Artificial Life Algorithm for Management of Multi-reservoir River Systems

The design and operation of civil engineering systems, particularly water resources systems, has been pursued from the perspective of minimizing costs and related negative impacts, maximizing benefits, or a combination thereof. Due to the complex, nonlinear nature of the majority of systems, together with an increase in digital computing capabilities, global search algorithms are becoming a common means of meeting these objectives. This paper employs an artificial life algorithm, derived from the artificial life paradigm. The algorithm is evaluated using standard optimization test functions and is subsequently applied to determine optimal dam operations in multi-reservoir river systems. The optimal dam operation scheme is that which indirectly minimizes environmental impacts caused by short-term water level fluctuations. Optimal releases are sought by coupling an artificial life algorithm with FLDWAV, a one-dimensional, steady flow simulation model. The resulting multi-reservoir management model is successfully applied to a portion of the Illinois River Waterway.     

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.     

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.

     

Development of Operational Policy for a Multi-reservoir System in India using Genetic Algorithm

A genetic algorithm (GA) and a backward moving stochastic dynamic programming (SDP) model has been developed for derivation of operational policies for a multi-reservoir system in Kodaiyar River Basin, Tamil Nadu, India. The model was developed with the objective of minimizing the annual sum of squared deviation of desired target releases. The total number of population, crossover probability and number of generations of the GA model was optimized using sensitivity analysis, and penalty function method was used to handle the constraints. The policies developed using the SDP model was evaluated using a simulation model with longer length of inflow data generated using monthly time stepped Thomas–Fiering model. The performance of the developed policies were evaluated using the performance criteria namely, the monthly frequency of irrigation deficit (MFID), Monthly average irrigation deficit (MAID), Percentage monthly irrigation deficit (PMID), Annual frequency of irrigation deficit (AFID), Annual average irrigation deficit (AAID), and Percentage annual irrigation deficit (PAID). Based on the performance, it was concluded that the robostic, probabilistic, random search GA resulted in better optimal operating policies for a multi-reservoir system than the SDP models.     

Optimal Operation of Multi-reservoir Systems Considering Time-lags of Flood Routing

Operations of multi-reservoir systems are nonlinear and high-dimensional problems, which are difficult to find the optimal or near-optimal solution owing to the heavy computation burden. This study focuses on flood control operation of multi-reservoir systems considering time-lags caused by Muskingum flood routing of river channels. An optimal model is established to jointly minimize the flood peak on the downstream flood control station for the multi-reservoir systems. A hybrid algorithm, Progressive Optimality Algorithm and Successive Approximation (POA-SA), is improved to solve the multi-reservoir operation model by modifying the POA. The POA-SA uses the DPSA to reduce the spatial dimensionality due to the multiple reservoirs, and adopts an improved POA to alleviate the temporal dimensionality caused by the time-lags of the Muskingum flood routing. Linear programming is then implemented to verify the solution of the POA-SA method with a linear approximation of the discharge capacity curve. The multi-reservoir systems of China’s Xijiang River is selected for a case study. Results show that the flood peak of Wuzhou station can be averagely decreased by 6730 m³/s (12.8 %) for the 100-year return period floods, indicating that the proposed method is efficient to operate the multi-reservoir systems and resolve the time-lags issues.     

Reducing Irrigation Deficiencies Based Optimizing Model for Multi-Reservoir Systems Utilizing Spider Monkey Algorithm

Continuous droughts and water scarcity have led to the need for optimal exploitation of dams’ reservoirs. Thus, the new meta-heuristic algorithm, spider monkey, is suggested for complex modeling of the multi-reservoir system in Iran with the aim of decreasing irrigation deficiencies. Golestan and Voshmgir dams’ operations are optimized with the spider monkey algorithm. The algorithm based on the exchange of information between local and global leaders with the other monkeys which improves the convergence speed. Average deficiencies for Golestan dam is computed as 2.1 and 1.9 MCM by spider monkey algorithm while it is respectively computed as 6.7, 16.4, 11.1, 4.1, 14.6, 19 MCM by particle swarm algorithm, harmony search algorithm, imperialist competitive algorithm, water cycle algorithm, genetic algorithm, and standards operation policy method. Also, the computation time of the spider monkey algorithm is 50 and 47 s for the Golestan and Voshmgir dams while the genetic algorithm optimizes the problem in 172.6 s and 112 s and the particle swarm algorithm needs 117.4 s and 100 s for the Golestan and Voshmgir, respectively. Also, root means square error (RMSE) and mean absolute error between demand and released water for the spider monkey algorithm have the least values among the applied evolutionary algorithms. Thus, the spider monkey algorithm is suggested as an appropriate method for optimizing the operation policy for the dam and reservoir systems.     

Optimizing Multiple Linear Rules for Multi-Reservoir Hydropower Systems Using an Optimization Method with an Adaptation Strategy

Water Resources Management - Water resources crisis has a significant impact on hydropower energy production, which highlights the importance of water resources management. Reservoirs are effective...     

Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm

The optimal hydropower operation of reservoir systems is known as a complex nonlinear nonconvex optimization problem. This paper presents the application of invasive weed optimization (IWO) algorithm, which is a novel evolutionary algorithm inspired from colonizing weeds, for optimal operation of hydropower reservoir systems. The IWO algorithm is used to optimally solve the hydropower operation problems for both cases of single reservoir and multi reservoir systems, over short, medium and long term operation periods, and the results are compared with the existing results obtained by the two most commonly used evolutionary algorithms, namely, particle swam optimization (PSO) and genetic algorithm (GA). The results show that the IWO is more efficient and effective than PSO and GA for both single reservoir and multi reservoir hydropower operation problems.     

汤浦水电站优化调度

根据对汤浦水库多年水文资料及近年来运行实际情况的分析,年均都有一定的弃水量,特别是丰水年,平均弃水量可达1 17×108m3/a。在满足供水要求基础上,为了合理利用多余的水量,最大可能地减少弃水,更好地发挥电站的防洪、发电等综合效益,创造更多的经济效益,现对汤浦电站调度方案进行优化,以便作为今后电站调度的依据。     

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.     

徐村水电站计算机监控系统

徐村水电站计算机监控系统采用H9000系统，它是一种全新的、全分布开放式总线型计算机系统，采用光纤以太网结构。该系统具有实时性、安全性能高、便于维护、功能齐全等特点，满足徐村水电站“无人值班”（少人值守）的技术要求。     

Short-Term Flood Control and Long-Term Energy Maximization in Multi-reservoir Systems Using Improved Particle Swarm Optimization

Water Resources Management - In this study, a model is developed for short-term flood control of a complex multi-reservoir system located on one of the largest basins in Turkey. The managing body...     

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.

     

清江流域梯级水电外送网络优化设计与探讨

从保证电网安全稳定 ,提高清江发电效益和电力市场竞争力出发 ,重点阐述了清江流域梯级水电站电力外送网络应结合三峡输变电系统的规划和建设 ,进行优化设计及科学合理的改造。对水布垭电站两回 5 0 0kV电力外送线路五种方案和隔河岩现有电力外送线路二种改造方案进行了分析 ,提出了建议     

非线性水电工程模型的参数优化

给出了非线性水电工程模型的参数优化的一种改进基因算法，并通过实例计算对该法与传统参数优化方法进行了比较，结果表明，该法直观，简单，拟合精度高，通用性强，易为广大水电工程技术人员理解和使用。     

白鹤水电站拱坝体型优化设计

针对浙江省重点工程─—白鹤水电站的拱坝设计，利用水利水电科学研究院的ADASO程序及浙江大学的ADAO程序对白鹤水电站拱坝进行了体型优化及应力分析，得出以下几点结论：（1）通过体型优化，减少坝体工程量约10％；（2）两套程序的优化体型基本相同；（3）对于中小型拱坝，拉应力是控制条件；（4）两套程序的拱圈厚度变化的α值取法不同．     

浅述丙村电站导流工程优化方案

论述了某电站导流施工方案的优化过程.     

利用GA-SA混合优化算法求解含水层参数

针对模拟退火算法串行优化能力差的问题,将具有并行搜索能力的遗传算法,用于引入到模拟退火算法中,并将两者结合组成遗传模拟退火算法,用于求解含水层参数反演的优化问题.遗传模拟退火算法充分发挥了遗传算法和模拟退火算法的优点,是一种优化能力更强、更高效、可靠性较高的优化方法.实际算例表明,遗传模拟退火算法用于含水层参数反演是可行的.     

水电站群装机容量优化选择模型及其应用

本文从电力系统和水电站群的角度出发，考虑电站的必然联系和水电群的整体运行，建立了规划水电站装机容量优化选择数学模型。该模型由水电群优化补偿调节计算子模型，水电群总工作容量优选子模型和规划水电站工作容量优化分配子模型三个子模型构成，通过三个子模型的逐步求解和多轮迭代，最后即可得到收敛的优化装机解。本文针对规划水电站工作容量优化分配子模型给出了增量判别求解方法。最后是将本模型方法应用于云南大朝山水电站     

红花水电站金属结构优化设计

根据红花水电站工程的特点，对金属结构的选型、结构型式、材料等进行比选优化，从而确定较佳方案，使设计的产品安全、可靠、方便、实用。