This report describes the study carried out to identify an appropriate modeling of uncertainty and risk in the Multi-Criteria Cost Benefit Analysis (CBA-MCA) of Smart Grid (SG) projects to evaluate the use of flexibility to develop the distribution system.

The study—which falls within the ‘Cost-Benefits’ WG3 of the IEA ISGAN TCP—is part of a strand of research that aims to develop a generally valid methodology for the design evaluation of development alternatives for SGs, which considers non-monetary impacts and externalities.
In the course of previous activities, a CBA-MCA web tool, called ‘smartgrideval,’ was developed and gradually improved, until it began its recent extension toward the evaluation of flexibility services, also taking into account the interaction of different carriers (sector coupling).

In order to make the tool more effective, improving the choice of alternatives and risk mitigation, the aspect of uncertainty (related to economic growth, the scenario analyzed or the method used)—one of the most critical for the creation of the flexibility market—was now addressed.

The scientific literature is rich in techniques to manage uncertainty in decision-making processes. However, not all approaches lend themselves to application to the various uncertainties.

The review carried out aims to provide an overview of the techniques developed to account for uncertainty in CBA and MCA.
It is inferred that the decision-making procedure in an uncertain context must start by identifying the sources of uncertainty. Computational burden and ease of use even by decision-makers, not necessarily insiders, must also be considered.

Scenario analysis—i.e., the analysis of several scenarios featuring simultaneous variations in one or more parameters—was found to be the methodology that achieves the best compromise between effectiveness of results and ease of use. In addition, it allows for the uncertainty of future scenarios to be taken into account by assigning each one a probability of occurrence and applying ‘minimization of maximum regret’ (min-max-regret) on the value of the alternatives.

After validation on test networks and comparison with other leading strategic investment evaluation tools, the plan is to update the smartgrideval tool by integrating the identified methodology.