Economic Analysis of Lagrangian and Genetic Algorithm for the Optimal Capacity Planning of Photovoltaic Generation

The optimal allocation problem for a stand-alone photovoltaic (SPV) generation can be achieved by good compromise between system objective and constraint requirements. The Lagrange technique (LGT) is a traditional method to solve such constrained optimization problem. To consider the nonlinear featu...

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Bibliographic Details
Published in:Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-7
Main Authors: Ling, Jeeng-Min, Liu, Ping-Hsun
Format: Article
Language:English
Subjects:
Algorithms
Allocations
Alternative energy
Battery
Component reliability
Constraints
Cost analysis
Cost function
Design
Economic analysis
Engineering
Evolution
Genetic algorithms
Lagrange multiplier
Mathematical analysis
Mutation
Optimization
Optimization techniques
Parameter estimation
Photovoltaic cells
Planning
Power reliability
Radiation
Renewable resources
Simulation
Solar cells
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Summary:The optimal allocation problem for a stand-alone photovoltaic (SPV) generation can be achieved by good compromise between system objective and constraint requirements. The Lagrange technique (LGT) is a traditional method to solve such constrained optimization problem. To consider the nonlinear features of reliability constraints evolving from the consideration of different scenarios, including variations of component cost, load profile and installation location, the implementation of SPV generation planning is time-consuming and conventionally implemented by a probability method. Genetic Algorithm (GA) has been successfully applied to many optimization problems. For the optimal allocation of photovoltaic and battery devices, the cost function minimization is implemented by GA to attain global optimum with relative computation simplicity. Analytical comparisons between the results from LGT and GA were investigated and the performance of simulation was discussed. Different planning scenarios show that GA performs better than the Lagrange optimization technique.
ISSN:1024-123X
1563-5147
DOI:10.1155/2015/713417