Forecasting CO 2 emissions of power system in China using Grey-Markov model

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1 Avalable onlne Journal of Chemcal and Pharmaceutcal Research, 014, 6(7): Research Artcle ISSN : CODEN(USA) : JCPRC5 Forecastng CO emssons of power system n Chna usng Grey-Markov model Je Xu 1, and Yuansheng Huang 1 1 School of Economcs and Management, North Chna Electrc Power Unversty, Bejng, Chna College of Management, Hebe Unversty, Baodng, Chna ABSTRACT The greenhouse effect and ts extenson affectng s one of the key ssues to governments and academcs currently. Study shows, CO produced from burnng fossl fuels are lable /3 of the above-mentoned problems. CO emssons n Chna are manly concentrated n the power plants. In ths paper, Gray - Markov method s used for long-term load forecastng, combned wth the predcton of power generaton coal consumpton value. Meanwhle, the carbon emssons of power system n are estmated through the predcton. The results show that: the power system 00, 030, 040 and 050 carbon emssons are:7.754 bllon tons, bllon tons, bllon tons and bllon tons. Bass on that, we analyze the trend of power system low-carbon development. Then provde some suggestons to reduce CO emssons of power system from the level of the natonal polcy, the level of power generaton components, power grd optmze and dspatchng n the future. Key words: Grey-Markov model, load forecastng, power system, CO emssons INTRODUCTION Wth the rapd development of the global economy, carbon doxde produced by excessve burnng of fossl fuels caused global warmng and serous envronmental problems. As a result, global warmng had become the envronmental problems that the entre human socety has to face, and attracted great attenton worldwde. Chna has acheved rapd economc development, at the same tme the carbon doxde emssons ncreased. At present, Chna's carbon doxde emssons ranked frst n the world exceedng the Unted States. Accordng to the data of Chna Energy Report (008) Carbon Emssons Research, carbon doxde emssons n Chna are manly concentrated n the power plants, ndustry and transportaton, these three sectors emssons are about 63-73% of total emssons. Power ndustry was one of the man sectors of carbon doxde emssons n the natonal economy, ts emssons are 38.76% of total emssons [1]. Therefore, the power ndustry has sgnfcant reducton space. Understandng the carbon emssons of power sector, and projectng ts future development trend have great sgnfcance n lowerng carbon doxde emssons of the power ndustry. To predct carbon emssons of the power system, we need accurately forecast the electrcty demand. In order to acheve emsson reducton targets establshed by the Chnese government, Power ndustry should accurately predct the power load, Make reasonable adjustments of the power structure whle satsfyng the demand of natonal economc development. When we understand the carbon emssons of power system, we can fnd ways to reduce carbon emssons. In ths paper, the power system carbon emssons were predcted by predctng the power load usng Gray - Markov model, combned wth supply coal consumpton predctve value. Based on the work, we put forward some suggestons of low-carbon power system development. 387

2 RESEARCH METHODOLOGY For load forecastng, there s systematc research, current trend n power load forecastng technology s: extrapolaton forecastng technques, regresson model forecastng technques, tme seres forecastng technques, gray predcton technology. Among them, the gray predcton technology s wdely used n the short, medum and long term power load forecastng for small sample sze, the hgher the predcton accuracy []. On ths bass, there s a lot of research of the applcaton of mproved gray predcton technology n load forecastng. Ths paper uses Grey - Markov model for load forecastng snce Grey model needs small sample sze but can gve long term predcton. Ths paper uses gray rollng forecastng technques, It uses GM (1,1) model to predct the next value, and then added them to the data at the same tme removed the oldest value, keepng the number of columns constant. Then feed the data to the GM (1,1) model, repeatng the process untl the predetermned goal and predcton accuracy are reached. In the end, we combne wth Markov technques. Gray model and Markov chan can both be used for tme seres predcton, gray system changes the processng method for stochastc problems, t takes the randomness of the system as a gray, has applcaton n systems when nformaton s unknown or unclear. But Grey forecastng s generally used wth less data, short tme frame and lttle fluctuaton, the predcted trend s a relatvely smooth curve, monotoncally decreasng or ncreasng. In the presence of random fluctuaton, there wll be predctons that are too hgh or too low. The Markov chan s the study of random varaton system, an n-order Markov chan conssts of a collecton of n states { a1, a,, a n } wth a set of transton probablty Pj (, j = 1,,, n) to determne. In ths process, any tme can only be n one status, f n the state a at tme k, then at tme k + 1 n the state a,transton probablty P reflect the nfluence degree of each factor, so the Markov chan s sutable for j j predctng dynamc process wth large random fluctuatons, and makes up for the weakness of the gray predcton. The two models complement each other and provde a practcalty. The establshment of gray model GM (1,1) GM (1,1) model establshed the dfferental equaton after generatng process to the raw data. Establsh GM (1,1) model requres only a few columns X = X X X n X ( (1), () ( )) : Let Take the seres 1-AGO (Accumulated Generatng Operaton), so (1) (1) (1) (1) X X X X n = ( (1), () ( )) = (1) (1) (1) X X + X X n + X n (1), (1) (), ( 1) ( )) In the formula k (1) X ( k) = ( X ( m)) k = 1,,, n m= 1 Generatng the orgnal seres by the accumulaton weakened the mpact of bad data n orgnal data, establshed the model after t became a regular sequence. Usng (1) X to consttute albno dfferental equatons: dx dt (1) (1) + ax = u Solvng Parameters a, u usng the least square method T T 1 T X = [ a, u] = ( B B) B Y N 1 (1) (1) X (1) + X () 1 1 (1) (1) X () + X (3) B = 1 1 (1) (1) X ( n 1) + X ( n) 1 388

3 (), (3), ( ) T YN = X X X n Get Gray model s: u u X k X e k a a ˆ ˆ (1) ˆ (1) X ( k + 1) = X ( k + 1) X ( k) ˆ (1) ak ( + 1) = [ (1) ] +,( = (0,1,, ) u = e X e k = a a ak (1 )[ (1) ],( (0,1,, ) The establshment of mproved Gray-Markov model The basc dea of Grey-Markov predcton model s: frst establsh the gray GM model, obtaned the ftted curve; accordng to fttng curve can be dvded nto several dynamc range, and then through the Markov transton probablty matrx predct the next state, calculate the predcted values. Specfc steps are as follows: The frst step: In accordance wth the aforementoned mproved gray model GM (1.1), calculated predcted value ˆx. The second step: state dvded Let the orgnal sequence s: x = x x x n { (1), () ( )} The analog values of orgnal sequence s: ˆ ˆ ˆ ˆ x = { x(1), x() x( n)} When the random sequence x meet the characterstcs of Markov chan, dvded (m +1) parallel curve to the varaton curve xˆ( k ) nto m states F1, F, Fm, where the value of m based on the orgnal sequence and research object. Frstly, forecasts for each thermal generatng capacty usng gray predcton model, obtans predcton resduals wth actual and predcted value. Dvdes resdual state as follows accordng to the proporton of the actual value, n ths paper makes the load forecastng results as follows: (1) The resdual proporton s down to -4%, whch means that the value of power load forecastng has been serously underestmated, called a state F 1 ; () The resdual proporton s n (-4%, 0], whch means that the value of power load forecastng was normal underestmated, called a state F ; (3) The resdual proporton s n (0, 4%], whch means that the value of power load forecastng s normal overestmated, called a state F 3 ; (4) The resdual proporton s up to 4%, whch means that the value of load forecastng was serously overestmated, called a state F 4. The thrd step: determned the state transton probablty matrx If M s the ntal sequence of samples n the state F, M j ( m) s the ntal data sample that F through the m-step transton to F, So the state transton probablty matrx s: p p p L p = L L L L ( m) ( m) ( m) pn 1 pn L pnn ( m) ( m) ( m) n ( m) ( m) ( m) ( m) p1 p L p1 n j M j ( m) pj ( m) = ( = 1,, L n) s called the state transton probablty. M 389

4 The fourth step: forecastng Accordng to Markov predcton model s = s * P; s = s * P ; L ; s = s * P n can be predcted n 0 DATA SOURCES Gross power generaton (bllon Producton (bllon Table 1: Chna's power structure and composton Thermal power Hydropower Nuclear power Others Producton Producton Producton Proporton Proporton Proporton (bllon (bllon (bllon Proporton Table 1 shows Chna's power structure, thermal power generaton was more than 80% of the total generatng capacty, and the generatng capacty ncreased year by year; hydropower generatng capacty mantans constant proporton of total generatng capacty at %, and nuclear power generaton has been very small proporton of the total generatng capacty, the hghest beng.30% n year 003 and 004, others such as wnd power, etc. s mnmal. Hydropower has almost zero carbon emssons, nuclear chan greenhouse gas emssons s equvalent to 13.71g CO ( -1, whle the coal chan greenhouse gas emssons s equvalent to 130 g CO ( -1 [3]. Thermal power s bascally domnated by coal generaton, meantme, the generators tend to have a longer servce lfe, whch mples that Chna's power ndustry has a strong carbon lock-n effect, that s the power ndustry CO emssons wll be "locked" by the current power structure n the future for a long perod of tme [4,5]. That s, the power system of carbon emssons wll be manly from thermal power generaton. So n ths paper, load forecast of power system for the next few years based on coal power load data for each year, and then carbon emssons of power system can be calculated accordng to the coal consumpton. RESULTS AND DISCUSSION Bases on the data of the power load n , accordng to the above method predcted power load for several years n the future. Takng nto account the power system of carbon emssons manly from thermal power, n ths paper, we select the thermal power data. Thermal power predctve value usng Grey-Markov method Accordng to the aforementoned gray model GM (1,1), take the Thermal power data n as the orgnal sequence for grey predcton, forecast values as follows: Table : Thermal power predctve value Generatng capacty Generatng capacty (bllon (bllon

5 generatng capcaty(bllon orgnal data forcastng value tme Fg.1: Orgnal data compared wth predcted values n Accordng to the aforementoned method, dvde the status of forecastng values n Table 3: Dvde the status of gray forecastng values Actual value Forecastng value Resduals Resduals (bllon (bllon proporton Status The frequency statstcs of the status transton from resduals can be obtaned by Table 4: Determne the state transton probablty matrx Table 4 Frequency statstcs of the resduals status transton Status 1 Status Status 3 Status 4 Status Status Status Status Total 10 State transton probablty matrx from the resduals frequency statstcs for status transton s: P = Accordng to Markov predcton model, s = s * P; s = s * P ; L ; s = s * P n, It can be obtaned the state transton forecastng value of 00, 030, 040, 050. S 0 = ( ) Fnal predctve value s: V = V (1 + P M ) n 0 391

6 In the formula, V s the fnal predctve value, V s the gray predctve value, P s the largest probablty value of the annual status, M s the medan, M = 0.5 ( U + L), U s the upper lmt of the status, L s the lower lmt of the status. Among them, the states 1 and 4 take drectly -% and %. So t can be obtaned the load forecastng values: 00 s bllon kwh, 030 s bllon kwh, 040 s bllon kwh, 050 s bllon kwh. Coal consumpton values forecast Most coal-power chan carbon emssons come from power generaton. The major factor that mpacts carbon emssons of power plants s coal consumpton level. Its level s drectly related not only to carbon emssons of the power plant,but also the coal-power chan carbon emssons. In recent years, a large number of old and small thermal powers were elmnated n Chna. Therefore, coal-fred power plants had a certan declne n coal consumpton. the specfc data are showed n table below. However, compared wth developed countres Chna's has a long way to go untl the problem of coal consumpton s solved. But coal consumpton trends and technologcal nnovatons n Chna show that coal consumpton of Chna can be greatly declned n the future. Table 5 Coal consumpton data Coal consumpton(g/ Coal consumpton(g/ Based on the above data, usng the mproved GM (1.1), that s gray rollng forecast, predcton results can be obtaned as follows: 00 s 93.8g/kwh, 030 s g/kwh, 040 s g/kwh, 050 s g/kwh. Calculaton of power system carbon emssons When power plant s n operaton, It produced greenhouse gases manly CO and a small amount of N O. NOx s generated by conventonal combuston mode n coal-fred power plants, NO accounted for about 90%, N O accounts for only about 1% [6]. Accordng to the recommended values by Natonal Development and Reform Commsson Energy Research Insttute and the Energy Handbook 006, One ton of standard coal s CO emsson coeffcent (t / tce) s.4567tco /tce, One ton of standard coal s NOx emsson coeffcent (t / tce) s tNOx/tce. In addton, accordng to the greenhouse gases GWP default gven by IPCC Thrd Assessment Report (001), CO s 1gCO equvalent / g greenhouse gases, N O s 96 gco equvalent / g greenhouse gases. Carbon emssons each year can be obtaned by calculatng. The result s: the CO emsson of 00 s bllon tons, 030 s bllon tons, 040 s bllon tons, 050 s bllon tons. Accordng to Chna's power ndustry development characterstcs and the characterstcs of prmary energy, the most effectve measures of CO emsson reducton s to adjust the ndustral structure. That s, orderly and vgorously develop hydropower, wnd and solar clean energy generaton under the premse of protecton of envronment. Substtutng hydropower, wnd power and solar power for thermal power can brng a decrease of CO emssons about 1kg per kwh, thus low-carbon benefts are obvous. Developng such energy plays an mportant role n achevng the development of low-carbon electrcty n Chna. Accordng to our calculatons, for example, f the proporton of thermal power generaton of total generated energy decrease by 0% n 050, the CO emssons would be reduced by about 6 bllon tons. However, clean energy also has a seres of problems to be solved, ncludng hgher cost and hgher demands of correspondng supportng of electrcty grd and cost requrements. Development of large-scale clean energy power wll cause a large ncrease of cost burden to power plants. In addton, another way for the power system to reduce carbon emssons s to reduce coal consumpton. Currently, Chna's coal unts are manly usng ultra unts and subcrtcal unt, whch are typcal examples of effcent power generaton technology, therefore there s consderable space to mprove the coal utlzaton effcency and reduce carbon emssons. For example, f the coal consumpton s decreased by 1g/kwh, CO emssons would be reduced by about 0.7 mllon tons n 050. Meanwhle, ntendng to reduce carbon emssons, there are two trends of development of coal power generaton technology outsde Chna: mprovng power generaton effcency and recyclng coal bolers, gas and steam boler. The typcal example of the former s Supercrtcal unts (SC) and Ultra-supercrtcal unts (USC) power generaton technology, of the latter s manly Integrated Gasfcaton 39

7 Combned Cycle (IGCC) power generaton technology. These technques really appled to power generaton need some tme and effort. CONCLUSION The results showed that: CO emssons from the power system have a rsng trend. If the exstng power structure s kept, emsson reducton works of power systems wll have a long way to go. To construct of low-carbon electrcty system, not only the exstng fossl fuel generators need to reduce emssons, but also renewable energy and other clean energy wll be reled on n the long term. So we can work from the followng aspects. On the level of the natonal polcy, we can levy "carbon tax" tmely and at approprate whch tax wll be able to be nternalze the external costs of the greenhouse gas emssons drectly and effectvely. It wll be of great beneft to settle the long-term envronmental problems. We can also establsh and mprove the carbon market. Carbon tradng market wll change wth prces and supply-demand changes n the overall operaton, but there are many ssues n the mplementaton of the carbon tradng market that need further study. Government departments need to establsh the ndustry's carbon emssons targets and strctly supervse executon, Implement dfferental prcng to dfferent power plants to encourage the mplementaton of low-carbon electrcty, and also ntroduce some ncentve measures to promote energy conservaton and emsson reducton to small and medum enterprses. On the level of power generaton components encourage the low-carbon development of power generaton enterprses, ncrease low-carbon technology nnovaton and applcaton of fossl fuel n power generaton, the development of recyclng economy; adjust carbon structure approprate, develop renewable energy vgorously, ncrease nuclear power and hydropower proporton approprate. On the level of the power grd optmze the layout of power plants and power grd, ncrease the smart power grd constructon; strengthen low-carbon electrcty schedulng constrants; contnue to buld UHA substaton and dgtal substaton. Acknowledgments The authors wsh to thank the Project supported by the key project of central unversty research n Educaton Mnstry, Chna. (ID. 1ZX1) REFERENCES [1] Ymng We, Lancu Lu,Yng Fan, et al. Chna Energy Report (008): CO Emssons Research, Scence Press. Bejng, 008. [] Julong Deng. Grey Predcton and Decson. Huazhong Unversty Press. Wuhan, [3] Zhongha Ma; Zqang Pan; Humn He. Chnese Journal of Nuclear Scence and Engneerng., 1999,19(3),68. [4] Kunmn Zhang, Jahua Pan, Dapeng Cu. Introducton of Low Carbon Economy, Chna Envronmental Scence Press, Bejng, 008. [5] Grubb M, Jamasb T, Polltt M. Delverng a Low-carbon Electrcty System, Cambrdge Unversty Press, UK, 008. [6] Chong-qng KANG; Tan-ru ZHOU; Q-xn CHEN; Jun GE. Power System Technology., 009,17,1-6. [7] Zewen Wang; Wen Zhang; Shufang Qu. Mathematcs n Practce and Theory., 009, 39(1), [8] Mn L ; Hu Jang ; Yn-hua Huang ; Xaomng Song. Proceedngs of the CSU-EPSA., 011, 3(), [9] Hu-mng Xa; Zh-gang Wang; Jn-ln Wu. Scence Technology and Engneerng., 01, 3(1), [10] Hsao-Ten Pao; Hsn-Cha Fu; Cheng-Lung Tseng. Energy., 01, 40(1), [11] We Sun; JIngmn Wang; Hong Chang. Journal of Computers., 013, 8, [1] Guo Ln Bao; Hong Q Hu. Advanced Materals Research., 01, 5,