A gasification model is developed and implemented in Matlab to simulate a downdraft gasifier using wood as feedstock. The downdraft gasifier was conceptually divided into three zones: the pyrolysis zone, the combustion/oxidation zone and the reduction zone. A typical tar composition and its mole fraction, as reported in the literature was supplied as an input parameter in the model. The concentration of syngas and profiles of temperature along the reduction zone length were obtained by solving the mass and energy balances across each control volume and taking into account the rate of formation/consumption of the species according to different gasification kinetics. The simulation results from the model agreed closely with the experimental results. The syngas concentration was found to be about 1.1%, 17.3%, 22.8%, 9.0% and 49.8% for CH4, H2, CO, CO2, and N2 respectively and the corresponding LHV, CGE, CCE and yield were 4.7 MJ/Nm 3 , 59.9%, 85.5% and 2.5 Nm 3 /kgbiomass respectively at ER of 3.1 and fuel moisture content of 18.5% wt. Sensitivity analysis was carried out with this validated model for different air-fuel ratios, moisture contents and inlet air temperature. The analysis can be applied to produce specific design data for a downdraft biomass reactor given the fuel composition and operating conditions.

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Wind power was integrated with both compressed air energy storage and biomass gasification energy storage for power generation.

Wind-based power is one of the renewable base power sources that are tipped to play a great role in decarbonising the globe. To achieve this potential, more wind farms are likely to be built. The capacity factor of a wind farm and hence its profitability is dependent on whether it is properly sized and sited. In fact, some wind power plants have failed wholly or underperformed, because the wind turbine plant installed did not match the wind site. In this paper, a new approximate capacity factor equation has been derived for matching wind turbines to potential site for optimum yield and profitability. The indexes of capacity factor and cost of electricity were used as metrics in the model. The proposed model was applied to the climatic conditions and wind turbine characteristics of Kappadagudda and Mailiao wind farms in India and Taiwan, respectively. The result obtained showed good agreement with measured data for the two wind farms. With respect to the Kappadagudda wind farm, the model computed CF of 38% is close to the Kappadagudda real wind farm annual CF of 36% representing an absolute error of 2% and a mean square error of 0.96%. In addition, it was found that the proposed model followed the same general trend with other six existing models compared.