The Risk Assessment Study for Electric Power Marketing Competitiveness Based on Cloud Model and TOPSIS

Transcription

1 IOP Conference Seres: Materals Scence and Engneerng PAPER OPEN ACCESS The Rsk Assessment Study for Electrc Power Marketng Compettveness Based on Cloud Model and TOPSIS To cte ths artcle: Cunbn L et al 2017 IOP Conf. Ser.: Mater. Sc. Eng Vew the artcle onlne for updates and enhancements. Ths content was downloaded from IP address on 09/10/2018 at 04:01

2 The Rsk Assessment Study for Electrc Power Marketng Compettveness Based on Cloud Model and TOPSIS Cunbn L 1, a), Y Wang 1, b) 1, c) and Shuashua Ln 1 Insttute School of Economcs and Management, North Chna Electrc Power Unversty, Bejng , Chna E-mal: a) lcb999@263.com, b) wy@ncepu.edu.cn Abstract. Wth the rapd development of the energy nternet and the deepenng of the electrc power reform, the tradtonal marketng mode of electrc power does not apply to most of electrc power enterprses, so must seek a breakthrough, however, n the face of ncreasngly complex marketng nformaton, how to make a quck, reasonable transformaton, makes the electrc power marketng compettveness assessment more accurate and objectve becomes a bg problem. In ths paper, cloud model and TOPSIS method s proposed. Frstly, buld the electrc power marketng compettveness evaluaton ndex system. Then utlze the cloud model to transform the qualtatve evaluaton of the marketng data nto quanttatve values and use the entropy weght method to weaken the subjectve factors of evaluaton ndex weght. Fnally, by TOPSIS method the closeness degrees of alternatves are obtaned. Ths method provdes a novel soluton for the electrc power marketng compettveness evaluaton. Through the case analyss, the effectveness and feasblty of ths model are verfed. 1. Introducton In recent years, wth the deepenng of the electrc power reform, the electrc power marketng [1] becomes more and more mportant for the basc work of electrc power enterprses. The effectve rsk assessment of electrc power marketng wll not only affect the stablty of enterprse and customer satsfacton, but also has an mportant sgnfcance for long-term development of enterprse. So, how to set up the electrc power marketng rsk assessment ndex and make an effectve rsk assessment s the frst problem of electrc power enterprse, and has an mportant theoretcal and practcal sgnfcance. At present, more and more researches have been done on power marketng. In the lterature [1], the nterval of the assessment level s studed by the concept of cloud measure, and the early warnng model of power marketng rsk assessment based on cloud measure s constructed. The fuzzy AHP and fuzzy synthess are used n lterature [2] and [3]. In order to evaluate the effectveness of the servce strategy of the power supply enterprse, the lterature [4] adopts the method of analytc herarchy process and lnear weghtng method to study the qualty of the power supply servce and the power supply enterprse's marketng work. Combnng the order relaton method and AHP method, Lterature [5] evaluates the marketng executon ablty of the power supply enterprse and selects the Yanta power supply company as an example. The lterature [6] analyzes the power marketng safety through expert nvestgaton and branstormng rsk dentfcaton, takng nto account the BP neural network, factor analyss and other methods of rsk analyss and evaluaton. Based on the above lteratures, we can see that: 1) At present, most of the current rsk research for power marketng has taken nto account the combnaton of qualtatve and quanttatve, but how to Content from ths work may be used under the terms of the Creatve Commons Attrbuton 3.0 lcence. Any further dstrbuton of ths work must mantan attrbuton to the author(s) and the ttle of the work, journal ctaton and DOI. Publshed under lcence by Ltd 1

3 ICAMMT 2017 IOP Conf. Seres: Materals Scence and Engneerng 242 (2017) do: / x/242/1/ convert the two s stll a problem to be solved urgently. 2) There are many lteratures on the evaluaton of electrc power marketng, but the research subjects rarely choose the rsk assessment of power marketng compettveness. Amng at the above two ponts, ths paper wll establsh a rsk assessment model of power marketng compettveness based on cloud model [7] and TOPSIS. How to descrbe nformaton s a major problem n the process of data mnng and knowledge acquston. The best way s to be expressed n natural language, because natural language can be better understood [8-10], whle natural language s also the bass of thnkng and ntellgence, but many of the concepts n natural language are unknown factors, such as ambguty, randomness, mprecson and ncompleteness. In these unknown factors, randomness and fuzzness have aroused people's attenton [12]. Although these uncertantes are dffcult to defne precsely, but ts exstence does not affect the exchange between people [11]. However, n order to make the computer have smlar to human understandng and judgment, for the study of ambguty and randomness s necessary [13]. Rough set and probablty theory are the two most effectve methods to deal wth the uncertanty. However, consderng the randomness of membershp degree and probablty, Professor Dey L put forward the cloud model theory on the bass of rough set and probablty theory n Ths model s an ndetermnate converson model that deals wth qualtatve and quanttatve descrptons. Because experts can t express ther subjectve professonal judgments n numercal form, they are often replaced by fuzzy numbers or nterval numbers, but both of the two ways have some lmtatons, and can t be lke the natural language expresson that ft the experts. On ths bass, put forward the cloud model to mplement the natural language evaluaton transformaton nto quanttatve values, provdes a good foundaton for quanttatve decson. 2. Compettveness Rsk ASSESMENT Index System Electrc power marketng compettveness assessment refers to the electrc power enterprse marketng aspects of the exstence of the unque, advantages, n the face of adversty can survve and develop the ablty to assess. Evaluaton of the compettveness of an enterprse should pay attenton to the followng four ponts:1) The operaton of the current electrc power marketng strategy; 2) What are the opportuntes and threats, strengths and weaknesses that companes face n marketng;3) Whether the prce and cost of the enterprse are compettve; 4) Relatve to other enterprses, marketng compettve poston. Consderng the above four ponts, and n order to make the evaluaton valuable, we must use the followng prncples n the establshment of the ndex system: systematc, dynamc, typcal, comparable and quantfable. Ths artcle establshed an evaluaton ndex system, ncludng three frstlevel ndcators and 20 secondary ndcators, as shown n Fgure 1. Market Sale Qualty Electrc Marketng Compettveness Rsk Assessment Index SystemMarketshareMarketsharechangePrcecompettvenessPrceconcentratonIndustryEnterDffcultyMarketDemandAlternatveenergycostElectrctySaleGrowthelectrctysalesrevenueGrowthElectrctySaleProftMargnAveragePrceRateSellngRateFeeRecoveryRateGreenEnergySaleGrowthGreenEnergySaleProftRateGreenEnergyAveragePrceRateGreenEnergySalePercentageLneLossStandard-ReachngRateFrequencyQualfedRateVoltageQualfedRateFIGURE 1. Electrc marketng compettveness rsk assessment ndex system.

4 3. Cloud model ntroducton 3.1 Defnton of cloud model The cloud model s a method to convert the qualtatve concept and the quanttatve numercal phase. The model combnes the knowledge of fuzzy mathematcs and statstcs to reflect the uncertanty of thngs and the ambguty and randomness of human subjectve thnkng. Defnton1 U s a feld wth exact values,c s a qualtatve concept n U, f there s a quanttatve varable x C, and x s a random mplementaton n the qualtatve concept C, the certanty u(x) [0,1] for C s a random number wth a stable tendency,such as equaton 0,then the dstrbuton n U of x s called cloud, each x s a cloud droplet. u: U [0,1] x Ux u( x) (0) The cloud model has three dgtal features, namely, expectaton valueex, entropy En and super entropy He. In the process of the assessment, these three numercal features can be used to realze the transformaton between qualtatve concept and electrc quantty data. Among them, the expectaton value Ex s the most representatve of the concept of qualtatve pont, and s the central nformaton value of fuzzy concept. It s not only the center gravty of the cloud poston, but also the most typcal sample ponts of quantfcaton for the qualtatve concept. Ths paper selects the expected value Ex as the quanttatve value for the converson of qualtatve concept. Entropy En reflects the uncertanty of qualtatve concepts, ncludng the randomness and ambguty of qualtatve concepts. In terms of randomness, entropy En manly reflects the degree of dscretzaton of cloud droplets, t also represents the randomness of cloud droplets. In terms of fuzzness, entropy En reflects the possblty of the qualtatve concept, and t represents the value range of the droplet. In general, the greater En represents the greater uncertanty. The super entropy He s the entropy of theen, whch s the uncertanty of the entropy En. It s determned by the randomness and fuzzness of the En. The super entropy He can ndrectly reflect the cloud thckness of the cloud droplets n certan doman. The thckness of the cloud s not the same, close to the concept center and away from the concept of the center of the cloud thckness s small, between the cloud thckness s large, wth the largest fuzzy value of the cloud thckness of the largest, as shown n Fgure 2. FIGURE 2. 20km membershp cloud chart Fgure 2 shows the membershp cloud of the concept of 20 km or so, n whch Ex 20, En =1, He =0.1, N (cloud droplet number) =3000. In the dstance of 20 km and 20 km away, because the concept s uncertan small, cloud thckness s small; not far from 20 km near the cloud thckness ncreases, because the concept of uncertanty, and the subjectve feelngs of people s consstent. 3.2 The language evaluaton value nto the cloud method Set the language of the experts to assess the scale as n (accordng to the actual stuaton arbtrarly 3

5 [ X, X ] mn max selected), experts specfy the effectve doman as, you can generate n clouds to express the language value. In ths paper, the language used n the evaluaton scale s 5, usng golden segmentaton to generate fve clouds. Set the mddle cloud as, the left and rght as, , , The characterstcs of these fve clouds are shown n Table 1. Table 1 Golden segmentaton method to generate fve cloud features dgtal calculaton method Cloud Ex En He max C Ex En He X En / He / ( 1, 1, 1) Ex ( X max X mn) / 2 Xmax Xmn C0( Ex0, En0, He 0) ( Xmn Xmax )/ max mn mn 0.618En ( ) / 6 He / Ex 0.382( X X ) / ( Xmax Xmn ) / 6 He / X En / He / Research on rsk assessment model of power marketng compettveness based on cloud model and grey TOPSIS Gven m schemes and n evaluaton ndexes of electrc power marketng compettveness, for schema, the evaluaton value of rsk factor C j s xj (0 m,0 j n), and the language evaluaton matrx s ( xj ) m n. Based on the cloud model and gray correlaton TOPSIS method, evaluaton calculaton steps are as follows: 4.1 Convert language evaluaton value Accordng to the expert's language evaluaton value, the method shown n Table 1 s transformed nto the normal cloud model, and the language evaluaton matrx gven by the expert s transformed nto the correspondng cloud model matrx. Gven the language evaluaton level V ={(hgh/hard/strong), (hgher/harder/stronger), (general), (lower/easer/weaker), (low/easy/weak)}. The vald doman of the cloud model s [0,1]. Let H 0.005, accordng to the method of Table 1, fve clouds } correspondng to the e language evaluaton level are generated, and the numercal characterstcs of the fve clouds are shown n Table 2. Table2 the characterstc number of 5 cloud Evaluaton Level Cloud Model Ex En He hgh/hard/strong C 2( Ex2, En2, He2) hgher/harder/stronger C 1( Ex1, En1, He1) general C0( Ex0, En 0, He0) lower/easer/weaker C 1( Ex 1, En 1, He1) low/easer/weak C2( Ex2, En2, He2) Normalze the decson matrx Because of the exstence of cost-type ndcators and effcency ndcators n the lsted ndex system, the ntal decson matrx ( xj ) m n s transformed nto a standardzed decson matrx D ( dj ) mn by normalzng the ndexes usng vector normalzaton. 4

6 xj d, 1,2,, m; j 1,2,, n (1) j m 1 d 2 j 4.3 Obtan the ndex weght. As the subjectve weghtng method has some lmtatons, here we take the entropy method to fnd the weght of the ndex, W ( w1, w2,, w n ). 1)Calculate the proporton p j of the schema plan under the j ndex. dj pj m (2) d 1 2)Calculate the entropy e j of the j ndex. 3)Calculate the entropy weght m j j j 1 j e k p ln p, k 1/ ln m (3) w of the j ndex. j n w (1 e ) (1 e ) j j j j1 (4) 4.4Fnd the weghted normalzaton matrx the formula s as follows. y1(1) y1(2) y1( n) y2(1) y2(2) y2( n) Y WD ym(1) ym(2) ym( n) (5) 4.5Determne the postve and negatve deal scheme The postve and negatve deal soluton of TOPSIS method s the optmal soluton and the worst soluton of the ndex under all schemes. U0 {(max y( j), jj ),(mn y( j), jj )} u0 ( j) 1 m 1 m (6) U {(mn y ( j), jj ),(max y ( j), jj )} u ( j) m 1 m J for effcency ndcator, J for cost-type ndcator. 4.6Determne the soluton to the postve and negatve deal soluton of the dstance. By calculatng the dstance of each scheme to the postve and negatve deal soluton, we can see the dstance between each scheme and the postve and negatve deal soluton. n D g( j) u0 ( j), 1,2,, m j1 n D g( j) u0 ( j), 1,2,, m j Determne the relatve degree of the program. Accordng to the sze of the close degree of the optmal sort, the greater the degree of close and (7) 5

7 negatve deal soluton that the greater the dstance, that s, the better the program. * D C (8) D D 5. Case study Table 3 shows the marketng compettveness data of an electrc power enterprse from July to December, whch quanttatve data are the actual data of the enterprse, qualtatve evaluatons are based on the expert evaluaton. The problem s a mult-attrbute decson-makng problem wth sx decson-makng schemes and 20 evaluaton ndexes, and to determne the order of compettveness n the fnal 6 months. Table 3 Power marketng compettveness orgnal data C C C C 4 general general hgher hgher Hgh hgh C 5 harder hard hard general hard hard C 6 strong strong stronger general strong strong C 7 hgher hgher general lower general general C C C 10 hgh hgher general lower lower general C C C C C C C C C C ) Accordng to Table 1 and Table 2, the qualtatve language n Table 3 s transformed nto a cloud model. Snce the expected value s the most representatve pont of qualtatve concept, the expected value Ex s used as the ndex value after converson. Table 4 Qualtatve language nto quanttatve values C C C C C ) Normalze the quanttatve data n Table 3 and Table 4 accordng to the formula (1) to obtan the normalzed matrx D ( d ) and use the entropy method of the formula (2-4) for the matrx to obtan j mn 20 evaluaton ndexes weghng, based on the known weght, we can use the formula (5) to calculate the weghted evaluaton matrx. Here, due to the lmted space, only the fnal weght value and weghted evaluaton matrx are lsted. 6

8 W={ 0.026, 0.045, 0.023, 0.009,0.005,0.013, ,0.085,0.113,0.068,0.033,0.053, 0.031,0.024,0.022,0.026,0.046,0.137,0.147} Weghted evaluaton matrx: Y WD ) The dstance from the scheme to the postve and negatve deal scheme s calculated by the formula (6-7) as shown below and plotted as shown n fgure 3. D {0.049, 0.036, 0.084, 0.082, 0.062, 0.050} D {0.068, 0.067, 0.019, 0.019, 0.051, 0.062} In the fgure 3, the abscssa represents 1-6 of the sx schemas, the ordnate s the dstance between the schema and the postve and negatve deal soluton, lne1 represents the dstance between the scheme and the postve deal soluton, and lne2 represents the dstance between the schema and the negatve deal soluton. From the two curves n the fgure can be roughly analyzed the 6 months of the enterprse's power marketng compettveness of the stuaton. 4) Fnally, accordng to the calculated postve and negatve deal soluton of the dstance from the formula (8) to get the proxmty of each schema. * C {0.577, 0.647, 0.181, 0.188, 0.451, 0.555} 7

9 FIGURE 3. The dstance between the schemes and the postve and negatve deal solutons 5) Sortng. The 6-month electrcty marketng compettveness s sorted accordng to the degree of proxmty n step 4): August> July> December> November> October> September. Because the electrc power marketng compettve ablty and proft drectly related, so the proft of the sx months of the comparatve analyss found that the sze of the proft and power marketng compettveness of the same order, whch also shows that the model bult n ths artcle has a scentfc and ratonalty, and can provde a bass for the decson-makng of electrc power marketng compettveness. 6. Concluson 1) Based on the comprehensve consderaton of market share, sales stuaton and product qualty, ths paper has establshed 20 comprehensve evaluaton ndexes system whch ncludes qualtatve and quanttatve, cost-effectve and cost-type. Through the study of evaluaton ndexes systems, t can be a deeper understandng about the status quo of the electrc power marketng compettveness. 2) In order to obtan more objectve evaluaton results, ths paper ntroduces the cloud model, realzes the qualtatve evaluaton of the scentfc and ratonal transformaton of the qualtatve evaluaton language to the quanttatve value, effectvely solves the fuzzness and randomness of the natural language evaluaton. At the same tme, the cloud model and TOPSIS method are combned to propose a more scentfc evaluaton method. In addton, n the calculaton of ndex weght, the use of entropy, effectvely weaken the subjectve factors of expert evaluaton, makng the evaluaton results more practcal and objectve. 3) An example s gven to analyze the marketng data of an electrc power enterprse for sx months. The evaluaton model proposed n ths paper s analyzed and verfed, whch provdes a new method and new dea for the evaluaton of power marketng compettveness. At the same tme, the model can also be used for electrc power target market state analyss, electrc power marketng evaluaton early warnng and other felds. Acknowledgments Ths artcle s sponsored by the Natonal Natural Scence Foundaton of Chna, No References [1] Yang Wu and Junyong L. Research on assessment and forewarnng model of power marketng condton based on cloud measure theory[j] (The Power System Protecton and Control, 2013, 1) pp [2] He Wang, Mng Zeng, Shan Chen, Jan Zhou and Zhxang Zhang. Comprehensve Evaluaton 8

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