Transmission Expansion Planning Using a Novel Meta-Heuristic Method

Ahmet Ova, Erdi Dogan, Sevki Demirbas

Abstract


Transmission Expansion Planning (TEP) is an optimization study aimed at determining new transmission lines to be added to the transmission network in order to expansion or reinforcement of the network within the scope of different purpose functions in parallel with the increase in demand and generation. In this study, the objective function of the TEP problem is determined as minimizing the investment costs of the lines to be added to the network and the loss of load cost. Forensic Based Investigation Optimization (FBIO), a meta-heuristic optimization method, is proposed for the first time to solve the TEP problem. The proposed method is applied for 4 different scenarios on IEEE 24-bus test system which is one of the most used test system using the DC model.  Obtained results are compared with the results which get using 5 different methods in literature. Furthermore, system security (N-1) is taken into account for the two scenarios. Python programming language is applied in modeling and solving the problem. Panda Power, an open source Python library, is used in modeling the IEEE 24- bus test system and carrying out power flows.

Keywords


Transmission expansion planning, meta-heuristic optimization, investment cost, cost of energy not supplied, dc model

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v12i4.13495.g8567

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