A Data-Driven Decision Support Tool at the service of Energy suppliers and Utilities for Tackling Energy Poverty: A case study in Greece

Energy poverty is mainly understood as the inability of households to maintain adequate levels of affordable energy services, within countries of developed economic context, and there is a diverse set of indicators to measure it, with arrears on utility bills being one of them. This has stimulated the interest of utilities and energy suppliers, especially those that are also obliged (through a ringfence) or incentivised (through administrative uplifts in savings) to implement a share of energy efficiency measures to vulnerable and energy poor households, under Article 7 of the Energy Efficiency Directive (2012/27/EU). As a result, a considerable number of energy companies are designing, adopting and implementing measures that help end consumers, and in particular energy poor and vulnerable consumers, improve the energy efficiency of their dwellings. In this context, the aim of this paper is to present a decision support tool to help utilities and energy suppliers effectively evaluate different energy poverty schemes in terms of cost, risk and energy savings and select the optimal one(s) to consider. The final combination of schemes (i.e., portfolios) meet a set of context-specific targets and constraints, and are elicited in the scope of minimising both cost and risk on the utilities’ end. The tool is established upon the basic principles of Multi-objective Programming and it is implemented in Python 3.0 programming language. In this way, the proposed tool sets the groundwork for utilities and energy suppliers to fulfil energy efficiency obligations, as well as tackle energy poverty.