The electricity system is suited to facing radical transformations in the medium-long term, in technical terms (new energy generation and storage technologies and new interactions between energy carriers and system elements), in social terms (new consumption habits and electrification of the heating and transport sectors) and in regulatory and economic terms (new market forms), as well as in the climate (greater onset of extreme weather events).

The financial resources for investments in large-scale infrastructure works are limited; however, it is desirable that the electricity system of the future maintains high standards of quality, adequacy, safety and sustainability, all this while paying attention to maximising the socio-economic welfare of the community. It follows that in optimal network planning, decision-making issues are very complex, multi-objective and multi-scenario issues.

In this research project, a new software tool has been developed that supports transmission network planners in identifying and evaluating possible actions for optimal development. In particular, we focus on finding a synergistic mix between optimal positioning of new transmission lines, optimal reinforcement of existing lines and optimal positioning of new storage systems, while also taking into account the constraints to the construction of new works. It is based on a Genetic Algorithm which iteratively proposes increasingly promising network development candidates, interpreting the economic/energy indicators derived from the analysis of one or more software tools that are interrogated at a lower hierarchical level in order to evaluate the effects of new potential expansion strategies.

This report presents the implementation phase of the modeling platform for generic, single- and multi-objective decision-making issues that can be solved using an ad-hoc Genetic Algorithm. This modeling environment will be used to address the issue of the optimal selection of network developments; it is absolutely generalistic and therefore, in the future it may allow for new analyses of strategic decisions or electrical and territorial planning, energy policy guidance and much more. Indeed, it is a useful tool for tackling integer or combinatorial optimisation issues.