Numerical and experimental assessment of a micro-combined cooling, heating, and power (CCHP) system based on biomass gasification

The goal of this paper is to analyse an innovative micro-combined cooling, heating, and power (CCHP) system, whose power generation unit is composed of an internal combustion engine fuelled with syngas coming from a woody biomass gasification process. The internal combustion engine is directly coupled to the gasifier. The analysis of the existing plant, realized in South Italy, was carried out experimentally. Moreover, a numerical model was developed. The model, in particular, considers the impact of gasifier operating conditions, i.e., biomass feed rate and equivalence ratio (ER) on engine performance: electric, thermal, and overall efficiency of the micro-CCHP system, running on woody biomass at full and partial loads conditions. Modelling the coupled system at partial load with real thermophysical properties of the syngas produced by the gasification process is surely a novel contribution but even more relevant is the presentation of experimental data on an existing plant. Once the model was validated, the system performance was evaluated by comparing two different biomass compositions: W1, characterized by a lower heating value of 14.5 MJ/kg and W2, having a lower heating value of 16.8 MJ/kg. Results show that the biomass composition affects more significantly the gasifier efficiency (72.4-82.6 %) and the engine thermal efficiency (55.3-50.6 %), while the engine electric efficiency is weakly affected (26-27 %). An overall efficiency of the entire micro-CCHP system amounting to 55.2 % and 61.1 % for W1 and W2, respectively, was estimated. Lastly, the economic feasibility of the micro-CCHP system was investigated and includes a sensitivity analysis of the current costs of energy vectors. The results demonstrate the affordability of the biomass based CCHP system, which is further increased in this period of contingent inter-national political crisis and a payback period of about 2 years is currently predictable.