Work

Risk-based optimal network planning considering resources remuneration and daily uncertainty

Year: 2025

Type: article

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. • Smart-grid economics: Third-party owner remuneration in medium voltage networks. • CVaR modeling: Uncertainty management for network planning with extreme events. • Efficient ESS placement: Optimizing storage and power lines for reliability. • Integrated model: Balancing uncertainty, remuneration, and network constraints. • Holistic planning integration and generation/remuneration/risk pattern analysis.

Source: Applied Energy

DOI: 10.1016/j.apenergy.2025.125531

Authors: Fábio Castro, Bruno Canizes, João Soares, José Almeida, Bruno Francois, Zita Vale