Supply-chain optimization model

This task provided a detailed analysis of two selected biomass projects/case-studies (Miajadas and Burgundy demonstration sites) and scenarios used for the case-studies. It aimed at synthesizing the economic, environmental and social assessments of these cases and their various scenarios, and at making recommendations regarding the biomass logistics system. For each demonstration site, a number of scenarios were developed in order to evaluate the availability of biomass (species/quantities/locations), new technologies, and frameworks (e.g. tax and incentives).

Assessing the performance of logistics chains based on a comprehensive set of sustainability criteria and identifying points of improvement was at the heart of the LogistEC project and one of its major research objectives. A holistic framework was developed to integrate chain components and assess the environmental, economic and social impacts of biomass supply chains based on the two case-studies of the project in Burgundy (France) and Miajadas (Spain). The framework enables an economic optimization of the supply chains and an overall sustainability assessment of technical and management options, also comprising environmental and social aspects, making the most of the progresses achieved in the logistics components – it detailed in this deliverable, which also encompasses application to the two case-studies.

As part of the framework a novel feedstock model with a high spatial resolution was developed to predict the most probable location of future fields planted to energy crops, in the vicinity of the biomass conversion plant, based on landscape features as well as yield potentials and farmers’ survey data. The model produces maps of energy crops fields and yields for a given production target and maximum transportation radius from the plant, which were used in the economic optimization. It is detailed in the following article, while the application to the cases are detailed in this deliverable in terms of feedstock supply expansion scenarios in Spain and France.

A mathematical programming supply-chain model was also developed to integrate and optimize the logistics system by choosing among different technology and management options including transport routes, storage sites, pre-conditioning and processing technologies (e.g. pelletization or briquetting), harvesting (cutting date or type of technology used), and feedstock type. Transportation distances are calculated with real road data, and the model can deal with certain kinds of uncertainty (see this web page for further details).

Miscanthus field near Aiserey, Burgundy ((c) B. Gabrielle).

In the case-study involving miscanthus biomass in Burgundy, the economic optimization model simulated the flows of three harvested products – chips and bales as long and short strands – through transport, storage up to the sales of the end-products: chips, bales as short strands, pellets and briquettes. Simply expanding miscanthus cropping areas and biomass consumption appeared neither a cost saving nor an environmental-friendly alternative to the current situation, due to the increase of transportation distances and storage lengths. This means that the concept of economy of scale did not apply here, but on the other hand costs and environmental impacts per tonne of biomass delivered did not increase as the biomass supply expanded.

By contrast, allowing innovative technologies such as decentralized briquetting with a mobile facility led to an increase of the overall profit up to +75% and a 15% reduction of climate change impact. At field gate, harvesting in autumn led to significantly higher climate change impact (+30%) due to the need for nitrogen fertilizers to compensate for fewer nutrients stored in rhizomes. However, at plant gate, such increase is compensated by a reduction of transport and storage leading to a climate change impact increase of only +5% and a significant increase of the profitability (+50%). See following journal article for detailed results.

Biomass power plant in Miajadas, Spain ((c) V. Troillard).

The case based on the Spanish Miajadas Biomass (energy) Plant (MBP) is quite different from the Burgundy case. While miscanthus is the only feedstock for the Burgundy Pellets plant, the MBP has been using approximately 20 different species fuelling power generation, including herbaceous and woody residues. The analysis of the MBP case takes into account a new regulatory framework enforced in 2014, after which special subsides for energy crops came to an end, and power tariffs earned by MPB no longer depends on the type of feedstock used. Thus, additional costs for energy crops have become a barrier rather than a reason for differentiated incentives in the bioenergy sector. Accordingly, the economic assessment of the MBP case has focused on the costs of using dedicated energy crops as fuel, and to contrast the use of energy crops to the use of agricultural residues for the herbaceous part of the feedstock supply. With the given purchase costs for straw and stover the energy crops (i.e triticale, sorghum and poplar SRC) are not competitive without dedicated subsidies. Energy crops appear to be more than twice as expensive as a fuel source compared to straw and stover. The average costs differ less than 5% between triticale and sorghum. SRC poplar seems to carry the highest costs, which appear to be related to the fact that irrigation is required – at least for the demonstrations carried out at Miajadas.

Spatially-explicit modelling of production fields, storage sites and transportation routes allowed for an optimization of the supply chains by combining the most appropriate technologies, as driven by the requirement of the biomass conversion processes, and the seasonality of end-product demand. The development of a comprehensive framework for sustainability assessment, encompassing economic, environmental and social criteria is intended to provide guidance in the chain optimization and to propose solutions tailored to various possible end-users, whether private stakeholders or policy makers. The tools and models are available upon request from the project’s coordination team.