Approach

To meet the aims described in section 1.3 a strategic sourcing method has been developed for biomass. The method is referred to as BioSS and comes in 3 distinct operating modes, BioSS.2, BioSS.3 and BioSS.Op which correlate to different stages of the project development and lifecycle. Within BioSS are three key elements that correspond to research problems within the thesis and challenges faced by bioenergy developers. These research problems are addressed in this thesis and come together to make the BioSS:

Fuels characterisation Supplier selection Order allocation

Table 1.2 shows the corresponding research objectives and outcomes for each of the three research elements. Sections 1.4.1 to 1.4.3 describe the approach taken for each of these research problems and section 1.4.4 shows how the elements combine to create the BioSS framework.

Table 1.2: Research problem, objective and outcome

Research Problem

Research Objective Research outcome

Fuels

characterisation

Create a fuels library that allows the user to estimate, based on secondary evidence, the salient properties of a feedstock given only the feedstock description

A fuels library

Supplier Selection

Determine the most important factors that should be considered when selecting a supplier of biomass. Determine the most relevant stakeholder groups and their requirements regarding the supply of biomass.

A register of stakeholders and representative actors within each stakeholder group.

A list of evaluating factors that can be used to satisfy stakeholder

requirements when selecting suppliers of biomass.

Order Allocation Develop a methodology for assisting with the allocation of orders between the shortlisted suppliers.

An optimisation module that provides a recommended portfolio of suppliers and how much material should be contracted for from each supplier. BioSS

implementation

Demonstrate the BioSS framework against two UK based scenarios over three

development stages

A demonstration of BioSS to show application for two scenarios where stakeholder importance and

technological constraints change as a project moves through development.

1.4.1 Fuels characterisation

Fuel characterisation and description is a problem in the early phases of project development, when feedstock characteristics are unknown and there is no incentive to test feedstock under laboratory conditions. Usually properties of the more commodity-like fuels can be estimated with reasonable accuracy. However to meet the aim of the research and open up the wider residue and waste biomass resource the BioSS requires some method of making estaimtes for fuel properties.

Estimates are possible based on a fuels library that has been created as one of the outputs of the research. This is a growing library of records collected from both secondary data and user-input data. The BioSS can look-up characteristics of biomass materials from this library to allow the decision maker to quickly assess if further investigation of potential biomass sources is worthwhile.

As more projects are developed and biomass materials are tested the fuels library grows, eventually becoming a valuable repository of information for the developer.

45 1.4.2 Supplier selection

The problem faced by biomass buyers sits neatly within the existing theoretical structure for supplier selection problems. In the problem being examined biomass is a raw material and the buyer requires some form of collaboration with the supplier and usually a supply contract to ensure that material being purchases is suitable. When selecting between potential suppliers the decision maker must balance the many complex requirements of the stakeholder group against the characteristics of the set of available suppliers.

In BioSS handling this multi-criteria decision process is done using the integrated QFD- AHP method as discussed in chapter 5. The QFD-AHP method allows the usually vague requirements of stakeholders to be translated into more specific factors which each have an importance score. These factors can then be used to compare and rank the available supplies of material that are available. In the case of biomass success in the eyes of the stakeholder group is not just an evaluation of the supplying company but also of the material that they are able to supply. For the material itself (as opposed to the supplying company) these factors are not directly related to quality of the material as this term is essentially redundant given the way that the framework aims to blend different sources together. Instead the factors relate to tacit elements about the material, where it has come from, its wider economic and environmental impact and the use of that material on the risk profile of the project. A preference score is therefore assigned to each available combination of supplier and biomass that can be supplied.

1.4.3 Order allocation

Having established the characteristics of available biomass materials and give each supplier- biomass combination a weighted preference score, orders can be allocated between the available supplier-biomass combinations. Following consultation with industry orders are usually allocated by tonnage of material, especially when arranging strategic supply contracts. This is different to other parts of the energy industry where total energy content is

used to determine total price but reflects the way that biomass is usually traded. To ensure that sufficient energy is delivered the buyer aims to procure a blend of material that has sufficient energy content for the conversion plant to operate properly. Energy content is one of 14 identified properties that must be controlled for the final fuel blend. These include impurities such as metals, ash content, moisture content and chemicals that can increase pollution or accelerate corrosion and acid creation such as sulphur and chlorine.

The order allocation model within BioSS uses a chance-constrained programming approach to find a final blend of material that meets the required specification whilst optimising the total stakeholder satisfaction score. The output of the model is a recommended distribution of orders (in tonnage per year) between the available supplier-biomass combinations. The model also has a Monte-Carlo analysis section that allows the user to examine the performance of any input portfolio.

The chance-constrained approach allows the decision maker to set a limit on how frequently each characteristic of the blend is allowed to exceed the corresponding constraint as shown in Figure 1.16. This part of BioSS allows buyers to either enter proposed supply portfolios and examine their performance against both chemical constraints and stakeholder requirements, or to enter available supplies and ask the model to give a recommended portfolio.

47 Figure 1.16: A chance constraint.

1.4.4 Contribution and implementation of BioSS

There are several contributions and outcomes from the individual sections of the BioSS and this thesis. The fuels library itself is a unique collection of material descriptions that is integral to BioSS and will be passed directly to Express Energy Ltd and made available for future research projects on biomass decision support schemes and supply chain management. The list of factors that biomass buyers look for and the allocated weightings is also novel, currently no structured research exists on exact factors that buyers look for outside of certification schemes. The application of QFD-AHP to provide weightings is also fairly novel as reflected by the publication of a paper on that part of the thesis (Scott et al., 2013). The optimisation module uses an unusual approach to a well-studied technique (chance constrained programming) in a novel application (bioenergy) to address a classic operations research problem of mixing or blending. The integration with Monte-Carlo analysis makes the model more robust and applicable. BioSS as an entire model running from excel will be passed to Express Energy following the research.

Chapter 7 shows the implementation of BioSS to two scenarios, the framework is demonstrated to operate through the early stage development in BioSS.2 and towards

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0 1 2 3 4 5 6 7 8 9 10 Pr o b ab ili ty d istr ib u tion fun ction ( PD )

Blend characteristic (unit's differ)

PD inside cosntraint PD outside constraint Constraint

Probability that constraint is exceeded

financial close in BioSS.3 mode. The BioSS framework is also demonstrated in the operational phase of the project under BioSS.Op mode.