Abstract
The Dynamic Economic Environmental Dispatch Problem (DEEDP) is a major issue in power system control. It aims to find the optimum schedule of the power output of thermal units in order to meet the required load at the lowest cost and emission of harmful gases. Several constraints, such as generation limits, valve point loading effects, prohibited operating zones, and ramp rate limits, can be considered. In this paper, a method based on Teaching-Learning-Based Optimization (TLBO) is proposed for dealing with the DEEDP problem where all aforementioned constraints are considered. To investigate the effectiveness of the proposed method for solving this discontinuous and nonlinear problem, the ten-unit system under four cases is used. The obtained results are compared with those obtained by other metaheuristic techniques. The comparison of the simulation results shows that the proposed technique has good performance.
Highlights
With the growing demand for electricity and rising fuel prices, electricity companies are constantly working to ensure continuous and reliable electrical power supply to their customers
In order to achieve this, system operators need to constantly adjust the control variables of power networks. This extremely difficult task is performed by the resolution of the Economic Dispatch Problem (EDP), which aims to determine the production levels of all thermal units which guarantee a balance between production and consumption at the lowest cost
A new metaheuristic called TeachingLearning-Based Optimization (TLBO) algorithm was used for solving the Dynamic Economic Environmental Dispatch Problem (DEEDP)
Summary
With the growing demand for electricity and rising fuel prices, electricity companies are constantly working to ensure continuous and reliable electrical power supply to their customers. The TLBO algorithm is based on two basic methods of learning: (i) through the teacher, known as the teacher phase, and (ii) through interaction with other students, called student phase In this optimization algorithm, a group of students is considered as a population and the different subjects offered to the students are considered to be the feasible solutions and a student's result is considered to be the value of the fitness function [16]. It has been shown that TLBO has the advantage of only requiring a few control parameters, such as the number of students in the class and the number of subjects presented for students, for its operation [17, 18] In this regard, a TLBO-based method is proposed for dealing with the problem of DEEDP. The simulation results obtained by the proposed method are compared with other metaheuristic techniques
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