Transmission line planning using global best artificial bee colony method
Introduction. Network expansion, substation planning, generating expansion planning, and load forecasting are all aspects of modern power system planning. The aim of this work is to solve network planning considering both future demand and all equality and inequality constraints. The transmission ne...
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| Format: | Article |
| Language: | English |
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National Technical University "Kharkiv Polytechnic Institute"
2023-08-01
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| Series: | Electrical engineering & Electromechanics |
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| Online Access: | http://eie.khpi.edu.ua/article/view/267178/279990 |
| Summary: | Introduction. Network expansion, substation planning, generating expansion planning, and load forecasting are all aspects of modern power system planning. The aim of this work is to solve network planning considering both future demand and all equality and inequality constraints. The transmission network design problem for the 6-bus system is considered and addressed using the Global Best Artificial Bee Colony (GABC) method in this research. The program is written in the Matrix Laboratory in MATLAB environment using the proposed methodology. Novelty of the work consist in considering the behavior of bees to find food source in most optimized way in nature with feature of user based accuracy selection and speed of execution selection on any scale of the system to solve Transmission Lines Expansion Problem (TLEP). The proposed method is implemented on nonlinear mathematical function and TLEP function. When demand grows, the program output optimally distributes new links between new generation buses and old buses, determines the overall minimum cost of those links, and determines if those linkages should meet power system limits. Originality of the proposed method is that it eliminated the need of load shedding while planning the future demand with GABC method. Results are validated using load flow analysis in electrical transient analyzer program, demonstrating that artificial intelligence approaches are accurate and particularly effective in non-linear transmission network planning challenges. Practical value of the program is that it can use to execute cost oriented complex transmission planning decision.
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| ISSN: | 2074-272X 2309-3404 |