Single Reservoir Operating Policies Using Genetic Algorithm

To obtain optimal operating rules for storage reservoirs, large numbers of simulation and optimization models have been developed over the past several decades, which vary significantly in their mechanisms and applications. As every model has its own limitations, the selection of appropriate model for derivation of reservoir operating rule curves is difficult and most often there is a scope for further improvement as the model selection depends on data available. Hence, evaluation and modifications related to the reservoir operation remain classical. In the present study a Genetic Algorithm model has been developed and applied to Pechiparai reservoir in Tamil Nadu, India to derive the optimal operational strategies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on the present case study it is concluded that GA model could perform better if applied in real world operation of the reservoir.     

Multi-Reservoir Operation Planning Using Hybrid Genetic Algorithm and Linear Programming (GA-LP): An Alternative Stochastic Approach

Many models have been suggested to deal with the multi-reservoir operation planning stochastic optimization problem involving decisions on water releases from various reservoirs in different time periods of the year. A new approach using genetic algorithm (GA) and linear programming (LP) is proposed here to determine operational decisions for reservoirs of a hydro system throughout a planning period, with the possibility of considering a variety of equally likely hydrologic sequences representing inflows. This approach permits the evaluation of a reduced number of parameters by GA and operational variables by LP. The proposed algorithm is a stochastic approximation to the hydro system operation problem, with advantages such as simple implementation and the possibility of extracting useful parameters for future operational decisions. Implementation of the method is demonstrated through a small hypothetical hydrothermal system used in literature as an example for stochastic dual dynamic programming (SDDP) method of Pereira and Pinto (Pereira, M. V. F. and Pinto, L. M. V. G.: 1985, Water Res. Res. 21(6), 779–792). The proposed GA-LP approach performed equally well as compared to the SDDP method.     

A Fuzzy Stochastic Dynamic Nash Game Analysis of Policies for Managing Water Allocation in a Reservoir System

In this paper a fuzzy dynamic Nash game model of interactions between water users in a reservoir system is presented. The model represents a fuzzy stochastic non-cooperative game in which water users are grouped into four players, where each player in game chooses its individual policies to maximize expected utility. The model is used to present empirical results about a real case water allocation from a reservoir, considering player (water user) non-cooperative behavior and also same level of information availability for individual players. According to the results an optimal allocation policy for each water user can be developed in addition to the optimal policy of the reservoir system. Also the proposed model is compared with two alternative dynamic models of reservoir optimization, namely Stochastic Dynamic Programming (SDP) and Fuzzy-State Stochastic Dynamic programming (FSDP). The proposed modeling procedures can be applied as an appropriate tool for reservoir operation, considering the interaction among the water users as well as the water users and reservoir operator.     

抽水蓄能电站运行优化的动态规划模型

按照电站抽水-发电循环效益最大化，满足电力电量平衡条件，满足库容、发电出力及抽水功率等限制条件，将抽水蓄能电站的运行优化表达为基于日或周运行优化的多阶段优化决策过程，即抽水蓄能电站运行优化的动态规划模型，并通过算例对算法及应用做了进一步说明。     

Folded Dynamic Programming for Optimal Operation of Multireservoir System

Dynamic Programming (DP) is considered as a good technique for optimal reservoir operation due to the sequential decision making and ease in handling non-linear objective functions and constraints. But the application of DP to multireservoir system is not that encouraging due to the problem `curse of dimensionality'. Incremental DP, discrete differential DP, DP with successive approximation, incremental DP with successive approximation are some of the algorithms evolved to tackle this curse of dimensionality for DP. But in all these cases, it is difficult to choose an initial trial trajectory, to get at an optimal solution and there is no control over the number of iterations required for convergence. In this paper, a new algorithm, Folded DP, is proposed, which overcomes these difficulties. Though it is also an iterative process, no initial trial trajectory is required to start with. So, the number of iterations is independent of any initial condition. The developed algorithm is applied to a hypothetical reservoir system, solved by earlier researchers.Operating policy obtained using the present algorithm has compared well with that of the earlier algorithm.     

Water Supply Reservoir Operation by Combined Genetic Algorithm – Linear Programming (GA-LP) Approach

Multi-reservoir operation planning is a complex task involving many variables, objectives, and decisions. This paper applies a hybrid method using genetic algorithm (GA) and linear programming (LP) developed by the authors to determine operational decisions for a reservoir system over the optimization period. This method identifies part of the decision variables called cost reduction factors (CRFs) by GA and operational variables by LP. CRFs are introduced into the formulation to discourage reservoir depletion in the initial stages of the planning period. These factors are useful parameters that can be employed to determine operational decisions such as optimal releases and imports, in response to future inflow predictions. A part of the Roadford Water Supply System, UK, is used to demonstrate the performance of the GA-LP method in comparison to the RELAX algorithm. The proposed approach obtains comparable results ensuring non zero final storages in the larger reservoirs of the Roadford Hydrosystem. It shows potential for generating operating policy in the form of hegging rules without a priori imposition of their form.     

A Bayesian Stochastic Optimization Model for a Multi-Reservoir Hydropower System

This paper presents the development of an operating policy model for a multi-reservoir system for hydropower generation by addressing forecast uncertainty along with inflow uncertainty. The stochastic optimization tool adopted is the Bayesian Stochastic Dynamic Programming (BSDP), which incorporates a Bayesian approach within the classical Stochastic Dynamic Programming (SDP) formulation. The BSDP model developed in this study considers, the storages of individual reservoirs at the beginning of period t, aggregate inflow to the system during period t and forecast for aggregate inflow to the system for the next time period t + 1, as state variables. The randomness of the inflow is addressed through a posterior flow transition probability, and the uncertainty in flow forecasts is addressed through both the posterior flow transition probability and the predictive probability of forecasts. The system performance measure used in the BSDP model is the square of the deviation of the total power generated from the total firm power committed and the objective function is to minimize the expected value of the system performance measure. The model application is demonstrated through a case study of the Kalinadi Hydroelectric Project (KHEP) Stage I, in Karnataka state, India.     

Hydropower Optimization for the Lower Seyhan System in Turkey using Dynamic Programming

Abstract

Dynamic programming with successive approximation has been used in the past for optimizing multi-reservoir water resources systems. In this study, the State Incremental Dynamic Programming (SIDP) model is developed for energy optimization of multi-reservoir systems. A random file access method is used for reaching initial and intermediate data to cope with the curse of dimensionality of dynamic programming. A conventional dynamic programming method is used for each single reservoir to find the initial trajectory of the reservoirs. Then, the computer program developed in the study is applied to the multipurpose-multi-reservoir system in Lower Seyhan Basin, which has six reservoirs, some of which are serial and some parallel. First, extended historical flows were used to maximize firm energy in the critical period, and then total energy in the total flows. The program was run with 50-year long segments (20 flow scenarios) of the synthetic flow data generated by using the HEC-4 generalized computer program to take into account the stochastic nature of stream flows. An increment of approximately 20 percent in total energy was obtained by using the model for the Lower Seyhan System, as compared to that calculated previously by conventional methods.     

水电厂经济运行的优先表动态规划方法

基于常规动态规划方法，将负荷最优分配与开停机组合寻优过程同时在线计算，机组组合方案按参与运行的机组台数递增选取，最优负荷分配亦随机组台数递推，排除不符合日发电计划中负荷要求的机组组合，以流量最小为优先原则，存储有限项可行组合，减少中间数据存储，降低寻优维数，缩短计算时间，适合机组特性数据经常变化的要求。对12台机组的实例计算表明，合理选择有限项的个数，可较快地解算出最优开停机计划。     

Multi-objective Planning for Conjunctive Use of Surface and Subsurface Water Using Genetic Algorithm and Dynamics Programming

The consideration of fixed cost and time-varying operating cost associated with the simultaneous conjunctive use of surface and subsurface water should be treated as a multi-objective problem due to the conflicting characteristics of these two objectives. In order to solve this multi-objective problem, a novel approach is developed herein by integrating the multi-objective genetic algorithm (MOGA), constrained differential dynamic programming (CDDP) and the groundwater simulation model ISOQUAD. A MOGA is used to generate the various fixed costs of reservoirs’ scale, generate a pattern of pumping/recharge, and estimate the non-inferior solutions set. A groundwater simulation model ISOQUAD is directly embedded to handle the complex dynamic relationship between the groundwater level and the generated pumping/recharge pattern. The CDDP optimization model is then adopted to distribute the optimal releases among reservoirs provided that reservoir capacities are known. Finally, the effectiveness of our proposed integrated model is verified by solving a water resources planning problem for the conjunctive use of surface and subsurface water in southern Taiwan.     

遗传模拟退火和小生境遗传算法在水库优化调度中的比较

根据溪洛渡水库的具体情况,建立了以发电量最大为目标的水库优化调度非线性数学模型,并利用遗传模拟退火算法(GSA)和小生境遗传算法(NGA)分别求解模型.结果表明,GSA和NGA的收敛速度和计算结果都明显优于基本遗传算法;且两者相比,GSA的收敛性更强,但计算时间较长.而在求解水库长系列优化调度问题时,各遗传算法占用机时太多,且收敛能力较差.     

基于动态搜索线性混合整数法的机组组合新算法

针对常用机组组合算法计算量过大的问题，提出了一种基于动态搜索的线性混合整数规划法，该方法在基于分支的线性混合整数规划法的计算框架中引入了动态搜索的概念，采用单独考察与时间相关的约束和功率约束等措施来减少进入分支阶段的数量，在实际运用中，可针对不同规模的系统通过调节计算参数灵活地控制计算时间和计算精度。算例分析结果表明，该方法快速、有效，适合求解大规模电力系统安全约束机组组合问题。     

基于多年平均年发电量最优的动态规划方法探讨

根据动态规划理论,以多年平均年发电量最优为准则,建立相应的水库动态规划模型,并详细介绍推求水库最优放水过程。将该方法应用于广西澄碧河水库水电站,结果多年平均年发电量比原来增加了8.5%,取得较好的经济效益。该方法求解过程较为简单,工程应用容易操作,因此能为指导已建水库调度运行提供参考。     

Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir

This paper presents a Genetic Algorithm (GA) model for finding the optimal operating policy of a multi-purpose reservoir, located on the river Pagladia, a major tributary of the river Brahmaputra. A synthetic monthly streamflow series of 100 years is used for deriving the operating policy. The policies derived by the GA model are compared with that of the stochastic dynamic programming (SDP) model on the basis of their performance in reservoir simulation for 20 years of historic monthly streamflow. The simulated result shows that GA-derived policies are promising and competitive and can be effectively used for reservoir operation.     

基于改进遗传算法的生态友好型水库调度

以锦屏梯级水库为案例,从系统工程论的角度出发,将梯级水库作为物理系统,以年发电量为目标函数,建立了梯级水库调度优化模型。为减少水库调度对河道生态系统的影响,在鱼类栖息地模拟的研究基础上,引入目标物种的生态需水过程对调度模型进行动态约束,并采用改进的遗传算法进行求解。研究得到了满足目标鱼类生态需水条件下发电量最大的梯级水库调度策略,并对生态流量满足程度与工程效益损失之间的定量响应关系进行了研究,提出了折中方案选择的基本原则。     

蜜蜂进化型遗传算法在水库优化调度中的应用

提出了一种基于蜜蜂进化型遗传算法的水库优化调度问题的求解方法,并通过实例对蜜蜂进化型遗传算法和标准遗传算法的性能做了比较.结果表明,在进化代数相同的条件下,由于蜜蜂进化型遗传算法在配种选择算子上使用种群的最优个体作为蜂王,提高了种群收敛速度;再者,在代进化过程中引入一个随机种群,保持了群体的多样性,提高了算法的勘测能力.     

改进的遗传算法在水库调度中的应用

为了改善遗传算法在水库优化调度中的应用效果,采用自适应遗传算法和广度变异模块相结合的分层收敛算法:第一层采用广度变异和外部存档的方式改善种群的多样性;第二层嵌套广度变异模块,并采用自适应遗传算法进行全局搜索。通过比较自适应遗传算法和分层进化算法,结果显示:基于遗传算法的分层算法具有高效的全局搜索能力,避免了自适应遗传算法陷入局部最优的缺陷,在一定收敛条件下得到了更接近全局最优的目标值。     

岩体动态施工力学与动态规划原理在工程中的应用

提出了岩体动态施工力学这一新的研究方向及其原理,先以二滩工程洞室群不同施工方案的围岩非线性数值稳定分析为例,说明施工顺序优化研究的重要性。再以广蓄电站洞室群的施工方案优化问题为例,应用动态规划原理,用洞周破损区面积表示收益函数,给出一种搜索洞室群最佳施工方案的方法。最后结合广蓄电站地下洞室进行了四种不同应力情况下的优化分析,并评估了实际的施工步骤的稳定性。     

遗传算法在水库(群)优化调度研究中的应用综述

介绍了遗传算法在水库(群)优化调度中的应用背景及算法的收敛性,讨论了水库(群)优化调度中遗传算法的基本应用步骤以及存在的问题,给出了算法的各种改进方法,并对遗传算法的应用前景进行了展望.     

混沌差分进化算法在水库优化调度中的应用研究

针对差分进化算法在求解水库调度等复杂优化问题时,算法初始种群的随机性导致其在解空间中的代表性不足,算法的贪婪选择策略又极易导致种群迅速趋同而"早熟"收敛。提出初始种群的混沌生成策略,利用混沌因子的遍历性提高算法初始种群的代表性。同时,以动态概率接受适应值较差的个体作为子代个体参与进化,从而提高算法跳出局部最优解的能力。将改进的差分进化算法模拟乌江梯级电站优化调度问题,模拟计算结果表明,改进的差分进化算法具有较高全局搜索能力,大幅提高了求解的精度,适合求解水库优化调度等问题。