Optimal Operation of Multi-Objective Hydropower Reservoir with Ecology Consideration

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1 Journal of Waer Resource and Proecion, 2011, 3, doi: /jwarp Published Online December 2011 (hp:// Opimal Operaion of Muli-Objecive Hydropower Reservoir wih Ecology Consideraion Absrac Xuewen Wu 1, Xianfeng Huang 2, Guohua Fang 2, Fei Kong 1 1 College of Compuer and Informaion, Hohai Universiy, Nanjing, China 2 College of Waer Conservancy and Hydropower, Hohai Universiy, Nanjing, China {hhuwxw, ghfang}@hhu.edu.cn, {hxfhuang2005, ongfei3261}@163.com Received Sepember 23, 2011; revised Ocober 25, 2011; acceped November 28, 2011 Aiming a he problem ha radiional opimal operaion of hydropower reservoir pays lile aenion o ecology, an opimal operaion model of muli-objecive hydropower reservoir wih ecology consideraion is esablished which combines he ecology and power generaion. The model aes he maximum annual power generaion benefi, he maximum oupu of he minimal oupu sage in he year and he minimum shorage of ecological waer demand as objecives, and waer quaniy balance of reservoir, reservoir sorage, discharge flow, oupu and so on as consrains. Chaoic geneic arihmeic is developed o solve he opimal model. An example is sudied, showing ha he annual generaion of he proposed model is 8 million W h less han ha model wihou ecology consideraion, which is abou 0.28 percen. Bu he proposed model is in favor of river ecology proecion, and can promoe he susainable uilizaion of waer resources. So i is worhy and necessary for he opimal operaion of hydropower reservoir wih ecology consideraion. Keywords: Hydropower Saion Reservoir, Opimal Operaion, Chaoic Geneic Algorihm, Ecology 1. Inroducion As a ind of clean and renewable energy, hydropower resources, once being used fully and reasonably, can no only reduce he consumpion of primary energy bu also lessen he environmenal sress caused by hermal power and oher energy sources, hus i can cu down greenhouse gases emissions. Therefore, every counry aaches grea imporance o hydropower developmen. Since 2008, he hydropower insalled capaciy in China has reached 172 million W, raning firs worldwide, and annual elecriciy generaion has reached billion W h, accouning for 16.4% of he oal naional annual energy oupu. A presen, hydropower occupies an imporan posiion in Chinese energy consrucion and exising energy srucure. The consrucion and operaion of reservoirs have played an imporan role on flood conrol, elecriciy generaion, urban and rural waer supply and irrigaion, however, i has also brough negaive effecs o he fragile river ecosysem due o reservoir consrucion iself and is operaion mode. The former can be solved by ecological resoraion, and he laer may be eased hrough consideraion of ecological reservoir operaion mode. A he momen, scholars home and abroad have conduced re- searches owards he ecological issues of reservoir scheduling and achieved considerable resuls. Cavallo [1], Lence ec. [2] sudied he ecological scheduling model of reservoir and applied he model in pracical operaion. Domesic Changjiang Waer Resources Commiee has sudied he ecological scheduling of he Three Gorges Reservoir o guaranee he ecological and environmenal waer demands in Yangze River region [3,4]. Yellow River Conservancy Commission conduced prooype ess of waer and sedimen regulaion owards he joinscheduling of China Xiaolangdi Reservoir, ec. Dong ec. [5] poined ou ha a he same ime of realizing muliple objecives of social economy, river ecological requiremen should also be aen ino consideraion and muliobjecive ecological scheduling of reservoir should be enforced. Liu [6], Yang, ec. [7] raised he quaniaive calculaion mehod of ecological waer demand o preven river sedimen deposiion and hey assered ha he average annual waer ha can mainain river balance and can preven he river sedimen in he downsream of he Yellow River is 18.4 billion on. Hu, ec. [8] proposed he reservoir ecological scheduling mehod based on ecological flow process line. Yu, ec. [9] discussed he noion and mehod concerning reservoir ecological waer

2 X. W. WU ET AL. 905 level and ecological capaciy of preparaory reservoir. Consequenly, ecological facor has become a significan issue ha deserves grea concern in reservoirs scheduling. Based on previous research achievemens, and by coupling reservoirs ecological and generaion scheduling, his paper discusses opimal operaion of hydropower reservoir wih ecology consideraion and employs Chaoic Geneic Algorihm (CGA) o see he soluion. 2. Mahemaical Model for Opimizaion of Ecology-oriened Muli-Objecive Hydropower Reservoir Convenional reservoirs scheduling norm mainly aims a flood conrol and benefi promoion raher han ecology facor. To enforce ecological reservoirs scheduling needs newly-esablished sandard and clear awareness of he prioriies of flood conrol, benefi promoion and ecology. In general, reservoirs scheduling should adhere o he operaion principle ha giving absolue prioriy o flood conrol while harmonize, unify and overall consider economical and ecological scheduling Objecives Objecives of ecology-oriened muli-objecive reservoirs opimizaion include he maximum annual power generaion benefi, he maximum oupu of he minimal oupu sage in he year and he minimum shorage of eco-environmen waer demand in reservoir region and downsream river. 1) Maximum annual power generaion benefi Taing maximum annual power generaion benefi as opimizaion crierion, and under he seady operaion of elecrical power sysem, adoping he maximum annual power generaion benefi in he year as he objecive funcion of reservoirs opimal operaion model. T max E AQ H M (1) 1 where, E is he annual generaion of hydropower saions, W h. A is he power generaion coefficien. Q is he urbine release waer discharge for power generaion a ime period, m 3 /s. H is he average head a ime period, m. T is he oal period coun wihin a year, T = 12. M is he amoun of hours a ime period. Taing maximum annual power generaion benefi as opimizaion crierion means o ae consideraion of he on-grid price difference in high, normal and low flow periods and o increase he generaion benefis during high and normal flow period as much as possible while enhancing he generaing income during low flow period via reservoir regulaion. T max F Ap Q H M (2) 1 where, F is annual generaion benefis (RMB). P is he elecriciy price facor during ime period. 2) Maximum oupu of he minimal oupu sage in he year. Maximum oupu of he minimal oupu sage in he year is required o achieve. The effec of his objecive is o provide possibly maximized, uniform and reliable oupu for he power grid, o give full play o hydropower capaciy benefis and o replace he hermal power capaciy. max NP max min AQH where, NP is he maximum oupu of he minimal oupu sage in he year (MW). 3) Minimum shorage of eco-environmen waer demand in reservoir region and downsream river. min N () () 1 (3) Z RL VL (4) where, Z is he shorage of eco-environmen waer demand. RL() is he shorage of eco-environmen waer demand of downsream waercourse during ime period. VL() is he shorage of eco-environmen waer demand in reservoir region. N is he oal amoun of ime periods. For he calculaion of eco-environmen waer demand please refer o lieraure [10]. Concerning ha here are sill dispues over he researches of eco-environmen waer demand, some scholars have raised he noion of minimum and appropriae eco-environmen waer demands. In he paper, calculaion of eco-environmen waer demand is based on he minimum eco-environmen waer demand. Hence, Equaion (4) can be urned ino Z RD VD RS VS (5) min N () () () () 1 where, RD(), VD() are he minimum eco-environmen waer demand of he downsream river and he reservoir iself during ime period respecively. RS(), VS() are he waer supplies of downsream river and reservoir iself during period respecively. Convenional reservoir scheduling mainly considers social and economic objecives and rarely pay aenion o he eco-environmenal demands, hus causing he ecological deerioraion in reservoir iself and he downsream waercourse. Taing he minimum shorage of ecoenvironmen waer demand in reservoir region and downsream river as opimizaion crierion fully reflecs awareness of eco-environmenal proecion of reservoir regions and is downsream waercourses. In his way, ba-

3 906 X. W. WU ET AL. sic waer usage of human life and eco-environmenal demand are being regarded equally imporan, which is significan o river proecion as well as he promoion of susainable uilizaion of waer resources Consrains Consrains of hydropower saions Opimizaion are mainly as follows: 1) Waer balance equaion V V q Q K (6) 1 where, V 1 is he reservoir sorage volume a he end of ime period, m 3. V is he reservoir sorage volume a he beginning of ime period, m 3. q is he average reservoir inflow a ime period, m 3 /s. Q is he urbine release waer discharge for power generaion a ime period, m 3 /s. K is he conversion coefficien of ime lengh. 2) Reservoir sorage capaciy limis A any ime, he volume of reservoir sorages should be beween he minimum and he maximum. V V V (7),min,max where, V,min is he minimum consen waer volume of reservoir a ime period, m 3. V,max is he maximum consen waer capaciy of reservoir a ime period, m 3. 3) Reservoir discharge limis Q Q Q (8),min,max where, Q,min is he minimum waer discharge of reservoir a ime period, m 3 /s. Q,max is he maximum waer discharge of reservoir a ime period, m 3 /s. 4) Hydropower saion power generaion limis. N AQ H N (9),min,max where, N,min is he hydro plan minimum power generaion consrain of reservoir a ime period, W. N,max is he maximum consen waer volume of reservoir a he beginning of period, m 3. 5) Boundary consrains V V, (10) K,1 Kn where, VK,1 is he iniial reservoir sorage volume for K ime of ieraion. V K, n is he final reservoir sorage volume afer K ime of ieraion. 6) Variable non-negaive consrain X 0 (11) where, X is he variable ha formed by decision variable. 3. Opimal Operaion Model Based on Chaoic Geneic Algorihm Opimizaion of hydropower saions is a raher complicaed and nonlinear issue. Scholars from home and abroad have sudied he issue wih various mehods, among which here are he relaively commonly-used dynamic programming (DP) [11], paricle swarm opimizaion (PSO) [12], geneic algorihm (GA), [13,14] and chaoic opimizaion algorihm (COA) [15]. However, hese mehods also have obvious defecs: DP mehod occupies oo much compuer memory, compues slowly and has he problem of curse of dimensionaliy. POA mehod is easily rapped ino local opimum and hus reduces compuaion speed grealy. Large Sysem Hierarchical mehod needs o add coordinaion facor and hus he compuaion becomes more complex and convergence speed is low. PSO mehod relies on lile empirical parameers and has a high convergence speed, bu here are also deficiencies of low accuracy and easily being rapped ino local opimum. GA mehod has he deficiencies of precociy and low searching efficiency. Wang [16] conduced a research owards reservoir scheduling model based on he coupling of chaoic and geneic algorihms and his coupled algorihm shows beer performance compared wih he convenional GA, unforunaely he geneic algorihm ha he auhor employed is basic algorihm, hus is no unsaisfacory. Based on previous research achievemens, an opimal operaion algorihm of hydropower reservoir based on CGA is pu forward Basic Idea of Chaoic Geneic Algorihm CGA couples Chaoic Opimizaion Algorihm wih Geneic Algorihm and benefis from he complemenary advanages. Firs, ae advanage of ergodiciy of COA and amplify he chaoic sequences produced by Logisic mapping o he range of he opimized variable. Then, carry ou operaions of selecion, crossover and muaion by opimized search mechanism of he GA. Meanwhile, add chaoic disurbance operaor o ge he new populaion, hen via generaions of ieraive opimizaion o ge he opimum soluion saisfying he convergence condiion, hen add anoher chaoic disurbance o he iniial opimum soluion and launch local elaborae search o see he opimum soluion of he original problem. The chaoic disurbance operaors in he CGA are added for wo reasons. Firs, o ge he new populaion o avoid precociy by relying on he chaoic disurbance of he chromosome in he geneic operaion process. Second, o launch local elaborae search wih he help of chaoic disurbance of he iniial opimum soluion afer he convergence of he GA operaion. Chaoic disurbance of he chromosome in he geneic operaion process: Suppose g as imes of he curren ieraion, X g M n (M is populaion scale. n is he amoun of decision variable) as he new populaion afer opera-

4 X. W. WU ET AL. 907 ions of selecion, crossover and muaion afer g imes of ieraion. Suppose M, j ( j 1, 2,, n) as he sequence produced by he Number j Logisic mapping, YM n as he array composed wih n chaoic sequences. Sum he g original populaion X M n wih he array obained via chaoic mapping correspondingly o ge he new populag ' g ion Z Mn X MnY M n. Chaoic disurbance of iniial opimum: map he iniial opimum ( x 1, x 2,, x n ) ha saisfy he ieraion ime o chaoic variable inerval (0, 1) respecively o ge he iniial decision variable, as ', ge chaoic sequence afer K imes ieraion of chaoic mapping funcion (K is he lengh of chaoic sequence), hen is he h variable of he chaoic sequence ( 1, 2,, K ), suppose is he variables ha consiss of n, hen ' can be obained by Equaion (12). ' 1 1,2,,K (12) ' where, is a consan beween (0, 1) and can be chosen adapively, is larger in iniial searching phase and smalller in laer phase. can be defined by Equaion (13). 1 1 m (13) where, m is posiive ineger and is defined based on he number of funcions, usually m 2. is ieraive imes Seps for Hydropower Saion Opimizaion Model Based on CGA CGA is used o solve he hydropower saion scheduling, and he seps are as follows: Sep 1: Divide he ime period for he opimizaion of hydropower saion, and deermine he decision variables and is range. This mahemaical model is divided ino T ime period, and aes he waer levels of reservoir as decision variables, wih he range beweenai, b i. Sep 2: Parameer seing. Se he number of variables, populaion size of geneic algorihm is M, he Terminaion ieraion of GA is T, probabiliy of crossover is p c, muaion rae is pm. Sep 3: Objecive funcion processing. Change he former Muli-objecive problem o Single objecive opimizaion problems by muli-objecive decision-maing echnique. Sep 4: Consrain processing. For decision variables ha correspond o unsaisfacory chromosome, is finess value ae one small numerical close o zero unil all chromosomes ha searched ou can mee he consrains. Sep 5: Populaion Iniializaion. Firs choose he n differen iniial, hen chaoic sequence i, j is produced by Logisic Mapping Equaion, i 1, 2,, n, j 1, 2,, p, p is he lengh of chaoic sequence. The Logisic Map- ping Equaion is (14): 1 1,2,,, 1,2,, i j i j i j i n j p (14) 1,,, where, is he conrol parameer. Then by mapping various variables above according o Equaion (15), we can ge he iniial populaion. i, j i i i i, j x a ( b a ) i 1, 2,, n, j 1, 2,, p (15) where, a and i bi are he lower limi and he upper limi of decision variable x i, j. Sep 6: Coding. Code he decision variable by he floaing-poin yards. Sep7: Operaions of Selecion, crossover and muaion. The probabiliy of selecion is seleced by Sochasic Tournamen Model, he crossover operaor is he arihmeical crossover, and muaion operaor is Uniform muaion operaor. Sep 8: Calculae he funcion. Selec proper finess funcion, calculae finess value. Sep 9: Reenion sraegies. sequencing finess value from big o small, ae he superior 10% o he nex generaion direcly, hen conduc selecion, crossover and muaion operaions again, calculae he new finess value and sequence finess value from big o small, replace he 10% bad chromosome in finess value in populaion wih he reserved 10% chromosome in former generaion, conduc chaoic muaion owards oher chromosome o ge a new populaion. Sep 10: Oupu he iniial opimal soluion. Rearrange he populaion; calculae he difference value beween Maximum finess value and he average finess value. If he difference value is among he permissible error ha se, or he ieraion reach maximum ime ha se, hen oupu iniial opimal soluion, oherwise urn o Sep 9 Sep 11: The chaoic muaion of iniial opimal soluion. Conducing chaoic muaion owards he opimal decision vecor corresponding o he iniial opimal soluion, chaoic sequence is also produced by Logisic mapping equaion, he lengh of sequence is K, ge he chaoic decision-maing vecor afer K group chaoic muaion. Sep 12: Oupu he opimal soluion. Map he chaoic muaion variables above o he feasible region, calculae and compare he finess values, he maximum is he opimum, he corresponding decision variable is he opimum decision variable, oupu he opimum. 4. Case Sudy Tae Wanjiazhai Hydropower Saion on he Yellow River as an example. The sage ~ discharge curve of downsream channel and sage ~ sorage capaciy curve are nown. The oal capaciy of he reservoir is 896 million.

5 908 X. W. WU ET AL. The uilizable sorage is 445 million. The dead waer level is 948 m. The normal waer level is m. The flood limi level is m. The comprehensive efficiency coefficien is deermined as 8.3. The guaraneed oupu is 185 MW. The insalled capaciy of he hydropower saion is 1080 MW. The maximum discharge capaciy is 1000 m 3 /s. Based on he incoming runoff daa in normal flow year, he opimized scheduling calculaion wih he aforemenioned model and algorihm is carried ou. The model aes he maximum annual power generaion benefi, he maximum oupu of he minimal oupu sage in he year and he minimum shorage of eco-environmen waer demand as objecives. In he process of pracical soluion, urn he maximum oupu and he minimum shorage of eco-environmen waer demand ino consrain condiions hrough consrain mehod. The concree operaion ideas are as follows. Firs, amplify he guaraneed oupu as he low limi of oupu in minimal oupu sage, which needs o be deermined via repeaing program operaion. This paper finally regards 189 MW as minimum oupu for every ime period. Second, urn he minimum shorage of eco-environmen waer demand o wo consrains, i.e. he minimum shorage of eco-environmen waer demand in downsream waercourse, and he minimum shorage of eco-environmen waer demand of reservoir iself. A las, aiming a he maximum elec- riciy generaion, and ae a waer year as a cycle, divide he scheduling period ino 12 sessions wih every monh as a period. The comprehensive efficiency coefficien is deermined as 8.3. The head loss is 0.5 m. In he model, he iniial value of Logisic mapping randomly lies beween 0.51 and 0.74 wih he conrolling value decided as = 4, he lengh of he chaoic sequence decided as 1000, he iniial populaion of he model decided as 1000, he crossover rae decided as 0.9, he muaion rae decided as 0.1, he allowable error decided as , he maximum ieraions decided as 200 (Table 1). As for he consrain for minimum eco-environmen waer demand in downsream waercourse, calculae he minimum eco-environmen waer demand in downsream waercourse hrough Tennan and minimum monhly runoff mehod and choose he larger value. Tae he reservoir le-down flow as he eco-environmenal waer supply in waercourse, which minus he minimum of ecoenvironmen waer demand in downsream waercourse we can ge he minimum shorage of eco-environmen waer demand in downsream waercourse. Obviously, a 0 minimized minimum shorage of eco-environmen waer demand in downsream waercourse is good for river healh. Ecological river flow process in normal flow year is shown in Table 2. As for he minimum eco-environmen waer demand in reservoir, and considering he inacive sorage capaciy of Wanjiazhai reservoir being Monh Iniial waer level (m) Table 1. Ecology-oriened reservoir opimizaion resuls. Final waer level (m) Inflow Generaing flow Oupu (10 4 W) Power generaion Toal Mehods Opimizaion scheduling Table 2. Operaion resuls for differen algorihm. Convenional scheduling Increasing oupu Increasing raio (%) Execuion ime (second) DP GA COA CGA

6 X. W. WU ET AL million m 3, hen ae he inacive sorage capaciy as he eco-environmenal waer supply in he reservoir region, hen he minimum eco-environmen waer demand in reservoir is less han he inacive sorage capaciy. Therefore, he minimum eco-environmen waer demand in reservoir iself is saisfied. The ecological impoundmen level is 948 m. From Table 1 we can see ha he waer levels a he beginning and end of each monh mee he level bound and each monh s oupu is wihin he conrol range. Meanwhile, he hydropower generaion flow rae saisfies he eco-environmenal waer demand in downsream waercourses. Consequenly, he calculaion resuls fulfill he requiremen. Afer opimized scheduling, he average annual energy oupu is billion W h. Apply he same model and mehod o he incoming runoff condiions of high (P = 10%) and low (P = 90%) flow annum, hen he average annual energy oupu is respecively and billion W h. Therefore, he average annual energy oupu calculaed hrough he model and algorihm in his paper is billion W h which has an billion W h (3.32%) increase in conras wih he 2.75 billion W h under rouine scheduling. Then compare DP, GA, COA and CGA wih one anoher, he resuls are shown in Table 2. From Table 2 we can see ha afer coupling GA and COA, CGA mehod can ge he maximum average annual energy oupu. Hence, CGA is a good algorihm for solving reservoir opimized scheduling model which has advanages of high search efficiency, fine convergence and being closer o he global opimum soluion, ec. Of course, i has a longer compuaion ime han DP and GA mehod because of he demand for larger number of populaions o realize ergodic search. This paper has also sudied reservoirs scheduling wihou ecology consideraion. Tae he maximum annual power generaion benefi and he maximum oupu of he minimal oupu sage in he year as objecives, ignore he ecological waer demand for consrain condiions, emply he same mehod o explore he scheduling of Wanjiazhai Hydropower Saion. The resuls of normal flow annum condiion are shown in Table 3. Under normal flow annum condiion, compare he scheduling resuls wih ecology consideraion and wihou. (see Table 4). Table 3. Corresponding scheduling resuls wihou ecology consideraion. Monh Iniial waer level (m) Final waer level (m) Inflow Generaing flow Oupu (10 4 W) Power generaion Toal Table 4. Comparison of scheduling resuls wih/wihou ecology consideraion. Monh Ecological flow Ecology-conside-red flow Ecology-ignored flow

7 910 X. W. WU ET AL. If ecology is no considered, annual elecriciy generaion of opimized scheduling is billion W h which is billion W h more han he ecology-considered siuaion. Bu from Table 4 we can see, he le-down flow rae in boh January and February is less han he minimum eco-environmenal waer demand flow rae. Similarly, apply he opimized scheduling mehod o he incoming runoff condiions of high (P = 10%) and low (P = 90%) flow annum wihou eco-environmenal consideraion, hen he average annual energy oupu afer scheduling in high and low flow annum is respecively and billion W h. Therefore, he average annual energy oupu calculaed hrough he model and algorihm in his paper is billion W h which has an increase of billion W h (3.7%) in conras wih he 2.75 billion W h under rouine scheduling. Under average annual condiions, he comparison of he scheduling resuls wih and wihou ecology consideraion is shown in Table 5. From Table 5, he average annual energy oupu has an increase of only billion W h (abou 0.28%) compared wih scheduling ha wih ecology consideraion. However, if ecology is no considered, here will be cerain negaive influence on he river ecology and environmen. The reservoir opimized scheduling wih ecology consideraion will be beneficial o he proecion of river ecology and environmen and he promoion of susainable use of waer resources. Thus i s worhy and necessary o gain he harmonious developmen of social economy and environmen a he cos of lile elecriciy generaion benefi. 5. Conclusions Wih he deepened awareness of eco-environmen of people and he proposal of he concep of harmonious coexisence beween man and waer, he fuure rend of reservoirs scheduling is o couple ecological scheduling wih generaion scheduling, and o research on reservoir opimized scheduling mode ha adaps o he social economy developmen and ecological proecion. This paper aes full consideraion of eco-environmen facors in generaion scheduling, and adds he objecive of he Table 5. Comparison of he scheduling resuls wih and wihou ecology consideraion (annual average). Ecologyconsidered Ecologyignored Decremen of annual Power eneraion Decreasing raio (%) minimum shorage of eco-environmen demand in reservoir and he downsream waercourse. In pracical calculaion, urn his objecive o wo consrains hrough consrain mehod, i.e. saisfy he minimum eco-environmen demand in reservoir and in he downsream waercourse. Case sudy indicaes ha he elecriciy generaions of opimized scheduling wih and wihou ecology consideraion are nearly he similar, and ha here will be cerain negaive influence on he river ecology and environmen if ecology is no considered. In addiion, his paper adops he Chaoic Geneic Algorihm in disurbance model and CGA has advanages of high search efficiency, fine convergence and being closer o he global opimum soluion, ec. All ha menioned above conribues o he enrichmen and developmen of he heory and mehods of reservoir opimized scheduling. Of course, he Chaoic Geneic Algorihm needs a longer compuaion ime han GA mehod because of he demand for larger number of populaions o realize ergodic search. In addiion, here are sill dispue over he compuing mehods of he eco-environmenal waer demand in reservoir regions and downsream waercourses, hus more scienific and reasonable eco-environmen waer demand paern and compuing mehod are essenial. These are he aspecs in need of furher improvemen. 6. References [1] A. Czvallo and M. Di Naale, A Fuzzy Conrol Sraegy for he Regulaion of an Arificial, Susainable World, Susainable Planning and Developmen, Vol. 6, 2003, pp [2] B. J. Lence, M. I. Laheef and D. H. Burn, Reservoir Managemen and Thermal Power Generaion, Journal of Waer Resources Planning and Managemen, Vol. 118, No. 4, 1992, pp doi: /(asce) (1992)118:4(388) [3] Q. H. Cai, Taing Eco-Sysem Proecion ino Fully Consideraion and Improving Reservoir Regulaion Process, China Waer Resources, Vol. 2, 2006, pp [4] Z. J. Yin, W. Huang and J. Chen, On Managemen Sysem of Ecological Regulaion of Cascade Reservoirs in he Yangze Basin, Yangze River, Vol. 39, No. 20, 2008, pp [5] Z. R. Dong, D. Y. Sun and J. Y. Zhao, Muli-Objecive Ecological Operaion of Reservoirs, Waer Resources and Hydropower Engineering, Vol. 38, No. 1, 2007, pp [6] L. Liu, Z. C. Dong and G. B. Cui, The Lowes Ecological Waer Demand o Preven he River Sedimen Accumulaion, Journal of Lae Science, Vol. 38, No. 1, 2007, pp [7] Z. F. Yang, J. L. Liu, T. Sun, e al., Rules of Eco-Environmen Waer Demand in a Drainage Basin, Science Press, Beijing, 2006.

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