Probabilistic simulation-based multi-objective optimization approach for hybrid power systems. • Study the uncertainties of renewable resources availability, load demand, and components failure. • Post-optimization sensitive analysis leads to unfeasible solutions. • Optimization with uncertainties implies higher costs for the same level of reliability. • Useful decision making tool to design optimum and robust power systems.

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In the present work is performed a technical and economic analysis of a combined heat and power 9 generation system (CHP), designed to operate coupled to an internal combustion engine (ICE) fuelled with 10 biomass producer gas, in order to generate electricity and hot water for isolated communities of power 11 distribution network. In the proposed system configuration, the energy of the engine's hot exhaust gases is 12 recovered (cogeneration), making this system more attractive in relation to conventional configurations, which 13 are normally used to produce solely mechanical and electrical energy. The proposed system is composed of a 14 modified downdraft gasifier, Imbert technology; coupled to an internal combustion engine, model ZIL−130. 15The system is designed and built in the laboratory of fluid mechanics at the University of Camagüey. The 16 feedstock studied for the gasifier was Dichrostachys Cinerea, collected in neighboring areas to the proposed 17 place of installation. The main energy flows and the costs associated with the production of producer gas were 18 determinate. From the mass and energy balances, the thermal and electric efficiencies of the cogeneration 19 systems resulted in η hw =32.4% and η ge =23.4% respectively, whereas the overall efficiency led to η global =33.3%. 20 In the economic analysis were studied the Internal Rate of Return (IRR), the Net Present Value (NPV) and time 21 of return on investment (TRI) or payback, considering a project lifespan of 15 years. For the annual interest rate 22 of 12%, the electricity should be sold at 0.3USD/kWh in order to the project be feasible. The IRR resulted in 23 12%, the NPV was 20,571 USD and payback period resulted in 5.3 years. In the proposed configuration, the 24 system consumes 1.46 kg of biomass per kWe produced, with a maximum cost of generated electricity of 0.022 25 USD/kWh.26 27 Nomenclature ݉ሶ − mass flow rate [kg/s or m 3 /h] n −r.p.m cw − cool water T − temperature [K] HHV-High hating value exh − flue gas N − power [kW] LHV-low heating value [kJ/kg or MJ/m 3 ] elec − electricity C 1 ,C 2 ,C 3 − coefficients of the engine Subscript e-electric ge − specific fuel consumption[g/kWh] bio − biomass net -net Tr − torque [kgf.m] i− each parameter of engine gas-producer gas Cp − average specific heat [kJ/kgK] N − nominal parameter of engine csys-cogeneration systems E − energy [kWh] out − outlet Superscripts I − Investment [USD$] in − inlet t-amortization periods C M − maintenance cost [USD$/kWh] hw − hot water Greek symbols C − cost [USD$/kWh] hw HE 1,2,4 -hot water in heat exchangers ߟ− efficiency Fp − adjustment factor air HE3 -hot air in heat exchanger 3 r-annual interest rate th − thermal q -factor eng/ger − engine generator

The objective of the present work is to study the temperature dependence of the flammability limits for pure compounds, and to develop a methodology to determine these limits in air at atmospheric pressure and at different initial temperatures of the mixture. A method to determine the lower flammability limits in those conditions was developed and compared with other methods available in the literature. The developed method shows an average absolute relative error of 3.25% and a squared correlation coefficient of 0.9928. Particularly, in the case of compounds with more than 5 carbon atoms, the method presents better accuracy than other available methods. Likewise, a method to determine the upper flammability limits was developed and compared with other widely accepted method. In this case, the developed methodology shows an average absolute relative error of 3.60% and a squared correlation coefficient of 0.9957, showing better accuracy than the available method.

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