An alternative approach to the operation of multinational reservoir systems: Application to the Amistad &; Falcon system (Lower Rio Grande/Río Bravo)

An optimization approach for the operation of international multi-reservoir systems is presented. The approach uses Stochastic Dynamic Programming (SDP) algorithms – both steady-state and real-time – to develop two models. In the first model, the reservoirs and flows of the system are aggregated to yield an equivalent reservoir, and the obtained operating policies are disaggregated using a non-linear optimization procedure for each reservoir and for each nation's water balance. In the second model a multi-reservoir approach is applied, disaggregating the releases for each country's water share in each reservoir. The non-linear disaggregation algorithm uses SDP-derived operating policies as boundary conditions for a local time-step optimization. Finally, the performance of the different approaches and methods is compared. These models are applied to the Amistad-Falcon International Reservoir System as part of a binational dynamic modeling effort to develop a decision support system tool for a better management of the water resources in the Lower Rio Grande Basin, currently enduring a severe drought.     

Simulation-optimization model of reservoir operation based on target storage curves

This paper proposes a new storage allocation rule based on target storage curves. Joint operating rules are also proposed to solve the operation problems of a multi-reservoir system with joint demands and water transfer-supply projects. The joint operating rules include a water diversion rule to determine the amount of diverted water in a period, a hedging rule based on an aggregated reservoir to determine the total release from the system, and a storage allocation rule to specify the release from each reservoir. A simulation-optimization model was established to optimize the key points of the water diversion curves, the hedging rule curves, and the target storage curves using the improved particle swarm optimization （IPSO） algorithm. The multi-reservoir water supply system located in Liaoning Province, China, including a water transfer-supply project, was employed as a case study to verify the effectiveness of the proposed join operating rules and target storage curves. The results indicate that the proposed operating rules are suitable for the complex system. The storage allocation rule based on target storage curves shows an improved performance with regard to system storage distribution.     

跨流域供水水库群联合调度规则研究

针对跨流域供水水库群联合调度存在的主从递阶结构,提出了调水规则和供水规则相结合的跨流域供水水库群联合调度规则。其中,调水规则由一组基于各水库蓄水量的调水控制线表示,根据其间的相对位置关系,决定是否调水,调水量如何分配等;供水规则由各库供水调度图表示,对应于不同用水户的限制供水线将水库的兴利库容分为若干调度区。建立了适合于主从递阶结构的水库群联合调度二层规划模型,采用并行种群混合进化的粒子群算法对模型进行求解。中国北方某大型跨流域调水工程的实例研究证明了模型的合理性和有效性。     

A Novel Optimization Method for Multi-Reservoir Operation Policy Derivation in Complex Inter-Basin Water Transfer System

Joint operation of multiple reservoir system in inter-basin water transfer-supply project is a complex problem because of the complicated structure and cooperated operation policy. The combination of high-dimensional, multi-peak and multiple constraints makes it incredibly difficult to obtain the optimal rule curves for multi-reservoir operation. In view of this, we constructed a joint optimization operation model, considering both water supply and transfer, and proposed the concept of “shape constraints”. To obtain the solution of this high-dimensional optimization model, a novel progressive optimum seeking method, namely Progressive Reservoir Algorithm-Particle Swarm Optimization (PRA-PSO), is presented based on the nature of progressive optimization algorithm (POA) and standard particle swarm optimization (PSO). The water transfer project in northeast China, consisting of three routes eight reservoirs, is selected as a case study. The results show that (1) PRA-PSO is yielding much more promising results when compared with other optimization techniques; (2) shape constraints would narrow the scope of feasible solution area but increase the convergence of algorithm; (3) because of the strong interaction between water transfer and water supply action, the progressive setting of PRA-PSO should be in accordance with the order of reservoir water transfer. The case study indicates the novel optimization method could effectively increase the chance of jumping out of local optimal points, thereby searching for better solutions.     

Improved particle swarm optimization algorithm for multi-reservoir system operation

In this paper, a hybrid improved particle swarm optimization (IPSO) algorithm isproposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization (PSO) algorithm is improved in two ways: (1) The linearly decreasing inertia weight coefficient (LDIWC) is replaced by a self-adaptive exponential inertia weight coefficient (SEIWC), which could make the PSO algorithm more balanceable and more effective in both global and local searches. (2) The crossover and mutation idea inspired by the genetic algorithm (GA) is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China,where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation (DPSA) algorithm.     

Optimization of Multi-Reservoir Operating Rules for a Water Supply System

To obtain the optimal releases of the multi-reservoir system, two sets of joint operating rules (JOR-I and JOR-II) are presented based on the aggregation-disaggregation approach and multi-reservoir approach respectively. In JOR-I, all reservoirs are aggregated to an equivalent reservoir, the operating rules of which, the release rule of the system is optimized following operating rule curves coupled with hedging rules. Then the system release is disaggregated into each reservoir according to water supply priorities and the dynamic demand partition approach. In JOR-II, a two-stage demand partition approach is applied to allocate the different demand priorities to determine the release from each reservoir. To assess the reliability and effectiveness of the joint operating rules, the proposed rules are applied to a multi-reservoir system in Liaoning province of China. Results demonstrate that JOR-I is suitable for high-dimensional multi-reservoir operation problems with large-scale inflow data, while JOR-II is suitable for low-dimensional multi-reservoir operation problems with small-scale inflow data, and JOR-II performs better than JOR-I but requires more computation time. The research provides guidelines for the management of multi-reservoir system.     

Multi-Objective Optimization of the Proposed Multi-Reservoir Operating Policy Using Improved NSPSO

Severe water shortage is unacceptable for water-supply reservoir operation. For avoiding single periods of catastrophic water shortage, this paper proposes a multi-reservoir operating policy for water supply by combining parametric rule with hedging rule. In this method, the roles of parametric rule and hedging rule can be played at the same time, which are reducing the number of decision variables and adopting an active reduction of water supply during droughts in advance. In order to maintain the diversity of the non-dominated solutions for multi-objective optimization problem and make them get closer to the optimal trade-off surfaces, the multi-population mechanism is incorporated into the non-dominated sorting particle swarm optimization (NSPSO) algorithm in this study to develop an improved NSPSO algorithm (I-NSPSO). The performance of the I-NSPSO on two benchmark test functions shows that it has a good ability in finding the Pareto optimal set. The water-supply multi-reservoir system located at Taize River basin in China is employed as a case study to verify the effect of the proposed operating policy and the efficiency of the I-NSPSO. The operation results indicate that the proposed operating policy is suitable to handle the multi-reservoir operation problem, especially for the periods of droughts. And the I-NSPSO also shows a good performance in multi-objective optimization of the proposed operating policy.     

Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation

This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation–optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computations, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems.     

Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights

Based on conventional particle swarm optimization(PSO), this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW) strategy, referred to as the ARIW-PSO algorithm, to build a multi-objective optimization model for reservoir operation. Using the triangular probability density function, the inertia weight is randomly generated, and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution, which is suitable for global searches. In the evolution process, the inertia weight gradually decreases, which is beneficial to local searches. The performance of the ARIWPSO algorithm was investigated with some classical test functions, and the results were compared with those of the genetic algorithm(GA), the conventional PSO, and other improved PSO methods. Then, the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China, including the Panjiakou Reservoir, Daheiting Reservoir, and Taolinkou Reservoir. The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.     

Simulation with RBF Neural Network Model for Reservoir Operation Rules

Reservoirs usually have multipurpose, such as flood control, water supply, hydropower and recreation. Deriving reservoirs operation rules are very important because it could help guide operators determine the release. For fulfilling such work, the use of neural network has presented to be a cost-effective technique superior to traditional statistical methods. But their training, usually with back-propagation (BP) algorithm or other gradient algorithms, is often with certain drawbacks. In this paper, a newly developed method, simulation with radial basis function neural network (RBFNN) model is adopted. Exemplars are obtained through a simulation model, and RBF neural network is trained to derive reservoirs operation rules by using particle swarm optimization (PSO) algorithm. The Yellow River upstream multi-reservoir system is demonstrated for this study.     

Water Transfer Triggering Mechanism for Multi-Reservoir Operation in Inter-Basin Water Transfer-Supply Project

This paper proposes a new water transfer triggering mechanism for multi-reservoir system to divert water from abundant to scarce regions with a constant diversion flow in an inter-basin water transfer-supply project. Taking into account of the uncertain nature of inflow, the storage of reservoir is taken as a signal for decision-making to indicate water abundance or water scarcity. In this study, a set of rule curves based on storage of donor reservoir and storage of recipient reservoir are used together to determine when to start water transfer. To initiate water diversion to each recipient reservoir effectively, several water transfer rule curves of the donor reservoir are set for each recipient reservoir respectively in the multi-reservoir system with one donor reservoir and several recipient reservoirs, which is the main difference in comparison with other water transfer triggering mechanisms. In addition, a systematic framework is developed to integrate the water transfer rule curves with hedging rule curves to simultaneously solve the water transfer and water supply problems, since they interact with each other during the operation process. In order to verify the utility of the new water transfer triggering mechanism, an inter-basin water transfer-supply project in China is used as a case study and an improved particle swarm optimization algorithm (IPSO) with a simulation model is adopted for optimizing the decision variables. The results show that the proposed water transfer triggering mechanism can improve the operation performances of the inter-basin system.     

Design-Operation of Multi-Hydropower Reservoirs: HBMO Approach

To illustrate and test the applicability and performance of the innovative honey-bee mating optimization (HBMO) algorithm in highly non-convex hydropower system design and operation, two problems are considered: single reservoir and multi-reservoir. Both hydropower problems are formulated to minimize the total present net cost of the system, while achieving the maximum possible ratio for generated power to installed capacity. The single hydropower reservoir problem is approached by the developed algorithm in 10 different runs. The first feasible solution was generated initially and later improved significantly and solutions converged to a near optimal solution very rapidly. In the application of the proposed algorithm to a five-reservoir hydropower system with 48 periods and a total of 230 decision variables, in early mating flights, the first feasible solution was identified and the results converged to a near optimal solution in later mating flights. In the case of the multi-reservoir problem, an efficient gradient-based nonlinear-programming solver (LINGO 8.0) failed to find a feasible solution and for the single hydropower reservoir design problem it performed much worse than the proposed algorithm.     

鲸鱼优化算法在水库优化调度中的应用

为验证鲸鱼优化算法在水库优化调度求解中的可行性和有效性,采用4个典型测试函数对鲸鱼优化算法进行仿真验证,并与布谷鸟搜索算法、差分进化算法、混合蛙跳算法、粒子群优化算法、萤火虫算法和SCE-UA算法共6种算法的仿真结果进行对比分析;将鲸鱼优化算法与6种对比算法应用于某单一水库和某梯级水库中长期优化调度求解。结果表明:鲸鱼优化算法寻优精度高于其他6种算法8个数量级以上,具有收敛速度快、收敛精度高和极值寻优能力强等特点;鲸鱼优化算法单一水库和梯级水库优化调度结果均优于其他6种算法;鲸鱼优化算法应用于水库优化调度求解是可行和有效的。     

Chaotic Evolutionary Algorithms for Multi-Reservoir Optimization

The water sharing dispute in a multi-reservoir river basin forces the water resources planners to have an integrated operation of multi-reservoir system rather than considering them as a single reservoir system. Thus, optimizing the operations of a multi-reservoir system for an integrated operation is gaining importance, especially in India. Recently, evolutionary algorithms have been successfully applied for optimizing the multi-reservoir system operations. The evolutionary optimization algorithms start its search from a randomly generated initial population to attain the global optimal solution. However, simple evolutionary algorithms are slower in convergence and also results in sub-optimal solutions for complex problems with hardbound variables. Hence, in the present study, chaotic technique is introduced to generate the initial population and also in other search steps to enhance the performance of the evolutionary algorithms and applied for the optimization of a multi-reservoir system. The results are compared with that of a simple GA and DE algorithm. From the study, it is found that the chaotic algorithm with the general optimizer has produced the global optimal solution (optimal hydropower production in the present case) within lesser generations. This shows that coupling the chaotic algorithm with evolutionary algorithm will enrich the search technique by having better initial population and also converges quickly. Further, the performances of the developed policies are evaluated for longer run using a simulation model to assess the irrigation deficits. The simulation results show that the model satisfactorily meets the irrigation demand in most of the time periods and the deficit is very less.     

Improved Krill Algorithm for Reservoir Operation

Much of the world is facing water scarcity during one or the other part of the year. Hence, water resources management and optimal operation of water resources system take on added importance these days. This study introduces an improved version of krill algorithm for reservoir operation. The algorithm is based on adding an onlooker search mechanism to avoid being trapped in local optima and then updating its position. The new krill algorithm is tested using a case study for irrigation management. The computation time is 33 s for the new algorithm but is 54, 59, and 60 s for krill algorithm, particle swarm optimization and genetic algorithm, respectively. Also, the improved krill algorithm can meet 97% of irrigation demands and has the lowest value of vulnerability index among genetic algorithm, particle swarm optimization, and simple krill algorithm. Also, the average solution of improved krill algorithm is close to the global solution. Results indicate that the improved krill algorithm has high potential for application in water resource management.     

改进的粒子群算法在水库优化调度中应用

针对水库优化调度中存在的规模庞大、结构复杂,涉及大量的决策变量和复杂的约束条件,呈现出高维度、非线性、强约束特性,传统的优化方法难以直接求解或者计算效率低,存在早熟等问题。为了提高粒子群算法全局搜索能力和收敛性能,把下山搜索策略引入到粒子群智能算法中,提出了改进的粒子群算法。函数测试证明该方法改进了算法的鲁棒性,提高了算法求解效率。上述优化算法应用于水库优化调度模型求解中,计算结果表明:该方法易于实现,求解效率高,为水库优化调度模型求解提供了新的途径。     

Reservoir Operation Using a Dynamic Programming Fuzzy Rule–Based Approach

A dynamic programming fuzzy rule–based (DPFRB) model for optimal operation of reservoirs system is presented in this paper. In the first step, a deterministic dynamic programming (DP) model is used to develop the optimal set of inflows, storage volumes, and reservoir releases. These optimal values are then used as inputs to a fuzzy rule–based (FRB) model to establish the general operating policies in the second step. Subsequently, the operating policies are evaluated in a simulation model. During the simulation step, the parameters of the FRB model are optimized after which the algorithm gets back to the second step in a feedback loop to establish the new set of operating rules using the optimized parameters. This iterative approach improves the value of the performance function of the simulation model and continues until the satisfaction of predetermined stopping criteria. This method results in deriving the operating policies, which are robust against the uncertainty of inflows. These policies are derived by using long-term synthetic inflows and an objective function that minimizes its variance. The DPFRB performance is tested and compared to a model, which uses the commonly used multiple regression–based operating rules. Results show that the DPFRB performs well in terms of satisfying the system target performances and computational requirements.     

基于分段粒子群算法的梯级水库多目标优化调度模型研究

提出了约束破坏向量、分段粒子群算法以及多目标分段粒子群算法,有效解决了在时间步长较小、计算时段数目较多时,传统智能优化算法解水库优化调度问题的寻优效率低下甚至无可行解的问题。该方法基于粒子群算法框架,引入约束破坏向量、分段操作和特殊变异操作来增强进化过程中的种群质量,从而提高算法的计算效率。闽江流域金溪梯级水库多目标优化调度的实例分析表明,在解时间步长较小、计算时段数目较多的水库优化调度问题时,分段粒子群算法、多目标分段粒子群算法相对其他算法具有明显优势。     

电力市场交易背景下水电站优化调度研究

电力市场交易是影响我国未来电力体制改革走向的重要落地措施之一。通过对电力市场交易背景下水电站优化调度特点分析,构建了分时电价下水电站优化调度模型。在粒子群算法基础上,提出了改进惯性因子、加速因子以及迭代速度的改进策略,并将其运用于模型求解,以广东省梅州市青溪水电站为实例进行了研究,验证了模型和算法的有效性和适用性,该研究成果为电力市场背景下的水电站优化调度提供了新思路。     

混沌变异粒子群算法在水库供水调度规则制定中的应用

本文针对基于调度图规则的水库供水调度问题,建立了以水库供水保证率高且缺水量少为目标的优化调度模型。同时应用混沌变异减缓粒子群算法收敛速度,当算法进化停滞步数大于停滞步数阀值时,随机选取其中20%的粒子进行混沌变异操作,将原本聚集的粒子群"驱散开来",达到增加种群多样性、避免算法早熟收敛的目的,并将该算法引入到调度图的获取中。并以白石水库为例,得到了满足各项用水保证率的水库调度图,验证了该方法的可行性。