Ant colony search algorithm for unit commitment

Abstract

In this paper, ant colony search algorithm (ACSA) is proposed to solve thermal unit commitment problem. ACSA is a new cooperative agents approach, which is inspired by the Observation of the behaviors of real ant colonies on the topic of ant trial formation and foraging methods. In the ACSA, a set of cooperating agents called "ants" cooperates to find good solution for unit commitment problem of thermal units. The merits of ACSA a r e parallel search and optimization capabilities. The problem is decomposed in two subproblems. The unit commitment sub-problem is solved by the ant colony search algorithm method and the economic dispatch sub-problem is solved by the lambda-iteration method. The unit commitment problem is formulated as the minimization of the performance index, which is the sum of objectives (fuel cost, start-up cost) and some constraints (power balance, generation limits, spinning reserve, minimum up time and minimum down time). This proposed approach is tested and compared to conventional Lagrangian relaxation (LR), genetic algorithm (GA), evolutionary programming (EP), Lagrangian relaxation and genetic algorithm (LRGA) on the 10 unit system.

Unit Commitment by an Improved Priority List Method

“…Also towards policy making and planning, this UC is useful as a tool to perform market simulations and assess the impact of specific measures. On the other hand, from the viewpoint of a single market player, a price-based UC problem can be considered, optimizing output towards maximum profit, based on electricity price forecasts [3,4,5 ].A wide range of solution techniques for the UC problem have been proposed and developed over the years. Examples include priority listing (heuristics), Lagrangian relaxation, dynamic programming, genetic algorithms, etc., together with hybrid methods combining several of these.…”

Unit commitment scheduling using binary differential evolution algorithm

“…Also, the Lagrangian relaxation method [11][12][13][14][15][16] is viewed as a applicable method to solve the problem in large-scale power system. Furthermore, various artificial intelligent algorithms such as genetic algorithm [17][18][19][20][21][22][23], simulated annealing [24][25][26], tabu search [27], ant colony [28], memetic algorithm [29], evolutionary programming [30] and particle swarm optimization [31][32][33][34] are applied.…”

Economic dispatch with valve point effect using iteration particle swarm optimization

“…Step 5: Update the swarm The new position of each particle in d dimensional search space is computed using (8). If the evaluation function of ith particle is better than the previous pbest, the current value is set to be pbest.…”

“…Modern heuristic optimization methods are proposed by researcher based on artificial intelligence concepts such as evolutionary programming [6], genetic algorithm [7], simulated annealing, ant colony optimization [8], tabu search [9], artificial immune system [10] and particle swarm optimization [1,11]. These methods do not always guarantee to find global best solution but they often achieve a fast and near global optimum solution.…”