Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation

Abstract

In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search algorithm (GSA) was chosen as the fundamental execution framework; the opposition learning strategy was adopted to increase the convergence speed of the swarm; the mutation search strategy was chosen to enhance the individual diversity; the elastic-ball modification strategy was used to promote the solution feasibility. Additionally, a practical constraint handling technique was introduced to improve the quality of the obtained agents, while the technique for order preference by similarity to an ideal solution method (TOPSIS) was used for the multi-objective decision. The numerical tests of twelve benchmark functions showed that the EGSA method could produce better results than several existing evolutionary algorithms. Then, the hydropower system located on the Wu River of China was chosen to test the engineering practicality of the proposed method. The results showed that the EGSA method could obtain satisfying scheduling schemes in different cases. Hence, an effective optimization method was provided for the multi-objective operation of hydropower system.

Optimal Operation of Hydropower System by Improved Grey Wolf Optimizer Based on Elite Mutation and Quasi-Oppositional Learning

“…For most evolutionary methods, all the individuals may fail to jump out of local optima and it is necessary to find some methods to enlarge the hunting range of the swarm [59]- [61].…”

“…In recent years, the opposition-based learning comparing the performances of the current solution and its opposite solution at the same time is often used to improve the performance of evolutionary method [59]- [61]. For any one solution, Equation ( 13) can be used to obtain the quasi-opposite position.…”

Advances in Hydrologic Forecasts and Water Resources Management

“…Due to climate change, the importance of reservoirs is likely to increase, not only for water storage purpose but also for maximizing water use benefits and mitigating climate extremes. Four papers [9][10][11][12] employ advanced optimization methods to derive reservoir operating rules for multi-reservoir systems and/or optimize multi-objective reservoir operation. In [9], the authors conduct a multi-target single dispatching study on ecology and power generation in the lower Yellow River to solve the single-objective and the multi-objective optimal schema using the genetic algorithm (GA) and an improved non-dominated genetic algorithm (NSGA-II).…”

“…Multidisciplinary research and advanced methodologies in hydrological forecasts, especially in extreme floods and droughts, are widely implemented for water planning and management, which ultimately lead to improved optimum water resources management and effective control under a changing environment. Among them, artificial intelligence (AI) techniques are efficient tools for extracting the key information from complex highly dimensional input-output patterns and are widely used to tackle various hydrological problems such as flood forecasts discussed in this Special Issue [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Over the last decades, many studies have demonstrated that artificial intelligence (AI) techniques, such as machine learning (ML) methods, can produce flood forecasts in a few hours [15][16][17][18][19] while extending to seasonal forecasts many months in advance for larger river basins [20][21][22][23][24].…”

Multi-Verse Algorithm based Approach for Multi-criteria Path Planning of Unmanned Aerial Vehicles

“…In the multiobjective optimization framework, many methods have been developed for the selection of an optimal solution from a given set of Pareto non-dominated ones [28,29]. In this work, the technique for order of preference by similarity to ideal solution TOPSIS is used to make decision about the optimal solution for problem (5).…”