Biomethane from Municipal Waste: ACEA PINEROLESE Case Study and Financial Replication Model for Romania

Abstract

Based on the ACEA Pinerolese case study (Pinerolo, Italy), the research investigates the possibility of establishing a municipal biomethane hub in Romania. In order to generate technical-energy indicators and build a scenario-based financial model, the analysis includes operational and financial data for the year 2024, including biomethane 33 464 MWh, cogenerated electricity 18 938 MWh and treated wet waste 48 566 kt. The resulting energy intensity, which is used as a reference point in the scenarios for Romania, is approximately 0.69 MWh/tonne of derived organic waste, or 3.35 million Nm³/year (assuming a LHV » 10 kWh/Nm³). The scenario matrix fluctuates the biomethane price (70/90/110 €/MWh), the capital expenditure (8/12/20 million €) and the aggregate operational expenditure (6-10% of the capital expenditure). The results show a profitable area at price combinations ³ 90 €/MWh and capital expenditure £ 12 million € for a medium scale (50 kt/year) (e.g. ~5.6 years at 12 million € and 90 €/MWh; ~3.2 years at 8 million € and 90 €/MWh). The scale effect is visible, by diluting the fixed costs and using economies of scale in modernization/connection, the transition from 30 to 80 kt/year reduces the payback time of the investment from about 13.3 to 3.0 years. The following are best practices: pursuing dual sampling (grid injection + CNG/biomethane for captive fleets), securing feedstock through functional separate collection and impurity control, arranging grid connection in advance, adopting hub integration with heat recovery and transparent internal transfers. Future research will focus on explicit quantification of support systems, Monte Carlo sensitivity and LCA/ESG analysis. Limitations include the use of internal transfer prices as proxies, LHV-based conversions and a single year data set. Municipal projects become bankable at sizes larger than tens of kt/year when integrated design reduces operational expenses and predictability of average market prices.