The Vision of Self-Management in Cognitive Organic Power Distribution Systems

Abstract

Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.

Analyzing the Propagation of Disturbances in CPES considering the States of ICT-enabled Grid Services

“…A promising approach for operating such complex systems with system-wide ICT, a large number of actors, and a high degree of automation is to shift from a hierarchical system, operated mainly in a centralized manner by humans, to a distributed one, mainly operated autonomously but with several actors interacting with each other as well as with humans. Consequentially, such CPESs can be regarded as organic computing systems [27,34] as they continuously and dynamically adapt to exogenous and endogenous changes on various time scales -from significant changes in generation technologies driven by external regulatory and political factors to unforeseen demand fluctuations due to stochastic human behaviour. To cope with these dynamics, the system is characterized by self-* properties, e.g., self-organization of battery storage swarms [41], self-configuration of SCADA and protection systems [43], self-explanation, and context awareness [5].…”