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The implementation of a multistate planning model for power generation-transmission expansion, as opposed to traditional independent planning frameworks, addresses the escalating energy demand and the need for efficient planning. This study applies a multistate DC-planning model to the 230 kV network of Ecuador’s National Interconnected System (NIS), aiming to minimize operational costs and the construction of new infrastructure. By employing a mixed-integer nonlinear programming (MINLP) approach and a neural network-generated demand forecasts, the electricity expansion spanning the period from 2023 to 2033 is analyzed. This methodology facilitates the delineation of expansion zones, thereby mitigating long-term costs.

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Optimization Model for Coordinated Multistage Planning of the Generation-Transmission System with Demand Forecasting Using Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Carlos Quinatoa,
  • Alex Chasi,
  • Vicky Puetate,
  • Alexander Casilimas,
  • Luis Camacho,
  • Josue Ortiz

摘要

The implementation of a multistate planning model for power generation-transmission expansion, as opposed to traditional independent planning frameworks, addresses the escalating energy demand and the need for efficient planning. This study applies a multistate DC-planning model to the 230 kV network of Ecuador’s National Interconnected System (NIS), aiming to minimize operational costs and the construction of new infrastructure. By employing a mixed-integer nonlinear programming (MINLP) approach and a neural network-generated demand forecasts, the electricity expansion spanning the period from 2023 to 2033 is analyzed. This methodology facilitates the delineation of expansion zones, thereby mitigating long-term costs.