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.     

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.     

Reliability Improved Stochastic Dynamic Programming for Reservoir Operation Optimization

Stochastic Dynamic Programming (SDP) is widely used in reservoir operation problems. Besides its advantages, a few drawbacks have leaded many studies to improve its structure. Handling the infeasible conditions and curse of dimensionality are two major challenges in this method. The main goal of this paper is proposing a new method to avoid infeasible conditions and enhance the solution efficiency with new discretization procedure. For this purpose, an optimization module is incorporated into regular SDP structure, so that, near optimal values of state variables are determined based on the available constraints. The new method (RISDP) employs reliability concept to maximize the reservoir releases to satisfy the downstream demands. Applying the proposed technique improves the reservoir operating policies compared to regular SDP policies with the same assumptions of discretization. Simulation of reservoir operation in a real case study indicates about 15% improvement in objective function value and elimination of infeasible conditions by using RISDP operating policies.     

Feasibility Improved Stochastic Dynamic Programming for Optimization of Reservoir Operation

Water Resources Management - Stochastic Dynamic Programming (SDP) is a major method for optimizing reservoir operation. Handling non-linear, non-convex and non-differentiable objective functions...     

Optimal Reservoir Operation for Flood Control Using Folded Dynamic Programming

Folded Dynamic Programming (FDP) is adopted for developing optimal reservoir operation policies for flood control. It is applied to a case study of Hirakud Reservoir in Mahanadi basin, India with the objective of deriving optimal policy for flood control. The river flows down to Naraj, the head of delta where a major city is located and finally joins the Bay of Bengal. As Hirakud reservoir is on the upstream side of delta area in the basin, it plays an important role in alleviating the severity of the flood for this area. Data of 68 floods such as peaks of inflow hydrograph, peak of outflow from reservoir during each flood, peak of flow hydrograph at Naraj and d/s catchment contribution are utilized. The combinations of 51, 54, 57 thousand cumecs as peak inflow into reservoir and 25.5, 20, 14 thousand cumecs respectively as peak d/s catchment contribution form the critical combinations for flood situation. It is observed that the combination of 57 thousand cumecs of inflow into reservoir and 14 thousand cumecs for d/s catchment contribution is the most critical among the critical combinations of flow series. The method proposed can be extended to similar situations for deriving reservoir operating policies for flood control.     

Operation Policies through Dynamic Programming and Genetic Algorithms,for a Reservoir with Irrigation and Water Supply Uses

Water Resources Management - In this study, operation policies were obtained for a reservoir in Michoacán, Mexico, used for irrigation and domestic water supplies. The main purpose of these...     

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。     

Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm

The efficient utilization of hydropower resources play an important role in the economic sector of power systems, where the hydroelectric plants constitute a significant portion of the installed capacity. Determination of daily optimal hydroelectric generation scheduling is a crucial task in water resource management. By utilizing the limited water resource, the purpose of hydroelectric generation scheduling is to specify the amount of water releases from a reservoir in order to produce maximum power, while the various physical and operational constraints are satisfied. Hence, new forms of release policies namely, BSOPHP, CSOPHP, and SHPHP are proposed and tested in this research. These policies could only use in hydropower reservoir systems. Meanwhile, to determine the optimal operation of each policy, real coded genetic algorithm is applied as an optimization technique and maximizing the total power generation over the operational periods is chosen as an objective function. The developed models have been applied to the Cameron Highland hydropower system, Malaysia. The results declared that by using optimal release policies, the output of power generation is increased, while these policies also increase the stability of reservoir system. In order to compare the efficiency of these policies, some reservoir performance indices such as reliability, resilience, vulnerability, and sustainability are used. The results demonstrated that SHPHP policy had the highest performance among the tested release policies.     

Long-Term Stochastic Reservoir Operation Using a Noisy Genetic Algorithm

To deal with stochastic characteristics of inflow in reservoir operation, a noisy genetic algorithm (NGA), based on simple genetic algorithms (GAs), is proposed. Using operation of a single reservoir as an example, the results of NGA and Monte Carlo method which is another way to optimize stochastic reservoir operation were compared. It was found that the noisy GA was a better alternative than Monte Carlo method for stochastic reservoir operation.     

串联供水库群优化调度的模糊规划模型

针对随机动态规划在解决多个水库联合优化调度时存在“维数灾”问题,尝试基于模糊集理论来解决该优化调度问题。以4个串联供水水库系统为例,目标为各供水片区最小的缺水率最大,将水库的入流过程视为模糊集,而需水过程视为确定性的,建立了模糊规划模型,并引入可靠度和满意度对优化调度结果进行评价。实例分析表明,该模型既可以刻画入流的不确定性,又可以简化问题,具有一定的实用性。     

基于FDDI的水库运行监控系统设计

将传统意义上的大坝安全自动监测与评价系统、水情自动测报系统、闸门自动监控等系统采用网络结构实现统一后,结合办公自动化组成了一套完整的水库监控自动化系统。文章给出了典型系统结构和数据流程,同时对有关技术问题进行了探讨。     

Basin/Reservoir System Integration for Real Time Reservoir Operation

This paper demonstrates the basin/reservoir system integration as a decision support system for short term operation policy of a multipurpose dam. It is desired to re-evaluate and improve the current operational regulation of the reservoir with respect to water supply and flood control especially for real time operation. The most innovative part of this paper is the development of a decision support system (DSS) by the integration of a hydrological (HEC-HMS) and reservoir simulation model (HEC-ResSim) to guide the professional practitioners during the real time operation of a reservoir to meet water elevation and flood protection objectives. In this context, a hybrid operating strategy to retain maximum water elevation is built by shifting between daily and hourly decisions depending on real time runoff forecasts. First, a daily hydro-meteorological rule based reservoir simulation model (HRM) is developed for both water supply and flood control risk. Then, for the possibility of a flood occurrence, hourly flood control rule based reservoir simulation model (FRM) is used. The DSS is applied on Yuvac?k Dam Basin which has a flood potential due to its steep topography, snow potential, mild and rainy climate in Turkey. Numerical weather prediction based runoff forecasts computed by a hydrological model together with developed reservoir operation policy are put into actual practice for real time operation of the reservoir for March – June, 2012. According to the evaluations, proposed DSS is found to be practical and valuable to overcome subjective decisions about reservoir storage.     

多沙水库汛限水位实时动态控制方案研究

水库在汛期使用单一汛限水位有利于保障水库的防洪安全,却大大降低了调度的灵活性和发电效益。对于多沙水库,在进行汛限水位动态控制的研究时,也应对泥沙因素予以考虑。为制定合理的多沙水库汛限水位动态控制方案,先采用预泄能力约束法,基于一维非均匀流不平衡输沙模型,选定水库汛限水位分期动态控制的范围,而后依据汛限水位动态控制预蓄预泄的基本原理,拟定某多沙水库的汛限水位实时动态控制方案。结果表明:在保证防洪安全和冲沙调度的前提下,对水库实施汛期分期及汛限水位动态控制方案,能够有效地提高水库的兴利效益。     

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.     

Improved Reservoir Operation Using Hybrid Genetic Algorithm and Neurofuzzy Computing

A hybrid genetic and neurofuzzy computing algorithm was developed to enhance efficiency of water management for a multipurpose reservoir system. The genetic algorithm was applied to search for the optimal input combination of a neurofuzzy system. The optimal model structure is modified using the selection index (SI) criterion expressed as the weighted combination of normalized values of root mean square error (RMSE) and maximum absolute percentage of error (MAPE). The hybrid learning algorithm combines the gradient descent and the least-square methods to train the genetic-based neurofuzzy network by adjusting the parameters of the neurofuzzy system. The applicability of this modeling approach is demonstrated through an operational study of the Pasak Jolasid Reservoir in Pasak River Basin, Thailand. The optimal reservoir releases are determined based on the reservoir inflow, storage stage, sideflow, diversion flow from the adjoining basin, and the water demand. Reliability, vulnerability and resiliency are used as indicators to evaluate the model performance in meeting objectives of satisfying water demand and maximizing flood prevention. Results of the performance evaluation indicate that the releases predicted by the genetic-based neurofuzzy model gave higher reliability for water supply and flood protection compared to the actual operation, the releases based on simulation following the current rule curve, and the predicted releases based on other approaches such as the fuzzy rule-based model and the neurofuzzy model. Also the predicted releases based on the newly developed approach result in the lowest amount of deficit and spill indicating that the developed modeling approach would assist in improved operation of Pasak Jolasid Reservoir.     

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.     

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.     

Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy

In the present study the WEAP-NSGA-II coupling model was developed in order to apply the hedging policy in a two-reservoir system, including Gavoshan and Shohada dams, located in the west of Iran. For this purpose after adjusting the input files of WEAP model, it was calibrated and verified for a statistical period of 4 and 2 years respectively (2008 till 2013). Then periods of water shortage were simulated for the next 20 years by defining a reference scenario and applying the operation policy based on the current situation. Finally, the water released from reservoirs was optimized based on the hedging policy and was compared with the reference scenario in coupled models. To ensure the superiority of the proposed method, its results was compared with the results of two well-known multi-objective algorithms called PESA-II and SPEA-II. Results show that NSGA-II algorithm is able to generate a better Pareto front in terms of minimizing the objective functions in compare with PESA-II and SPEA-II algorithms. An improvement of about 20% in the demand site coverage reliability of the optimum scenario was obtained in comparison with the reference scenario for the months with a higher water shortage. In addition, considering the hedging policy, the demand site coverage in the critical months increased about 35% in compared with the reference scenario.     

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.