279165 Optimisation of Bioelectricity Supply Chains
There has been a growing interest in the development and use of renewable energy technologies in the recent years to tackle major problems such as global climate change, depleting fossil fuel resources and increasing oil prices. Among the renewable energy technologies, bioenergy is considered to be a promising near-term solution. Bioenergy is obtained from the conversion of a variety of biomass feedstock including wood, dedicated energy crops, food crops and biomass waste. This diversity of types of biomass offers a significant advantage in terms of contributing to energy security through a diversified energy mix. Bioenergy can be used for different purposes such as dispatchable electricity generation, high grade heat and production of liquid biofuels. Mandates and targets have been set around the globe to promote the use of renewable energy. The UK is committed to supplying 15% of its energy demand from renewable resources and reduce its greenhouse gas emissions by at least 34% by 2020. Moreover, when combined with CO2 capture and storage, the co-firing of biomass with fossil fuels leads to CO2 negative energy. This is important in the context of mitigating anthropogenic CO2 emissions, and eventually reducing atmospheric CO2 concentration. Therefore, bioenergy is expected to play an important role as a part of the renewable energy mix to meet these long term renewables and emissions reductions targets.
A bioenergy supply chain is a multi-echelon network consisting of biomass cultivation sites, bioenergy production facilities and demand centres. Application of supply chain optimisation to such systems means consideration of all these nodes in the chain as well as transport of biomass and bioenergy between these nodes. This work presents a spatially-explicit optimisation framework for a bioenergy supply chain network based on a “neighbourhood flow” approach. Linear models of co-fired coal-based power plants with amine-based CO2 capture have been derived from correlations of detailed simulations of the whole system using ASPEN plus.
The model applicability is highlighted with a case study of bioelectricity generation in the UK. The bioelectricity demand is determined based on a combination UK renewables and carbon mitigation targets. Availabilities of different types of woody biomass in the UK are accounted for in the generation of bioelectricity. Similarly, land-use-change (LUC) emissions are also incorporated in the carbon balance. Biomass imports are considered as a possible option to meet the domestic bioenergy demand
The model aims to minimise the total cost and/or total emissions of the whole network by optimising a variety of variables such as the material flows within the network, biomass supply and bioenergy production amounts in each region as well as the extent of biomass co-firing and CO2 capture at a given power station.