Abstract
We consider a Unit Commitment Problem (UCP) addressing not only the economic objective of minimizing the total production costs—as is done in the standard UCP—but also addressing environmental concerns. Our approach utilizes a multi-objective formulation and includes in the objective function a criterion to minimize the emission of pollutants. Environmental concerns are having a significant impact on the operation of power systems related to the emissions from fossil-fuelled power plants. However, the standard UCP, which minimizes just the total production costs, is inadequate to address environmental concerns. We propose to address the UCP with environmental concerns as a multi-objective problem and use a metaheuristic approach combined with a non-dominated sorting procedure to solve it. The metaheuristic developed is a variant of an evolutionary algorithm, known as Biased Random Key Genetic Algorithm. Computational experiments have been carried out on benchmark problems with up to 100 generation units for a 24 h scheduling horizon. The performance of the method, as well as the quality, diversity and the distribution characteristics of the solutions obtained are analysed. It is shown that the method proposed compares favourably against alternative approaches in most cases analysed.
Highlights
The power system generation scheduling is composed of two tasks [1,2]: On the one hand, one must determine the scheduling of the turn-on and turn-off of the thermal generating units; on the other hand, one must determine the economic dispatch (ED), which assigns the amount of power that should be produced by each on-line unit in order to minimize the total operating cost for a specific time generation horizon
The current population of solutions is evolved by the GA operators onto a new population as follows: the elite set is formed by 20% of best solutions; 40% of the new population is obtained by introducing mutants; and the remaining 60% of the population is obtained by biased reproduction, which is accomplished by having both a biased selection and a biased crossover
Even if we look at the most relative performance of the Biased Random Key Genetic Algorithm (BRKGA), which occurs for the problem with 80 generation units, it can be seen that the BRKGA dominates in about 59%, 34.6% and 21.1% of the non-dominated solutions found by NSGA II, Niched Pareto Genetic Algorithm (NPGA), and SPEA2, respectively
Summary
The power system generation scheduling is composed of two tasks [1,2]: On the one hand, one must determine the scheduling of the turn-on and turn-off of the thermal generating units; on the other hand, one must determine the economic dispatch (ED), which assigns the amount of power that should be produced by each on-line unit in order to minimize the total operating cost for a specific time generation horizon. The rapid growth in the use of fossil fuels has led to the emission of a large amount of atmospheric pollutants, that are continuously released into the environment. The increased public awareness regarding the harmful effects of atmospheric pollutants on the environment, as well as the tightening of environmental regulations, namely due to the goals imposed by the Kyoto protocol and later by the Paris Agreement [3], have forced power utilities to search for different operational. These new strategies must lead to a reduction in pollution and environmental emissions. Power utilities should look for solutions that in addition to being cost-effective must be environmentally friendly. The carbon emissions produced by fossil-fueled thermal power plants need to be minimized. We are in the presence of a problem with two, usually conflicting objectives
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
