Development of hybridized evolutionary algorithm for solving economic environmental dispatch problem in Nigerian power system
By: Oyajide, Depo O
.
Contributor(s): Olanrewaju, Olanite.
Publisher: Johannesburg AkiNik Publications 2022Edition: Vol.3(1), Jan-Jun.Description: 9-17p.Subject(s): Electrical EngineeringOnline resources: Click here
In:
International journal of advances in electrical engineeringSummary: Economic Pollution Dispatch (EED) has been the subject of several approaches in recent years. EED-
based algorithms may be used in place of traditional optimization approaches. Many different types of
modern power generation units include non-linear properties such as valve charging effects, ramp rate
constraints, and polynomial estimates of fuel cost and pollution that these approaches haven't been
proven to explain. All of this may lead to poor results and lengthy calculations. Because of the global
optimum of functional EED problems, conventional techniques have found it difficult to produce a
range of metaheuristic solutions in the last two decades. EP, PSO, DSO, self-adapting modified firefly
algorithm, chaotic quantum genetic algorithm. Various methods for addressing the EED issue have
been described in the literature. As a result, traditional approaches that rely on derivate are unable to
provide large ranges of Pareto fronts since the real EED problem is significantly non-linear. Load
scheduling becomes more difficult because of these constraints. Meta-heuristic optimization is
becoming more popular among academics as a way to get around the limits of traditional approaches.
Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Classification Based Surrogate Aided
Algorithm (CSEA) were used in this work to solve the issue of convergence to a diverse assortment of
Pareto front solutions for the EED problem. This method will handle convex and non-convex dispatch
economic issues, as well as microgrid dispatch problems. With demand for the power system and
operational limit limitations, the convex ED problem yields the quadratic cost structure. The non-
convex ED problem explains the engine's nonlinearities, such as valve point loads, restricted working
zones, and various power alternatives. This problem aims to find a strategy for distributing a generation
of dedicated generators in order to meet demand while minimizing fuel costs and pollutant emissions
while adhering to different fairness and inequality restrictions. Nonlinear and linear constraints are
used in a multi-objective optimization problem with the purpose of reducing costs and pollution. This
research will provide the groundwork for more efficient power generator dispatch, allowing power
system managers to make more informed choices.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-2067 |
Economic Pollution Dispatch (EED) has been the subject of several approaches in recent years. EED-
based algorithms may be used in place of traditional optimization approaches. Many different types of
modern power generation units include non-linear properties such as valve charging effects, ramp rate
constraints, and polynomial estimates of fuel cost and pollution that these approaches haven't been
proven to explain. All of this may lead to poor results and lengthy calculations. Because of the global
optimum of functional EED problems, conventional techniques have found it difficult to produce a
range of metaheuristic solutions in the last two decades. EP, PSO, DSO, self-adapting modified firefly
algorithm, chaotic quantum genetic algorithm. Various methods for addressing the EED issue have
been described in the literature. As a result, traditional approaches that rely on derivate are unable to
provide large ranges of Pareto fronts since the real EED problem is significantly non-linear. Load
scheduling becomes more difficult because of these constraints. Meta-heuristic optimization is
becoming more popular among academics as a way to get around the limits of traditional approaches.
Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Classification Based Surrogate Aided
Algorithm (CSEA) were used in this work to solve the issue of convergence to a diverse assortment of
Pareto front solutions for the EED problem. This method will handle convex and non-convex dispatch
economic issues, as well as microgrid dispatch problems. With demand for the power system and
operational limit limitations, the convex ED problem yields the quadratic cost structure. The non-
convex ED problem explains the engine's nonlinearities, such as valve point loads, restricted working
zones, and various power alternatives. This problem aims to find a strategy for distributing a generation
of dedicated generators in order to meet demand while minimizing fuel costs and pollutant emissions
while adhering to different fairness and inequality restrictions. Nonlinear and linear constraints are
used in a multi-objective optimization problem with the purpose of reducing costs and pollution. This
research will provide the groundwork for more efficient power generator dispatch, allowing power
system managers to make more informed choices.
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