A Grid-Structure Based Multi-Region Optimisation Model for the Development of Power Generation Sector in China

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1 439 A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 45, 2015 Guest Editos: Peta Sabev Vabanov, Jiří Jaomí Klemeš, Shaifah Rafidah Wan Alwi, Jun Yow Yon, Xia Liu Copyiht 2015, AIDIC Sevizi S..l., ISBN ; ISSN The Italian Association of Chemical Enineein DOI: /CET A Gid-Stuctue Based Multi-Reion Optimisation Model fo the Development of Powe Geneation Secto in China Zhen Guo, Pei Liu*, Linwei Ma, Zhen Li State Key Lab of Powe Systems, Depatment of Themal Enineein, Tsinhua Univesity, Beijin,100084, China liu_pei@tsinhua.edu.cn Powe demand in China has inceased ove yeas and is expected to futhe expand in the futue. The dispaity of esouces distibution and load dispatch fo eions equies eional analyses athe than takin China as a sinle entity. Meanwhile subsidy policies fo clean eney and powe pices will laely affect the development of the powe secto in a lon-tem. In this pape, a multi-eion model based on id stuctues is built aimin fo maximisin pofits ained by China s powe eneation secto fom 2013 to A case study is pefomed demonstatin deployment tends fo technoloies and the utility of tansmission capacity. The esults indicate diffeent developin stateies fo eions and the impotance of eney subsidies. In addition, powe tansmission utility is analysed and discussed. 1. Intoduction China has expeienced a apid economic owth as well as shap incease in powe demand, fom 1,347.3 TWh in 2000 to 4,976.8 TWh in 2012 (NBSC, 2014). Electicity demand was expected to futhe incease in the futue (Hu et al., 2009) and pathways fo the development of powe capacity ae woth studyin. Many studies focus on analysin China s powe secto by takin the whole county as a sinle entity. Cai et al. (2007) analysed the development of China s powe secto in vaious policy scenaios by the Lonane Eney Altenatives Plannin System (LEAP) and ave CO 2 eduction potential. Zhu and Fan (2010) adapted potfolio theoy to optimise China s powe secto in thee scenaios. Zhan et al. (2012) developed a model optimisin costs fo China s powe secto while considein cabon makets and CCS technoloies. Howeve, these studies do not distinuish eional natual esouces and powe demand diffeences. Gnansounou and Don (2004) consideed powe tansmission between two povinces in China and inteate thei electicity makets. Wan and Nakata (2009) divided China into coastal and inland aeas and discussed the development of clean coal technoloies concenin diffeent envionmental policies. Notably, these simplified divisions cannot eflect the actual situation of China s powe secto. Chen et al. (2015) divided China into 10 pats based on physical id stuctues and minimised total costs with the consideation of electicity tansmission. Nevetheless, this wok does not conside tansmission capacity and coss-eion tansmission. Thouh studies fo India (Paikh and Chattopadhyay, 1996) and Geece (Koltsaklis et al., 2014) took eional vaiations and electicity tansmission into account, electicity makets and policies in these counties ae much diffeent fom China. Thus, this pape established a multi-eion model, considein actual id stuctues and capacity, esouce distibution and eney subsidy policies, in ode to ive an insiht into the lon-tem development of China s powe eneation secto. 2. Methodoloy 2.1 Model stuctues and assumptions Based on the cuent physical stuctue of ids, China is divided into seven eions: Notheast, Noth, Cental, East, South, Nothwest and Tibet (Zhou et al., 2010). Amon these ids, Tibet id, as well as Hainan, Hon Kon, Macau and Taiwan, ae elatively independent ones and thei load ae insinificant compaed to the othes, thus they ae not consideed in this model. In addition, natual esouce Please cite this aticle as: Guo Z., Liu P., Ma L., Li Z., 2015, A id-stuctue based multi-eion optimisation model fo the development of powe eneation secto in china, Chemical Enineein Tansactions, 45, DOI: /CET

2 440 endowment ae impotant. Accodin to the Ulta-hih Voltae (UHV) tansmission pojects poposed by the State Gid Copoation of China (SGCC, 2014), Inne Monolia and Xinjian will become main electicity expotin povinces due to thei abundant coal eseves and developable enewable esouces. Owin to these consideations, China is modelled as 8 eions eflectin physical id stuctues as well as natual esouces. As shown in Fiue 1, 8 eions ae as follows: Notheast (Heilonjian, Jilin and Liaonin), Noth (Beijin, Tianjin, Hebei, Shanxi and Shandon), Inne Monolia, Cental (Jianxi, Hubei, Hunan, Henan, Sichuan and Chonqin), East (Shanhai, Jiansu, Zhejian, Anhui and Fujian), South (Yunnan, Guizhou, Guanxi and Guandon), Nothwest (Shaanxi, Gansu, Ninxia and Qinhai), and Xinjian. Fiue 1: Reional division of China based on id stuctues and natual esouces In tems of powe eneation technoloies, 7 types ae consideed: pulveised coal (PC), ulta-supecitical coal (USC), natual as combined cycle (NGCC), nuclea (NU), hydoelectic (HD), wind powe (WD) and photovoltaic (PV). These technoloies have the potential to be laely deployed acoss the county (The State Council, 2013). Based on thei diffeent feed-in taiff policies, they ae divided into two cateoies. The fist one includes PC, USC, NGCC and HD, whose on-id pices ae eulated by the ovenment at any time. The second cateoy includes NU, WD and PV, whose on-id pices ae eithe a stike pice o a feed-in taiff decided by the time when the plant is put into opeation. Due to the lack of CCS expeiences and cuent ovenmental policies nationwide, CCS equipment is not consideed in this model. In ode to intoduce powe tansmission between eions, this model consides cuently built exta-hih and ulta-hih voltae powe tansmission lines, poposed ulta-hih voltae powe tansmission lines and thei tansmission losses. Intenational tansmission is nelect due to its elatively low amount compaed to national powe demand (NBSC, 2012). 2.2 Mathematical equations Mathematical equations of the model ae pesented in this section. These equations can be classified into two oups. The fist oup includes objective function and expessions of its elative vaiables. The second oup indicates physical constains. Fou sets, t,, and f, stand fo time, eion, powe eneation type and fuel type, espectively. Meanwhile, t and t, and shae the same set in the equations Objective function The objective function of this model is to maximise accumulated pofits ained by powe secto fom 2013 to As expessed in Eq(1), the sum of eional pofits is leveaed to 2013 and totted up. atc ( eve c ) 2050 t, t, (1) ( t2013) t2013 (1 I) Pofits equals to evenues less costs. Costs consist of thee pats: capital costs fo constuctin new capacity, opeation and maintenance costs, and fuel costs, and can be calculated by Eq(2). ct, tinvt, tomt, tfct, (2) Reional evenues Fo the fist and second oups of technoloies, evenues can be calculated by Eq(3) and Eq(4). Opeatin hous ae efeed to statistical mateials (SERC, 2011) and thei on-id pices conside

3 ovenmental documents, includin the latest feed-in taiff policies fo wind (NDRC, 2014b) and sola PV (NDRC, 2013). On-id pices fo the fist oup ae decided by the time when new capacity is added. Those fo the second oup ae eulated by the ovenment at any time. t eve nbc OH OGP t,,, t ',,,, t ' t ' ttlt (3) eve ic OH OGP (4) t,,, t,,,, t Reional costs Thee pats of costs can be calculated by Eqs(5) to (7), espectively. Capital costs efein to OECD s epot (OECD, 2010) ae leveaed to annum. Total lifetimes (TLT) ae efeed to IEA s epot (OECD, 2010). O&M costs ae assumed to be pat of capital costs. Fuel costs ae decided by Fuel Pice (FP), Fuel Consumption Rate (FCR) and the powe eneated. t I inv, t, CAP t,' nb, t ', t ' ttlt 1 (1 I) 1 (1 I) TLT tomt, om, t, ic, t, tfc, fc f, t, FP f, t, fd f, t, t FP f, t, fd, f, t, FP f, t, p, t, FCR f,, t f f f f Physical constains Powe demand and supply Reional Powe Demand (PD) is met by electicity eneated in its own eion and tansmitted in o out (losses ae included). Ideal tansmission powe fom to equals to the neative value of that fom to, and is limited by tansmission capacity(sgcc, 2014). These expessions can be witten as Eqs(8) to (11). PDt, p, t, ttt, ic, t, OH, ttt, (5) (5) (7) (8) it t,, ' tt t,, ' [1 TRLOSS, ' ] itt,, ' it it t,, ' t, ', ( is the expotin eion) ( is the impotin eion) (9) (10) it TRLIMIT t,, ', ', t Reional powe demand in yea t is extapolated based on histoical powe demand (NBSC, 2012) as well as ovenmental pojections (Hu et al., 2009). Tansmission loss ates between eions ae calculated efein studies on UHV tansmission (Zhao et al., 2009) and actual distances Installed capacity This model assumes that all types of technoloies will be decommissioned at the end of thei lifespan. Thus the installed capacity of type in eion in yea t can be expessed as Eq(12). (11) t ic, t, nbc, t ', t ' ttlt 1 (12) Installed capacity fo existin plants efe to statistical books (China Electicity Council, 2009) and annual epots (EBCEPY, 2013). Considein esouce endowment, maximum eional installed capacity fo enewable (CAE, 2010) limits thei deployment, as shown in Eq(13). New built capacity is constained by constuction pacticality, efein to histoical expeiences (EIA, 2011), as noted in Eq(14). In addition, taets fo clean eney (NDRC, 2014a) ae included. Futhemoe, it is assumed no nuclea plants will be added in Nothwest, Xinjian and Inne Monolia due to its abundant coal eseves. ic ub, t, IC, (13)

4 442 ub nbc,, t NB (14) Fuel supply Annual total fuel demand must not exceed Fuel Supply Capacity (FSC), as expessed in Eq(15). The capacity is both limited by domestic poductivity (NBSC, 2013) and intenational availability (IAEA, 2009). ub tfd ft, FSC f Assumptions fo economical paametes Capital costs fo technoloies ae expected to decline adually thouh to 2050 with diffeent ates. The model assumes that on-id pices fo coal-fied plants will emain stable, and those fo othes will be eulated by policies (The State Council, 2014). Fuel pices take the cuent coal and as pices (China's Chemical Poducts Web, 2015) and ae set to be flat. Discount ate is assumed to be at 7 %. 3. Results and discussions The Linea Poammin Solve of the Geneal Alebaic Modellin System (GAMS) was used in this pape fo modellin and optimisation. 3.1 Installed capacity fo technoloies in eions Installed capacity in Noth, East, Cental and South eions will shae some similaities as shown in Fiue 2. Hydoelectic powe will be exploited, notably in Cental and South. Sola and wind will not be well deployed due to insufficient subsidy policies. In ode to meet inceasin powe demand, themal powe plants will emain the majoity and USC plants will adually eplace PC plants thouh to NGCC will not be deployed in these aeas concenin hih as pices. Nuclea powe will have the pioity to develop in the East to fulfil the national clean eney taets as well as meet its demand by (15) Fiue 2: Installed capacity fo technoloies in East, Noth, Cental and South In tems of eions with abundant wind and sola souces, wind powe will develop apidly by 2020 in Notheast and Nothwest because of the national clean eney taets as well as the cease of feed-in taiff fo wind by 2020, and thouh to 2050 in Xinjian owin to declinin capital costs (shown in Fiue 3). PV will expeience apid development by 2020 in Xinjian and Inne Monolia, but no new addition in Notheast and Nothwest due to insufficient subsidies. These indicate the impotance of subsidies fo wind and sola powe as well as the ston national taets. PC will emain popula in Nothwest, Xinjian and Inne Monolia until 2035 owin to low coal pices. NGCC will expand in the thee aeas because of low as pices and subsidies fo as plants. Uncetainties fo fuel pices and capital costs may laely affect the esults. Fo instance, if as pice could dop sinificantly in the futue, install capacity fo NGCC would incease notably compaed to the case pefomed above. On the othe hand, if capital costs fo wind and sola could decline faste than the ate set in the case, moe capacity would be installed. Howeve, the pape aims at pefomin a case study based on easonable assumptions and ivin insihts into the powe secto athe than pedicts the futue.

5 443 Fiue 3: Installed capacity fo technoloies in Notheast, Nothwest, Xinjian and Inne Monolia 3.2 Powe tansmission Actual tansmitted powe is compaed to theoetical tansmission capacity to examine the utility of poposed UHV lines. By analysin the esults between 2040 and 2050, some tansmission lines will each its maximum capacity. These ae fom Noth to East, Cental to East and South, Nothwest to East and Noth, and Inne Monolia to Notheast (black aows in Fiue 4). The est is unused, fom Xinjian to the othe eions, Noth to Cental, Nothwest to Cental, Notheast to Noth, and Inne Monolia to East and Cental. East is load cented, and will eceive powe fom othe eions due to its hih fuel pices. Cental aea tends to expot powe athe than impot due to its abundant hydopowe. Fo the thee esouce-ich eions, Nothwest is the only one expotin lae amount of powe. Xinjian and Inne Monolia lacks the momentum to expot powe due to elatively low on-id pices and lon-distance tansmission losses. Fiue 4: Powe tansmission schematics between 2040 and Conclusions In this study, a model eflectin natual esouce endowment and id stuctues is built aimin fo maximisin pofits fo powe eneation secto in China. Vaious eney subsidy policies ae consideed in ode to delive pathways fo the development fom 2013 to Fo themal powe, USC will adually eplace PC, but elatively late in Nothwest, Xinjian and Inne Monolia whee coal is elatively cheap, and NGCC will only develop in these thee eions due to lowcost as. With espect to clean eney, hydopowe will be fully exploited. Eney subsidies and national taets ae impotant to the deployment of wind and PV both in the shot tem and lon tem. Nuclea will be competitive in the East and Notheast due to hih eional pices fo fossil fuel. Utility of tansmission lines between eions vaies sinificantly. Fo load-cented eions with hih fuel pices, such as East, tansmission capacity will be fully used. Reions with abundant hydopowe like Cental will tend to dispatch its eional powe athe than impotin powe. With espect to esouce-ich eions, subsidies fo enewable and incentives fo coal plants ae equied to stimulate powe expotin.

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