Data platform guidelines and prototype for microgrids and energy access: matching demand profiles and socio-economic data to foster project development

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Fioriti, Davide, Stevanato, Nicolo, Ducange, Pietro, Marcelloni, Francesco, Colombo, Emanuela, Poli, Davide
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access to electricity; Biological system modeling; Data collection; Distributed generation; Economic development; Economic growth; energy access; Energy utilization; Estimation; Guidelines; Home appliances; Households; Literature reviews; load estimation; Microgrids; NoSQL database; open data; Planning; Platforms; Project development; Prototypes; software platform; Time series
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3294841

Energy Access is a pivotal need for socio-economic growth. Proven to be a key enabler of development and progress, access to electricity has been prioritized by governments using grid extension actions and off-grid solutions, namely microgrids and home systems technologies, fed by renewable sources. However, achieving universal access to energy is still highly challenging, given the lack of resources and the large population currently unserved. The lack of adequate socio-economic data at granular scale and of a good understanding of demand uptake led by economic growth is a barrier for efficient energy planning. Access to cojoint demand and socio-economic data at local level is crucial, yet hard to obtain: often such data are unavailable or very difficult to collect, and current data platforms often lack the ability to conjointly store variegated socio-economic and time series data. For these reasons, in this paper, we present a comprehensive methodology that, based on an extensive literature review, draws guidelines for developing data-sharing platforms in energy access, develops a proposed architecture to support the data collection of conjoint socio-economic and time-series data, and proposes a prototype of the final application. The methodology leverages on a novel extensive literature review to identify the major determinants of demand uptake and the corresponding consuming entities: villages, households and appliances. The proposed architecture is able to capture numeric, categorical and time series information for all consuming entities, based on state-of-the-art NoSQL databases. Finally, a prototype implementation with a web-based interface developed with Angular and Spring is proposed and discussed.