High-resolution modeling for the integrated planning of generation, storage, and transmission in energy networks

The increasing integration of Intermittent Renewable Energy Sources (IRES) and the need for decarbonization are driving significant transformations in the expansion planning of electrical power systems. In this context, the literature highlights the need to integrate long-term strategic decisions with short-term operational constraints in planning models, while preserving high resolution across time, space, and techno-economic detail. This paper addresses this challenge by proposing a high-resolution optimization model for the integrated expansion planning of generation, storage, and transmission in electrical power systems. The proposed model combines high temporal resolution, a multi-node spatial configuration, and a Clustered Unit Commitment (CUC) formulation to account for key operational constraints while limiting computational cost. The practical applicability of the model in real-world contexts is demonstrated through a multi-scenario analysis focused on the Italian electricity system, including a 2021 baseline scenario and 2030 future scenarios differing in terms of carbon tax and IRES support level. The validation of the model outputs in the baseline scenario against the 2021 historical electricity generation mix results in a Mean Absolute Percentage Error (MAPE) of 0.8%. In the 2030 scenarios, the renewable share in electricity generation increases from 40.0% in the 2021 baseline to 51.0–56.5%, while average emission intensity decreases from 0.315 to 0.196–0.177 kg CO₂-eq/kWh. These outcomes are associated with targeted IRES investments, especially in southern Italy and the islands, together with storage deployment and additional transmission capacity along the south–north connections, highlighting the value of integrated high-resolution planning for capturing generation-storage-transmission interactions.