An Efficient Scheduling for Security Constraint Unit Commitment Problem Via Modified Genetic Algorithm Based on Multicellular Organisms Mechanisms

TL;DR

This paper introduces a novel modified genetic algorithm based on Multicellular Organisms Mechanisms to efficiently solve the complex Security Constraint Unit Commitment problem in power grid operations, improving convergence speed and solution quality.

Contribution

The paper proposes a new GAMOM algorithm tailored for SCUC, combining genetic algorithms with multicellular mechanisms to enhance optimization performance.

Findings

Faster convergence compared to traditional methods

Reduced total operating costs in simulations

Effective constraint satisfaction in power system scheduling

Abstract

Security Constraint Unit commitment (SCUC) is one of the significant challenges in operation of power grids which tries to regulate the status of the generation units (ON or OFF) and providing an efficient power dispatch within the grid. While many researches tried to address the SCUC challenges, it is a mixed-integer optimization problem that is difficult to reach global optimum. In this study, a novel modified genetic algorithm based on Multicellular Organisms Mechanisms (GAMOM) is developed to find an optimal solution for SCUC problem. The presentation of the GAMOM on the SCUC contain two sections, the GA and modified GAMOM sections. Hence, a set of population is considered for the SCUC problem. Next, an iterative process is used to obtain the greatest SCUC population. Indeed, the best population is selected so that the total operating cost is minimized and also all system and units constraints are satisfied. The effectiveness of the proposed GAMOM algorithm is determined by the simulation studies which demonstrate the convergence speed. Finally, the proposed technique is compared with well-known existing approaches.