An Improved Ant Colony Search Algorithm for Unit Commitment Application

Abstract

This paper presents an improved ant colony search algorithm that is suitable for solving unit commitment (UC) problems. Ant colony search algorithm (ACSA) is a metaheuristic technique for solving hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback, distributed computation as well as constructive greedy heuristic. Positive feedback is for fast discovery of good solutions, while the greedy heuristic helps find adequate solutions in the early stages of the search process, and finally distributed computation avoids early convergence. The ACSA was inspired by the behavior of real ants that are capable of finding the shortest path from food sources to the nest without using visual cues. The constraints used in the solution of the UC problem using this approach are: real power balance, real power operating limits of generating units, spinning reserve, start up cost, and minimum up and down time constraints. The approach determines the units schedule followed by the consideration of unit transition related constraints. The proposed approach is expected to yield a better operational cost for the UC problem and use less computational resources compared to the traditional ACSA.

EADPSODV Technique for Solving UC Problem

“…So for the mutation operator selection the probabilities obtained from step 2 are compared with each other and the better value among them is passed to select the required appropriate value. For illustration purpose, following mutation operators are engaged [16]:…”

“…EADPSODV will be using the ant colony search system to realize the appropriate mutation operator for a faster pursuit in attaining a global solution. Here, the mutation operation of DE is combined with velocity part of PSO [13][14][15][16].…”

Unit commitment with nature and biologically inspired computing

“…ACSA clearly shows in a single cycle its effectiveness to find the better and faster solution. El-sharkh et al [78] presented an improved ACSA for solving UC with a property that cooperating agents can exchange information among them. A Gaussian load distribution which makes the test system as working in realistic environment is discussed by Simon et al [79] for UC Problem.…”

Ant Colony Optimization for Resolving Unit Commitment Issues by Considering Reliability Constraints

“…ACO is an optimization method that mimics ant behavior in determining the shortest distance between nests to food source. Some studies show that ACO has a better solution than other methods used in the study [2]- [4]. Nevertheless, the use of ACO as a UC solution has not been used in scheduling a largescale power generator because it still uses ten generating units as a test system.…”