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

Abstract

As one of the most important renewable energy, hydropower is often asked to satisfy the load demand of power system at peak periods. Thus, the optimal operation of hydropower system is modelled to minimize the standard deviation of the residual load series obtained by subtracting the total power outputs of all the involved hydropower plants from the original load curve. Hence, this paper develops an improved grey wolf optimizer (IGWO) to effectively address the complex constrained optimization problem. In the proposed method, the quasi-oppositional learning is used to enhance the convergence rate of the swarm; the elite mutation operator is used to increase the probability of escaping from local optima; the elastic-ball strategy and heuristic constraint handling method are used to help infeasible individuals rebound to feasible space. Numerical experiments of 12 classical test functions demonstrate the feasibility of the IGWO method in the global optimization problems. Then, the developed method is applied to solve the optimal operation of two cascade hydropower systems. The results indicate that the proposed method outperforms several traditional methods in smoothing the peak loads of power system. To sum up, an effective solution tool is provided for the hydropower system operation optimization problem. INDEX TERMS Hydropower system, grey wolf optimizer, quasi-oppositional learning, elite mutation, elastic-ball strategy, constraint handling method.

Design of Reverse Logistics Network for Remanufacturing Waste Machine Tools Based on Multi-Objective Gray Wolf Optimization Algorithm

“…The effectiveness and feasibility of the model and algorithm proposed in this paper were verified. This will provide decision support and basis for machine tool remanufacturing enterprises to recycle used machine tools [22][23][24][25][26].…”

An Improved Slime Mold Algorithm and its Application for Optimal Operation of Cascade Hydropower Stations

“…Figure 6 shows the flow chart of the process of optimizing the hydropower stations by using the ISMA. The obtained results of the proposed scheme ISMA are compared with the other schemes, e.g., the PSO [44], IMFO [46], DE [45], GWO [47], and SMA [24] algorithms for the operation hydropower stations. Setting the experiment environment for the algorithms is such as the search agent is set to 100; the total number of iterations is 500; the number of runs for algorithm involved independently is set to 30.…”

“…In general, the comparison data analysis results of the ISMA with other schemes for the optimal operation of cascade hydropower stations shows that the ISMA scheme provides robustness and saves significantly better energy than the SMA, PSO, DE, GWO, and MFO algorithms. show the curves of the obtained optimum result chart of the power generation in the typical year ( rainy, ordinary, and dry year) of the ISMA, PSO [44], SMA [24], DE [45], IFMO [46], and GWO [47] algorithms for the operation hydropower stations. It can be seen from the diagrams corresponding to several specific years that ISMA can obtain the largest generation of performance and maintain optimization ability in optimization.…”

“…In recent years, with the rise of the metaheuristic algorithms [42], such as the mentioned swarm intelligence algorithms [19][7], the optimal reservoir operation has entered a new stage of being dealt with by swarm intelligence algorithms [43]. Several works developed concerning the dealing with the optimal reservoir operation, e.g., Particle swarm optimization (PSO) algorithm for the optimal dispatch of cascade hydropower stations [44], Differential evolution (DE) algorithm for the optimal transform of cascade hydropower stations [45], Improved moth flame optimization (IMFO) algorithm for the cascade reservoirs operations [46], and Grey wolf optimizer (GWO) for the optimal operation of cascade reservoirs [47]. Although the swarm intelligence algorithms can provide a unique solution for cascade reservoir operation [43][48][49], it still also has disadvantages, e.g., falling optimal local, low optimization accuracy, and poor convergence performance [48][50].…”

Forecasting Monthly Runoff Time Series by Single-Layer Feedforward Artificial Neural Network and Grey Wolf Optimizer

“…In the search process, all wolves will enhance the global exploration in the entire state space at the early evolutionary stage, while the local exploitation will be improved with the increasing number of iterations. In this way, the swarm can gradually converge to promising areas of the complex global optimization problem [51][52][53].…”