Optimization of Reservoir Operation with a New Approach in Evolutionary Computation Using TLBO Algorithm and Jaya Algorithm

Reservoir operation and management are complex engineering problems, due to the stochastic nature of inflow, various demands and as well as tailwater in the downstream. The complexity increases when the number of reservoirs gets increased such as multi-reservoir system or chain system. To obtain optimal operation in such condition become more difficult. It requires powerful optimization algorithm to solve aforesaid problems. Teaching Learning Based Optimization (TLBO) algorithm and Jaya Algorithm (JA) are recently developed advanced optimization techniques a novel approach comparatively simple, easy, and robust. The main advantages of these algorithms are it only requires the common control parameters such as number of iterations and population size. In the present study, three different benchmark problems were evaluated to check the applicability and performance of TLBO and JA in multi-reservoir operation problems. The benchmark problems are the discrete time four-reservoir operation (DFRO), the continuous time four-reservoir operation (CFRO), and the ten-reservoir operation (TRO). The results from the TLBO and JA are compared with different approaches from the literature. The optimal net benefits obtained from JA for DFRO, CFRO and TRO problems are 401.44, 308.40 and 1194.59, respectively, and that of TLBO algorithm are 401.33, 308.30 and 1194.44, respectively. It is found that both JA and TLBO algorithms provided a satisfactory solution as other optimization techniques, from literature. In conclusion, JA outperformed over TLBO.     

Optimal Operation of Reservoir Systems using Simulated Annealing

A stochastic search technique, simulated annealing (SA), is used to optimize the operation of multiple reservoirs. Seminal application of annealing technique in general to multi-period, multiple-reservoir systems, along with problem representation and selection of different parameter values used in the annealing algorithm for specific cases is discussed. The search technique is improved with the help of heuristic rules, problem-specific information and concepts from the field of evolutionary algorithms. The technique is tested for application to a benchmark problem of four-reservoir system previously solved using a linear programming formulation and its ability to replicate the global optimum solution is examined. The technique is also applied to a system of four hydropower generating reservoirs in Manitoba, Canada, to derive optimal operating rules. A limited version of this problem is solved using a mixed integer nonlinear programming and results are compared with those obtained using SA. A better objective function value is obtained using simulated annealing than the value from a mixed integer non-linear programming model developed for the same problem. Results obtained from these applications suggest that simulated annealing can be used for obtaining near-optimal solutions for multi-period reservoir operation problems that are computationally intractable.     

Generation of Clean Hydropower Energy in Multi-Reservoir Systems Based on a New Evolutionary Algorithm

Lingering droughts and shortage of water sources signify the importance of optimal utilization of water reservoirs such as multi-reservoir systems. These systems could be employed not only as a storage system to manage the water utilization but also as a power generation system. To rise the generated power besides the management of water utilization, an optimization algorithm should be used. In this study, the kidney algorithm in three different scenarios, namely the wet, normal, and dry years is employed to fulfill such an engineering operation in a four-reservoir system in China. Simulations show well compatibility of the water level inside the reservoir with real statistical indices in terms of RMSE and MAE. Results also reveal that using the kidney algorithm not only reduces the required calculation but also increases the convergence pace with respect to other algorithms that have been used (bat, shark, abundance of particles, and genetic algorithms). Moreover, it increases the amount of the generated energy by a factor of 2.2–3.2 with respect to the aforementioned algorithms. Results indicate the capability of the kidney algorithm in the management of water sources and engineering operations.

     

含翻水线的两库系统联合调度优化方法研究

丰水地区水库和泵站系统水资源优化调度的目标应该是解决系统季节性缺水、弃水、补水共存的矛盾。针对含有翻水线的两库系统,对系统的联合调度控制参数设计正交试验,然后采用动态规划对子系统模型进行求解,可以同时获得两座水库最优的供、弃水量过程和两座泵站最优的提水量过程。将该方法应用于江苏省南京市六合区山湖水库与泥桥水库及其翻水线的联合调度方案中,以2016年为例,在满足系统需水的前提下,可以减少25. 7%的系统年总补水量,节约245 h的泵站运行时间,降低了系统的运行成本。     

Partially and Fully Constrained Ant Algorithms for the Optimal Solution of Large Scale Reservoir Operation Problems

This paper presents a constrained formulation of the ant colony optimization algorithm (ACOA) for the optimization of large scale reservoir operation problems. ACO algorithms enjoy a unique feature namely incremental solution building capability. In ACO algorithms, each ant is required to make a decision at some points of the search space called decision points. If the constraints of the problem are of explicit type, then ants may be forced to satisfy the constraints when making decisions. This could be done via the provision of a tabu list for each ant at each decision point of the problem. This is very useful when attempting large scale optimization problem as it would lead to a considerable reduction of the search space size. Two different formulations namely partially constrained and fully constrained version of the proposed method are outlined here using Max-Min Ant System for the solution of reservoir operation problems. Two cases of simple and hydropower reservoir operation problems are considered with the storage volumes taken as the decision variables of the problems. In the partially constrained version of the algorithm, knowing the value of the storage volume at an arbitrary decision point, the continuity equation is used to provide a tabu list for the feasible options at the next decision point. The tabu list is designed such that commonly used box constraints for the release and storage volumes are simultaneously satisfied. In the second and fully constrained algorithm, the box constraints of storage volumes at each period are modified prior to the main calculation such that ants will not have any chance of making infeasible decision in the search process. The proposed methods are used to optimally solve the problem of simple and hydropower operation of “Dez” reservoir in Iran and the results are presented and compared with the conventional unconstrained ACO algorithm. The results indicate the ability of the proposed methods to optimally solve large scale reservoir operation problems where the conventional heuristic methods fail to even find a feasible solution.     

Extracting Optimal Policies of Hydropower Multi-Reservoir Systems Utilizing Enhanced Differential Evolution Algorithm

Deriving the optimal policies of hydropower multi-reservoir systems is a nonlinear and high-dimensional problem which makes it difficult to achieve the global or near global optimal solution. In order to optimally solve the problem effectively, development of optimization methods with the purpose of optimizing reservoir operation is indispensable as well as inevitable. This paper introduces an enhanced differential evolution (EDE) algorithm to enhance the exploration and exploitation abilities of the original differential evolution (DE) algorithm. The EDE algorithm is first applied to minimize two benchmark functions (Ackley and Shifted Schwefel). In addition, a real world two-reservoir hydropower optimization problem and a large scale benchmark problem, namely ten-reservoir problem, were considered to indicate the effectiveness of the EDE. The performance of the EDE was compared with the original DE to solve the three optimization problems. The results demonstrate that the EDE would have a powerful global ability and faster convergence than the original DE to solve the two benchmark functions. In the 10-reservoir optimization problem, the EDE proved to be much more functional to reach optimal or near optimal solution and to be effective in terms of convergence rate, standard deviation, the best, average and worst values of objective function than the original DE. Also, In the case of two-reservoir system, the best values of the objective function obtained 93.86 and 101.09 for EDE and DE respectively. Based on the results, it can be stated that the most important reason to improve the performance of the EDE algorithm is the promotion of local and global search abilities of the DE algorithm using the number of novel operators. Also, the results of these three problems corroborated the superior performance, the high efficiency and robustness of the EDE to optimize complex and large scale multi-reservoir operation problems.     

Assessment of Stochastic Operation Optimization for Reservoirs of Contrasting Scales

Deriving optimal release policies for dams and corresponding reservoirs is crucial for the sustainable water resources management of a region as they directly control the distribution of water to several users. Mathematical optimization algorithms can help in finding efficient reservoir operating strategies taking into account complex system constraints and hydrologic uncertainty. The robustness of operation optimization models may be influenced by physical reservoir characteristics such as size and scale and the effectiveness of a model for a particular case study does not always guarantee the same level of success for another application. This research focused on assessing the applicability of an implicit stochastic optimization (ISO) procedure to derive rule curves for two different dams of contrasting reservoir scales in terms of physical and operational characteristics. The results demonstrated the feasibility of the proposed technique for both small- and large-scale systems in view of the lower vulnerability provided by the ISO-derived policies in contrast to operations carried out by the standard reservoir operating policy as well as the proximity of the ISO operations with those by perfect-forecast deterministic optimization. The ISO procedure also provided operating rules similar to, and even less vulnerable than, those derived by stochastic dynamic programming.     

Robust Water Supply Chain Network Design under Uncertainty in Capacity

This paper focuses on the capacity uncertainty in water supply chains that occurs when facilities face disruption. A combination of scenario-based two-stage stochastic programming with the min-max robust optimization approach is proposed to optimize the water supply chain network design problem. In the first stage, the decisions are made on locations and capacities of reservoirs and water-treatment plants while recourse decisions including amount of water extraction, amount of water refinement, and consequently amount of water held in reservoirs are made at the second stage. The proposed robust two-stage stochastic programming model can help decision makers consider the impacts of uncertainties and analyze trade-offs between system cost and stability. The literature reveals that most exact methods are not able to tackle the computational complexity of mixed integer non-linear two-stage stochastic problems at large scale. Another contribution of this study is to propose two metaheuristics - a particle swarm optimization (PSO) and a bat algorithm (BA) - to solve the proposed model in large-scale networks efficiently in a reasonable time. The developed model is applied to several hypothetical cases of water resources management systems to evaluate the effectiveness of the model formulation and solution algorithms. Sensitivity analyses are also carried out to analyze the behavior of the model and the robustness approach under parameters variations.

     

Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos

Genetic algorithms (GA) have been widely applied to solve water resources system optimization. With the increase of the complexity and the larger problem scale of water resources system, GAs are most frequently faced with the problems of premature convergence, slow iterations to reach the global optimal solution and getting stuck at a local optimum. A novel chaos genetic algorithm (CGA) based on the chaos optimization algorithm (COA) and genetic algorithm (GA), which makes use of the ergodicity and internal randomness of chaos iterations, is presented to overcome premature local optimum and increase the convergence speed of genetic algorithm. CGA integrates powerful global searching capability of the GA with that of powerful local searching capability of the COA. Two measures are adopted in order to improve the performance of the GA. The first one is the adoption of chaos optimization of the initialization to improve species quality and to maintain the population diversity. The second is the utilization of annealing chaotic mutation operation to replace standard mutation operator in order to avoid the search being trapped in local optimum. The Rosenbrock function and Schaffer function, which are complex and global optimum functions and often used as benchmarks for contemporary optimization algorithms for GAs and Evolutionary computation, are first employed to examine the performance of the GA and CGA. The test results indicate that CGA can improve convergence speed and solution accuracy. Furthermore, the developed model is applied for the monthly operation of a hydropower reservoir with a series of monthly inflow of 38 years. The results show that the long term average annual energy based CGA is the best and its convergent speed not only is faster than dynamic programming largely, but also overpasses the standard GA. Thus, the proposed approach is feasible and effective in optimal operations of complex reservoir systems.     

An Efficient Linearization Method for Long-Term Operation of Cascaded Hydropower Reservoirs

The hydropower reservoir operation is a challenging optimization problem due to the nonlinear factors, where the water head, reservoir storage, release, generating capacity, and water rate are interconnected. To solve such a difficult problem in an efficient and stable way based on mathematical programming, efficient linearization method with high accuracy is of vital importance. This paper simplifies the hydropower output as the function of average reservoir storage and release, and presents an efficient piecewise linearization method that concaves the hydropower output function with a series of planes, which transforms the original nonlinear problem into a linear programming one without introducing any integer variables. The presented method is applied to a long-term hydropower scheduling (LHS) problem with 7 cascaded reservoirs, and a nonlinear direct search procedure is then employed to search further. The performance is compared with that of another linearization method that uses special ordered sets of type two, case study shows that LHS using the presented linearization method runs much faster and obtains results very close to that of the latter one. The presented method, as a high performance exact algorithm, should be very promising in solving the real-world hydropower operation problems.     

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。     

Simulation and Optimization of Multi-Reservoir Operation in Inter-Basin Water Transfer System

Inter-basin water transfer projects are usually considered as one of the most effective facilities to balance the non-uniform temporal and spatial distribution of water resources and water demands by diverting water from surplus to deficient area. However, the operation of these projects are always daunting, especially for projects with multi-donor reservoirs but only one recipient reservoir. In this study, a set of water transfer rule curves are firstly proposed to determine when, where and how much water should be diverted from each donor reservoir. In addition, a simulation-optimization model with the objective to minimize both water shortage risk and vulnerability is established to derive the optimal operation rule curves. Following that, the new transfer rules are applied to provide guidelines for the operation of a water transfer-supply project with two donor reservoirs in central China. The effects of water diversion on each reservoir are evaluated under different scenarios including no diversion, diversion from the donor reservoir with relatively sufficient water, diversion from the donor reservoir with relatively limited water, and diversion from both donor reservoirs. The results show the advantages of improving the performance of whole water diversion system and demonstrate the feasibility of the proposed approach.     

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。     

面向多区域防洪的长江上游水库群协同调度模型

长江上游水库群是长江流域防洪工程体系的重要组成部分,承担着水库所在河流、川渝河段以及长江中下游的防洪任务。长江流域面积广大,水系众多,洪水地区组成与遭遇十分复杂,防洪需求众多,防洪对象分散,且要兼顾发电、航运、供水、生态、库区安全等多种因素,水库群防洪调度面临大规模、多区域、多层次等协同调度技术难题。以长江上游25座控制性水库为研究对象,基于防洪格局和防洪任务将水库群防洪调度划分为核心、骨干和群组水库,阐明了水库群多区域协调防洪的调度节点和角色定位,提出了兼顾"时-空-量-序-效"多维属性的模型功能结构,构建了长江上游水库群多区域协同防洪调度模型,并在长江流域防洪调度形成示范应用,以挖掘长江上游水库群防洪调度潜力,进而提升长江流域防洪调度管理水平。     

Reliability-based Operation of Reservoirs Using Combined Monte Carlo Simulation Model and a Novel Nature-inspired Algorithm

One of the critical issues in surface water resources management is the optimal operation of dam reservoirs. In recent decades, meta-heuristics algorithms have gained attention as a powerful tool for finding the optimal program for the dam reservoir operation. Increasing demand due to population growth and lack of precipitation for reasons such as climate change has caused uncertainties in the affecting parameters on the planning of reservoirs, which invalidates the operational plans of these reservoirs. In this study, a novel optimization algorithm with the combination of genetic algorithm (GA) and multi-verse optimizer (MVO) called multi-verse genetic algorithm (MVGA) has been developed to solve the optimal dam reservoir operation issue under influence of the joint uncertainties of inflow, evaporation and demand. After validating the performance of MVGA by solving several benchmark functions, MVGA was used to find the optimal operation program of the Amirkabir Dam reservoir in 132 months, in both deterministic and probabilistic states. Minimizing the deficit between downstream demand and release from the reservoir during the operation period was considered as the objective function. Also, the limitations of the reservoir continuity equation, storage volume, and reservoir release equation were applied to the objective function. For modeling the effect of uncertainty, Monte Carlo simulation (MCS) is coupled to MVGA. The results of model implementations showed that the MVGA-MCS model with the best value of the objective function equal to 26 in the 1st rank and MVGA, MVO, and GA, with 15%, 34%, and 46% increase in the value of the objective function compared to the MVGA-MCS stood in the second to fourth ranks, respectively. Also, the results of the resiliency, and vulnerability indices of the reservoir operation showed that MVGA-MCS and MVGA models have better performance than other models.

     

黄河上游梯级水库联合调水调沙及合理库容研究

随着对黄河水资源利用要求的提高,需明确未来调水调沙及南水北调西线工程对黄河上游梯级水库所需库容的影响,探求未来多工况用水要求下合理的梯级水库兴利库容。考虑黄河上游兰州断面需水、梯级水库发电、调水调沙、防洪、防凌等要求,首先建立了以缺水量最小为目标的梯级水库联合调度模型,设置了现状年及远景年共8个方案;其次采用自迭代模拟优化算法求解模型,用长系列径流资料进行优化调度计算;最后对模型求解结果采用时历法推求梯级水库所需兴利库容,确定梯级水库合理库容。从总调沙次数、调沙频率、多年供水量、供水保证率、多年平均发电量、发电保证率等指标分别量化了调水调沙、供水和发电效益。未来梯级水库所需合理库容将因综合需水和调沙次数的增加而增大,而西线工程的实施将有助于减小合理库容规模。在各调沙方案中,仅当2030年西线调水80亿m3时现有的梯级水库兴利库容能够满足综合用水需求,适当降低调沙力度与调沙频率或许是解决这一矛盾的折衷选择。研究成果量化了未来新水资源利用形势下的梯级水库合理库容,为科学指导黄河上游总体工程布局提供了决策依据。     

Technical Communication: Two-Phase Stochastic Dynamic Programming Model for Optimal Operation of Irrigation Reservoir

A two-phase stochastic dynamic programming model is developed for optimal operation of irrigation reservoirs under a multicrop environment. Under a multicrop environment, the crops compete for the available water whenever the water available is less than the irrigation demands. The performance of the reservoir depends on how the deficit is allocated among the competing crops. The proposed model integrates reservoir release decisions with water allocation decisions. The water requirements of crops vary from period to period and are determined from the soil moisture balance equation taking into consideration the contribution of soil moisture and rainfall for the water requirements of the crops. The model is demonstrated over an existing reservoir and the performance of the reservoir under the operating policy derived using the model is evaluated through simulation.     

Multidimensional Parallel Dynamic Programming Algorithm Based on Spark for Large-Scale Hydropower Systems

The “curse of dimensionality” is a major problem in dynamic programming (DP) algorithms for large-scale hydropower systems. This study proposes a parallel DP algorithm based on Spark (PDPoS) to alleviate the “curse of dimensionality”. Parallel computing experiments are formulated by varying the number of reservoirs, the number of discrete water levels and the number of CPU cores to analyze the quality and efficiency of PDPoS. The methodologies were applied to a cascade reservoir system made up of eight reservoirs in the Yuanshui River Basin in China. The results are as follows. (1) The number of discrete water levels is the dominant factor in the solution quality, while the number of reservoirs is the dominant factor in the solving efficiency. (2) The runtime of PDPoS is markedly affected by the calculational scale (determined by the number of reservoirs and discrete water levels), and the relationship between the number of CPU cores and the runtime is triphasic with increasing calculational scale. (3) The larger the calculational scale is, the better the parallel performance (i.e., the parallel speedup and parallel efficiency). The proposed PDPoS method has strong generality, high parallel performance, and high practical value.

     

广东省大中型水库现状及可持续利用对策研究

本文分析了广东省水旱灾害基本情况及大中型水库的现状.根据大中型水库在防洪安全、兴利效益、管理运行等方面存在的主要问题,从水库功能优化、防洪减灾体系、水质生态环境、管理运行体制、信息化建设和人才队伍建设等六个角度提出了大中型水库可持续利用的对策,以实现大中型水库对社会经济可持续发展的支撑作用.     

洪泽湖多目标水量调度决策模型及其应用

针对洪泽湖水量利用与生态水位维持这一矛盾开展多目标水量调度决策方法研究,构建贴近适宜生态水位（生态效益）、缺水率最小、引水量最小、入湖水量改变度最小等目标的多目标调度模型,考虑生态效益目标与水资源利用目标的不可共度性及决策者偏好模糊性特征,采用多目标模糊决策法从非劣解集中筛选最适宜调度方案。结果表明：多目标模型解集反映生态效益和经济社会效益的置换关系,贴近适宜生态水位的调控方式在一定程度上降低洪泽湖调蓄能力,与水资源利用形成矛盾关系;模糊决策法筛选的均衡调度策略可以有效反映决策者的偏好情况,并提供适用于不同情景的优化调度方式;生态目标优先方案通过减少供水、增加引水量、调节入湖水量等方式有效补充生态用水,可将生态水位偏离差降低至0.30 m,可为生态优先原则下的适宜调度策略制定提供参考。