Operation Of Reservoir Systems Research Articles

Simulation-Optimization Model for Dokan Reservoir System Operation

Reservoir operation system is the essential part of water resources management, and each reservoir has a special policy for operation. Simulation and optimization are two different techniques for the operating process of any reservoir. Combined simulation-optimization (S-O) model as a new technique in recent years to minimize the deficit in hydropower generation and irrigation demand has been developed for Dokan reservoir system in Kurdistan Region, Iraq. The model combines the Simulink and genetic algorithm (GA) as techniques for simulation and optimization respectively. For comparison, one traditional simulation model based on the standard operating policy (SOP) and two optimization models using nonlinear programming (NLP) and discrete differential dynamic programming (DDDP) optimization methods was developed. In the present study, three performance evaluation criteria, namely; reliability, resiliency and vulnerability have been used for comparing and evaluating the developed models.
 The proposed models were run over a period of 54 years using monthly time step interval, i.e. 648 months starting from Jan-1958 to Dec-2011. The results reveal that the SOP model (Model-I) has serious deficit events in minimum downstream demands, although, it has a higher reliability in the irrigation demand (0.94). In addition, the other models; NLP, DDDP and S-O by considering weight factors and almost have the same reliability, 0.90, 0.90 and 0.91 respectively. Furthermore, the results show a low resilience for NLP model and a high vulnerability for S-O modelwhich causes higher severity of failure events. Moreover, for the operation period from 1995 to 2011, the annual productions of hydropower are 1280, 1339, 1344and 1296 MW by increasing of 24.9, 30.64, 31.16 and 26.46 % more than the actual hydropower production (1025 MW) for SOP, NLP, DDDP and S-O models respectively.
 Finally, the conclusions present that the DDDP optimization model provides high reliability as well as more power generation at the same time. The model can be more easily applied to solve the nonlinear and multi objective problems with less computational time.

Determination of cost coefficients of a priority-based water allocation linear programming model – a network flow approach

Abstract. This paper presents a method to establish the objective function of a network flow programming model for simulating river–reservoir system operations and associated water allocation, with an emphasis on situations when the links other than demand or storage have to be assigned with nonzero cost coefficients. The method preserves the priorities defined by rule curves of reservoir, operational preferences for conveying water, allocation of storage among multiple reservoirs, and transbasin water diversions. Path enumeration analysis transforms these water allocation rules into linear constraints that can be solved to determine link cost coefficients. An approach to prune the original system into a reduced network is proposed to establish the precise constraints of nonzero cost coefficients, which can then be efficiently solved. The cost coefficients for the water allocation in the Feitsui and Shihmen reservoirs' joint operating system of northern Taiwan was adequately assigned by the proposed method. This case study demonstrates how practitioners can correctly utilize network-flow-based models to allocate water supply throughout complex systems that are subject to strict operating rules.

An adaptive particle swarm optimization algorithm for reservoir operation optimization

Reservoir operation optimization (ROO) is a complicated dynamically constrained nonlinear problem that is important in the context of reservoir system operation. In this study, improved adaptive particle swarm optimization (IAPSO) is proposed to solve the problem, which involves many conflicting objectives and constraints. The proposed algorithm takes particle swarm optimization (PSO) as the main evolution method. To overcome the premature convergence of PSO, adjusting dynamically the two sensitive parameters of PSO guides the evolution direction of each particle in the evolution process. In the IAPSO method, an adaptive dynamic parameter control mechanism is applied to determine parameter settings. Moreover, a new strategy is proposed to handle the reservoir output constraint of ROO problem. Finally, the feasibility and effectiveness of the proposed IAPSO algorithm are validated by the Three Gorges Project (TGP) with 42.23bkW power generation and XiLuoDo Project (XLDP) with 30.10bkW. Compared with other methods, the IAPSO provides a better operational result with greater effectiveness and robustness, and appears to be better in terms of power generation benefit and convergence performance. Meanwhile, the optimal results could meet output constraint at each interval.

Quarter-Hourly Operation of Large-Scale Hydropower Reservoir Systems with Prioritized Constraints

AbstractThis paper deals with the optimization of short-term hydropower scheduling when a mathematically feasible solution cannot be achieved. Generally, the numerical search procedure terminates with the detection of infeasibility when an optimization problem is being solved. More often, however, operators of hydropower reservoirs prefer violating the mathematical constraints in order of priority, such that a satisfactory solution to the real-world problem can always be guaranteed. Previous studies rarely addressed this issue in relation to short-term operations of hydropower reservoirs. The present study relaxes the prioritized constraints with extra variables and solves the model by weighted goal programming. The weights are designed such that solving the problem is equivalent to a lexicographical optimization. The solution procedure is also efficient in dealing with large-scale problems using the following techniques: temporal and spatial decomposition of the hydro system, separate solution of plant-b...

Reservoir Operation to Minimize Sedimentation

The Wonogiri Reservoir capacity decreases rapidly, caused by serious sedimentation problems. In 2007, JICA was proposed a sediment storage reservoir with a new spillway for the purpose of sediment flushing / sluicing from The Keduang River. Due to the change of reservoir storage and change of reservoir system, it requires a sustainable reservoir operation technique. This technique is aimed to minimize the deviation between the input and output of sediments. The main objective of this study is to explore the optimal Wonogiri reservoir operation by minimizing the sediment trap. The CSUDP incremental dynamic programming procedure is used for the model optimization. This new operating rules will also simulate a five years operation period, to show the effect of the implemented techniques. The result of the study are the newly developed reservoir operation system has many advantages when compared to the actual operation system and the disadvantage of this developed system is that the use is mainly designed for a wet hydrologic year, since its performance for the water supply is lower than the actual reservoir operations. Doi : 10.12777/ijse.6.1.16-23 [How to cite this article: Wulandari, D.A., Legono, D., and Darsono, S., 2014. Reservoir Operation to Minimize Sedimentation. International Journal of Science and Engineering , 5(2),61-65. Doi: 10.12777/ijse.6.1.16-23 ] Normal 0 false false false EN-US X-NONE X-NONE

Evaluation of Real-Time Operation Rules in Reservoir Systems Operation

Reservoir operation rules are logical or mathematical equations that take into account system variables to calculate water release from a reservoir based on inflow and storage volume values. In fact, previous experiences of the system are used to balance reservoir system parameters in each operational period. Commonly, reservoir operation rules have been considered to be linear decision rules (LDRs) and constant coefficients developed by using various optimization procedures. This paper addresses the application of real-time operation rules on a reservoir system whose purpose is to supply total downstream demand. Those rules include standard operation policy (SOP), stochastic dynamic programming (SDP), LDR, and nonlinear decision rule (NLDR) with various orders of inflow and reservoir storage volume. Also, a multi-attribute decision method, elimination and choice expressing reality (ELECTRE)-I, with a combination of indices, objective functions, and reservoir performance criteria (reliability, resiliency, and vulnerability) are used to rank the aforementioned rules. The ranking method employs two combinations of indices: (1) performance criteria and (2) objective function and performance criteria by using the same weights for all criteria. Results show that the NLDR gives an appropriate rule for real-time operation. Moreover, NLDR validation is presented by testing predefined curves for dry, normal, and wet years.

Optimization of Water Supply Reservoir in the Framework of Climate Variation

This paper proposed a new methodology adaptation for the Pedu-Muda reservoir operation policies to enhance the capability and reliability of the reservoir operation fit to the climate change impact. These reservoirs are responsible to irrigate water supply for the largest double paddy cultivation area in Malaysia - the Muda Irrigation Scheme as staple food among people. In this study, statistical downscaling model (SDSM) was used to project the changes in climate trend for the study area, a hydrological model was used to simulate the runoff under projected change in climate, and a crop water model was used to re-arrange the crop water demand for two-times cultivation season. Two optimization models were applied; NSGA-II and LP model represents as evolutionary algorithm and classical method respectively to enhance the reservoir workability under changing climate. As a result, the rule-curves were proposed as guidelines to the operators for long-term reservoir operation. The performances of these models were compared via reliability, resilience, and vulnerability index. The results revealed that both optimization models were successful in theory to provide more/sufficient water for the irrigation purposes more than 4% than historical record. The system can also reduce the water transfer from Muda reservoir and water spill of Pedu reservoir. By performance results, the NSGA-II model showed a better improvement in the reservoir operation system and mitigated possible water crisis sufficiently due to climate change rather than LP model.

Sampling/stochastic dynamic programming for optimal operation of multi-purpose reservoirs using artificial neural network-based ensemble streamflow predictions

Due to limited water resources and the increasing demand for agricultural products, it is significantly important to operate surface water reservoirs optimally, especially those located in arid and semi-arid regions. This paper investigates uncertainty-based optimal operation of a multi-purpose water reservoir system by using four optimization models. The models include dynamic programming (DP), stochastic DP (SDP) with inflow classification (SDP/Class), SDP with inflow scenarios (SDP/Scenario), and sampling SDP (SSDP) with historical scenarios (SSDP/Hist). The performance of the models was tested in Zayandeh-Rud Reservoir system in Iran by evaluating how their release policies perform in a simulation phase. While the SDP approaches were better than the DP approach, the SSDP/Hist model outperformed the other SDP models. We also assessed the effect of ensemble streamflow predictions (ESPs) that were generated by artificial neural networks on the performance of SSDP/Hist. Application of the models to the Zayandeh-Rud case study demonstrated that SSDP in combination with ESPs and the K-means technique, which was used to cluster a large number of ESPs, could be a promising approach for real-time reservoir operation.

Tasks and functions of a decision support system for water resources management in the Lower Volga

The Lower Volga is a Russian region, which has a unique natural resources and a territory which is the area of collision of the interests of different economic branches that use water resources. The economic development of the region requires effective water resources management in harmony with natural conditions and the operation of the Volga-Kama reservoir system. A reliable management tool is a computer decision support system for the Lower Volga. The problems to be solved with this system answer the current water problems of the region. Data on the programs under implementation and to be developed are given; water apportioning model is given; and the way of coordinated choice of management regime of the Volga-Kama reservoir system and water resources use in the Lower Volga is demonstrated.

A Factorial-based Dynamic Analysis Method for Reservoir Operation Under Fuzzy-stochastic Uncertainties

In this study, a factorial-based fuzzy-stochastic dynamic programming (FFS-DP) method is developed for tackling multiple uncertainties including fuzziness, randomness and their interaction in reservoir operation management (ROM). FFS-DP is framed on the integration of stochastic dynamic programming, fuzzy-Markov chain, vertex analysis and factorial analysis techniques. It can not only deal with the conventional optimization problem for reflecting dynamic and uncertain features in ROM, but also obtain detailed effects of uncertain parameters and their interactions on the system performance. The developed method is applied to a case study of a reservoir operation system, where the local authority is in charge of allocating relative scant water to the downstream municipality. The results obtained can help the local authority identify desired water release policies under uncertain system conditions. Besides, the results simultaneously indicate that significant factors and their interactions can be identified in ROM. Moreover, the results can be further analyzed for generating optimal parameter inputs to obtain maximized system benefits.

Use of parallel deterministic dynamic programming and hierarchical adaptive genetic algorithm for reservoir operation optimization

Reservoir operation optimization (ROO) is a complicated dynamically constrained nonlinear problem that is important in the context of reservoir system operation. In this study, parallel deterministic dynamic programming (PDDP) and a hierarchical adaptive genetic algorithm (HAGA) are proposed to solve the problem, which involves many conflicting objectives and constraints. In the PDDP method, multi-threads are found to exhibit better speed-up than single threads and to perform well for up to four threads. In the HAGA, an adaptive dynamic parameter control mechanism is applied to determine parameter settings, and an elite individual is preserved in the archive from the first hierarchy to the second hierarchy. Compared with other methods, the HAGA provides a better operational result with greater effectiveness and robustness because of the population diversity created by the archive operator. Comparison of the results of the HAGA and PDDP shows two contradictory objectives in the ROO problem-economy and reliability. The simulation results reveal that: compared with proposed PDDP, the proposed HAGA integrated with parallel model appears to be better in terms of power generation benefit and computational efficiency.

Short Term Hydro Thermal Scheduling using Lambda Gamma Iteration Method

Abstract: Short term hydrothermal scheduling (STHTS) is a daily planning proposition in power system operation. The main purpose of hydro thermal generation scheduling is to minimize the overall operation cost and to satisfy the given constraints by optimally scheduling the power outputs of all hydro and thermal units under study periods, given electrical load and limited water resource. This paper presents the lambda-gamma iteration method for fixed head hydro-thermal problems. Also, hydro power input-output model with constant head has been developed for Sewa Hydro Electric Power Plant by using the curve fitting techniques by least square method in MATLAB 7.9 version software. From scheduling results, operational decisions can be made for the best performance under changing conditions of load, head, unit availability and other important constraints. Furthermore, these decisions may be used as an effective input to the on-line decision support system for real-time operation of reservoir systems in availability based tariff (ABT) context that results in increased power production and enhanced revenue earnings in the process of planning and management of a water resources project.DOI: http://dx.doi.org/10.3126/hn.v9i0.7068 Hydro Nepal Vol.9 July 2011 20-26

An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System

A new approach for optimization of long-term operation of large-scale reservoirs is presented, incorporating Incremental Dynamic Programming (IDP) and Genetic algorithm (GA) . The immense storage capacity of the large scale reservoirs enlarges feasible region of the operational decision variables, which leads to invalidation of traditional random heuristic optimization algorithms. Besides, long term raised problem dimension, which has a negative impact on reservoir operational optimization because of its non-linearity and non-convexity. The hybrid IDP-GA approach proposed exploits the validity of IDP for high dimensional problem with large feasible domain by narrowing the search space with iterations, and also takes the advantage of the efficiency of GA in solving highly non-linear, non-convex problems. IDP is firstly used to narrow down the search space with discrete d variables. Within the sub search space provided by IDP, GA searches the optimal operation scheme with continuous variables to improve the optimization precision. This hybrid IDP-GA approach was applied to daily optimization of the Three Gorges Project-Gezhouba cascaded hydropower system for annual evaluation from the year of 2004 to 2008. Contrast test shows hybrid IDP-GA approach outperforms both the univocal IDP and the classical GA. Another sub search space determined by actual operational data is also compared, and the hybrid IDP-GA approach saves about 10 times of computing resources to obtain similar increments. It is shown that the hybrid IDP GA approach would be a promising approach to dealing with long-term optimization problems of large-scale reservoirs.

Real-Time Operation of Reservoir System by Genetic Programming

Reservoir operation policy depends on specific values of deterministic variables and predictable actions as well as stochastic variables, in which small differences affect water release and reservoir operation efficiency. Operational rule curves of reservoir are policies which relate water release to the deterministic and stochastic variables such as storage volume and inflow. To operate a reservoir system in real time, a prediction model may be coupled with rule curves to estimate inflow as a stochastic variable. Inappropriate selection of this prediction model increases calculations and impacts the reservoir operation efficiency. Thus, extraction of an operational policy simultaneously with inflow prediction helps the operator to make an appropriate decision to calculate how much water to release from the reservoir without employing a prediction model. This paper addresses the use of genetic programming (GP) to develop a reservoir operation policy simultaneously with inflow prediction. To determine a water release policy, two operational rule curves are considered in each period by using (1) inflow and storage volume at the beginning of each period and (2) inflow of the 1st, 2nd, 12th previous periods and storage volume at the beginning of each period. The obtained objective functions of those rules have only 4.86 and 0.44 % difference in the training and testing data sets. These results indicate that the proposed rule based on deterministic variables is effective in determining optimal rule curves simultaneously with inflow prediction for reservoirs.

이상홍수를 고려한 퍼지 최적 저수지 운영

본 연구에서는 이상홍수 발생 시 저수지의 안전성을 확보함과 동시에 두 개의 저수지를 효율적으로 연계운영하여 하류의 홍수피해를 줄이기 위한 모형을 개발하였다. 이상홍수 발생 시 저수지 최적연계운영을 위해 선형계획법을 사용하였고, 저수지 운영에서 유입량, 저수지 수위, 유입량의 증감 등의 요소에 포함된 불확실성을 해결하기 위해 퍼지제어기법을 도입하였다. 선형계획법을 이용하여 이상홍수 유입에 따른 저수지 최적연계운영규칙을 찾아내고, 퍼지제어기법을 사용하여 신속하고 정교한 운영이 요구되는 이상홍수 유입상황에 사용할 수 있는 모형을 개발하였다. 본 연구에서는 낙동강 상류에 위치한 안동댐과 임하댐을 대상으로 각 댐의 저수지에 100년 빈도의 홍수와 PMF가 유입될 때 안동시의 계획홍수량을 초과하지 않도록 저수지최적연계운영을 실시하였고, 최적연계운영규칙과 퍼지제어기법을 사용하여 댐 하류지점의 유량과 각 댐별 저류량, 유입량, 유입량의 증감에 따라 방류량을 결정하는 모형을 개발하였다. 개발된 모형은 안동댐과 임하댐의 각 저수지 및 안동시의 유황에 따라 결정되는 224개의 퍼지규칙으로 정리되었으며, GUI를 통해 현재 유황을 입력하면 각 댐의 방류량을 간단히 결정할 수 있다. In this study, the model enhancing the safety of reservoirs and reducing the downstream flood damage by reservoirs system operation during abnormal flood was developed. Linear programming was used for the optimal reservoirs system operation during an abnormal flood and fuzzy inference system was introduced to solve the uncertainty problem which is included in hydrological factors like inflow, water level and inflow variation of reservoir operation. The linear programming model determined the optimal reservoir system operation rules and could be used in situation where water demands varies rapidly during the abnormal flood events using fuzzy control technique. In this study, the optimal reservoirs system operation for Andong and Imha reservoirs located in the upper basin of Nakdong river was performed in order that the design flood discharge at Andong city would not be exceeded for the design flood of 100 year and PMF(Probable Maximum Flood). And the model that determines the release according to the downstream flow discharge, the reservoir storage, the inflow and the inflow variation of each reservoir was developed using the optimal system operation result and fuzzy control technique. The developed model consisted of 224 fuzzy rules according to the conditions of Andong reservoir, Imha reservoir and Andong city. And the release from each reservoir could be determined when the current data are used as input data through the developed GUI.

Multiobjective Evolutionary Approach to Optimal Reservoir Operation

This paper presents an application of the Multiobjective Differential Evolution (MoDE) algorithm for the optimal operation of a complex multipurpose reservoir system. The developed algorithm (MoDE) is compared with the Genetic Algorithm (NSGA-II) using a set of common test problems and the case study. The case study includes part of a complex water supply system located in southwest Brazil that provides water for approximately 20 million people in the Sao Paulo metropolitan area (SPMA). The objectives of the case study include minimization of demand shortage (the difference between demand for water and available water supply), maximization of water quality (or minimization of the deviation from the water quality standards), and minimization of pumping cost. MoDE is applied to the case study using two inflow scenarios: (1) a drought period with inflows below historical average; and (2) a wet period with inflows above historical average. Multiobjective analysis is done by comparing two pairs of objective functions: minimization of demand shortage versus minimization of pumping cost and minimization of demand shortage versus minimization of the deviation from water quality standards. The constraints for the analysis are reservoir capacity, tunnels and channel limitations, and minimum downstream flow for all reservoirs. The proposed MoDE algorithm is outperforming NSGA-II as it converges closer to and provides better spread coverage of the true Pareto front.

Optimisation of real time operation of a multi-reservoir system in an electricity market contest

this paper presents the new tool OASI (Italian acronym for Optimisation of a multi-reservoir system) which is used by ENEL to optimize the real time operation of a multi reservoir system in the context of electricity market. The objective of the optimisation is to minimise the economic penalties which are charged according to the market rules if the power producer is not able to respect the energy plans scheduled by the market for each 15 minutes of the day of delivery. The optimisation is referred to the interconnected hydraulic system and, besides routine activities, is triggered any time there is the need of a new rescheduling of the production of the day, due for instance to external factors such as sudden outage of generators or actual natural flows quite different from the forecasted ones. Examples are provided about the application of the optimisation of a hydraulic system in a typical real time situation.

Fuzzy constrained optimization of eco‐friendly reservoir operation using self‐adaptive genetic algorithm: a case study of a cascade reservoir system in the Yalong River, China

ABSTRACTWith the increasing demands for hydropower development in China, it is particularly important to implement eco‐friendly reservoir operation in consideration of the vulnerability of downstream aquatic environment. In this paper, a fuzzy constrained nonlinear programming model is presented. Fuzzy programming is used to deal with the inherent imprecision and vagueness in constraints, and a self‐adaptive genetic algorithm with simulated binary crossover is proposed for searching for the optimal reservoir operating rules. First, the fuzzy reservoir operation model is transformed into two deterministic sub‐models. Self‐adaptive genetic algorithm is then used to generate optimal solutions by applying penalty functions to integrate constraints into the objective function to form the fitness function. To achieve a final compromise between the fuzzy objective and the constraints, a ‘max‐min’ decision principle is incorporated into the optimization process to obtain satisfactory operating schemes. The methodology is demonstrated through a cascade system of reservoirs in the Yalong River, southwest China. A monthly reservoir operation model, which considered downstream ecological flow requirements as fuzzy constraints, is developed to optimize eco‐friendly reservoir operation with the objective of maximizing total hydropower generation. Monthly operating rules are generated for two cascade reservoirs, and the optimal hydropower generation is also obtained. Results indicate that the proposed approach can effectively improve the operation of the cascade reservoir system and fulfill the flow requirements of the downstream ecosystem that are expressed as fuzzy sets. It is a useful tool for generating optimal strategy to achieve an eco‐friendly reservoir operation under uncertainty. Copyright © 2011 John Wiley & Sons, Ltd.

Application of water rights priority and natural priority orders to river and reservoir operation systems

Surface water rights in Texas are developed based on “Prior Appropriation Doctrine”. The Texas Water Availability Model, a computer-based simulation model based on water rights priority order, has been used for computing the amount of water supply and determining the amount of water available for a newly requested water right for the past several years. This study compares the water rights priority and natural priority orders by applying it to the largest sixteen reservoirs in the Brazos River Basin, Texas. The water supply reliability, reservoir storage frequency, and stream flows are analyzed corresponding to each priority orders. The natural priority order increases water supply reliability for most reservoirs about 1 to 2% and mean reservoir storage volume located in the upper basin than water rights priority order. The stream flows based on water rights priority order are more regulated than natural priority order by 3 and 5%. The composite priority order is more effective than each priority order in increasing the water supply reliability.

A fuzzy-Markov-chain-based analysis method for reservoir operation

In this study, a fuzzy-Markov-chain-based stochastic dynamic programming (FM-SDP) method is developed for tackling uncertainties expressed as fuzzy sets and distributions with fuzzy probability (DFPs) in reservoir operation. The concept of DFPs used in Markov chain is presented as an extended form for expressing uncertainties including both stochastic and fuzzy characteristics. A fuzzy dominance index analysis approach is proposed for solving multiple fuzzy sets and DPFs in the proposed FM-SDP model. Solutions under a set of α-cut levels and fuzzy dominance indices can be generated by solving a series of deterministic submodels. The developed method is applied to a case study of a reservoir operation system. Solutions from FM-SDP provide a range of desired water-release policies under various system conditions for reservoir operation decision makers, reflecting dynamic and dual uncertain features of water availability simultaneously. The results indicate that the FM-SDP method could be applicable to practical problems for decision makers to obtain insight regarding the tradeoffs between economic and system reliability criteria. Willingness to obtain a lower benefit may guarantee meeting system-constraint demands; conversely, a desire to acquire a higher benefit could run into a higher risk of violating system constraints.