Application of PSO algorithm in short-term optimization of reservoir operation

Ecological operation of cascade hydropower stations is one of the most important methods for the ecological restoration of a river system. In order to balance the benefits between power generation and ecology of Qingjiang cascade hydropower station, an optimal equilibrium operation method is proposed in this paper. Firstly, the range of ecological flow of the Qingjiang cascade is calculated by using ten-day frequency calculation method. Secondly, an optimal equilibrium operation model by considering the balance between power generation and ecology is established. And then, in order to solve this complex model, a particle swarm optimization based model solution method is proposed, and the evaluation index system of operation schemes is established. Finally, through operation calculation in 2008-2010 years, the results show that compared with the operation scheme considering only power generation benefit, the optimal equilibrium operation scheme increases the runoff ecological dispersion coefficient by 6.90%，reduces the cascade ecological overflow by 7.739 billion m3 and the cascade ecological water shortage by 130 million m3, while the cascade power generation decreases slightly by 1.83%. This study provides an effective scientific method for the optimal operation of Qingjiang cascade under the background of Yangtze River protection.

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“…The particle swarm optimization (PSO) algorithm for Qingjiang cascade optimal scheduling is designed as follows [9] :…”

Section: Pso Based Solution Methodsmentioning

confidence: 99%

The optimal equilibrium operation between power generation and ecology of Qingjiang cascade hydropower stations

Lin

Guo

et al. 2018

MATEC Web Conf.

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“…In many studies such as [25][26][27][28][29][30][31][32], GA is used for those issues. Similarly, in some studies such as [9,[33][34][35][36][37][38][39][40], the PSO algorithm is used for the same problems and showed the adequacy of this algorithm. Chang et al [41] Thereafter, the developed model is implemented using each algorithm considering the farms′ water requirements and attainable water.…”

Section: Introductionmentioning

confidence: 96%

Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

Saeidian

Mesgari

Pradhan

et al. 2019

Water

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The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method. and Huang et al. [42] used the GA-PSO hybrid algorithm for the optimal management of water resources. Davijani, Banihabib, Anvar, and Hashemi [9] compared the use of PSO and GA algorithms for allocating water to agricultural, industrial, and municipal consumptions over one year.The ICA is successfully used in a variety of applications related to industrial, civil, mechanical, electrical, and computer engineering [43]. Hosseini-Moghari et al. [44] used this algorithm for water resource management. Karamouz et al. [45] compared the suitability of the ICA and PSO algorithms for allocating reservoir water to agricultural, industrial, and urban consumers. Many studies have used the BA for various applications [46]. However, to the best of our knowledge, it is not used in AW.Considering the preceding review on the use of these algorithms in water resource management and other applications, the reasons for selecting GA, BA, ICA, and PSO algorithms in this study are as follows: (1) They are among the most commonly used meta-heuristic algorithms.(2) On similar problems, the performance of these algorithms was acceptable. (3) The solution space of this study is continuous, while the ICA and PSO are inherently continuous, and GA and BA are also applicable to continuous issues.The aim of this study is to model the AW to farms as...

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“…When the models were solved by modern intelligent algorithms, it was not always able to satisfy the constraints in a short time. Because most intelligent algorithms adopted the way of random sampling, which also had difficulty in solving the problem of equality constraint (Birhanu et al 2014;SaberChenari et al 2016). However, the water demand constraint was an equality constraint that was used to minimize waste.…”

Section: Introductionmentioning

confidence: 99%

Optimal operation of a single unit in a pumping station based on a combination of orthogonal experiment and 0-1 integer programming algorithm

Wang

Cheng

2022

Water Supply

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The frequent start-stop scheme in the operation management of large pumping stations causes a series of problems, e.g., energy consumption and potential safety hazard. Therefore, we established a mathematical model for the optimal operation of a large-scale pumping station considering the start-stop loss of machine. The objectives of the optimization model included electricity charges for pumping station operation and the start-stop loss of pumping station. The blade angle and start-stop scheme were taken into account as the double decision variables, while the target water extraction (equality constraint), start-stop scheme and power were the constraint conditions. A combination of orthogonal experiment and 0-1 integer programming algorithm was proposed to exploring the optimal solution of the model. A real case of single-unit pump station in China was applied to prove the effectiveness of the algorithm. The results showed that the optimization efficiency of proposed algorithm was 4%–10% higher than that of genetic algorithm. Additionally, the maximum optimization benefit was obtained when the number of start-stop was less than 3 times during operation process. The proposed algorithm provided a reference for similar complex nonlinear models.

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Application of PSO algorithm in short-term optimization of reservoir operation

Cited by 39 publications

References 31 publications

The optimal equilibrium operation between power generation and ecology of Qingjiang cascade hydropower stations

The optimal equilibrium operation between power generation and ecology of Qingjiang cascade hydropower stations

Irrigation Water Allocation at Farm Level Based on Temporal Cultivation-Related Data Using Meta-Heuristic Optimisation Algorithms

Optimal operation of a single unit in a pumping station based on a combination of orthogonal experiment and 0-1 integer programming algorithm

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