Planning Framework for Distribution System Investments Under Energy Transition Uncertainty: A Multi-Scenario Approach

Abstract

The energy transition presents significant challenges to the planning of electrical distribution systems. Distribution companies must take into account a variety of uncertainties, including the adoption of electric vehicles, the growth of distributed generation, and changes in regulatory frameworks. Conventional deterministic planning methodologies have been demonstrated to be inefficient in addressing the intricacies inherent in such scenarios, a situation that may result in suboptimal investment decisions, and This work proposes a planning framework that systematically integrates economic, regulatory, demand, and financial uncertainties through a multi-scenario approach. The framework integrates a heuristic optimization technique, a pseudo-sequential Monte Carlo method, and modeling technology adoption and dissemination using the Gompertz function and logistic regression models. The proposed framework is designed to accommodate the evolving behaviors of prosumers and the dynamic nature of regulatory frameworks. The primary objective of this framework is to facilitate optimal long-term investment decisions within the broader context of energy transition scenarios.The methodology is validated through a Colombian case study and simulations, which demonstrate specific results. The findings indicate that the proposed approach enables distribution companies, planning and regulatory entities to formulate more robust decisions in the context of energy transition scenarios, thereby enhancing the technical and economic viability of their investment strategies.

The energy transition presents significant challenges to the planning of electrical distribution systems. Distribution companies must take into account a variety of uncertainties, including the adoption of electric vehicles, the growth of distributed generation, and changes in regulatory frameworks. Conventional deterministic planning methodologies have been demonstrated to be inefficient in addressing the intricacies inherent in such scenarios, a situation that may result in suboptimal investment decisions, and This work proposes a planning framework that systematically integrates economic, regulatory, demand, and financial uncertainties through a multi-scenario approach. The framework integrates a heuristic optimization technique, a pseudo-sequential Monte Carlo method, and modeling technology adoption and dissemination using the Gompertz function and logistic regression models. The proposed framework is designed to accommodate the evolving behaviors of prosumers and the dynamic nature of regulatory frameworks. The primary objective of this framework is to facilitate optimal long-term investment decisions within the broader context of energy transition scenarios.The methodology is validated through a Colombian case study and simulations, which demonstrate specific results. The findings indicate that the proposed approach enables distribution companies, planning and regulatory entities to formulate more robust decisions in the context of energy transition scenarios, thereby enhancing the technical and economic viability of their investment strategies.