Theoretical Investigation of Combined Use of PSO, Tabu Search and Lagrangian Relaxation methods to solve the Unit Commitment Problem

Abstract

Solving the Unit Commitment problem (UCP) optimizes the combination of production units operations and determines the appropriate operational scheduling of each production units to satisfy the expected consumption which varies from one day to one month. Besides, each production unit is conducted to constraints that render this problem complex, combinatorial and nonlinear. In this paper, we proposed a new strategy based on the combination three optimization methods: Tabu search, Particle swarm optimization and Lagrangian relaxation methods in order to develop a proper unit commitment scheduling of the production units while reducing the production cost during a definite period. The proposed strategy has been implemented on a the IEEE 9 bus test system containing 3 production unit and the results were promising compared to strategies based on metaheuristic and deterministic methods.

Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations

“…In this context, production unit operation planning should be established to select which of the production units to be available to supply the forecast load of the system over a future period. Many numerical optimization techniques have been proposed to address the UC problem, like dynamic programming [6][7][8][9], the Lagrangian relaxation method [10][11][12][13], mixed variable programming [14][15], and the branch-andbound method [16]. The dynamic programming method is simple but has a rather long computation time to converge to the optimal solution.…”