| Abstract |
The integration of renewable energy into power networks introduces challenges due to intermittency and unpredictability, making precise expansion planning essential. This research introduces a novel two-stage stochastic approach for distribution network expansion planning in smart grids with high renewable energy penetration, addressing uncertainty, risk, and distributed generators' remuneration. Key contributions include: the incorporation of third-party generation owners' economic remuneration into a risk-based stochastic model; the use of conditional value-at-risk to manage uncertainty and extreme events, with a detailed analysis of cost evolution for various confidence levels and risk aversion parameters; the optimization of energy storage systems sizing and placement, alongside the location and type of new power lines and substation transformers, ensuring a reliable and radial network topology; and the integration of multiple factors, including uncertainty, risk aversion, ESS allocation, remuneration, and reliability, into a unified model that ensures optimal network design under technical constraints. Tested on a 180-bus network in Leiria, Portugal and on a 13-bus smart city mockup from Salamanca, Spain, the approach proved economically viable, reducing extreme scenario costs by up to 34 % through CVaR-based risk management, and demonstrating its potential for sustainable, risk-averse network expansion. © 2025 The Authors |
