Evolutionary Algorithm Applied to Economic Load Dispatch

2018 ◽  
pp. 65-116
Keyword(s):  
Power Systems ◽  
Economic Load Dispatch ◽  
Grey Wolf ◽  
Solution Quality ◽  
Krill Herd Algorithm ◽  
Reaction Optimization ◽  
Krill Herd ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Statistical Results

This chapter introduces various evolutionary algorithms, namely grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO) algorithms, for solving the economic load dispatch (ELD) problem of various power systems. To demonstrate the superiority of the proposed approaches in solving non-convex, non-linear and constrained ELD problem, the aforesaid approaches are implemented on 10-unit, 15-unit, 40-unit, 80-unit, and 140-unit test systems. It is observed from the simulation results that HCRO exhibits significantly better performance in terms of solution quality and convergence speed for all the cases compared to other discussed algorithms. Furthermore, the statistical results confirm the robustness of the proposed HCRO algorithm.

Description of Various Evolutionary Optimization Techniques

2018 ◽  
pp. 1-45
Keyword(s):  
Power Systems ◽  
Behavioral Pattern ◽  
Optimization Techniques ◽  
Grey Wolf ◽  
Krill Herd Algorithm ◽  
Reaction Optimization ◽  
Krill Herd ◽  
Teaching Learning Based Optimization ◽  
Evolutionary Optimization Techniques ◽  
Teaching Learning

Despite the success of various classical optimization techniques, there remains a large class of problems where either these methods are unable to find the satisfactory results or the computational times are sufficiently large. Several heuristic methods have emerged in the recent years as complementary tools to various mathematical approaches. These methods include genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), differential evolution (DE), and so on. Researchers are constantly trying to learn from the behavioral pattern of organisms and implementing those ideas and philosophies in solving optimizing problems. In this chapter, a few efficient optimization algorithms, namely grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO) algorithms, and hybrid CRO (HCRO) are discussed, and in the subsequent chapters, the performance of the aforesaid algorithms are investigated by applying them in a few areas of power systems.

A Comparative Study of Improved Teaching Learning Based Optimization Technique on Economic Load Dispatch Problem with Generator Constraints

2016 ◽  
Vol 5 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Sumit Banerjee ◽  
Chandan Chanda ◽  
Deblina Maity
Keyword(s):  
Optimization Technique ◽  
Inequality Constraints ◽  
Test System ◽  
Economic Load Dispatch ◽  
Thermal Plant ◽  
Equality And Inequality Constraints ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Operational Constraints ◽  
Rate Limit

This article presents a novel improved teaching learning based optimization (I-TLBO) technique to solve economic load dispatch (ELD) problem of the thermal plant without considering transmission losses. The proposed methodology can take care of ELD problems considering practical nonlinearities such as ramp rate limit, prohibited operating zone and valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. I-TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. The effectiveness of the proposed algorithm has been verified on test system with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm.

scholarly journals An improved teaching learning–based optimization algorithm for congestion management with the integration of solar photovoltaic system

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1231-1237
Author(s):  
S T Suganthi ◽  
D Devaraj
Keyword(s):  
Power Systems ◽  
Congestion Management ◽  
Test System ◽  
Photovoltaic System ◽  
Management Technique ◽  
Optimization Approach ◽  
Solar Photovoltaic ◽  
Teaching Learning Based Optimization ◽  
Solar Photovoltaic System ◽  
Teaching Learning

In restructured power systems, transmission congestion is an imperative issue. Establishment of solar photovoltaic system at appropriate areas is likely to relieve congestion in transmission lines in the restructured power systems. Congestion management technique by utilizing solar photovoltaic sources, using an improved teaching learning–based optimization, is investigated in this article. Bus sensitivity factors which have the direct influence on the congested lines are utilized to locate the solar photovoltaic sources at appropriate areas. Congestion management is figured as an optimization problem with a goal of limiting the congestion management price utilizing the improved teaching learning–based optimization approach, which espouses the self-driven learning principle. IEEE-30 bus test system is simulated and tested in MATLAB environment so as to demonstrate the viability of the suggested methodology than different methodologies.

scholarly journals Noninferior Solution Grey Wolf Optimizer with an Independent Local Search Mechanism for Solving Economic Load Dispatch Problems

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2274 ◽  
Author(s):  
Jianzhong Xu ◽  
Fu Yan ◽  
Kumchol Yun ◽  
Lifei Su ◽  
Fengshu Li ◽  
...  
Keyword(s):  
Power Systems ◽  
Local Search ◽  
Optimization Problem ◽  
Search Strategy ◽  
Local Optimum ◽  
Economic Load Dispatch ◽  
Problem Solution ◽  
Main Task ◽  
Grey Wolf ◽  
Local Search Strategy

The economic load dispatch (ELD) problem is a complex optimization problem in power systems. The main task for this optimization problem is to minimize the total fuel cost of generators while also meeting the conditional constraints of valve-point loading effects, prohibited operating zones, and nonsmooth cost functions. In this paper, a novel grey wolf optimization (GWO), abbreviated as NGWO, is proposed to solve the ELD problem by introducing an independent local search strategy and a noninferior solution neighborhood independent local search technique to the original GWO algorithm to achieve the best problem solution. A local search strategy is added to the standard GWO algorithm in the NGWO, which is called GWOI, to search the local neighborhood of the global optimal point in depth and to guarantee a better candidate. In addition, a noninferior solution neighborhood independent local search method is introduced into the GWOI algorithm to find a better solution in the noninferior solution neighborhood and ensure the high probability of jumping out of the local optimum. The feasibility of the proposed NGWO method is verified on five different power systems, and it is compared with other selected methods in terms of the solution quality, convergence rate, and robustness. The compared experimental results indicate that the proposed NGWO method can efficiently solve ELD problems with higher-quality solutions.

scholarly journals Optimal Power Flow Incorporating FACTS Devices and Stochastic Wind Power Generation Using Krill Herd Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1043 ◽  
Author(s):  
Arsalan Abdollahi ◽  
Ali Ghadimi ◽  
Mohammad Miveh ◽  
Fazel Mohammadi ◽  
Francisco Jurado
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Wind Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Wind Power Generation ◽  
Krill Herd Algorithm ◽  
Facts Devices ◽  
Optimal Power ◽  
Krill Herd

This paper deals with investigating the Optimal Power Flow (OPF) solution of power systems considering Flexible AC Transmission Systems (FACTS) devices and wind power generation under uncertainty. The Krill Herd Algorithm (KHA), as a new meta-heuristic approach, is employed to cope with the OPF problem of power systems, incorporating FACTS devices and stochastic wind power generation. The wind power uncertainty is included in the optimization problem using Weibull probability density function modeling to determine the optimal values of decision variables. Various objective functions, including minimization of fuel cost, active power losses across transmission lines, emission, and Combined Economic and Environmental Costs (CEEC), are separately formulated to solve the OPF considering FACTS devices and stochastic wind power generation. The effectiveness of the KHA approach is investigated on modified IEEE-30 bus and IEEE-57 bus test systems and compared with other conventional methods available in the literature.

A Novel Hybrid Krill Herd Algorithm for Improving the Performance of Electric Power Systems Operation

2019 ◽  
Vol 92 (2-4) ◽  
pp. 86-96
Author(s):  
Aboubakr Khelifi ◽  
Bachir Bentouati ◽  
Saliha Chettih ◽  
Ragab El-Sehiemy
Keyword(s):  
Power Systems ◽  
Electric Power ◽  
Electric Power Systems ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Power Systems Operation

A novel sufficient bio-inspired optimisation method based on modified krill herd algorithm to solve the economic load dispatch

2014 ◽  
Vol 6 (6) ◽  
pp. 416 ◽  
Author(s):  
Abdollah Kavousi Fard ◽  
Mohammad Reza Akbari Zadeh ◽  
Bahram Dehghan ◽  
Farzaneh Kavousi Fard
Keyword(s):  
Economic Load Dispatch ◽  
Krill Herd Algorithm ◽  
Krill Herd
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