Abstract
Secure and economic operation of the power system is one of the prime concerns for the engineers of 21st century. Unit Commitment (UC) represents an enhancement problem for controlling the operating schedule of units in each hour interval with different loads at various technical and environmental constraints. UC is one of the complex optimization tasks performed by power plant engineers for regular planning and operation of power system. Researchers have used a number of metaheuristics (MH) for solving this complex and demanding problem. This work aims to test the Gradient Based Optimizer (GBO) performance for treating with the UC problem. The evaluation of GBO is applied on five cases study, first case is power system network with 4-unit and the second case is power system network with 10-unit, then 20 units, then 40 units, and 100-unit system. Simulation results establish the efficacy and robustness of GBO in solving UC problem as compared to other metaheuristics such as Differential Evolution, Enhanced Genetic Algorithm, Lagrangian Relaxation, Genetic Algorithm, Ionic Bond-direct Particle Swarm Optimization, Bacteria Foraging Algorithm and Grey Wolf Algorithm. The GBO method achieve the lowest average run time than the competitor methods. The best cost function for all systems used in this work is achieved by the GBO technique.
Highlights
Unit Commitment (UC) is one of the complex optimization tasks performed by power plant engineers for regular planning and operation of power system [1,2]
Researchers have used a number of metaheuristics (MH) such as GA, PSO, ACO, Grey Wolf Optimization (GWO) etc for solving this complex and demanding problem
For 4-unit system, best cost yielded by Gradient Based Optimizer (GBO) is 74379 $ which is less as compared to other algorithms
Summary
In [22], authors have modelled the unit commitment problem for hydropower plants considering multiple hydraulic heads by a two-layer nested optimization approach with Cuckoo Search (CS) and dynamic programming. In [25], the authors modelled a security constrained scenario-based unit commitment problem considering battery energy storage and solved the problem by deep learning. They concluded that incorporating battery energy storage could reduce the operating cost by 4.7%. In [27], authors formulated the unit commitment problem considering random generation of wind power and solved the same by Mixed Integer Linear programming. The convergence and robustness curves are performed for all used techniques
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