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In this paper a metaheuristic technique called Ant Colony Optimization (ACO) is proposed to derive operating policies for a multi-purpose reservoir system. Most of the realworld problems often involve non-linear optimization in their solution with high dimensionality and large number of equality and inequality constraints. Often the conventional te...
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... F gb is a fitness function corresponding to the global best tour within all the past iterations. Sometimes another type of global best ant updating rule called iteration best ant update is used. The updating rule is the same as given in Equation 4, but in Equation 5, F gb will be replaced by F ib , the fitness function corresponding to the best tour done by any ant in the current iteration. It can be noted that ACO is a problem dependent application. So to apply the algorithm, it requires appropriate representation of the problem and suitable heuristics in its solution construction (Dorigo and Di Caro, 1999). The case study considered in this paper is the Hirakud reservoir project in Orissa state, India. Hirakud dam is situated at latitude 21 ◦ 32 N and longitude 83 ◦ 52 E. The index map of Mahanadi river basin showing the location of Hirakud dam is presented in Figure 2. The reservoir has an active storage capacity of 5,375 Mm 3 (Million cubic meters) and a gross storage of 7,189 Mm 3 . The Hirakud project is a multi-purpose scheme and the water available in the dam is used in the following order of priority: for flood control, drinking water, irrigation, and power generation. Since the drinking water requirement is a very small quantity compared to other demands, this quantity is neglected in this particular model formulation. Water levels begin rising in July with the beginning of monsoon season in the region, and begin declining in October, at the end of the season. During monsoon season, the project provides flood protection to 9,500 km 2 of delta area in the districts of Cuttack and Puri. Also the project provides irrigation for 155,635 ha in wet season (Kharif) and for 108,385 ha in dry (Rabi) season in the districts of Sambalpur, Bargarh, Bolangir, and Subarnpur. The water released through the powerhouses after power generation, irrigates further 436,000 ha of command area in Mahanadi delta. Installed capacity of power generation is 198 MW through its two powerhouses at Burla (PH-I) located at the right bank and Chiplima (PH-II) located at 22 km downstream of the dam (Proc. of 3rd meeting of rule curve revision committee, 1988). The PH-I generates energy by utilizing water discharged directly from the Hirakud dam. Then the utilized water passes to the PH-II through a power channel to generate further power at Chiplima. Orissa state is having plenty of water during the wet season, so there is great possibility for hydropower improvement in that season. Net energy production is high during the monsoon period. However, unless the region experiences ...
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A direct search approach to determine optimal reservoir operating is proposed with ant colony optimization for continuous domains (ACOR). The model is applied to a system of single reservoir to determine the optimum releases during 42 years of monthly steps. A disadvantage of ant colony based methods and the ACOR in particular, refers to great amou...
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... Previous studies have shown that there are no standard algorithms for all problems due to distinct physical problems and operating characteristics (Ahmad et al. 2014). Several algorithms that have been used for optimization include Particle Swarm Optimization (PSO) (Riadi 2014;Allawi et al. 2019), Ant Colony Optimization (ACO) (Kumar and Reddy 2006;Kangrang and Lokham 2013), and Bee Colony Algorithm (BCA) (Hossain et al. 2018;Soghrati and Moeini 2020). The development of these algorithms was inspired by animal behavior or swarm intelligence. ...
... At present, the use of ACO algorithm for solving water resources management problems remains limited. The previous research used ACO to optimize multi-use reservoir and then compared the results with Genetic Algorithm (GA) (Kumar and Reddy 2006). The results showed that the method was superior in terms of fulfilling irrigation and hydropower needs. ...
... Managing multi-purpose and multi-year reservoir was a complex problem, which comprised several variables, nonlinear objectives, and a long duration. The scientific development of features for nonlinearity, and multi-objectives, such as reservoir operations could be effectively addressed using heuristic methods (Kumar and Reddy 2006). This study was an analysis of a single reservoir with a multi-use and multiyear treatment system. ...
This study aimed to develop Multi-Year Reservoir Operation Model (MYROM) to optimize water allocation in multi-purpose and multi-year reservoir. MYROM relied on Ant Colony Optimization (ACO) method to address non-linear optimization challenges associated with multiple objectives. In addition, water availability in the reservoir was assessed, which was an analysis of constraints in minimizing water shortages at study location. The weighted priority for each type of water use was then obtained from a model that followed ACO method. This priority weighting was carried out to provide an assessment regarding decisions related to water management, particularly in determining the sequence of use for various purposes, such as irrigation, domestic and industrial water supply, or hydroelectric power generation. The model used was tested at Wonorejo multi-purpose Reservoir in East Java, Indonesia using historical water availability and use from 2003 to 2021. The simulation results showed that MYROM succeeded in allocating water through the minimization of shortages by proposing parameters α = 1, β = 2, and ρ = 0.4, with the number of ants (m) = 100 in the water demand variable. In addition, the water demand variable was in the form of hydropower, irrigation needs, and raw water needs with a period of 10 days for the next 5 years. Based on these results, the use of MYROM with specified parameter values could serve as a practical approach to improve the planning of reservoir water allocation operations. In addition, this showed that the performance of future water allocation planning model depended on predicted water availability data.
... Popular MHAs for reservoir optimization include the genetic algorithm (GA) (Chang et al., 2005;Sharif & Wardlaw, 2000), particle swarm optimization (PSO) (Afshar, 2009;Al-Aqeeli & Mahmood Agha, 2020;Kumar & Reddy, 2007a), genetic programming (GP) (Ashofteh et al., 2015;Fallah-Mehdipour et al., 2012a, 2012bGarousi-Nejad et al., 2016), harmony search (HS) (Bashiri-Atrabi et al., 2015;Haddad et al., 2011;Kougias & Theodossiou, 2013), honey-bee mating optimization (HBMO) (Haddad et al., 2011;Soghrati & Moeini, 2019), and ant colony optimization (ACO) (Kumar & Reddy, 2006;Moeini & Afshar, 2012). GA, GP, and HS are population-based algorithms, inspired by the principle of natural selection and musical improvisation process, respectively. ...
The optimum utilization of water resources is a challenging issue across the world. This study aims to optimize a complex reservoir network in the Koshi basin, Nepal considering planned water projects. The best operating strategy of the planned five storage reservoirs was determined using an elitist-mutated particle swarm optimization model with the goal of energy maximization by ensuring water supply from Koshi to Bagmati and Kamala river basin through two inter-basin diversion projects. For this, three optimization approaches; stand-alone, cascade with and without diversion project were explored under dry, average, and wet flow scenarios. The dry season (Dec–May) energy and annual energy generation from the total system increased up to 30.1% and 6.78%, respectively, under cascade optimization compared to the stand-alone optimization of the projects. Cascade operation optimization led to increases in dry season and annual energy of up to 20.1% and 4.69%, respectively, while still meeting diversion criteria. The results present a clear trade-off between irrigation benefits from the inter-basin diversions and total energy generation from the whole system. Based on preliminary assessment, the inter-basin diversions are found to give a larger overall benefit from the water resources development of the Koshi basin. This study underscores the importance of using optimization techniques in basin level planning, designing and managing water resource projects to ensure the optimum benefit and sustainability of water resources.
Keywords Energy · Koshi basin · Multi-reservoir system · Particle swarm optimization · Storage hydropower
... Ants use a decision rule called pseudo-random proportional rule, in which, an ant k in node i will select node j to move as described in [19]. The state transition rule from the next node j that ant k chooses to go is described in [19]. ...
... Ants use a decision rule called pseudo-random proportional rule, in which, an ant k in node i will select node j to move as described in [19]. The state transition rule from the next node j that ant k chooses to go is described in [19]. In each iteration of the algorithm, each ant progressively builds a solution, by using the probability transition rule. ...
... In each iteration of the algorithm, each ant progressively builds a solution, by using the probability transition rule. The pheromone trail is updated both locally and globally as described in [19]. ...
... Water distribution systems and reservoir optimization are the primary areas where ACO has been predominantly utilized. For example, Jalali et al. [31] and Kumar and Reddy [32] used the ACO algorithm to optimize reservoir operation. Jalali et al. [31] proposed an improved ACO algorithm that uses a new heuristic function and a new pheromone updating rule to improve the convergence speed and the quality of the solution. ...
... The proposed improved ACO algorithm is tested on a case study of the Hirakud reservoir in India, and the results show that it outperforms the traditional ACO algorithm and other optimization algorithms in terms of solution quality and computational efficiency. Kumar and Reddy [32] ACO technique to derive operating policies for a multi-purpose reservoir system. ...
The use of optimization techniques in water resource management has received increasing attention in the past few decades. The main objective of optimization in water resource management is to maximize the use of the available resources while minimizing the negative impacts on the environment. Two main approaches are widely used in optimization, including global and local optimization. The main aim of the chapter is to explore the application of the Ant Colony Optimization (ACO) algorithm in water resource management. It is a flexible and powerful tool that can handle various types of hydrological models. The chapter will use different performance measures and case studies to comprehensively compare these techniques in water resource management. The results will be valuable for practitioners in choosing the appropriate optimization technique for their specific application.
... These include genetic algorithms [25][26][27], particle swarm optimization [28,29], artificial neural networks [30,31], ant colony optimization [32,33], simulated annealing [34,35], support vector machines [36], and fuzzy computation [37]. Early joint optimal water-sediment operation of reservoirs was usually solved by transforming the multi-objective problem into a single-objective one through weighted non-dimensionalization [38]. Further studies have used the analytical hierarchy process to determine the weights of objectives [39] and adopted a neural network prediction method to compute the amount of reservoir sedimentation [40]. ...
Reservoir operation optimization is a technical measure for flood control and is beneficial owing to its reasonable and reliable control and application of existing water conservancy and hydropower hubs, while ensuring dam safety and flood control, as well as the normal operation of power supply and water supply. Considering the beneficial functions of reservoirs, namely flood control and ecological protection, this paper firstly established a two-objective optimal operation model for the reservoir group in the middle reaches of the Yellow River. We aim to maximize the average output of the cascade reservoir group and minimize the average change in ecological flow during the operation period under efficient sediment transport conditions, with the coordination degree of water and sediment as the constraints of reservoir discharge flows. The paper aims to construct an evaluation index system for reservoir operation schemes, apply a combined approach of objective and subjective evaluations, and introduce the gray target and cumulative prospect theories. By uniformly quantifying the established scheme evaluation index system, screening the reservoir operation schemes with the fuzzy evaluation method, and selecting the recommended scheme for each typical year, this paper provides a new scientific formulation of the operation schemes of reservoirs in the middle reaches of the Yellow River. The selected schemes are compared with actual data, demonstrating the effectiveness of joint reservoir operation and for multidimensional benefits in terms of power generation, ecology, and flood control.
... The application of MHAs to water resources engineering is discussed by Reddy & Kumar (2020). A few examples of the applications of evolutionary algorithms to reservoir problems are Genetic Algorithms (GA) (Chang & Chen 1998;Jothiprakash & Shanthi 2006), Differential Evolution (DE) (Vasan & Raju 2004), Genetic Programming (GP) (Fallah-Mehdipour et al. 2012), Swarm Intelligence Algorithms such as Particle Swarm Optimization (PSO) (Moradi & Dariane 2009), Ant Colony Optimization (ACO) (Reddy & Kumar 2006), Honey Bee Mating Algorithm (HBMA) (Haddad et al. 2005), Bat Algorithm (BA) (Yang 2010;Ehteram et al. 2018), and Improved Bat Algorithm (IBA) (Ahmadianfar et al. 2016). Other algorithms include the Weed Optimization Algorithm (WOA) (Asgari et al. 2016), Harmony Search (HS) Algorithm (Bashiri-Atrabi et al. 2015), etc. ...
Operating a reservoir during flooding is a complex problem in which optimum decision-making is a difficult task. The present study demonstrates a solution for the operation of flooding problem in a multiple-purpose reservoir. A reservoir on River Narmada in central India is chosen as the case study. The multiple objective problems comprised maximization of hydropower releases, minimizing spills, and achieving stipulated target storage at the end of the operation period. The chosen optimization models are the Differential Evaluation Algorithm (DEA) and its variants: the Enhanced Differential Evolution Algorithm (EDEA) and the Modified Enhanced Differential Algorithm (MEDEA). The EDEA model is modified in the present study to MEDEA. The results of all three models applied to the same case study are compared on convergence to an optimal solution. All three algorithms were tested on two of the popular benchmark functions that are Ackley and Sphere. The results of both applications demonstrated that MEDEA proved to be the best in terms of converging to the optimal solution, exhibiting better stability, and quality of final results.
... Popular MHAs for reservoir optimization include the genetic algorithm (GA) (Chang et al., 2005;Sharif & Wardlaw, 2000), particle swarm optimization (PSO) (Afshar, 2009;Al-Aqeeli & Mahmood Agha, 2020;Kumar & Reddy, 2007a), genetic programming (GP) (Ashofteh et al., 2015;Fallah-Mehdipour et al., 2012a, 2012bGarousi-Nejad et al., 2016), harmony search (HS) (Bashiri-Atrabi et al., 2015;Haddad et al., 2011;Kougias & Theodossiou, 2013), honey-bee mating optimization (HBMO) (Haddad et al., 2011;Soghrati & Moeini, 2019), and ant colony optimization (ACO) (Kumar & Reddy, 2006;Moeini & Afshar, 2012). GA, GP, and HS are population-based algorithms, inspired by the principle of natural selection and musical improvisation process, respectively. ...
The optimum utilization of water resources is a challenging issue across the world. This study aims to optimize a complex reservoir network in the Koshi basin, Nepal considering planned water projects. The best operating strategy of the planned five storage reservoirs was determined using an elitist-mutated particle swarm optimization model with the goal of energy maximization by ensuring water supply from Koshi to Bagmati and Kamala river basin through two inter-basin diversion projects. For this, three optimization approaches; stand-alone, cascade with and without diversion project were explored under dry, average, and wet flow scenarios. The dry season (Dec-May) energy and annual energy generation from the total system increased up to 30.1% and 6.78%, respectively, under cascade optimization compared to the stand-alone optimization of the projects. Cascade operation optimization led to increases in dry season and annual energy of up to 20.1% and 4.69%, respectively, while still meeting diversion criteria. The results present a clear trade-off between irrigation benefits from the inter-basin diversions and total energy generation from the whole system. Based on preliminary assessment, the inter-basin diversions are found to give a larger overall benefit from the water resources development of the Koshi basin. This study underscores the importance of using optimization techniques in basin level planning, designing and managing water resource projects to ensure the optimum benefit and sustainability of water resources.
... Reservoir optimization is important to attain the paramount possible performance of a reservoir system [43,44]. It helps in arriving at decisions pertaining to storage over a period of time and release from reservoirs, taking into consideration the variations in inflows and demands. ...
Ant-inspired metaheuristic algorithms known as ant colony optimization (ACO) offer an approach that has the ability to solve complex problems in both discrete and continuous domains. ACOs have gained significant attention in the field of water resources management, since many problems in this domain are non-linear, complex, challenging and also demand reliable solutions. The aim of this study is to critically review the applications of ACO algorithms specifically in the field of hydrology and hydrogeology, which include areas such as reservoir operations, water distribution systems, coastal aquifer management, long-term groundwater monitoring, hydraulic parameter estimation, and urban drainage and storm network design. Research articles, peer-reviewed journal papers and conference papers on ACO were critically analyzed to identify the arguments and research findings to delineate the scope for future research and to identify the drawbacks of ACO. Implementation of ACO variants is also discussed, as hybrid and modified ACO techniques prove to be more efficient over traditional ACO algorithms. These algorithms facilitate formulation of near-optimal solutions, and they also help improve cost efficiency. Although many studies are attempting to overcome the difficulties faced in the application of ACO, some parts of the mathematical analysis remain unsolved. It is also observed that despite its popularity, studies have not been successful in incorporating the uncertainty in ACOs and the problems of dimensionality, convergence and stability are yet to be resolved. Nevertheless, ACO is a potential area for further research as the studies on the applications of these techniques are few.
... In this case study, multiple objectives were identified, including reducing flood risk, minimizing irrigation deficits, and maximizing hydropower generation as a priority. The final objective was to implement the developed model into the monthly reservoir operation [150]. ACO has been implemented to find a solution to the problem of hydropower reservoir operation that is defined as a partially connected graph consisting of a set of rule curves connecting the nodes of the graph. ...
This paper reviews applications of optimization techniques connected with reservoir simulation models to search for optimal rule curves. The literature reporting the search for suitable reservoir rule curves is discussed and examined. The development of optimization techniques for searching processes are investigated by focusing on fitness function and constraints. There are five groups of optimization algorithms that have been applied to find the optimal reservoir rule curves: the trial and error technique with the reservoir simulation model, dynamic programing, heuristic algorithm, swarm algorithm, and evolutionary algorithm. The application of an optimization algorithm with the considered reservoirs is presented by focusing on its efficiency to alleviate downstream flood reduction and drought mitigation, which can be explored by researchers in wider studies. Finally, the appropriate future rule curves that are useful for future conditions are presented by focusing on climate and land use changes as well as the participation of stakeholders. In conclusion, this paper presents the suitable conditions for applying optimization techniques to search for optimal reservoir rule curves to be effectively applied in future reservoir operations.
... Heuristic programming like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), etc. have also gained huge importance in the field of Reservoir Operations as an alternative to conventional techniques. (Ashutosh, Sandeep, and Chandra 2018;Kumar and Reddy 2006). Fuzzy rule-based models have also been used and successfully implemented in reservoir operations providing optimal solutions to the problem. ...
Reservoirs play an important role in the management of the water resources system. It serves various objectives, such as meeting municipal demands, irrigation demands, hydropower generation, and flood control. These objectives are often conflicting in nature and make the reservoir operation problem a complicated task. Over the last decades, the use of soft computing techniques like Neural Networks has gained huge importance in water resources engineering. In recent years, ANNs have been utilized more and more to forecast water resource factors, especially while operating reservoirs. The present study describes the use of a deep learning algorithm Artificial Neural Network(ANN) in the real-world problem of reservoir operations of the Hirakud reservoir. ANN approaches problem-solving more quickly
and flexibly, and it is highly adaptable to newer environments. Data set of three hourly time steps of the Hirakud reservoir has been taken from the year 2018-2021(4773 points). Input parameters for the model were taken as Inflow and evaporation while the model predicts the outflow and storage of the reservoir. Exploration data analysis has been done on the dataset and a correlation is found between inflow and outflow parameters. Feedforward structure and backpropagation algorithm have been used to design and train the ANN model The task of expressing an input dataset to trigger the generation of a specific output pattern is executed by a neural network. MAPE and the coefficient of determination (R2) were used as model parameters. The outcomes of the training, testing, and validation runs of the ANN models were compared to the observed data. The neural network's projected values closely matched the measured values. The reservoir operation decision and future updating are aided by the use of the ANN technique. It has been concluded that the monthly storage can be predicted with good accuracy using the ANN approach.
