Optimal Reservoir Operation Using Multi-Objective Evolutionary Algorithm

This paper presents a Multi-objective Evolutionary Algorithm (MOEA) to derive a set of optimal operation policies for a multipurpose reservoir system. One of the main goals in multi-objective optimization is to find a set of well distributed optimal solutions along the Pareto front. Classical optimization methods often fail in attaining a good Pareto front. To overcome the drawbacks faced by the classical methods for Multi-objective Optimization Problems (MOOP), this study employs a population based search evolutionary algorithm namely Multi-objective Genetic Algorithm (MOGA) to generate a Pareto optimal set. The MOGA approach is applied to a realistic reservoir system, namely Bhadra Reservoir system, in India. The reservoir serves multiple purposes irrigation, hydropower generation and downstream water quality requirements. The results obtained using the proposed evolutionary algorithm is able to offer many alternative policies for the reservoir operator, giving flexibility to choose the best out of them. This study demonstrates the usefulness of MOGA for a real life multi-objective optimization problem.     

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.     

Application of the Grasshopper Optimization Algorithm (GOA) to the Optimal Operation of Hydropower Reservoir Systems Under Climate Change

Hydropower is a low-carbon energy source, which may be adversely impacted by climate change. This work applies the Grasshopper Optimization Algorithm (GOA) to optimize hydropower multi-reservoir systems. Performance of GOA is compared with that of particle swarm optimization (PSO). GOA is applied to hydropower, three-reservoir system (Seymareh, Sazbon, and Karkheh), located in the Karkheh basin (Iran) for baseline period 1976–2005 and two future periods (2040–2069) and (2070–2099) under greenhouse gases pathway scenarios RCP2.6, RCP4.5, and RCP8.5. GOA minimizes the shortage of hydropower energy generation. Results from GOA optimization of Seymareh reservoir show that average objective function in baseline is 85 and minimum value of average objective function in 2040–2069 would be under RCP2.6 (equal to 0.278). Optimization of Seymareh-reservoir based on PSO shows that average value of objective function in baseline is less (that is, better) than value obtained with GOA (10.953). Optimization results for two-reservoir system (Sazbon and Karkheh) based on GOA optimization show that objective function in baseline is 5.44 times corresponding value obtained with PSO, standard deviation is 2.3 times that calculated with PSO, and run-time is 1.5 times PSO’s. Concerning three-reservoir systems it was determined that objective function based on PSO had the best value (the lowest energy deficit), especially in future. GOA converges close to the best objective function, especially in future-periods optimization, and convergence to solutions is more stable than PSO’s. A comparison of performance of GOA and PSO indicates PSO converges faster to optimal solution, and produces better objective function than GOA.

     

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.     

Optimisation of Multiple Hydropower Reservoir Operation Using Artificial Bee Colony Algorithm

In this study, the Artificial Bee Colony (ABC) algorithm was developed to solve the Chenderoh Reservoir operation optimisation problem which located in the state of Perak, Malaysia. The proposed algorithm aimed to minimise the water deficit in the operating system and examine its performance impact based on monthly and weekly data input. Due to its capability to identify different possible events occurring in the reservoir, the ABC algorithm provides promising and comparable solutions for optimum release curves. The optimal release curves were then used to stimulate the reservoir release under different operating times under different inflow scenarios. To investigate the performance of both the monthly and weekly ABC optimisation employed in the reservoir, the well-known reliability, resilience and vulnerability indices were used for performance assessment. The indices tests revealed that weekly ABC optimisation outperformed in terms of reliability and vulnerability leading to the development of a better release policy for optimal operation.     

混沌蚁群算法在水库优化调度中的应用

提出一种基于混沌优化算法和蚁群算法相结合的混合算法,在求解水库优化调度问题的方法。根据混沌变量的随机性和遍历性,利用混沌变量进行优化搜索,从而有效地克服了蚁群算法存在的效率低、易于演化停滞及陷入局部最优等问题。又利用蚁群算法信息素正反馈的优点,改善了混沌搜索的盲目性,提高了搜索的效率。通过实例计算,结果表明该算法具有效率高及较强的全局寻优能力。     

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.     

关门岩水电站水库优化调度分祈

文章作者根据关门岩水电站运行经验,分析了几种可提高关门岩水电站发电效率的有效方法,并根据经验与需要提出了上下游电站实现同步运行和加强雨水情预报的具体措施.     

水电站水库汛期洪水退水段优化调度研究

有防洪任务的水电站水库,洪水退水段的调度对发电非常重要,退水段调度好坏直接影响水库全年兴利效益。以北方某水电站水库为例,在预测退水段径流过程的条件下,建立了退水段发电量最大模型,并利用遗传算法对该模型进行求解。遗传算法与常规调度结果的对比结果表明,前者能获得更优的调度结果。该方法对指导水电站水库汛期洪水退水段的调度,有较强的实用价值。     

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。     

Optimal Reservoir Operation Based on Conjunctive Use of Surface Water and Groundwater Using Neuro-Fuzzy Systems

Reservoir operation cannot be carried out without due heed to surface water and groundwater resources, since neglecting either will have irreversible consequences. Optimal operation of the Zayandehrood Dam which supplies water into the Zayandehrood River basin in the central plateau of Iran is a case in point which warrants due consideration paid to both dam operation and the climate conditions in the region suffering from a history of successive droughts. The main objective of the present research is to develop operation rules for the Zayandehrood reservoir through a combined perspective of both surface and ground water resources using the fuzzy inference system, and adaptive neuro-fuzzy inference system. The objective is to determine the share of the Zayandehrood reservoir in meeting downstream water demands. For this purpose, the water shortage and the dramatic groundwater drawdown in the Zayandehrood River basin faced with in recent years have been studied in an attempt to develop operation models capable of controlling groundwater drawdown. The models indicate that not only can groundwater drawdown be controlled, but that it is also possible to establish a greater sustainability. Different operation models have been compared in terms of their operation criteria. Results show that the ANFIS model composed of optimal data enjoys a higher sustainability compared to others.     

改进粒子群优化算法在水电站群优化调度中的应用研究

针对常规粒子群优化算法易早熟，后期收敛慢且易陷入局部最优解的不足，提出一种新的惯性权重系数更新策略—自适应指数惯性权重系数（SEIWC）代替线性递减惯性权重系数（LDIWC），同时，将遗传算法中的染色体交叉、变异思想引入粒子的更新策略，提高粒子的多样性，增强算法的全局搜索能力。使用Rosenbrock函数和Schaffer函数验证了改进粒子群优化算法的有效性。以福建电网闽江流域水电站群优化调度为例，建立基于改进粒子群优化算法的库群长期优化调度模型，计算结果表明，该模型的调度结果显著优于常规粒子群优化算法，与逐步优化算法获得的结果达到相当水平。     

Optimal Operation of a Multi-Purpose Reservoir Using Neuro-Fuzzy Technique

Present paper is aimed to develop operation policy for a multi-purpose reservoir using Neuro-Fuzzy technique in an efficient way. Ramganga reservoir behind Ramganga dam, Kalagarh, India has been considered as a study reservoir. The developed policy minimizes the damage due to floods and droughts and determines optimum releases against demands for domestic supply, irrigation and hydropower generation for monsoon and non-monsoon periods. Three Fuzzy Rule Based (FRB) models for monsoon period and three for non monsoon period have been developed and tested. Actual releases have been used to formulate the general operation fuzzy rules. Releases computed from all developed models using Fuzzy Mamdani (FM) and ANFIS (Adaptive Neuro-Fuzzy Interactive System) – Grid and Cluster have been compared and it was found that ANFIS-cluster gives the best results but FM is more users friendly. For any expected inflow, reservoir level and demand, release can be calculated using developed GUI windows of the models.     

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.     

Optimization of Hydropower Reservoir Using Evolutionary Algorithms Coupled with Chaos

Over the past decade, several conventional optimization techniques had been developed for the optimization of complex water resources system. To overcome some of the drawbacks of conventional techniques, soft computing techniques were developed based on the principles of natural evolution. The major difference between the conventional optimization techniques and soft computing is that in the former case, the optimal solution is derived where as in the soft computing techniques, it is searched from a randomly generated population of possible solutions. The results of the evolutionary algorithm mainly depend on the randomly generated initial population that is arrived based on the probabilistic theory. Recent research findings proved that most of the water resources variables exhibit chaotic behavior, which is a projection depends upon the initial condition. In the present study, the chaos algorithm is coupled with evolutionary optimization algorithms such as genetic algorithm (GA) and differential evolution (DE) algorithm for generating the initial population and applied for maximizing the hydropower production from a reservoir. The results are then compared with conventional genetic algorithm and differential evolution algorithm. The results show that the chaotic differential evolution (CDE) algorithm performs better than other techniques in terms of total annual power production. This study also shows that the chaos algorithm has enriched the search of general optimization algorithm and thus may be used for optimizing complex non-linear water resources systems.     

Many-Objective Multi-Scenario Algorithm for Optimal Reservoir Operation Under Future Uncertainties

An increase in greenhouse gases in future can exacerbate the climate change phenomenon and may have negative consequences on different elements of hydrologic system, including rainfall, temperature, and streamflow. Since the reservoir operation is highly dependent on the timing and magnitude of inflow, the impact of potential climate change on inflow sequences should be considered in deriving the system operation rule. Nevertheless, existing algorithms are only able to optimize the operation policy for a single predetermined climate scenario. Thus, the derived operation rule would not work well if the scenario changes. This paper proposes an algorithm which is able to handle simultaneously multiple scenarios in finding optimum system operation rule. Thus, it can overcome drawbacks caused by uncertainties in the occurrence of future scenarios. The proposed algorithm is used to optimize reservoir operation policy considering various climate change scenarios (RCPs). To evaluate the performance of the proposed algorithm, a five-reservoir system within Tehran region with several objectives including municipal, agricultural, environmental, and hydropower demands is employed as the case study. Results show that in all cases the multi-scenario rule derived by the proposed method performs as good as the operation rule derived for any specific scenario using a powerful optimization algorithm when evaluated for that scenario. While, in all other models as the future scenario changes to the one other than that used in deriving the operation rule, the model performance declines as compared to the proposed model.     

水库多目标优化调度技术比较研究

多目标智能优化算法种类繁多,不断涌现,在水库优化调度中得到了广泛应用,但多目标智能优化技术仍然是目前水库群综合利用优化调度研究中的热点和难点之一。已有的研究算法大多是关于水库优化调度中适用性的应用研究,且实际问题简化多,在算法算子的选择、算法性能的探讨和比较、特别是多目标优化等方面还不够深入。为此,选择应用较为广泛的NSGA-Ⅱ和DEMO算法,从变量规模、约束处理技术等方面,对其在水库多目标优化调度中的应用效果进行初步分析、比较和评估,为水库多目标优化调度算法的选择提供了参考。     

Optimal Reservoir Operation Using Bat and Particle Swarm Algorithm and Game Theory Based on Optimal Water Allocation among Consumers

Water Resources Management - Water resources scarcity and competition among stakeholders in water allocation always highlights the optimal operation of water resources. This research examines the...     

越南占化水电站水库运行方式优化

越南占化水电站为低水头日调节电站,提高电站发电利用水头是增加本电站发电效益的关键。占化水电站相邻上游梯级宣光水电站水库具有多年调节性能,并在电网中承担调峰作用,经常以满负荷调峰运行,下游尾水位经常处在高水位工况;占化水电站与宣光水电站同步联合运行,具有优化水库运行方式抬高水库水位的空间,从而提高电站发电利用水头,提高电站装机规模和发电效益。通过越南占化水电站水库运行方式优化,电站装机容量从45 MW提高到48 MW,年发电量从186.30 GW.h增加到196.30～198.60 GW.h,增幅达5.3%～6.6%,发电效益增加显著。     

Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems

Water Resources Management - Reservoirs’ optimal operation is a critical issue in the management of surface water resources. In the present study, after combining the whale optimization...