A Hybrid Method for Optimal Scheduling of Short-Term Electric Power Generation of Cascaded Hydroelectric Plants Based on Particle Swarm Optimization and Chance-Constrained Programming

2008 ◽  
Vol 23 (4) ◽  
pp. 1570-1579 ◽  
Author(s):  
Wu Jiekang ◽  
Zhu Jianquan ◽  
Chen Guotong ◽  
Zhang Hongliang
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Electric Power ◽  
Hybrid Method ◽  
Optimal Scheduling ◽  
Chance Constrained Programming ◽  
Short Term ◽  
Swarm Optimization ◽  
Hydroelectric Plants ◽  
Constrained Programming

An Algorithm for Solving Stochastic Chance-Constrained Programming Problem

2014 ◽  
Vol 912-914 ◽  
pp. 1138-1141 ◽  
Author(s):  
Ning Xiao
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Chance Constrained Programming ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Complex Calculation ◽  
Long Time ◽  
Hybrid Intelligence ◽  
Constrained Programming ◽  
Stochastic Particle

Stochastic chance-constrained programming which is one of important stochastic programming widely exits in different fields. For searching an algorithm that can more effectively solve this problem,a new algorithm for its combined stochastic particle swarm optimization with stochastic simulation for approximation of the fitness function and checking feasibility of solution is presented. It overcomes the defaults such as needing a long time, complex calculation,resapsing into local optimum in the hybrid intelligence algorithm based on GA. After testing its performance and comparing with GA, the results show that the algorithm is more preferable.

scholarly journals Efficient Energy Load Distribution Model using Modified Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2 (4) ◽  
pp. 226-231
Author(s):  
Vijayakumar T ◽  
Vinothkanna R
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Electric Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Economic Load Dispatch ◽  
Electric Power Generation ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Modified Pso

Reduction of emission and energy conservation plays a major role in the current power system for realizing sustainable socio-economic development. The application prospects and practical significance of economic load dispatch issue in the electric power market is remarkable. The various generating sets must be assigned with load capacity in a reasonable manner for reducing the cost of electric power generation. This problem may be overcome by the proposed modified particle swarm optimization (PSO) algorithm. The practical issue is converted and modelled into its corresponding mathematical counterpart by establishing certain constraints. Further, a novel interdependence strategy along with a modified PSO algorithm is implemented for balancing the local search capability and global optimization. Multiple swarms are introduced in the modified PSO algorithm. Certain standard test functions are executed for specific analysis. Finally, the proposed modified PSO algorithm can optimize the economic load dispatch problem while saving the energy resources to a larger extent. The algorithm evaluation can be performed using real-time examples for verifying the efficiency. When compared to existing schemes like artificial bee colony (ABC), genetic algorithms (GAs), and conventional PSO algorithms, the proposed scheme offers lowest electric power generation cost and overcomes the load dispatch issue according to the simulation results.

scholarly journals Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Zhifeng Zhong ◽  
Chenxi Yang ◽  
Wenyang Cao ◽  
Chenyang Yan
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Prediction Accuracy ◽  
Particle Swarm Optimization Algorithm ◽  
Theory Model ◽  
Grey Model ◽  
Grey Theory ◽  
Short Term ◽  
Swarm Optimization ◽  
Photovoltaic Power

Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV) system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives.

scholarly journals Optimal short-term hydro-thermal scheduling using multi-function global particle swarm optimization

2020 ◽  
Vol 20 (1) ◽  
pp. 537
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Power Generation ◽  
Particle Swarm Optimization ◽  
Power Plants ◽  
Thermal Power ◽  
Particle Swarm ◽  
Test System ◽  
Thermal Power Plants ◽  
Fuel Cost ◽  
Short Term ◽  
Swarm Optimization

<p>An optimal short-term hydro-thermal scheduling (ST-HTS) problem is solved in this paper using the multi-function global particle swarm optimization (MF-GPSO). A multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, power generation and net head is considered in this paper. The ST-HTS problem determines the optimal power generation of hydro and thermal generators which is aimed to minimize total fuel cost of thermal power plants during a determined time period. Effects of valve point loading and prohibited operating zones in the fuel cost function of the thermal power plants is examined. Power balance, reservoir volume, water balance and operation constraints of hydro and thermal plants are considered. The effectiveness and feasibility of MF-GPSO algorithm is examined on a standard test system, and the simulation results are compared with other algorithms presented in the literature. The results show that the MF-GPSO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques reported in the literature.</p>

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