A New Approach for the Monte-Carlo Method to Locate and Size DGs in Distribution Systems
This paper proposes a new approach for a Parallel implementation of Monte-Carlo method aimed for optimal location and sizing of distributed generators in distribution networks. In this approach, a reduction of the solution space is performed, using heuristic strategies, to improve processing times,...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8859
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8859
- Palabra clave:
- Distributed generators
Monte-carlo method
Optimization techniques
Parallel processing
Distributed power generation
Electric load flow
Electric losses
Functions
Genetic algorithms
Location
MATLAB
Software testing
Distributed generators
Distribution systems
Mathematical formulation
Optimization strategy
Optimization techniques
Parallel implementations
Parallel Monte Carlo
Parallel processing
Monte Carlo methods
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| Summary: | This paper proposes a new approach for a Parallel implementation of Monte-Carlo method aimed for optimal location and sizing of distributed generators in distribution networks. In this approach, a reduction of the solution space is performed, using heuristic strategies, to improve processing times, power losses and voltage profiles considering the location of distributed generators in electric distribution networks. The mathematical formulation of the problem considers a single-objective function, which is composed by weighting factors associated with active power losses and square voltage error minimization; moreover, classical power flow constraints and distributed generation capabilities are considered as restrictions. A master-slave optimization strategy is used to solve the problem: the master stage corresponds to the proposed parallel Monte-Carlo with space solution reduction, which performs the optimal location of the distributed generators; the slave strategy is in charge of solving the resulting optimal power problem. Classical 33-node and 69node test systems are used to validate the proposed approach via MATLAB/MATPOWER software. For comparison purposes, the loss sensitivity factor (LSF), genetic algorithm (GA) and classical parallel Monte-Carlo (PMC) solutions are also tested. The simulations confirm that the proposed reduction to the space solution for the PMC provides improved results in comparison with the existing approaches. © 2018 IEEE. |
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