Abstract

This paper presents an optimization method for solving the Power Economic Dispatch (PED) problem of thermal generation units with multiple fuels and valve point loadings. The proposed optimizer is a variant of Differential Evolution (DE) characterized as a Stud Differential Evolution (SDE), which has been proposed earlier and implemented on a hydrothermal energy system. In SDE, an operator named Stud Crossover (SC) is introduced in the conventional DE during the trial vector updating process. In SC operator, a best vector gives its optimal information to all other population members through mating. The proposed algorithm’s effectiveness to solve Multiple Fuel PED problem, with and without Valve Point Loading Effects (VPLEs), has been validated by testing it on 10 machine multiple fuel standard test systems having 2400 MW, 2500 MW, 2600 MW, and 2700 MW load demands. The results depict the strength of SDE over various other methods in the literature.

Highlights

  • The Power Economic Dispatch (PED) is one of the essential steps in operation and planning of a power system

  • In order to validate the effectiveness of the proposed Stud Differential Evolution (SDE), it has been tested on two 10-machine multiple fuel test systems

  • The following conclusions can be made: and it can be concluded that the proposed algorithm can effectively and efficiently explore the SDE is a potential solution methodology for the PED problem, as it addresses the convex and

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Summary

Introduction

The Power Economic Dispatch (PED) is one of the essential steps in operation and planning of a power system. Until now the effectiveness of stud incorporated DE (SDE) has not been examined as a competent solution to convex/non-convex power economic dispatch problems as well as a potential search approach, thereby rendering a research gap in the literature. Another major reason behind the development of SDE is the incompetency of conventional DE in solving complicated multi-modal problems efficiently as it does not always proceed to the global optimum solution.

Problem Formulation
Objective Function
Equality Constraint
Power Economic Dispatch Considering Multiple Fuel Options Only
Differential
1: Randomly
Simulation Results
System 1
Methods
10 Machine
Conclusions
Full Text
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