Planning, modelling and optimization of hydrogen valleys to foster local decarbonization targets

Driven by the urgent need to achieve net-zero emissions by 2050 and align with the European Union’s Renewable Energy Directive III (RED III), green hydrogen has emerged as the most promising molecular carrier to decarbonize energy-intensive industrial hubs and heavy transport sectors. However, large-scale adoption is historically hindered by a chicken-and-egg dilemma between supply and local demand. This PhD thesis proposes the “Hydrogen Valley” model—a localized, integrated ecosystem spanning the entire hydrogen value chain—as the essential catalyst to overcome this deadlock. To bridge the current literature gap in district-level planning and holistic optimization, this research develops advanced simulation and techno-economic evaluation models applied to the Province of Taranto. Characterized by massive renewable generation potential combined with concentrated heavy-industry demand, primarily driven by the steel manufacturing sector, this area serves as a scalable blueprint for other territorial clusters. The methodology unfolds in two interconnected phases: strategic parametric planning and dynamic multi-objective optimization. The first phase maps the territory’s renewable potential and evaluates the value chain’s economic feasibility across three time horizons (Today, 2030, 2050). Results project the Levelized Cost of Hydrogen (LCOH) to drop from current values of 3.66–4.90 e/kg to 1.41–1.94 e/kg by 2050, a trajectory consistent with the long-term projections of the International Energy Agency (IEA). This establishes a rigorous decarbonization merit order, prioritizing gas grid blending and mobility (FCEVs). Complementary investigations further validate the framework’s versatility, demonstrating that optimal decentralized micro-grid sizing yields highly competitive local hydrogen at 3.82 e/kg, while Power-to-Fuel analyses project a steep cost decline for synthetic green methanol down to 72 e/MWh by 2050. To deeply investigate complex grid interactions, the second phase transitions to a dynamic modelling of the local energy system. Leveraging EnergyPLAN coupled with the MaT4EnergyPLAN toolbox, a multi-objective optimization framework was developed to simultaneously balance the Total Annual Cost (TAC) of the entire Taranto provincial area, CO2 abatement, and Critical Excess Electricity Production (CEEP). Comparing a 2030 Business-as-Usual (BAU) baseline to optimized low and high hydrogen penetration scenarios (L-H2V and H-H2V), the model reveals the fundamental systemic value of Sector Coupling. By utilizing electrolysis and synthesis reactors as smart loads to absorb massive renewable excesses that would otherwise be curtailed, storing them as chemical energy and ensuring overall grid stability, the levelized cost of alternative fuels plummets. Leveraging the infrastructure lowers liquid e-fuels (such as green methanol) to 0.28 e/kWh (down to 0.22 e/kWh in best scenarios) and synthetic natural gas (SNG), itself a gaseous e-fuel, to 0.25 e/kWh. Achieving this deep integration requires a radical macro-economic paradigm shift. The optimized H-H2V scenario reduces carbon emissions by an additional 30% compared to the BAU baseline (4.1 Mt CO2), capping the provincial system’s TAC at 1.34 billion euros. This investment transforms the local ecosystem from a vulnerable, OPEX-intensive model reliant on volatile fossil fuels into an independent, resilient, CAPEX-intensive energy economy. In conclusion, this research demonstrates that Hydrogen Valleys transcend theoretical modeling to become tangible, engineerable infrastructures, acting as the foundational nodes for a mature global hydrogen market and turning the imperative of climate neutrality into an unprecedented driver for industrial renaissance.