The Energy Community Platform (ECP) is a modular system conceived to promote a conscious use of energy by the users inside local energy communities. It is composed of two integrated subsystems: the Energy Community Data Platform (ECDP), a middleware platform designed to support the collection and the analysis of big data about the energy consumption inside local energy communities, and the Energy Community Tokenization Platform (ECTP), which focuses on tokenizing processed source data to enable incentives through smart contracts hosted on a decentralized infrastructure possibly governed by multiple authorities. We illustrate the overall design of our system, conceived considering some real-world projects (dealing with different types of local energy community, different amounts and nature of incoming data, and different types of users), analyzing in detail the key aspects of the two subsystems. In particular, the ECDP acquires data of a different nature in a heterogeneous format from multiple sources and supports a data integration workflow and a data lake workflow, designed for different uses of the data. We motivate our technological choices and present the alternatives taken into account, both in terms of software and of architectural design. On the other hand, the ECTP operates a tokenization process via smart contracts to promote good behaviors of users within the local energy community. The peculiarity of this platform is to allow external parties to audit the correct behavior of the whole tokenization process while protecting the confidentiality of the data and the performance of the platform. The main strengths of the presented system are flexibility and scalability (guaranteed by its modular architecture), which allow its applicability to any type of local energy community.
A big data platform exploiting auditable tokenization to promote good practices inside local energy communities / Gagliardelli, Luca; Zecchini, Luca; Ferretti, Luca; Beneventano, Domenico; Simonini, Giovanni; Bergamaschi, Sonia; Orsini, Mirko; Magnotta, Luca; Mescoli, Emma; Livaldi, Andrea; Gessa, Nicola; De Sabbata, Piero; D’Agosta, Gianluca; Paolucci, Fabrizio; Moretti, Fabio. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - 141:(2022), pp. 595-610. [10.1016/j.future.2022.12.007]