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

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.