Future smart energy software houses

Forskningsoutput: TidskriftsbidragArtikelVetenskapligPeer review

Sammanfattning

Software is the key enabling technology (KET) as digitalization is cross-cutting future energy systems spanning the production sites, distribution networks, and consumers particularly in electricity smart grids. In this paper, we identify systematically what particular software competencies are required in the future energy systems focusing on electricity system smart grids. The realizations of that can then be roadmapped to specific software capabilities of the different future software houses' across the networks. Our instrumental method is software competence development scenario path construction with environmental scanning of the related systems elements. The vision of future software-enabled smart energy systems with software houses is mapped with the already progressing scenarios of energy systems transitions on the one hand coupled with the technology foresight of software on the other hand. Grounding on the Smart Grid Reference Architecture Model (SGAM), it tabulates the distinguished software competencies and attributes them to the different partiesincluding customers/consumers (Internet of People, IoP)involved in future smart energy systems. The resulting designations can then be used to recognize and measure the necessary software competencies (e.g., fog computing) in order to be able to develop them in-house, or for instance to partner with software companies, depending on the future desirability. Software-intensive systems development competence becomes one of the key success factors for such cyber-physical-social systems (CPSS). Further futures research work is chartered with the Futures Map frame. This paper contributes preliminarily toward that by identifying pictures of the software-enabled futures and the connecting software competence-based scenario paths.
Originalspråkengelska
Artikelnummer1
TidskriftEuropean Journal of Futures Research
Volym7
Utgåva1
Antal sidor25
ISSN2195-2248
DOI
StatusPublicerad - 19 jan 2019
MoE-publikationstypA1 Tidskriftsartikel-refererad

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap

Citera det här