Abstract:Effective reservoir operation is achieved using suitable rule curves to control the release of water following the release criteria. Conditional cuckoo search (CCS) algorithms and conditional particle swarm optimisation (CPSO) were linked with a reservoir simulation model to identify adaptive optimal reservoir rule curves. Three cases (normal, dry and wet years) were considered when producing the rule curves depending on the inflow pattern and inflow period. Lampao reservoir in Thailand was used to illustrate … Show more
“…The results demonstrated that the shape of new historic rule curve in every cases were homologous to the existing. The characteristics of these new historic rule curves are similar to the optimal rule curves of the reservoirs in Thailand that were previously studied (Ashofteh et al., 2015; Kangrang et al., 2017), mainly due to the effects of seasonal inflow changes.Fig. 7Optimal historic rule curves of Ubolrat Reservoir using minimize water shortage and minimize excess spill water with CTSA model.Fig.…”
Section: Resultssupporting
confidence: 55%
“…It can be concluded that the proposed CTSA model is another search suitable technique, so the outcomes are close to optimal goal of the CGA model depend on the identical condition. Nevertheless, the ability of each method was determined by many researches (Bozorg-Haddad et al., 2015; Ming et al., 2015; Kangrang et al., 2017). Include connecting to the multi-condition allowing the reservoir to be managed properly and with the highest efficiency regardless of any situation.…”
Section: Resultsmentioning
confidence: 99%
“…Over the past decade, the optimization techniques, especially in the meta-heuristic search group, which include: genetic algorithm (GA), bat algorithm (BA), ant colony optimization (ACO), simulated annealing algorithm (SA), particle swam optimization (PSO), weed optimization (WO) and cuckoo search (CS) have been applied to develop the optimal rule curves (Kangrang et al., 2011, 2017; Ming et al., 2015; Afshar, 2013; Afshar et al., 2015; Akbari-Alashti et al., 2015; Ashofteh et al., 2015; Bozorg-Haddad et al., 2015; Ji et al., 2015; Azizipour et al., 2016). The reservoir rule curves developed from the use of optimization techniques are necessary to go through effective improvements and integrations which must be carried out along with important physical parameters such as historic inflow into the reservoir, quantity of water requirement from the downstream area.…”
Multi-objective future rule curves are imperative recommendations for operating multipurpose reservoirs throughout long term periods. This research utilized the conditional tabu search algorithm (CTSA) and conditional genetic algorithm (CGA) combining to the reservoir simulation model through contemplating the multiple-purpose functionals when exploring processes for finding adaptable rule curves of a single reservoir. The historic inflow data during 1966–2016 (51 years) including the future inflow during 2017–2041 (25 years) in case of the B2 scenario of IPCC for the Ubolrat Reservoir in Thailand were applying to create the searching conditions. The 500 synthetic events of historical inflow and 25 years of future inflow were used to calculate the reservoir operation process for assessing the obtained rule curves. As a result, the predicament of water scarcity and spill water were illustrated in terms of frequency scale and duration along with the maintained water at the edge of the rainy period. The operation outcomes suggest that the multi-objective rule curves developed by the CGA can alleviate the frequency of flooding and drought situations appropriately than the CTSA during the future period. However, the rule curves obtained from both optimization techniques indicate better performance correlated to the actual rule curves along with having more maintained water at the end of the rainy period (November), which has continued benefits betwixt the dry period because the reservoir can discharge water in sufficient quantities to the downstream area.
“…The results demonstrated that the shape of new historic rule curve in every cases were homologous to the existing. The characteristics of these new historic rule curves are similar to the optimal rule curves of the reservoirs in Thailand that were previously studied (Ashofteh et al., 2015; Kangrang et al., 2017), mainly due to the effects of seasonal inflow changes.Fig. 7Optimal historic rule curves of Ubolrat Reservoir using minimize water shortage and minimize excess spill water with CTSA model.Fig.…”
Section: Resultssupporting
confidence: 55%
“…It can be concluded that the proposed CTSA model is another search suitable technique, so the outcomes are close to optimal goal of the CGA model depend on the identical condition. Nevertheless, the ability of each method was determined by many researches (Bozorg-Haddad et al., 2015; Ming et al., 2015; Kangrang et al., 2017). Include connecting to the multi-condition allowing the reservoir to be managed properly and with the highest efficiency regardless of any situation.…”
Section: Resultsmentioning
confidence: 99%
“…Over the past decade, the optimization techniques, especially in the meta-heuristic search group, which include: genetic algorithm (GA), bat algorithm (BA), ant colony optimization (ACO), simulated annealing algorithm (SA), particle swam optimization (PSO), weed optimization (WO) and cuckoo search (CS) have been applied to develop the optimal rule curves (Kangrang et al., 2011, 2017; Ming et al., 2015; Afshar, 2013; Afshar et al., 2015; Akbari-Alashti et al., 2015; Ashofteh et al., 2015; Bozorg-Haddad et al., 2015; Ji et al., 2015; Azizipour et al., 2016). The reservoir rule curves developed from the use of optimization techniques are necessary to go through effective improvements and integrations which must be carried out along with important physical parameters such as historic inflow into the reservoir, quantity of water requirement from the downstream area.…”
Multi-objective future rule curves are imperative recommendations for operating multipurpose reservoirs throughout long term periods. This research utilized the conditional tabu search algorithm (CTSA) and conditional genetic algorithm (CGA) combining to the reservoir simulation model through contemplating the multiple-purpose functionals when exploring processes for finding adaptable rule curves of a single reservoir. The historic inflow data during 1966–2016 (51 years) including the future inflow during 2017–2041 (25 years) in case of the B2 scenario of IPCC for the Ubolrat Reservoir in Thailand were applying to create the searching conditions. The 500 synthetic events of historical inflow and 25 years of future inflow were used to calculate the reservoir operation process for assessing the obtained rule curves. As a result, the predicament of water scarcity and spill water were illustrated in terms of frequency scale and duration along with the maintained water at the edge of the rainy period. The operation outcomes suggest that the multi-objective rule curves developed by the CGA can alleviate the frequency of flooding and drought situations appropriately than the CTSA during the future period. However, the rule curves obtained from both optimization techniques indicate better performance correlated to the actual rule curves along with having more maintained water at the end of the rainy period (November), which has continued benefits betwixt the dry period because the reservoir can discharge water in sufficient quantities to the downstream area.
“…A summary of applied the CS to reservoir optimization is indicated in Table 10. Kangrang et al (2017) published a paper dealing with the application of the CS to reservoir optimization. The conditional cuckoo search (CCS) was applied to derive rule curves for a single reservoir in Thailand.…”
Section: Algorithmsmentioning
confidence: 99%
“…The conditional cuckoo search (CCS) was applied to derive rule curves for a single reservoir in Thailand. Kangrang et al (2017) showed that the CCS is efficient in calculating adaptive rule curves under variable inflows. Ming et al (2015) employed the CS to maximize the power generation of a multi-reservoir system in China.…”
Successful operation of reservoir systems to guarantee the optimal use of available water resources has been the subject of many studies. The advent and applications of evolutionary algorithms (EAs) in the field of reservoir operation have led to significant advances in our capacity to improve the planning and management of complex reservoir systems. This study reports a review of the applications of animal‐inspired EAs to reservoir operation optimization selected among a large number of available papers in this area of research. The animal‐inspired EAs herein identified concern algorithms that mimic biologic traits of animal (wild) species. Among the animal‐inspired EAs ant colony optimization (ACO), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA), artificial bee colony (ABC), honey bee mating optimization (HBMO), firefly algorithm (FA), cuckoo search (CS) and the bat algorithm (BA) are the best‐known ones selected for this review. This paper presents a brief description of the algorithmic characteristics and various employed improved versions or varieties thereof of each of the stated EAs. Furthermore, the differences between the proposed animal‐inspired EAs and their improved versions are identified by comparing the performance of the implemented animal‐inspired EAs in the reviewed literature. PSO and its varieties have the largest number of reported applications. Our comparison results revealed that constrained, discrete and randomized varieties of the animal‐inspired EAs outperformed unconstrained, continuous and deterministic varieties, respectively because of larger feasible search space, better solution quality and shorter computational time. Moreover, all the animal‐inspired EAs outperformed traditional methods of reservoir optimization, such as nonlinear programming (NLP) and dynamic programming (DP).
The construction and operation of upstream reservoirs have significantly altered downstream hydrologic regime. Appropriate and quantifiable assessment method for the alteration of hydrologic regime is considerably vital and emergent for ecological protection and restoration. The Range of Variability Approach (RVA) and modified RVA methods have been widely used in practice to assess the hydrological alteration. However, these methods have failed to concurrently describe the distribution of indicator and morphological features in detail, which might inevitably lead to the misjudgment of alteration. This paper proposes a Joint Probability Density Difference Approach (JPDDA) method to address the major drawbacks of these previous methods with the introduction of Gaussian Kernel Density Estimation (KDE) and copula function. The annual average flow is selected as the reference variable to construct a proper joint probability density function between itself and other hydrological alteration indicators. The JPDDA method could describe the marginal distribution in detail through Gaussian KDE and also link the morphological features with copula function. Along with pervious methods, the hydrological alterations at Yichang hydrological station, Yangtze River are estimated based on the measured flow from 1949 to 2022. It is shown that the hydrologic regime has suffered from a moderate or even heavy alteration under the influence of massive upstream cascade reservoirs, and the JPDDA outperforms the other methods in terms of rationality and stability for practical assessments. Thus, the proposed JPDDA method is strongly advised to handle the hydrological alteration and could provide a reasonable reference for ecological operation.
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