Binary differential evolution for the unit commitment problem

Abstract

The Unit Commitment Problem (UCP) is the task of finding an optimal turn on and turn off schedule for a group of power generation units over a given time horizon to minimize operation costs while satisfying the hourly power demand constraints. Various approaches exist in the literature for solving this problem. This paper reports the results of experiments performed on a series of the UCP test data using the binary differential evolution approach combined with a simple local search mechanism. In the future stages of the project, the algorithm will be applied to solve the UCP for the Turkish interconnected power system.

Maximum likelihood decoding based on pseudo-captured image templates for image sensor communication

“…Figure 4 shows a flowchart for the proposed computational complexity reduction algorithm. BDE, a modified version of DE [27], is a population-based algorithm that operates in binary search spaces [23][24][25][26]. There are two main studies [23,24] in literature, which modify the DE for application to binary optimization problems.…”

“…There are two main studies [23,24] in literature, which modify the DE for application to binary optimization problems. As a result of the experiments performed in [25,26], the BDE in [24] has been used in this study. BDE includes four main steps: initialization, mutation, recombination, and selection.…”

“…In the case of this study, the number of candidates is equivalent to the number of LEDs included in the transmitter. To reduce the computational complexity of our MLD algorithm, this paper also proposes a novel MLD process that uses the binary differential evolution (BDE) algorithm, which is a swarm intelligence technique [23][24][25][26]. Computer simulations are then used to evaluate the BER reduction gained with this step.…”

A Priority List-Based Binary Crow Search Algorithm for Unit Commitment Problem

“…Recently, various optimization techniques have been applied to the UCP such as simulated annealing (SA) [8 and 9], genetic algorithm (GA) [10 and 11], particle swarm optimization algorithm (PSO) [12][13][14][15], whale optimization algorithm [16], differential evolution [17], seeker optimization algorithm [18], [19] and ant colony algorithm [20]. These modern techniques have several advantages since they can handle different complicated constraints searching for a better solution quality.…”

“…In [16], whale optimization algorithm which is inspired from the hunting behavior of the humpback whales has been carried out for the solution of the UCP. In [17], differential evolution algorithm has been combined with a local search mechanism to solve the UCP. In [18], [19], seeker optimization algorithm has been applied for the UCP in the deregulated power environment.…”