Use cases and business models of multi-agent system (MAS) ICT solutions for LV flexibility management

J.M. Espeche, T. Messervey, Z. Lennard, Riccardo Puglisi, M. Sissini and Meritxell Vinyals
Additional contact information
J.M. Espeche: R2M Solution
T. Messervey: R2M Solution
Z. Lennard: R2M Solution
M. Sissini: Smart Metering Systems plc
Meritxell Vinyals: LADIS (CEA, LIST) - Laboratoire d'analyse des données et d'intelligence des systèmes (CEA, LIST) - DM2I (CEA, LIST) - Département Métrologie Instrumentation & Information (CEA, LIST) - LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies - DRT (CEA) - Direction de Recherche Technologique (CEA) - CEA - Commissariat à l'énergie atomique et aux énergies alternatives - Université Paris-Saclay

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Abstract: This paper describes the use cases and business models opportunities of a Multi-Agent System (MAS) ICT solution for LV Flexibility Management. The MAS platform provides a technological solution that enables new collaboration opportunities between actors in the LV portion of the grid, namely, distribution system operators, ESCOs (in particular Telecoms) and consumers/ prosumers. MAS have potential for efficient decision-making in the LV part of the grid due to the large number devices, users and variables and which makes more efficient a decentralized decision making approach. To support the new collaborations and business strategies amongst these actors, new business models are required and the ecosystem forms series of multi-sided platform business models. In this paper, the approach to business model development is detailed and 17 resultant business model opportunities are identified. These business models are then mapped to the use cases for future analysis.

Keywords: Smart grid; Aggregators; Distribution systems; Decentralized decision making; Technological solution; multi-agent system; Business model development; Capacity management; Smart power grids; Optimization; Decision making; Electric power transmission networks; Multi-sided platforms; artificial intelligence; distributed learning (search for similar items in EconPapers)
Date: 2017-01
Note: View the original document on HAL open archive server: https://cea.hal.science/cea-01809215v1
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Published in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2017, 175, pp.136-142. ⟨10.1007/978-3-319-47729-9_14⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:cea-01809215

DOI: 10.1007/978-3-319-47729-9_14

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