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The European Union has set ambitious targets for energy efficiency to promote energy sustainability and decarbonisation of energy systems by introducing performance indicators that can be used in different contexts at international, national or regional level. In particular, the macroeconomic indicator of Electrical Intensity (EI) and Sustainable Electrical Intensity (SEI) can be used to measure energy performance on different scales, to identify the amount of energy used per unit of Gross Domestic Product (GDP), thus providing a picture of sustainability in economic and spatial terms. To support just urban and territorial policies that can have a positive effect not only on GDP, more generally on the fair and sustainable wellbeing of a community, the complexity of the EI index and SEI index must be addressed by assessing the impacts of energy efficiency interventions and the development of renewable energy installations. The aim of the paper is to develop a comparative approach between Italian spatial distribution of the total electricity consumption (by EI index) and the total renewable electrical consumption (by SEI index) with a dataset of BES indicators - Equitable and Sustainable Well-being - to evaluate of the just electric transition. [ABSTRACT FROM AUTHOR]

In the restructured electricity industry, the engineering aspects of planning need to be reformulated even though the goal to attain remains substantially the same, requiring various objectives to be simultaneously accomplished to achieve the optimality of the power system development and operation. In many cases, these objectives contradict each other and cannot be handled by conventional single optimization techniques. In this paper, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed. The methodology adopted permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, cost of energy not supplied, and cost of energy required by the served customers. The implemented technique is based on a genetic algorithm and an e-constrained method that allows obtaining a set of noninferior solutions. Application examples are presented to demonstrate the effectiveness of the proposed procedure. Index Terms--Distributed generation, distribution networks, genetic algorithms, multiobjective programming.