Emanuela Colombo

Energy is increasingly regarded as a strategic national asset. Long-term planning must therefore evaluate environmental sustainability, security of supply, system reliability and the affordability of system costs together. This energy trilemma becomes harder to assess when systems rely more heavily on variable renewable energy. As a result, energy planning requires more robust modelling tools, able to connect long-term system evolution with operational performance under realistic conditions. Energy-system models used for long-term planning often simplify chronology through representative or averaged time slices. This abstraction makes large multi-year optimisation problems tractable, but it may understate the operational consequences of variable renewable energy, prolonged low-resource events and hourly adequacy requirements. This paper develops and applies a soft-linked modelling framework for Italy in which a dynamic multi-carrier energy-system optimisation model, Hypatia, defines long-term pathways across electricity, heat and hydrogen, while an hourly model, Calliope, tests selected target-year configurations under 8760-hour chronology. The coupling transfers Hypatia capacities for 2030, 2040 and 2050 to Calliope, where all non-battery technologies are fixed at their inherited levels, while additional battery capacity can be selected endogenously to meet hourly flexibility requirements.. The hourly stage is run under a Typical Meteorological Year and three critical historical weather years selected from a 45-year solar and wind dataset, including the lowest combined capacity-factor year and one-week and two-week Dunkelflaute events. Four policy-relevant cases are analysed: a conservative reference pathway, a VRES-led net-zero pathway with doubled renewable deployment limits, and two nuclear-plus-CCS sensitivities with high and low nuclear capital costs. Results show that temporal aggregation materially affects scenario interpretation. In the VRES-led net-zero case, Calliope increases 2050 battery requirements from 473 GWh in Hypatia to 26107 GWh, while the corrected system cost rises from 699 to 1866 billion € under the two-week Dunkelflaute case. By contrast, firm low-carbon capacity reduces the adequacy correction: in the low-costs nuclear case, 2050 battery needs remain within 161–183 GWh and the cost correction is within 10 billion €. The results show that operational and weather-year stress testing can fundamentally change the assessment of long-term decarbonisation pathways, highlighting the role of nuclear capacity in achieving carbon neutrality while maintaining system adequacy and limiting the cost and storage requirements associated with prolonged renewable scarcity.

The energy transition requires balancing the three dimensions of the Energy Trilemma: decarbonization, affordability, and security of supply. In this context, Small Modular Reactors (SMRs) have emerged as a promising low-carbon technology, although their role in long-term energy planning analyses remains insufficiently explored.This study assesses the potential contribution of SMRs to the Italian energy system using an energy system optimization model (ESOM) with a sector-coupling approach integrating electricity, heat, and hydrogen. SMRs are modelled as flexible cogeneration technologies coupled with thermal energy storage, enabling the joint supply of electricity and process heat. Four policy-driven scenarios are compared against a baseline scenario, consistent with the Italian National Energy and Climate Plan (PNIEC) targets, and results are evaluated via a structured set of indicators aligned with the Energy Trilemma - the Decision Scoreboard.Results show that, while achieving comparable decarbonization levels by 2050, scenarios significantly differ across the other Trilemma dimensions. Nuclear scenarios reduce reliance on solar photovoltaic and battery capacity, limit renewable curtailment, and enhance energy mix diversification. When deploying the entire nuclear fleet in cogeneration mode, SMRs further reduces heat-sector CO2 intensity by 50% compared to the baseline, while lowering system costs and natural gas penetration. However, nuclear deployment introduces dependence on imported uranium fuel, which requires dedicated supply chain diversification strategies to genuinely strengthen energy security.These findings emphasize the importance of sector-coupling approaches to fully capture the value of multi-product technologies such as SMRs, and inform evidence-based policymaking on nuclear integration in low-carbon energy systems.

Sub-Saharan Africa (SSA) continues to face profound energy access deficits, with over 500 million people lacking electricity. Addressing this gap equitably requires moving beyond techno-economic energy planning to meaningfully integrate Energy Justice (EJ) principles. Through a scoping review of recent literature, this study examines the disconnect between energy modelling and socio-political justice in SSA. Our analysis reveals that even in SSA-focused reviews, techno-economic dimensions dominate 60–80% of analytical attention, while political and procedural justice remains marginal. In response, we propose an interface to bridge energy modelling and policy formulation, tailored to SSA contexts. This interface outlines two complementary integration pathways: a soft-link, where modelling outputs are evaluated using EJ-informed multi-criteria decision support; and a hard-link, where justice metrics are directly embedded as objectives or constraints within optimization models. Both pathways represent pragmatic entry points to embed justice into planning, though neither fully reconceptualizes modelling from a justice-first foundation. The study concludes that advancing just energy access in SSA requires context-sensitive methodologies, strengthened local modelling capacity, and institutionalized participatory processes. By translating justice principles into operational planning tools, this approach supports the development of energy strategies that are not only technically sound but also socially equitable and rooted in African realities.

As a key material for manufacturing clean energy technologies, steel is crucial for energy transition, but its production causes 2.6 Gton of CO₂ emissions at global level each year. In 2020 the European Union (EU) set a net-zero emissions target by 2050, fostering innovation in the steel industry to reduce its environmental impact. However, a scenario-oriented and technologically comprehensive analysis assessing prospected environmental and market implications of steel decarbonisation strategies remains a gap, which is addressed in this paper. The analysis adopts a hybrid input-output-based life-cycle assessment model built in the MARIO framework, extending the Exiobase database to represent the supply chains of the most promising low-carbon steelmaking technologies in the EU, such as hydrogen- or charcoal-injected blast furnaces and natural gas- and hydrogen-based direct reduction routes. The penetration of these technologies is explored by formulating scenarios resembling European climate targets. The results show a reduction in the carbon footprint of steel across all scenarios, ranging up to −26% in 2030 and to −60% in 2050. However, the extent of footprint reduction is highly dependent on the share of clean electricity in the European supply mix, highlighting the relevance of holistic decarbonisation strategies. Economic implications affect steel prices, which rise up to 25% in 2030 and 56% in 2050, opening discussions on the need for suitable policies such as CBAM to avoid protectionism and encourage international technological progress.

Access to affordable, reliable, and modern energy remains a critical goal under the Agenda 2030 for Sustainable Development, especially in remote areas of developing countries. Based on traditional engineering approaches, many energy solution planning tools have been developed to identify the optimal solution in these areas to assess the competition across different technological options. Nevertheless, these approaches, based on an economic optimum, do not necessarily grant long-term sustainability of the solution in specific local contexts, since they are not able to capture the social implications within the Energy-Development nexus. Moreover, also in light of the 2030 Agenda, scientific and grey literature on energy access highlights how energy solutions planning methodologies developed in the last decades need to be complemented by a more comprehensive view, able to integrate evidence from various disciplines, especially engineering and social sciences. Based on the above considerations, this paper introduces a novel framework under the name of CESP, where three social sciences-based phases complement three engineering phases, each one characterized by specific tools, to offer an informed decision framework for the local planner. CESP encompasses a set of techno-economic and socio-technical actions to prevent potential failure as evidenced by a counterfactual analysis used to identify the reasons behind past project failures. The CESP framework presents a sequential and iterative structure that underlines the cyclic perspective of a holistic decision process where social sciences feed the engineering analysis and vice versa. Finally, CESP emerges as a practical and applicable framework for supporting energy access planning in critical areas.