• Optimal electric vehicle parks’ placement for strategic resource allocation. • Energy storage systems positioning aids system operator investment decisions. • Multi-period investment flexibility reduces upfront costs for investors. • Adaptive framework considers resource remuneration, uncertainty, and emissions. • Economic viability of complete distribution network planning model studied. The transition toward a sustainable and low-carbon energy system presents major challenges for distribution networks, which must adapt to increasing renewable penetration, electrification, and operational uncertainty. Effective long-term planning is essential to ensure that network investments remain technically sound, economically viable, and environmentally aligned with decarbonization objectives. This study proposes a multi-stage stochastic optimization model that jointly minimizes planning costs and CO 2 emissions while addressing uncertainty in renewable generation and demand. The framework integrates the optimal placement of energy storage systems and electric vehicle parks, considers remuneration mechanisms for distributed generation, and enables investment decisions across multiple planning stages. The model is applied to a realistic 180-bus, 30 kV medium-voltage distribution network in Leiria, Portugal, over a 30-year horizon. The findings provide relevant insights into strategies for sustainable and cost-efficient network expansion, highlighting how emission-aware stochastic planning can support utilities and policymakers in balancing economic and environmental objectives. The model’s financial evaluation further demonstrates its practical feasibility, achieving an internal rate of return of 11.53% over the project lifetime. Overall, the proposed framework contributes to bridging the gap between long-term sustainability goals and real-world implementation, offering actionable guidance for network operators, investors, and planners engaged in the transition toward smarter and cleaner power distribution systems.
Castro et al. (Sun,) studied this question.