1.- Comprehensive overview of the project

An state of the art was developed in order to provide a comprehensive overview of the project.

D1.2 Comprehensive overview of the project. State of the art, problem, proposed approach, and outcome

2.- Identifying key variables, overall influence and dependence

Task 2.1 Defining relevant variables

In the first task, the variables that are considered relevant for the proposal were identified, analysed, and reduced according to their representability, before proceeding with the structural analysis. Variables, internal and external, refer to the characterizing of both technical (e.g. water temperature, flying ashes characterisation, waste heaps soil characterisation, etc.), and evaluation criteria (e.g. cost, competitiveness, etc.) related with renewable energy technologies, scale energy storage, assets, resources and circular economy contributions.

An unsorted list of variables is the output of this step. Of course, not all the sources agreed in the importance of the variables or even in identifying what aspects should be formalized as a variable or which should not. Detailed explanation of the variables is provided, allowing a better perception of the relations between these variables further in the analysis.

D2.1 Unsorted list of relevant variables of the system

Lessons learnt within this task were that each partner originally came with its own view, understanding, approach and methodology based on structured expert judgment and multi-criteria analysis. Based on improving information discussion among the project partners (internal stakeholders), it became evident the adoption of a framework to facilitate a common understanding upon the different options and opinions. Pre-defining a common methodology/framework can significantly reduce the time to bring a consensus and free resources for more technical details.

Task 2.2 Specifying the relations between the variables

In the second task, the influence that each variable has over the rest of variables of the system was stated by different groups of experts. The groups of experts provided a n x n integer matrix that states these influences, based on their knowledge and expertise. The entries of the matrix are generally qualitative, adjusting the intensities of the relations among the variables, as in a systemic vision a variable does not exist other than as part of the relational web with the other variables. This phase helps to put for n variables n x n-1 questions (4,692 for 69 variables), by means of direct brainstorming sessions or panel sessions. It was developed with a two-round Delphi-based study. This procedure allows not only avoiding errors, but correcting inconsistencies within the first Delphi round, and giving the opportunity to redefine the variables and thus refine the system’s analysis.

With the information collected and after the two-round Delphi-based study, a Matrix of Direct Influence describing the relation of direct influences between the variables defining the system was then developed.

D2.2 Matrix of Direct Influence

Lessons learnt within this task were that the discrepancies between the responses in the matrix required a second round of Delphi study. The revision of the results showed that in some cases experts were able to evaluate the relation between two variables but without proper identification of the direction of influence, i.e.: Influence of variable no. 69 (Companies manufacturers of goods and/or suppliers of services) on variable no. 58 (Access / proximity to gas pipeline network connections) does not exist, but the opposite one was identified (access / proximity to gas pipeline network connections may affect companies manufacturers of goods and/or suppliers of services). The second round of Delphi study benefited from the knowledge and experience of external experts, who positively influenced the final results.

Task 2.3 Identifying the key variables

During the work developed in this task, structural analyses of mutual influences and relationships between variables were carried out. The MICMAC software was used to analyse direct, indirect and potential influences. The result of the analysis was a structured database of grouped variables.

Two methods were applied: the direct method, which estimates the overall direct influence and direct dependence of a variable in the system directly from the Matrix, and the indirect method, which estimates the overall influence and dependence of a variable through other system variables. The comparison of the results (direct and indirect classification) enables the confirmation of the importance of certain variables and reveals certain variables that, because of their indirect actions, play a dominant role (and which the direct classification did not allow revealing). Therefore, the comparison of the hierarchy of the variables in the various classifications is rich in information, providing the key variables of the system.

D2.3 List of key variables of the system

Two were the main lessons learnt within this task. In the first place, the main problem when performing structural analyses was the wide variety and range of variables. The number and diversity of variables on the one hand accounted for the high content value of the matrix, and on the other hand posed a challenge for the appropriate selection of parameters for the analyses.

The structural analysis performed for all variables clearly indicated that the key variables for the whole system are only those from the “Power plant” group. This was mainly due to the fact that the variables in the “Mining” group referred to both the underground and surface parts of the mine, which meant that most of them did not show any influence/dependence on the others. In contrast, there was a greater number of influences/dependencies between the variables in the “Power plant” group, which consequently caused the variables in this group to ‘dominate’ the results of the analyses. Therefore, it was decided, in addition to the system-wide analysis, to conduct analyses for three groups of variables: “Power plant”, “Surface mining” and “Underground mining” separately. The results obtained from these analyses allowed the identification of key variables in the above areas, which would not have been possible with a holistic analysis.

In the second place, it was observed that in three cases, variables with similar characteristics occupied places close to each other in the system, which allowed them to be combined into one variable without any negative impact on the system.

3.- Scenarios planning and assessment in a multi-stakeholder environment

Task 3.1 Constructing exploratory scenarios

In this task, Morphological analysis was used as the methodology to explore possible recombinations of the elements that make up the studied system. This method is used primarily for the construction of scenarios developing business models that rely on renewable energy, contribute to the circular economy or scale energy storage, but is equally well suited for both technological forecasting and creating potentially new products or services through the recombination of technologies. The MORPHOL tool, that was developed by the Institut d’Innovation Informatique pour l’Entreprise 3IE, was used for this purpose.

Apart from the morphological analysis, some scenarios were obtained via ideas from the consultation process, as well as via a brainstorming among the partners, in order not to leave specific combinations of variables that, due to time constraints, did not appear during the analysis.

D3.1 Exploratory scenarios

Lessons learnt within this task were that the process of identifying the final variables was rather complicated, as sometimes several variables could be combined into one, and some of them represented development hypotheses for a certain working horizon of the variable. In other cases, variables were directly related to other variables and were therefore eliminated from the analysis. Also, several variables did not condition the development hypothesis for a given working horizon, so these variables were not taken into account in the analysis. In some cases it was possible to combine several variables into one variable.

After this process, the final number of variables selected from the three groups for the morphological analysis was only ten. However, the combination of the different hypotheses for each variable allows a total of 100,000 possible scenarios to be calculated. Thus, the scenario space was large enough to allow for an extremely deep analysis of the system.

Finally, in some cases it was difficult to decide whether it was a normal scenario or a micro-scenario. However, once any scenario has been selected, it is important to review any additional possibilities that are not incompatible with the main scenario.