The newest Distributed Ledger Technology platforms, which delegate the execution of complex tasks in the form of Smart Contracts, make it possible to devise novel local electricity market frameworks, which are performed in a fully automated fashion. This paper proposes a novel fully automated platform for energy and ancillary service markets in distribution networks, able to run in a decentralized fashion, bypassing the need for a physical central authority. The proposed platform, able to perform the role of Virtual Decentralized Market Authority, shows excellent potential applications in the management of local ancillary service markets in local energy communities of various sizes. The proposed Virtual Decentralized Market Authority showed reasonable running costs and comparable technical management capabilities with respect to a physical, centralized managing authority.

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Techno-economic assessment methodologies are fundamental tools both for defining strategic policies and for selecting the worthy investment options. Since smart grid transition and the increasing demand for novel decision support tools, a joined Multi Criteria -Cost Benefit Analysis (MC-CBA) is presented in this paper. The aim is to provide an assessment framework for smart grid projects which considers monetary and non-monetary impacts. The joint approach is proposed for outclassing the weaknesses of both CBA and MCA by emphasizing their strengths. The MC-CBA framework is employed for identifying the best option among a set of active network distribution planning alternatives. The monetisation of all impacts is not required; hence, the MC-CBA is suitable for assessing social and technical impacts without introducing any underlying bias. The aim of the proposed MC-CBA is to help companies and government bodies in strategic planning.

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The energy transition for decarbonization requires consumers’ and producers’ active participation to give the power system the necessary flexibility to manage intermittency and non-programmability of renewable energy sources. The accurate knowledge of the energy demand of every single customer is crucial for accurately assessing their potential as flexibility providers. This topic gained terrific input from the widespread deployment of smart meters and the continuous development of data analytics and artificial intelligence. The paper proposes a new technique based on advanced data analytics to analyze the data registered by smart meters to associate to each customer a typical load profile (LP). Different LPs are assigned to low voltage (LV) customers belonging to nominal homogeneous category for overcoming the inaccuracy due to non-existent coincident peaks, arising by the common use of a unique LP per category. The proposed methodology, starting from two large databases, constituted by tens of thousands of customers of different categories, clusters their consumption profiles to define new representative LPs, without a priori preferring a specific clustering technique but using that one that provides better results. The paper also proposes a method for associating the proper LP to new or not monitored customers, considering only few features easily available for the distribution systems operator (DSO).