Artificial Immune-Based Optimization Technique for Solving Economic Dispatch in Power System

2006 ◽  
pp. 338-345 ◽  
Author(s):  
Titik Khawa Abdul Rahman ◽  
Saiful Izwan Suliman ◽  
Ismail Musirin
Keyword(s):  
Power System ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Artificial Immune

scholarly journals Application of Whale Optimization Algorithm for Environmental Constrained Economic Dispatch of Fixed Head Hydro-Wind-Thermal Power System

2019 ◽  
Vol 9 (1) ◽  
pp. 5608-5616
Keyword(s):  
Power System ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Electric Power System ◽  
Whale Optimization Algorithm ◽  
Multi Objective ◽  
Whale Optimization ◽  
The Cost

This work applies whale optimization algorithm for emission constrained economic dispatch of hydrothermal units including wind power. As the wind power has a characteristic of cleanliness and is renewable, this is convincing to include this for better operation of electric power system keeping in view both economic and environmental aspects. Hydrothermal scheduling integrated with wind power establishes a multi-objective problem that becomes economic emission hydro-thermal-wind scheduling problem while taking into consideration the cost due to wind uncertainty. Whale optimization algorithm is proposed to solve this emission constrained economic dispatch problem with competing objectives. This algorithm is recently developed and gives the best solution among other nature inspired algorithms. The objectives minimum generations as well as emission cost, both are optimized together including different constraints. A daily scheduling of all the three types of systems - hydro, thermal and wind is considered to evaluate the competency of this optimization technique to get a solution for this multi-objective problem. The experiments are carried out on two systems for determining the effectiveness of the suggested method. Besides, results found using the whale optimization technique have been compared with the results obtained from other evolutionary methods. From the comparison, it is experimentally justified that the whale optimization works faster and the cost of generation as well as cost of emission are lower than the other approaches.

scholarly journals Dynamic Economic Dispatch Assessment Using Particle Swarm Optimization Technique

2018 ◽  
Vol 7 (3) ◽  
pp. 458-464
Author(s):  
Muhammad Murtadha Othman ◽  
Mohd Affendi Ismail Salim ◽  
Ismail Musirin ◽  
Nur Ashida Salim ◽  
Mohammad Lutfi Othman
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Transmission Lines ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Particle Swarm ◽  
Optimal Operation ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Dynamic Economic Dispatch

This paper presents the application of particle swarm optimization (PSO) technique for solving the dynamic economic dispatch (DED) problem. The DED is one of the main functions in power system planning in order to obtain optimum power system operation and control. It determines the optimal operation of generating units at every predicted load demands over a certain period of time. The optimum operation of generating units is obtained by referring to the minimum total generation cost while the system is operating within its limits. The DED based PSO technique is tested on a 9-bus system containing of three generator bus, six load bus and twelve transmission lines.

Artificial Immune System for Multiple-Fuel-Constrained Generation Scheduling

2013 ◽  
Vol 376 ◽  
pp. 272-275
Author(s):  
Chao Lung Chiang
Keyword(s):  
Immune System ◽  
Power Systems ◽  
Power System ◽  
Artificial Immune System ◽  
Lagrange Function ◽  
Economic Dispatch ◽  
Numerical Results ◽  
Artificial Immune ◽  
Generation Scheduling ◽  
Size Population

This paper proposes an artificial immune system with a multiplier updating method (AIS-MU) for multiple-fuel-constrained generation scheduling of power systems. The artificial immune system (AIS) equips with a migration can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty of solution searching. The proposed method integrates the AIS and the MU that has merits of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population for the economic dispatch problem (EDP) of the multiple-fuel-constrained generation scheduling. Numerical results indicate that the proposed algorithm is more suitable than previous approaches in the practical economic dispatch of power system.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

scholarly journals Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

2021 ◽  
Vol 13 (12) ◽  
pp. 6644
Author(s):  
Ali Selim ◽  
Salah Kamel ◽  
Amal A. Mohamed ◽  
Ehab E. Elattar
Keyword(s):  
Power System ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Optimization Technique ◽  
Mathematical Formulation ◽  
Optimization Techniques ◽  
Sensitivity Factor ◽  
Hybrid Technique ◽  
Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

scholarly journals Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1581
Author(s):  
Deepak Kumar Gupta ◽  
Amitkumar V. Jha ◽  
Bhargav Appasani ◽  
Avireni Srinivasulu ◽  
Nicu Bizon ◽  
...  
Keyword(s):  
Power Plant ◽  
Power System ◽  
Frequency Control ◽  
Optimization Technique ◽  
Load Frequency Control ◽  
Frequency Deviation ◽  
Source Power ◽  
Load Frequency ◽  
Tie Line

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.

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