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Transmission network expansion planning involves determining the optimal selection, routes, types, and quantities of new electrical circuits required to satisfy future electricity demand at minimal financial cost. This challenge represents a complex, non-linear, mixed-integer optimization problem. To address this, the multi-verse optimizer algorithm was applied alongside security constraints to deliver economic and reliable network expansion strategies. The optimization approach benefits from a straightforward architecture, adaptive control parameters, and the capacity to avoid local stagnation. Future electricity demand up to 2030 was projected using an adaptive neuro-fuzzy inference system. The optimization method was tested on two real-world Egyptian transmission networks, specifically the West Delta System and the 500 kilovolt Extra High Voltage System. Simulation outcomes demonstrate that the algorithm successfully generates secure transmission routes while minimising overall investment costs.
Planning electrical grid expansions is essential for preventing power shortages and blackouts as energy demand grows. By identifying the most economical combination and placement of new power circuits while maintaining grid security, transmission operators can prevent unnecessary capital expenditure. This enables power authorities to align major infrastructure investments directly with reliable projections of future electricity consumption.
This method offers a computational planning tool for electrical utilities, system operators, and infrastructure consultants seeking to design cost-effective, secure network upgrades. The approach is at an applied research stage, having been validated through simulated case studies on two realistic Egyptian transmission systems rather than deployed in live operations. Transitioning this research to market would require integration into commercial power system planning software suites.
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Transmission Network Expansion Planning (TNEP) is an important issue in electrical power systems. It is a mixed integer, non-linear, non-convex optimization problem which aims to optimal selection of the routs, types, and number of the added circuits to face the expected future predicted load forecasting at minimum costs. This paper proposes the application of Multi-Verse Optimizer (MVO) for solving the TNEP with security constraints. MVO has various merits of being simple structure, having adaptive control parameter, and operating with high ability to escape the local optima stagnation. The MVO has been developed and applied to solve the TNEP problem for two realistic transmission Egyptian networks of West Delta System (WDS) and 500 kV of Extra High Voltage System (EHVS). The predicted load forecasting up to 2030 is considered based on the adaptive neuro-fuzzy inference system (ANFIS). The simulation results for the two systems show the capability of the proposed MVO to solve efficiently the TNEP problem. The MVO superiority is proven to produce economic planning and secure transmission routes.
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DOI: 10.1109/itce.2019.8646329
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