A parallel multi-objective particle swarm optimization for cascade hydropower reservoir operation in southwest China

Niu, Wen-jing; Feng, Zhong-kai; Cheng, Chuntian; Wu, Xinyu

doi:10.1016/j.asoc.2018.06.011

Cited by 100 publications

(42 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to plenty of physical constraints in practice, some individuals may fall into the infeasible zones during the evolutionary process [53,59]. To address this case, an effective constraint handling method based on penalty coefficient and heuristic modification strategies is proposed, where the variables violating the feasible zone are forced to be the boundary range, and then the constraint violation is merged into the objective function.…”

Section: Constraint Handling Methodsmentioning

confidence: 99%

“…Energies 2019, 9, x 5 of 24 Similar to the other evolutionary methods, the SCA method is often trapped into local optima because the population diversity gradually decreases during the evolution process [51]. Hence, it is important to find some strategies that can help individuals search in different directions [52,53]. To achieve this goal, a novel random mutation strategy is proposed to effectively increase the diversity of the swarm.…”

Section: Random Mutation Strategy To Increase the Population Diversitymentioning

confidence: 99%

See 1 more Smart Citation

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

et al. 2019

Self Cite

View full text Add to dashboard Cite

In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs.

show abstract

Section: Constraint Handling Methodsmentioning

confidence: 99%

Section: Random Mutation Strategy To Increase the Population Diversitymentioning

confidence: 99%

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The above-mentioned traditional methods might fail to address the complexity due to various defects, like dimensionality problem [25], high computational burden [26], duality gap [27], or parameter tuning [28][29][30]. In recent years, with the booming development of computer technology, many evolutionary algorithms have been proposed to resolve these kind of problems [31][32][33], like genetic algorithm (GA) [34], differential evolution (DE) [35,36], particle swarm algorithm (PSO) [37][38][39][40], cuckoo search (CS) [41], Covariance Matrix Adaptation Evolution Strategy with a Directed Target to Best Perturbation (CMA-ES-DTBP) [42], and a clustered adaptive teaching-learning-based optimization (CATLBO) [43]. Compared with traditional methods, the evolutionary algorithms can produce satisfying solutions in most cases, regardless of the problem features (like continuity or nonconvexity) [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

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

Feng

Liu

Jiang

et al. 2019

Water

View full text Add to dashboard Cite

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.

show abstract

“…Hydropower generated by fast running or falling water has become an important part of renewable energy [2,3]. The total generation of hydropower in China exceeded 1 trillion kWh by the end of 2015, while the total installed capacity of hydropower reached 320 million kilowatts [4]. Therefore, the operation of cascade hydropower stations is becoming an important issue in the optimization of power systems in China.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Hydropower Generation Scheduling of Large-Scale Cascade Reservoirs Using Chaotic Adaptive Multi-Objective Bat Algorithm

Yang

et al. 2019

Water

View full text Add to dashboard Cite

With growing concerns over renewable energy, the cascade hydropower reservoirs operation (CHRO), which balances the development of economic benefits and power supply security, plays an increasingly important role in hydropower systems. Due to conflicting objectives and complicated operation constraints, the CHRO problem considering the requirements of maximizing power generation benefit and firm power output is determined as a multi-objective optimization problem (MOP). In this paper, a chaotic adaptive multi-objective bat algorithm (CAMOBA) is proposed to solve the CHRO problem, and the external archive set is added to preserve non-dominant solutions. Meanwhile, population initialization based on the improved logical mapping function is adopted to improve population diversity. Furthermore, the self-adaptive local search strategy and mutation operation are designed to escape local minima. The CAMOBA is applied to the CHRO problem of the Qingjiang cascade hydropower stations in southern China. The results show that CAMOBA outperforms the multi-objective bat algorithm (MOBA) and non-dominated sorting genetic algorithms-II (NSGA-II) in different hydrological years. The spacing (SP) and hypervolume (HV) metrics verify the excellent performance of CAMOBA in diversity and convergence. In summary, the CAMOBA is demonstrated to get better scheduling solutions, providing an effective approach for solving the cascade hydropower reservoirs operation (CHRO).

show abstract

A parallel multi-objective particle swarm optimization for cascade hydropower reservoir operation in southwest China

Cited by 100 publications

References 63 publications

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

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

Long-Term Hydropower Generation Scheduling of Large-Scale Cascade Reservoirs Using Chaotic Adaptive Multi-Objective Bat Algorithm

Contact Info

Product

Resources

About