Nandor Verba, Pablo Baldivieso-Monasterios, Siyuan Dong, Andrei Braitor, George Konstantopoulos, Elena Gaura, Euan Morris, Alison Halford and Colin Stephen
16 December 2021
Sustainable, responsible, and fair energy transitions to address environmental goals, reduce energy costs, and strive for greater energy equity requires a connected and intelligent eco-system of energy assets that has the potential for significant optimisation of electricity production and use. Central to this shift is the adoption of distributed, agent-based approaches to Smart Local Energy System (SLES) management informed by AI and machine learning (ML). Critically, participants in the energy eco-system need to consider how SLES can unify allocating roles, functional responsibilities, and technical requirements to bounded systems that foreground the development of fully digitised, flexibly interconnected, multi-layer plug and play architectures for energy systems.
By defining and standardising the energy system component groups by roles and responsibilities, this paper:
- Explores the way whole system design can be achieved by integrating control, markets and analytics into each system to meet and improve the energy efficiency of existing assets and processes.
- Proposes the use of physical, control, market and service layers to create a ‘system of systems’ representation that work towards the democratisation of energy.
- Improves understanding of the value of optimising cyber-physical interactions when designing and implementing effective SLES networks.
Tags: Cyber-physical systems; SLES; Integrating control, markets, and analytics; AI and Multi-Agent Systems