng Ren, Xiaolu Chen,
and Xinyu Wang
Two Stage Stochastic Scheduling Model of Integrated Energy
System with Renewable Energy Considering Demand Response . . . . . . 144
Qiao Chen, Yimin Qian, Kai Ding, Yi Wang, Chengliang Zhu,
and Sen Wang
Evaluation of Distribution Equipment Utilization Based
on Data Driven. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
Xintong Li, Shuo Liang, Yangjun Zhou, and Xiaoyong Yu
Approaches to Validation of Quantification of the Variable
“Relationship Between Users” in the Context of Social
Engineering Attacks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Anastasiia Khlobystova, Maxim Abramov, and Tatiana Tulupyeva
Parametric Oscillations at Delays in the Forces of Elasticity
and Damping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Alishir A. Alifov
A Coordinated Dispatching Model for HDR-PV Hybrid Power
System: A Zero-Sum Game Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 189
Qingmiao Zhang, Yang Si, Xuelin Zhang, and Xiaotao Chen
Category Splices and Modeling with Their Help Chemical Systems
and Biomolecules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Georgy K. Tolokonnikov
Consensus Algorithm Based Distributed Coordinated Control
for ESSs Integrated Off-grid PV Station . . . . . . . . . . . . . . . . . . . . . . . . 213
Xiaoling Su, Zhengxi Li, Yang Si, Yongqing Guo, and Wenhao Xu
Contents vii
Algebraic Harmony in Genomic DNA-Texts and Long-Range
Coherence in Biological Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
Sergey V. Petoukhov
Comparative Analysis of Inductive Density Clustering Algorithms
Meanshift and DBSCAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
Zhengbing Hu, Irina Lurie, Oleksii K. Tyshchenko, Nataliia Savina,
and Volodymyr Lytvynenko
Author Index.. .... .... .... ..... .... .... .... .... .... ..... .... 243
Optimal Dispatch of Combined Cooling, Heating
and Power Microgrid with Advanced Adiabatic
Compressed Air Energy Storage
B
ManshangWang1,JiayuBai2( ),BoshengChen1,LaijunChen3,andWeiWei2
1 StateGridZhenjiangPowerSupplyCompany,Zhenjiang212001,China
2 StateKeyLabofControlandSimulationofPowerSystemsandGenerationEquipments,
DepartmentofElectricalEngineering,TsinghuaUniversity,HaidianDistrict,
Beijing100084,China
3 NewEnergy(Photovoltaic)IndustryResearchCenter,QinghaiUniversity,
Xining810016,China
Abstract. Advancedadiabaticcompressedairenergystorage(AA-CAES)isa
promising large-scale energy storage technology inherently combined cooling,
heatingandpower(CCHP)generation,withtheadditionalmeritsofhighenergy
efficiency, long service time and zero carbon emission. In this paper, an inte-
gratedenergysystemdispatch-orientedAA-CAESmodelisdeveloped,wherethe
relationshipsbetweencharging,discharging,heatingandcoolingpower,andthe
stateofcharges(SOC)ofairstorage(AS)andthermalenergystorage(TES)is
established around air and heat transfer oil mass flow rate. The economic dis-
patch of a CCHP microgrid with AA-CAES is then modeled as mixed integer
quadraticprogramming(MIQP).Thesimulationresultsdemonstratethatthepro-
posed AA-CAES dispatch model delivers more practical scheduling strategies
andalsoverifiestheeconomicbenefitsofAA-CAESoperatinginCCHPmode
forreducingsystemoperationcostandincreasingrenewablesaccommodation.
Keywords: Advancedadiabaticcompressedairenergystorage·Combined
cooling·Heatingandpower·Integratedenergysystem·Economicdispatch
1 Introduction
Facedwithgrowingseriousenergycrisisandenvironmentalpollution,itisinevitableto
developrenewableenergiesandtoimprovethecomprehensiveutilizationefficiencyof
energy.TheconceptofintegratedenergysystemisearlyproposedbyU.S.andEuropean
researchers,withtheaimtopromotethedeep-penetrationofrenewablegenerationand
improve the energy systemeconomy by dovetailing different energy systems [1].The
combinedcooling,heatingandpower(CCHP)microgrids,alsoreferredtoastrigener-
ation system, is a typical integrated energy system whose energy efficiencies is much
higherthanindependentlyoperatingenergysupplysystems,andwasputintopractice
insomeschoolsandhospitals[2].
©TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2021
Z.Huetal.(Eds.):AIPE2020,AISC1403,pp.1–14,2021.
https://doi.org/10.1007/978-3-030-80531-9_1
2 M.Wangetal.
A lot of researches have been carried out on the operation and dispatch of CCHP
based microgrid. Ref. [2] proposed a two-stage coordinated control scheme of CCHP
systemtocopewiththeforecasterrorofrenewablesoutputsandload,whichcontainsthe
first-stageeconomicdispatchandsecond-stagereal-timeadjusting.Ref.[3]established
the dynamic model of cooling and heating systems taking into consideration thermal
inertia, and proposed a joint-dispatch strategy of energy and reserve for CCHP-based
microgrids.ModelpredictivecontrolapproachwasemployedinRef.[4]torealizean
efficientonlineschedulingofCCHPmicrogrid.Thepartial-loadfeaturesofCCHPsare
modeledinRef.[5]andamultitime-scaledispatchmodelispresentedwiththeaimto
minimizetheimpactofmicrogridonthemaingrid.
Compressedairenergystorage(CAES)isrecognizedasoneofthelarge-scaleenergy
storage technologies and attracts lots of attention in recent years due to its metrics of
long service time, high reliability, and low environmental impact [6]. Compared with
conventionalCAES,theby-producedcompressionheatisrecoveredandfullyutilized
inadvancedadiabaticcompressedairenergystorage(AA-CAES),whichbringsabout
higherenergyefficiencyandfuelindependence[7].Inaddition,conventionalCAESas
wellasAA-CAESisinherentlycombinedcooling,heatingandpowergeneration,and
thuscanplayanimportantroleinintegratedenergysystems[8].
AA-CAESoperatinginCCHPmodeinvolvesconversionofmulti-energyflows,such
asmechanicalenergy,electricalenergy,thermalenergy,coldenergyandpressurepoten-
tial energy, etc., with complex coupling among control and state variables. There has
beenabundantresearchesonthethermodynamicmodelingofAA-CAESunderCCHP
mode, which was further applied in system efficiency evaluation [7, 9], and operation
strategyanalysis[8,10].However,fewresearchespayattentiontotheoptimaldispatch
of CCHP microgrid integrated with AA-CAES, where a multi-energy dispatch model
of AA-CAES is required, of which the model accuracy and computational efficiency
shouldbewellbalanced.
Theremainderofthepaperisarrangedasfollows.ThedispatchmodelofAA-CAES
combinedcooling,heatingandpowergenerationisestablishedinSect.2.Theoptimal
dispatch model of CCHP system integrated with AA-CAES is proposed in Sect. 3.
Moreover,casestudyiscarriedoutinSect.4andconclusionsaresumedupinSect.5.
2 ModelingofAA-CAESUnderCCHPMode
2.1 DescriptionofAA-CAESCombinedCooling,HeatingandPowerGeneration
TheschematicdiagramofAA-CAEScombinedwithcooling,heatingandpowergen-
erationisshowninFig.1,whichconsistsofmotor,multi-stagecompressors,e.g.,high-
pressure compressor (HPC), medium-pressure compressor (MPC) and low-pressure
compressor(LPC)asforthree-stagecompression,inter-coolersdenotedbyHX1,HX2
andHX3,multi-stageturbines,e.g.,high-pressureturbine(HPT)andlow-pressuretur-
bine(LPT)asfortwo-stageexpansion,inter-heatersdenotedbyHX4,HX5,generator,
airstorage(AS),low-temperaturethermalenergystorage(LTES)andhigh-temperature
thermal energy storage (HTES), oil pumps (OP) at the outside of HTES and LTES,
heating-sideheatexchanger(HX),andthrottlevalves(TV).
