Heuristic Optimization Techniques Applied to Power Systems
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 32339
Special Issue Editors
Interests: evolutionary computation applications to engineering; renewable energy; distribution power system; energy management; electric markets
Special Issues, Collections and Topics in MDPI journals
Interests: renewable energy; electricity storage; advanced batteries models; net metering; energy management; optimization algorithms
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The word heuristic comes from the Greek (heurískein) and means to find or to discover. Heuristic techniques find solutions that make it possible to solve problems that could hardly be solved with other techniques, such as linear and nonlinear programming, integer programming, and dynamic programming. Many heuristic techniques have been developed. Some of them have been inspired by nature, such as evolutionary algorithms. Others use different methodologies in order to discover good solutions in an efficient way. They have been successfully applied to solve a wide range of real‐world and complex engineering problems during the last few decades. One characteristic of these techniques is that they do not ensure that the solutions they provide are the best possible and that the necessary computational time is reasonable.
The topics of interest in this Special Issue include heuristic techniques applied to any field related to power systems, such as energy generation, distribution, and transmission networks; smart grids; energy storage; renewable energy integration; electric vehicles; electricity markets; electricity demands; etc.
Heuristic techniques of interest for this Special Issue include, but are not limited to, the following:
- Genetic algorithms;
- Evolution strategies;
- Evolutionary programming;
- Differential evolution;
- Particle swarm optimization;
- Ant colony algorithm;
- Tabu Search;
- Simulated annealing;
- Pareto multi-objective optimization;
- Pattern search.
Prof. Dr. José L. Bernal-Agustín
Prof. Dr. Rodolfo Dufo-López
Guest Editors
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Keywords
- heuristic techniques
- power systems
- optimization
- management
- operation.
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