access icon free Transmission expansion planning using AC-based differential evolution algorithm

The rapid growth of the transmission networks has brought more uncertainties and new requirements in the transmission expansion planning (TEP) to the planners. The existing methods of solving TEP problem have a drawback since the DC load flow and the relaxed load flow models have been utilized to solve TEP problem. In this work, the TEP problem is solved based on mixed integer nonlinear non-convex programming model. A meta-heuristic algorithm by the means of differential evolution algorithm (DEA) is employed as an optimisation tool. An AC load flow model is used in solving the TEP problem, where accurate and realistic results can be obtained. Furthermore, the work considers the constraints checking and system violation such as real and power generation limits, possible number of lines added and bus voltage limits. The proposed technique is tested on Garver's 6 bus system and IEEE 24 bus system and has shown high capability in considering the active and reactive power in the same manner and solving the TEP problem. The method produced improved results for the test systems. In terms of minimising the cost and the solution quality, the proposed method obtained good and challenging results comparing to the previous works.

Inspec keywords: power transmission economics; power transmission planning; reactive power; integer programming; power transmission lines; load forecasting; cost reduction; nonlinear programming; load flow; concave programming

Other keywords: bus voltage limit; active power; reactive power; metaheuristic algorithm; cost minimisation; AC-based differential evolution algorithm; DC load flow model; mixed integer nonlinear non-convex programming model; AC load flow model; IEEE 24 bus system; modified Garver 6-bus system; relaxed load flow model; optimisation tool; transmission expansion planning network; TEP; load forecasting; thermal limit; transmission line

Subjects: Power transmission, distribution and supply; Optimisation techniques; Power system management, operation and economics; Power system planning and layout

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http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2014.0001
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