Unit commitment with ramp rate constraints using the simulated annealing algorithm

Abstract

A Simulated Annealing (SA) algorithm has been developed for solving the unit commitment (UC) problem. The algorithm is used for the scheduling of the generating units, while a quadratic programming routine carries out the economic dispatch. New rules concerning the cooling schedule and the random generation of the initial solution of the system are presented. A new approach for generating feasible neighboring solutions contributes to the reduction of the required CPU time. Ramp rate constraints have also been added in order to obtain more realistic results. Numerical simulations have proved the effectiveness of the proposed model.

Review on Economic Load Dispatch and Associated Artificial Intelligence Algorithms

“…New techniques have been developed using stochastic approaches to solve optimization problems. Examples are an adaptive Hopfield neural network [4], the simulated annealing method [5] and genetic algorithms (GA).…”

Unit commitment using Micro Genetic Algorithm

“…The LR method provides a fast solution but it suffers from problem of numerical convergence. In order to get better solution, Artificial Neural Networks (ANN) techniques such as Hopfield Neural Network (HNN) [7,8], Heuristics methods such as Genetic Algorithm (GA) [9,10] and Simulated Annealing [11,12], Meta-Heuristic methods like Evolutionary Programming (EP) [13], Particle Swarm Optimization (PSO) [14,15], Ant Colony Search Algorithm (ACSA) [16] and Tabu Search Algorithm (TSA) [17] have been effectively used for solving the UC problem. Hybrid methods such as Lagrangian Relaxation and Genetic Algorithm (LRGA) [18], Lagrangian Relaxation and Particle Swarm Optimization (LRPSO) [19], Evolutionary Programming with Tabu Search Algorithm (EP-TSA) [20] and LR-EP [21] have been used for solving the UC problem.…”