Chen, Yi

Jin, Tongdan

English

Distributed electric system integrating wind and solar generation is reshaping the landscape of power industry. Distributed renewable generation allows the utility companies to lower the carbon footprints as well as deferring the expansion of grid infrastructure. However, power intermittency and equipment cost are the main hurdles confining the large adoption of wind- and solar-based energy solutions. This paper proposes a multistage, multicriteria approach maximizing the renewable energy throughput, while minimizing the levelised cost of energy. In particular, we optimize the sizing, siting, and maintenance of renewable sources under stringent reliability, power quality, and environmental constraints. A two-stage meta-heuristics, consisting of genetic algorithms and the gradient method, is developed, in order to search for the non-dominant solution set. A 13-node distribution network is used to demonstrate the performance of the proposed planning model. The results are compared with simulations and other meta-heuristics, and it is shown that the genetic algorithm excels in terms of computational time and quality of the results

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A two-stage multi-criteria decision for distributed wind and solar integrationJin, Tongdan ; Chen, YiPublication date:2015Document VersionPeer reviewed versionLink to publication in ResearchOnlineCitation for published version (Harvard):Jin, T & Chen, Y 2015, 'A two-stage multi-criteria decision for distributed wind and solar integration'.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.Take down policyIf you believe that this document breaches copyright please view our takedown policy at https://edshare.gcu.ac.uk/id/eprint/5179 for detailsof how to contact us.Download date: 29. Apr. 2020See	discussions,	stats,	and	author	profiles	for	this	publication	at:	http://www.researchgate.net/publication/281716832A	Two-Stage	Multi-Criteria	Decision	for	Distributed	Wind	and	SolarIntegrationCONFERENCE	PAPER	·	JULY	2015DOWNLOAD11	AUTHOR:Yi	ChenGlasgow	Caledonian	University39	PUBLICATIONS			107	CITATIONS			SEE	PROFILEAvailable	from:	Yi	ChenRetrieved	on:	14	September	2015A Two-Stage Multi-Criteria Decision for Distributed Wind and Solar Integration Tongdan Jin, Ph.D.Ingram School of Engineering, Texas State UniversityYi Chen, Ph.D.Glasgow Caledonian University, UK1l Toward Distributed Generationl Characterizing Wind and Solar GenerationlMulti-Criteria Planning Model l Numerical Experimentl ConclusionOutline3wind windwind3GascoalNuclearRenewable Portfolio Standards in 2040 US=25%EU=40%China=20-25%The Rise of Distributed Power ServiceDistributed Power Age1880 1910Central Power Period2000Rise of Distributed Power Onsite GenerationWind and Solar FarmsWind power is Scotland's fastest growing renewable energy technology, with 2574 MW of installed capacity as of April 2011. For example:Whitelee Wind Farm is the largest on-shore wind farm in the United Kingdom with 215 Siemens and Alstom wind turbines and a total capacity of 539 MW.The Clyde Wind Farm is a 350 MW on-shore wind farm near Abington in South Lanarkshire, Scotland.The Robin Rigg Wind Farm is a 180 MW development completed in April 2010, is an off-shore wind farm sited on a sandbank in the Solway Firth.Wind power in Scotlandhttps://en.wikipedia.org/wiki/Wind_power_in_Scotland#Large_wind_farmsLarge wind farms in Scotland:l Black Law Wind Farml Braes of Doune Wind Farml Clyde Wind Farml Crystal Rig Wind Farml Farr Wind Farml Hadyard Hill Wind Farm l Robin Rigg Wind FarmlWhitelee Wind FarmWind power in Scotlandhttps://en.wikipedia.org/wiki/Wind_power_in_Scotland#Large_wind_farmsThere is further potential for expansion, especially offshore given the high average wind speeds, and a number of large offshore wind farms are planned.Type of Distributed Generation (DG)1) Generation backup2) Onsite generation 3) Multi-node Generation4) Microgrid Systemhttp://www.efchina.org/Case-Study-en/case-2014122305-enA commercial micro-grid system in ChinaLocation: Turpan, XinjiangYear: 2009Capacity: 13.4 MW PVService: 7,000 homesFunded: Energy Foundation China’s Renewable Energy Program Up to 299 days w/o raining2,550-3,300 hours of sunlight/yearBackup DG Onsite DGMulti-NodeDGMicro-GridSystemKey Notationsx=decision variables representing the sizing and sitingt=the maintenance time of DG systemn=number of nodes in the power systemm=number of available DG equipment type l=number of link of the distribution network Dj=power demand of node j, for j=1, 2, …, nPj(x)=power generation at node j, for j=1, 2, …, nVj(x)=voltage at node j, for j=1, 2, …, nIk(x)=current in link k, for k=1, 2, …, lPr=rated wind turbine power output fw(y)=wind speed distributionS=hourly irradiance on PV, random variablePs(S)=power output of PVh=PV efficiencyA=PV areaTo=PV skin temperaturemixed integration programming problemModeling Variable Power OutputØ Time Series Model (.e.g. ARIMA Model)Ø DG SimulationsØ Astronomy/Physics Models (Observation)Ø Moment Methods (Mean and Variance)The models of ‘Wind’ and ‘Solar PV’ Powergeneration are given:Wind Power Generationïïîïïíì££££÷÷øöççèæ><£=srrrcrrscwvyvPvyvvyPvyvyyP3,00)(( ))()()()]([ 3 rsmvvvFvFPdyyfxyPErc-+ò= g3/ rr vP=g( ))()()()]([ 2622 rsmvvvFvFPdxxfxYPErc-+= òg( ) ( )22( ) [ ( )] [ ( )Var P Y E P Y E P Y= -Wherekcykw ecyckyf )/(1)( --÷øöçèæ÷øöçèæ=00.040.080.120.160 4 8 12 16pdf of yWind speed yWeibull Wind Speed Distributionc=7, k=2 c=10, k=4vc vr vs0Power (MW)wind speedcubic power curvePmy Prdensity function Piecewise FunctionsSolar Photovoltaics (PV) Generation111)()()()(--÷÷øöççèæ-÷÷øöççèæGG+G=bmamms ssssbasbasf( ))25(005.01)( --= os TASSP h( )ssomss baTAsaSPE+--=)25(005.01)]([ h( ))1()()25(005.01))(( 22222+++--=ssssomsss babaTAsbaSPVar h00.00050.0010.00150.0020.00250.0030 200 400 600 800 1000Probability DensitySolar Irradiance (W/m2)Beta Distribution for Solar Irradiancea=2, b=2, (I)a=4, b=4, (II)a=1.8, b=3, (III)a=3, b=1.8, (IV)smsWheresm=maximum solar irradiance (W/m2)Ps(S)=power output of PVh=PV efficiencyA=PV areaTo=PV panel temperaturedensity function Key Performance Measures of DG Systemq TechnicalØ Energy Supply ReliabilityØ Power Quality (i.e. voltage stability)Ø Line Thermal Stressq EconomicalØ Return On Investmentq Environmental-SocialØ Carbon Savings/Climate Changeq PoliticalØ Renewable Portfolio Standards 12Technical Constraints132maxmin })x(Pr{ a³££ VVVØ Reliability (loss-of-load probability):11})x(Pr{ a-³³ DPØ Power Quality:Ø Thermal Limits3max})x(Pr{ a³£ IIEconomic Goal14Cost=Install+Operation +Carbon Credits+Maintenanceåå= =÷÷øöççèæ-++=minjcijijijhhe PaxrrrC1 11)1()1()x(åå= ==minjcijijijo PbxC1 1)(xåå= ==minjijijijaic PcxtC1 1)(x( )å åò= = ÷÷÷øöçççèæ+++=minjijiipiiifiiiipiiifiaim xRtFtdttRRcFctCi1 10)()()()()()τ,x(ttttt),()()()(),( τxxxxτx mcoeDG CCCCC +++=1(x, τ) (x, τ)DGf C=Environmental and Political Goals15åå= ==minjijiiijai PAxtqf1 12 )(),( tτxØ Environmental Goal: Maximize Carbon SavingsØ Political Requirements][1jmicijij DEPx l£å=Renewable Portfolio Standardsfor j=1, 2, …, nA Multi-Criteria Approach to DG PlanningMin:Subject to:Max:Decision variables:  xij (binary)ti (positive) 2maxmin })(Pr{ a³££ VVV j x for  j=1, 2, …, n3max}Pr{ a³< kk II for  k=1, 2, …, l1(x, τ) (x, τ)DGf C=åå= ==minjijiiijai PAxtqf1 12 )(),( tτx1})(Pr{ a£< DP x][1jmicijij DEPx l£å=for  j=1, 2, …, nA Two-Stage Decision Making))(()]([)]([)]([)( 1)1(1 xxxxx DGcoe CVarZCECECEf q-+++=åå= ==minjijijai PExtqf1 1)1(2 ][)(x2/122)(1)( )(1 DPDP Z ssmm a ++³ - xx)(2/)1(max)()(2/)1(min 22 xxx jjj VVVZVZV sms aa -- +££-)(max)( 3 xx kk IkIZI sm a-£][1jmicijij DEPx l£å=)];([)()( )1(1)2(1 xτxxτ; mCEff +=å å= =÷÷øöççèæ=minjijijiiai PExAtqf1 1)2(2 ][)()( txτ;Stage 1: Determining xStage 2: Determining  tMin:Max:Subject To:Min:Max:Subject To: 0>itNumerical Experiment: A13-Node NetworkInput Data1)The mean and the standard deviation of Dj2)Wind speed and solar irradiance distributions3)Wind turbine (WT) power curve4)Costs associated with WT and PV maintenance5)Lifetime distribution of WT and PV units6) a1, a2, a3, and lOutput Data1)Where to place the WT/PV?2)The size of WT/PV units3)The maintenance time of WT/PV4)Annualized system cost5) Carbon savings estimation12 10114 8912D1D2D3D5D6D9D10D12D115D4D7 D836712D1310111312nodelink34567 89i DER Capacity (MW)1 WT1 1.02 WT2 1.53 WT3 2.04 PV1 0.55 PV2 1.0WT and PV Options (m=5)Testing on 13-Node Network (n=13)xtf1f12Pareto Solution in Stage 1A1.21.41.61.82.02.22.42.62.82.5 3 3.5 4 4.5 5 5.5Cost (Million in $) Carbon Savings (10,000 tons)Pareto Solution in Stage 1Pareto FrontierB12 345678Selected Pareto Solutions in Stage 2Solution No. 1 2 3 4 5 6 7 8Carbon (´104tons) 2.737 3.134 3.465 3.803 4.082 4.450 4.774 5.140Cost (´$106) 1.913 2.035 2.103 2.347 2.368 2.597 2.716 2.942j=1 SS SS SS SS SS SS SS SS2 5 4 4 4 5 3 1 33 4 5 5 5 4 5 5 54 5 5 5 5 5 5 5 55 0 2 2 2 2 1 2 26 0 2 2 3 1 3 3 37 5 1 1 2 2 3 3 38 2 2 2 2 3 3 3 39 1 0 0 2 0 1 1 210 2 1 3 1 3 2 3 211 1 2 0 1 2 1 3 312 4 5 5 1 2 1 1 313 1 0 2 1 2 2 2 2t1 (hour) 2,860 2,793 2,876 2,943 2,936 3,019 3,105 n/at2 (hour) 2,820 2,734 2,808 2,892 2,889 3,024 3,118 2,734t3 (hour) n/a n/a 2,869 2,925 2,957 3,041 3,195 2,773t4 (hour) 10,732 10,615 10,743 10,887 10,860 n/a n/a n/at5 (hour) 7,644 7,419 7,635 7,861 7,886 8,150 8,490 7,419(SS=Substation)Conclusionq The power industry is transitioning to distributed and renewablegeneration to mitigate the climate change.q A DG planning is a multi-criteria decision making, taking into 4 impacts: account economic, technical, environmental and regularly factors.  q Propose a two-stage optimization algorithm: 1) allocating DG siting and sizing; and 2) determining the maintenance times. q Demonstrated on a 13-node network, showing the benefits to reliability, voltage stability and thermal stress release.q Future research incorporates emerging technologies: vehicle-to-grid and demand response.21Thank You !Q and A 22

A two-stage multi-criteria decision for distributed wind and solar integration

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A two-stage multi-criteria decision for distributed wind and solar integration

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