Synergetic Optimal Operation of Cascade Reservoirs in Mainstream of Yellow River Responding to Drought

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Procedia 158 Energy Procedia 00(2019) (2017)6288–6295 000–000 10th International ConferenceEnergy on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, www.elsevier.com/locate/procedia China

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10 International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, Synergetic Optimal Operation ofChina Cascade Reservoirs in Mainstream of Yellow River Responding to Drought The 15th International Symposium on District Heating and Cooling Synergetic Optimal Operation of Cascade Reservoirs in Mainstream a,b a a a Peng Shaoming , Zheng Xiaokang *, Wang Yu , Li Kefei of Yellow River Responding to Drought AssessingYellow theRiver feasibility of Co.,Ltd., usingZhengzhou, the heat demand-outdoor Engineering Consulting Henan 450003, China State Key Laboratory of Basin water Cycle simulation of water resources Hydropower Research, Beijing a,b and regulation. China Academy a a a Pengfunction Shaomingfor , Zheng Xiaokang *, Wang Yu , LiandKefei temperature a long-term 100038, China district heat demand forecast a

b

a Yellow River Engineering Consulting Co.,Ltd., Zhengzhou, Henan 450003, China a,b,c a a b c c State Key Laboratory of Basin water Cycle simulation and regulation. China Academy of water resources and Hydropower Research, Beijing 100038, China a Abstract IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Cascade reservoirs are playing anÉnergétiques important role in dealing with the Kastler, space-time Département Systèmes et Environnement - IMTdroughts, Atlantique,whereas 4 rue Alfred 44300coordination Nantes, Franceand process b

I. Andrić

*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre

optimization of cascade reservoirs are difficult to realize. In this study, a multi-spatial-temporal scales synergetic optimal Abstract operation model of cascade reservoirs is built to control total water shortage in the basin and optimize the spatial and temporal distribution of waterareshortage and hydrology compensation of cascade reservoirs. MultiCascade reservoirs playingby anexploiting important storage role in compensation dealing with droughts, whereas the space-time coordination and process Abstractoptimization objective is transferred single-objective optimization with the application of penalty function. Improvedoptimal nested optimization of cascade reservoirs into are difficult to realize. In this study, a multi-spatial-temporal scales synergetic particle swarm (PSO) algorithm to solve model. In theinouter layer, and importance regions and operation modeloptimization of cascade reservoirs is builtistoused control totalthe water shortage the basin optimizeparameters the spatialofand temporal Districtare heating networks commonly inlimit the literature onemulti-year of thecompensation most effective for decreasing the periods set realize theare optimal controladdressed of drought water level for regulating storage reservoir. In theMultiinner distribution of to water shortage by exploiting storage compensation andashydrology of solutions cascade reservoirs. greenhouse gas emissions from the into building sector. These systems require high investments which returned through the heat layer, the optimization water storage and discharge rules and processes are optimized to application realize the of inter-annual regulation, intra-annual objective is transferred single-objective optimization with the penaltyare function. Improved nested sales. Due tocascade the changed climate conditions andtocoordination. building policies, heat in demand in the future could optimization, reservoirs’ synergy andisspatial The model developed this study is applied to regions the decrease, cascade particle swarm optimization (PSO) algorithm used solve therenovation model. In the outer layer, importance parameters of and prolonging the investment return period. reservoirs in the mainstream of the Yellow River, optimizing drought limit water levels of the Longyangxia Reservoir periods are set to realize the optimal control of drought limit water level for multi-year regulating storage reservoir. Inand the water inner The main scope storage of this paper is tofrom assess the feasibility using the heat – outdoor temperature function heat demand discharge of reservoirs 2012-2014. The of results thatdemand droughts andthe lowinter-annual water periods in thefor Yellow River layer, the processes water and discharge rules and processes areshow optimized to realize regulation, intra-annual forecast. Thedealt district of Alvalade, located inspatial Lisbon (Portugal), was used as a case study. The consisted of 665 Basin can be withreservoirs’ orderly and water shortage ratios of each year are controlled between 4.9%-5.7%. Theismodel developed optimization, cascade synergy and coordination. The model developed in this studydistrict is applied to the cascade buildings in boththe construction period and typology. Three weather (low, medium, high) andbetter three district in this study canvary improve water resources capacity dramatically in theofYellow River Basin for reservoirs inthat the mainstream of the Yellow River,scheduling optimizing drought limit waterscenarios levels the Longyangxia Reservoir andcoping water renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were mechanisms of droughts. discharge processes of reservoirs from 2012-2014. The results show that droughts and low water periods in the Yellow River compared results a dynamic heat demandratios model, developed andbetween validated by the authors. Basin can bewith dealt with from orderly and water shortage of previously each year are controlled 4.9%-5.7%. The model developed The that when only weather change is considered, margin of error be acceptable for for some applications Copyright © showed 2018 Elsevier Ltd. All rights reserved. in thisresults study can improve the water resources scheduling capacitythe dramatically in thecould Yellow River Basin better coping (the errorand inofannual demand was responsibility lower than 20% weathercommittee scenarios considered). However, after introducing Conference onrenovation Applied Selection peer-review under of for the all scientific of the 10th International mechanisms droughts. scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). Energy (ICAE2018). ©The 2019 The Authors. Published Elsevier Ltd.average within the range of 3.8% up to 8% per decade, that corresponds to the value slope coefficient increased on Copyright ©of 2018 Elsevier Ltd. by All rights reserved. This is an open access article under the CC of BY-NC-ND licensethe (http://creativecommons.org/licenses/by-nc-nd/4.0/) decrease in the number of heating hours 22-139h season (depending on the combination of on weather and Selection and peer-review under responsibility of theduring scientific heating committee of the 10th International Conference Applied Peer-review under responsibility of the scientific committee of ICAE2018 –increased The 10th International Conference on(depending Applied Energy. renovation scenarios considered). On the other hand, function intercept for 7.8-12.7% per decade on the Energy (ICAE2018). coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. by Elsevier Ltd. * Corresponding author.Published Tel.:+86-13592554786; fax:+86-371-66020908. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and E-mail address: [email protected] Cooling. 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. * Corresponding author. Tel.:+86-13592554786; fax:+86-371-66020908. Keywords: Heat demand; Forecast; Climate change Selection peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). E-mailand address: [email protected] 1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.445

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Keywords: coping mechanisms of droughts; Yellow River Basin; cascade reservoirs; synergetic optimization; nested double layer; improved particle swarm optimization

1. Introduction The Yellow River basin is located in the typical monsoon climate zone with the uneven distribution of precipitation along time and space, which result in frequent disasters in the basin[1]. Reservoirs are the efficient tools for regulating flow and exploring and utilizing the water resources. The great values on the optimizing basin water allocation, protecting droughts and reducing damages are given by scientifically regulating storage and discharge methods for cascade reservoirs in upstream and downstream, coordinating the relationships among reservoirs, maximizing the benefits of cascade reservoirs[2-3]. With the increasing drought issues, the situation of lack of irrigation water becomes more and more serious, the functions of the large reservoirs, which are stored water for drought protection through optimizing the drought limited water level, are focused on by researchers, and the regulations of reservoirs for drought protection are studied actively [4-7]. There are 26 cascade reservoirs built in main stream in Yellow River Basin, the total capacity is more than 7×10 11 m3 (figure 1). In this research, the systematic generalization is used as the method, the numerical simulation is complemented as the support, the collaborative optimization model for regulation of cascade reservoirs in Yellow River Basin is built, the collaborative regulation plans of cascade reservoirs in typical drought years in Yellow River Basin is proposed.

Figure 1. Distribution of the cascade reservoirs and irrigation areas in Yellow River Basin.

2. Basin drought recognition and irrigation water demand for agriculture 2.1. Basin drought recognition and evaluation The Palmer Drought Severity Index (PDSI) proposed by Palmer in 1965 is the most widely used drought index [8]. PDSI index can be applied to comprehensive consider the area precipitation, evapotranspiration, runoff, soil moisture, and other basic factors, also be used with differences, which inflect the moisture shortage at a specific time in a specific region, between the measured precipitation and precipitation with optimum climate. （1） d  P  P  P  ( PET   PR   PRO   PL) ~ Where: P and P are the measured precipitation and precipitation with optimum climate, d is the corresponding moisture difference. The value of P~ is based on four hydrologic coefficients including evapotranspiration coefficient α, soil water supply coefficient β, runoff coefficient γ, and soil moisture lost coefficient δ. PET, PR, PRO, PL stand for possible evapotranspiration, possible water supplement, possible runoff, and possible water lost, respectively.

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The value marked as Z by Palmer is obtained after d times a weightK′, Z is monthly moisture anomaly index, also known as Palmer Z index. Palmer Z index can clearly show the wet and dry conditions in one month, it is helpful for indicating the drought and flood for a short term (one month).  PE  E  PO 2.8  K  1.5  log10     0.5 D  ( P  L) D 17.67 Z  12 K d  DK 

（2） （3）

1

The value of PDSIi the drought index in this month is the linear combination of PDSI i-1 the drought index in last month and Zi moisture anomaly in this month[9]: Z （4） PDSI 0.897 PDSI  i  i

i 1

3

According to the PDSI, the drought regime is divided into 5 levels: extreme drought, drought, mid drought, light drought and drought free. Table 1. Drought level based on PDSI state PDSI Level

0 -1.0
1 -2.0
2 -3.0
3 -4.0
4 PDSI≤-4.0 Extreme drought

PDSI is the comprehensive drought index representing the drought status, when the PDSI is smaller than -1.0, the drought might occur, so that the smaller PDSI the severer drought. The reach and the basin are normally constituted by several sections, drought is not only inextricably related with the drought levels, but also impacted by the drought area corresponding to various drought levels. 1 n （5）  CRDI ( PDSI  A )



A i 1

i

i

Where: A is the total area of basin, Ai is the area of i section. 2.2. Relationship analysis between irrigation water demand and drought index The water sources used by crops including the effective precipitation, soil moisture and ground water supplement, the purpose of the irrigation is to supplement the water lost by plant transpiration and evaporation between trees. The irrigation water demand for crops can be simulated by dynamic balance of field water layers, net irrigation water demand is analyzed by water balance theory, and the equation of water balance is shown as below [10]: （6）  Qi ETci  Pe  Gei  W

Where: Qi is the net irrigation water demand for i crop (mm); ETci is the possible evapotranspiration of i crop (mm); Pe is the monthly effective precipitations (mm) during within the growth period of crops, which is computed based on monthly effective precipitations and coefficient of effective water use; GEi is the groundwater use (mm) during the growth period of crop, under the condition of the soil texture and crops, groundwater use is a function of depth and evapotranspiration condition; △ works as the difference (mm) of water storage of wet soil layer between the beginning and ending of the growth period, in terms of the crops water demand for long period, the change of the soil is too small to calculation, that is △≈0. From the formula (1) to formula (6), it is obvious that the precipitation P and evapotranspiration ET, which are the main meteorological factors impacting the PDSI, play an important role for crop water demand as well. The researches illustrate that there is a linear relationship between irrigation water demand Q and drought index PDSI in Yellow River Basin[11], the irrigation water demand is increasing with the drought index PDSI decreasing, the result is shown in table 2.

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Table 2. The irrigation water demand and regression of drought index PDSI in Yellow River Basin. Irrigation areas Above Longyangxia Section Long-Liu Section Liu-Wan Section Wan-Three Section Three-Xiao Section Below Xiaolangdi Section

Regression equation for irrigation water demand and drought index Q = -0.5983PDSI + 2.35 Q = -0.7645PDSI + 7.15 Q = -2.8136PDSI + 172.45 Q = -2.8136PDSI + 79.24 Q = -3.9569 PDSI + 38.413 Q = -10.431 PDSI + 92.292

test value t 15.634 23.619 48.488 53.221 61.463 98.731

3. The collaborative regulation model of cascade reservoirs coping with the drought 3.1. Model built Minimizing the comprehensive water shortage in drought years. In drought years, the comprehensive water shortage for basin can be controlled in the minimum through simulating the reasonable operation method for Longyangxia Reservoir, Liujiaxia Reservoir, Wanjiazhai Reservoir, Sanmenxia Reservoir and Xiaolangdi Reservoir in order to find the optimal collaborative regulation for cascade reservoirs, with optimizing the spatial-temporal distribution process of runoff, and improving the water supply and reducing the water shortage in drought years.

 MinWSS

I

T

 (i, t )(QD(i, t )  QS (i, t )T )

（7）

i 1 t 1

Where: WSS is the comprehensive water shortage of basin in regulation period, QD(i, t) is the water demand on i note in t time, QS(i, t) is the water supply on i note in t time, i=1,2,…,40; T is the total time of computation, t=1,2,…,T. (i,t ) is the weight of water supply on i note in t time, which determined by analytic hierarchy process based on the differences during water supply by local department, the importance of irrigation water use is depended on water demand for crops on the various growth periods, and the environmental water use is consider on difference between the water for transporting sands and environmental water. Minimizing the difference of drought damages. In order to realize the minimum of the differences between the water shortage ratio of basin and mean water shortage ratio of basin, the process of intra-annual discharging water by cascade reservoirs is optimized, the shortage balance is controlled in regulation period, moreover, the serious lack of water which may lead to a huge lost is protected from intensively occurring in partial regions and reaches.

 MinRss MinMax（r (i, t )  r）

（8）

Where: r (i, t )  QD(i, t )  QS (i, t ) is the water shortage radio on i area in t time; r is the mean water shortage ratio of QD(i, t ) basin in regulation period, which is based on the water demand and available water supply in regulation period. In terms of the objective functions, a threshold of the average difference of water shortage rate in the regulation period was set up in this study, the additional water shortage is the part where difference between water shortage rate and mean water shortage rate is larger than the range of the threshold [12], which is the penalty coefficient times the water shortage in that period[13]. If water shortage rate on i area and t time is overrate, the severe damage might be caused intensively, especially, the tremendous loss of the agriculture is irrecoverable even though the water supply for irrigation increases in the later period; whereas, if the water shortage rate on i area and t time is too low, it might lead to the large rate of water shortage on another area or another time. Therefore, the multi-objectives optimization problem can be transformed into a single objective optimization problem of minimum comprehensive water shortage, including the objective function of minimum water shortage of basin and penalty function of the water shortage balance:

MinWSS  Where:

I

T

1

1

 t (i,t )(QD(i,t )  QS(i,t ))T i



 t  max((r(i,t )  r )  e ),0)QS(i,t )T i I

T

1

1

 is penalty coefficient, e is the allowable threshold of the water shortage rate difference.

（9）

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3.2. Solving method The collaborative regulation of cascade reservoirs coping with drought is a high-dimensional, multistage and dynamic nonlinear problem with a plenty of complex constraints and a large quantities of status variables, the solution accuracy is related with the number of additional statuses and discrete grids, the solution dimensions exponential is increasing with the argument of the additional statuses and discrete grids, which will lead to the curse of dimensionality [14]. The PSO algorithm is used to optimizing the discharge process of cascade reservoirs. PSO algorithm includes the outer control and inner iteration. In terms of outer control, the mutual method is applied with reference of water supply weight on one region and one time, in order to lead the Longyangxia Reservoir to regulate the water over years, and output the optimal drought limit water level, then realize the many-year regulation and inter-annual balance to feedback the effects, on coping the drought in basin, they are water shortage rate in drought years and distribution of water shortage among areas and time, which are the basis of the inner iteration. Account for inner iteration, the intra-annual optimization of storing and discharging process of cascade reservoirs in Yellow River Basin is achieved based on the drought limit water level of Longyangxia Reservoir optimized by outer control, then the discharge process of reservoirs on main stream of Yellow River is initialized, the basin comprehensive water shortage is used as fitness function to compute the particle fitness value, the monthly water shortage on reaches and areas is regarded as the basis of PSO, the speed and direction of location update and evolution of particles are controlled, the intra-annual spatial-temporal balance is judged, in order to lead the optimizing discharge process of the reservoirs in main stream, then the optimal discharge process of every reservoir in main stream is output. 4. Case study 4.1. Basic data Research shows that the runoff of the Yellow River varies with a periodicity of 3 years. Therefore, 3 years was regarded as a basic period for making water resources allocation plans and decision variables were water discharges of each reservoir in 36 months. To show advantages of the synergetic optimal operation model, the natural runoff data and measured drought data from 2012-2014 were input into the model in this case study. Then the optimization results were compared with the actual condition. Hydrological year was applied, meaning that the optimization period started from July 2012 and ended at June 2015. The calculation step was set as month. The results were compared with actual condition.  Runoff and reservoir water storage. The natural annual runoff of the Yellow River in 2012, 2013, and 2014 was 62.725, 50.639, and 47.439 billion m3 respectively.  Droughts and irrigation water demands. Drought ranks were assessed according to Equation (1), using measured rainfall, evaporation, runoff and soil moisture data from 2012-2014.  The results showed that irrigation water demand was 35.617 billion m3 and 36.321 billion m3 in 2012 and 2013 respectively, and in 2014 the irrigation water demand reached 40.879 billion m3 .  Flow constraints in major sections. Considering the maintenance of ecosystems in Ningxia-Inner Mongolia reach, the minimum environmental flow in the Hekouzhen section was 250 m3/s. The maximum flow in the Hekouzhen section was 4000 m3/s in the flood season to ensure the flood control security. In Lijin section, the minimum flow was 50 m3/s, in the non-flood season the flow should be no more than 500 m3/s as a result of water shortage, and in flood season the maximum flow was set as 4000 m3/s for safety.b = manuscript reference code 4.2. Reservoir operation processes Considering the natural annual runoff and water demand in the Yellow River Basin, the average water shortage ratio was evaluated as 6% and the threshold of water shortage ratio variation was set as 2%. The synergetic optimal operation model was applied to figure out reservoir operation plans to cope with droughts.

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(1) Drought limit water levels of the Longyangxia Reservior at the end of hydrological years. During 2012-2014, the Longyangxia Reservoir supplied 0.69 billion m3 of water to the downstream, storing 4.07 billion m3 of water in the wet year (2012) and suppling the stored water in the dry year (2014). Inter-annual regulation was realized through the synergetic optimal operation model. (2) Optimization of the storage and discharge of cascade reservoirs. The discharge process of each reservoir was optimized on the basis of inflow process and water demand in the downstream to improve the water supply capacity in dry years and important periods. The results show that Longyangxia Reservoir stored water in the flood season and released water in the non-flood season. The year 2014 was a dry year with heavy drought. The water storage of the Longyangxia Reservoir was 15.08 billion m3 at the beginning of July 2014. In 2014, the Longyangxia Reservoir supplied 3.91 billion m3 of water to the downstream, as shown in Figure 2. The Liujiaxia Reservoir, Wangjiazhai Reservoir and Sanmenxia Reservoir stored 1.271, 0.268 and 0.021 billion m3 of water in the flood season respectively. Through the optimization of flood control water level, the Xiaolangdi Reservoir stored 0.88 billion m3 of water in the flood season (Figure 3). By the synergy of discharge processes of cascade reservoirs, the optimal allocation of water among different periods and regions was realized and the drought was dealt with properly.

Figure 2. Water storage and discharge of the Longyangxia Reservoir and the change of total water storage

Figure 3.Water storage and discharge of the Xiaolangdi Reservoir and the change of total water storage

4.3. Effects of optimal operation (1) Water supply in the dry year increased as a result of inter-annual operation. The year 2012 was a wet year without drought. The Longyangxia Reservoir stored 4.07 billion m3 of water by adopting the drought limit water level as a constraint. At the same time, water demand was almost satisfied this year. Agricultural water shortage ratio was 8.1% and agricultural water at key period was ensured. The year 2013 was a normal year without drought. The Longyangxia Reservoir supplied 0.85 billion m3 of water stored in 2012, reducing agricultural water shortage ratio to 10%. The year 2014 was a dry year with heavy drought. Through synergetic optimal operation of cascade reservoirs, water supply in the whole basin was 4.79 billion m3 larger than actual water supply in 2014. (2) The temporal and spatial distribution of runoff was varied through the synergetic optimal operation of cascade reservoirs, which realized the temporal and spatial balance of water shortage and controlled annual water shortage ratio (Figure 4 and Figure 5). There was no drought in 2012 and 2013, and the water shortage ratio was controlled to be 4.9% and 5.2% respectively. Heavy drought happened in 2014, thus the Longyangxia Reservoir increased water discharge. Through the synergetic optimal operation of the Liujiaxia Reservoir, Wangjiazhai Reservoir, Sanmenxia Reservoir and Xiaolangdi Reservoir, irrigation water demand was satisfied during the key periods of agricultural water use, the water shortage ratio of the whole basin can be controlled as 5.7%. Agricultural water shortage ratios of the upper, middle and lower reaches at each period were confined between 7%-10%. The evenly distributed water shortage avoided serious agricultural water shortage, which restrained drought from being hazard.

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a) Discharge of the Longyangxia Reservoir and water demand in the downstream

b) Discharge of the Liujiaxia Reservoir and water demand in the downstream

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c) Discharge of the Xiaolangdi Reservoir and water demand in the downstream

Figure 4. Discharge processes and downstream water demand processes of major reservoirs

Figure 5. Variation of agricultural water shortage ratio of the Yellow River Basin during 2012-2014

(3) Runoff process was optimized to increase drought resistance ability. Results of the synergetic optimal operation model revealed that 3.91 billion m3 of water was released to the downstream with the constraint of drought limit water levels. Compared with actual condition, 4.79 billion m3 more water was supplied under optimization, reducing water shortage ratio to 5.7% (Figure 6a). The long-term runoff and drought were not fully considered in actual condition. Hence the discharge of cascade reservoirs could not match the irrigation water demand, leading to drastic variations of agricultural water shortage and causing agricultural water shortage exceed 15% in dry year. In contrast, the synergetic optimal operation model could take long-term runoff and drought into account to balance inter-annual and intra-annual agricultural water shortage (Figure 6b).

a) Comparison of water storage and discharge of b) Comparison of agricultural water shortage the Longyangxia Reservoir Figure 6. Comparison of synergetic optimal operation and actual operation 5. Conclusions The Yellow River Basin has serious water shortage problems and frequently happened droughts. Moreover, water resources allocation is very complicated in this basin. To solve these problems, this paper built a synergetic optimal

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operation model of cascade reservoirs to cope with droughts through optimal technologies like decomposition, coordination, coupling, controlling and so forth. Improved nested PSO algorithm is adopted to figure out the discharge process of cascade reservoirs. The model built in this paper was used to make synergetic optimal operation plans of cascade reservoirs for drought resistance during 2012-2014. The results showed that drought limit water level of the Longyangxia Reservoir was optimized through runoff analysis, drought assessment and agricultural water demand prediction, therefore water was stored in wet year and discharged in dry year. The heavy drought happened in the dry year 2014 could be dealt with properly. Through this model the water shortage ratio of the whole basin in 2012, 2013 and 2014 could be controlled to 4.9%, 5.2% and 5.7% respectively. By optimizing the synergetic operation of the Liujiaxia Reservoir, Wanjiazhai Reservoir, Sanmenxia Reservoir and Xiaolangdi Reservoir, the balance of the spatial and temporal distribution of water shortage was realized, combining agricultural water shortage ratio between 7%-10%. Thus irrigation water demand of key periods could be satisfied and drought could be stopped from becoming hazard. Acknowledgements This work was financially supported by The National Key Research and Development Program of China (2017YFC0404406 and 2017YFC0404404). Reference

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