The effect of biomass fuel moisture content to the power generation value chain
Wang, Guangxuan (2019)
Diplomityö
2019
School of Energy Systems, Energiatekniikka
Kaikki oikeudet pidätetään.
Julkaisun pysyvä osoite on
http://urn.fi/URN:NBN:fi-fe2019121247800
http://urn.fi/URN:NBN:fi-fe2019121247800
Tiivistelmä
With global energy demand increased rapidly, biomass as a sustainable and renewable source can be a substitution of fossil fuel to response to energy crisis and climate change. Finland has positive attitude to increase the use of renewable energy with the target that share renewable energy of energy consumption up to 38% in 2020. Wood fuels to total energy consumption grow to 27% in Finland, of which 40% were forest chips.
The primary aim of the study was to evaluate to what extent moisture content of biomass effect to power generation value chain based on a dynamic model. The analysis includes moisture content prediction model, procurement cost and profitability comparisons between different forest chips biomass supply chain, energy production analysis for heating and electricity generation. Additionally, optimization model of biomass supply chain was also evaluated to minimum supply chain cost with MC constraints.
According to the results of the study, biomass procurement cost and energy production cost varies with different harvest and storage time which affect biomass moisture content change. Tree volume is the most impact for supply chain cost, following MC, storage period, forward distance, interest rate and transport distance separately. For heat generation, fuel price is the most impact, following operation hour, interest rate and MC. Optimization model reveal that total supply chain cost and harvest volume both sensitive with MC constraints, supply chain cost after optimization had a significant decrease.
The primary aim of the study was to evaluate to what extent moisture content of biomass effect to power generation value chain based on a dynamic model. The analysis includes moisture content prediction model, procurement cost and profitability comparisons between different forest chips biomass supply chain, energy production analysis for heating and electricity generation. Additionally, optimization model of biomass supply chain was also evaluated to minimum supply chain cost with MC constraints.
According to the results of the study, biomass procurement cost and energy production cost varies with different harvest and storage time which affect biomass moisture content change. Tree volume is the most impact for supply chain cost, following MC, storage period, forward distance, interest rate and transport distance separately. For heat generation, fuel price is the most impact, following operation hour, interest rate and MC. Optimization model reveal that total supply chain cost and harvest volume both sensitive with MC constraints, supply chain cost after optimization had a significant decrease.