Economic generation schedule on thermal power system considering emission using grey wolves optimization

Kadali, Kalyan Sagar; Rajaji, L.; Moorthy, V. Prasanna; Viswanatharao, J.

doi:10.1016/j.egypro.2017.05.177

Cited by 7 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, it has a limited control agent to adjust, and superior convergence. Some studies have found that GWO has better numerical characteristics that enable it to prevent local optimum compared to other traditional optimization models, and it has been suggested as a convenient stochastic method to solve highly nonlinear, multivariate and multimodal optimization problems [56]. GWO, a novel metaheuristic algorithm technique, was initially proposed by Mirjalili et al [57], and was inspired by grey wolves' hunting and social hierarchy.…”

Section: Grey Wolf Optimizationmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

View full text Add to dashboard Cite

The effects of developing technology and rapid population growth on the environment have been expanding gradually. Particularly, the growth in water consumption has revealed the necessity of water management. In this sense, accurate flow estimation is important to water management. Therefore, in this study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model is proposed for streamflow forecasting. In the study, daily flow data of Üçtepe and Tuzla flow observation stations located in various water collection areas of the Seyhan basin were utilized. In the test and training analysis of the models, the first 75% of the data were used for training, and the remaining 25% for testing. The accuracy and success of the hybrid model were compared via the comparison model and linear regression, one of the most basic models of artificial neural networks. The estimation results of the models were analyzed using different statistical indexes. Better results were obtained for the GWO-GRU hybrid model compared to the benchmark models in all statistical metrics except SD at the Üçtepe station and the whole Tuzla station. At Üçtepe, the FMS, despite the RMSE and MAE of the hybrid model being 82.93 and 85.93 m3/s, was 124.57 m3/s, and it was 184.06 m3/s in the single GRU model. We achieved around 34% and 53% improvements, respectively. Additionally, the R2 values for Tuzla FMS were 0.9827 and 0.9558 from GWO-GRU and linear regression, respectively. It was observed that the hybrid GWO-GRU model could be used successfully in forecasting studies.

show abstract

Section: Grey Wolf Optimizationmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…The optimization process of GWO involves the steps of hierarchy, tracking, surrounding, and attacking the prey. Kadali et al [ 30 ] used the gray wolf optimization algorithm to optimize the scheduling of thermal power systems. Zheng et al [ 31 ] used the gray wolf optimization algorithm to balance the load among smart microgrids, user demand, and service providers.…”

Section: Economic Dispatching Model Of Integrated Energy Systemmentioning

confidence: 99%

Integrated energy system optimal scheduling considering the comprehensive and flexible operation mode of pumping storage

et al. 2022

View full text Add to dashboard Cite

The integrated energy system (IES) optimal scheduling under the comprehensive flexible operation mode of pumping storage is considered. This system is conducive to the promotion of the accommodation of wind and solar energy and can meet the water, electricity and heat needs of coastal areas far away from the energy center. In this study, the joint dispatch between double pumped storage power stations is used to accommodate wind and solar energy better and smooth their fluctuations on the grid. Through the scheduling of water between the high and low reservoirs of double pumped storage power stations and the reservoir of the seawater desalination plant, the impact of storage capacity constraints on pumped storage power plants is reduced. Moreover, the objective function to build the IES optimization scheduling model is to achieve minimum economic cost. The results reveal that the integrated energy system accommodates all wind energy and solar energy. As such, the system increases the average working time of the pumped storage unit by 1.9 hours and reduces the economic cost by 31.50%. Based on the simulation results, the model can enhance the accommodation capability of wind energy and solar energy and improve the system economy.

show abstract

“…At the same time, this effect becomes more obvious as the scale of optimization problems increase.The grey wolf optimizer (GWO) is a swarm intelligence optimization algorithm proposed by Mirjalili et al in 2014, which simulates the group behavior of grey wolves preying on prey and the leadership mechanism. The algorithm is widely used in parameter optimization [5][6][7] , knapsack problem 8,9 , economic scheduling problem [10][11][12] , shop scheduling problem 13,14 , fault diagnosis [15][16][17] , feature selection [18][19][20] , image processing [21][22][23] and many other fields due to its features of few parameters and easy implementation. However, in actual optimization projects, the GWO algorithm has problems of slow convergence speed, insufficient global search ability, and easy to fall into local optimal solution, which has attracted the attention of many scholars and launched a series of studies on Grey Wolf Optimization algorithm.…”

mentioning

confidence: 99%

Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy

Huang

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The traditional Grey Wolf Optimization algorithm (GWO) has received widespread attention due to features of strong convergence performance, few parameters, and easy implementation. However, in actual optimization projects, there are problems of slow convergence speed and easy to fall into local optimal solution. The paper proposed a Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy (CG-GWO) in response to the above problems. The Cauchy-Gaussian mutation operator is introduced to increase the population diversity of the leader wolves and improve the global search ability of the algorithm. This work retains outstanding grey wolf individuals through the greedy selection mechanism to ensure the convergence speed of the algorithm. An improved search strategy was proposed to expand the optimization space of the algorithm and improve the convergence accuracy. Experiments are performed with 16 benchmark functions covering unimodal functions, multimodal functions, and fixed-dimension multimodal functions to verify the effectiveness of the algorithm. Experimental results show that compared with four classic optimization algorithms, particle swarm optimization algorithm (PSO), whale optimization algorithm (WOA), sparrow optimization algorithm (SSA), and farmland fertility algorithm (FFA), the CG-GWO algorithm shows better convergence accuracy, convergence speed, and global search ability. The proposed algorithm shows the same better performance compared with a series of improved algorithms such as the improved grey wolf algorithm (IGWO), modified Grey Wolf Optimization algorithm (mGWO), and the Grey Wolf Optimization algorithm inspired by enhanced leadership (GLF-GWO).

show abstract

Economic generation schedule on thermal power system considering emission using grey wolves optimization

Cited by 7 publications

References 6 publications

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Integrated energy system optimal scheduling considering the comprehensive and flexible operation mode of pumping storage

Grey Wolf Optimization algorithm based on Cauchy-Gaussian mutation and improved search strategy

Contact Info

Product

Resources

About