ADREM: Adaptive clustering for Decentralised Resilient Energy Management

Adaptive clustering for Decentralised Resilient Energy Management

ADREM: Adaptive clustering for Decentralised Resilient Energy Management
The systems are to be evaluated by distributed simulation and emulation during their design, with both simulated and live data.

ADREM: Adaptive clustering for Decentralised Resilient Energy Management

  • Thema: products and services
  • Onderwerp: SG: Markets

Self-optimizing and self-healing clusters

This proposal focuses on the design of a framework for Distributed Energy Resource (DER) management based on self-optimizing and self-healing clusters of consumers and producers.

  • Consumers and producers participate in clusters based on negotiated service level agreements (SLAs)
  • Clusters are (approximately) autarkic and adaptive
  • Cluster membership and SLAs can be (re-)negotiated due to changes in the environment, the (forecasted) availability of energy resources, the overall energy market, but also participants? forecasts of their own needs and possibilities
  • This allows for local, decentralised S/D management based on SLAs, reducing complexity on a wider scale;
    and it provides the basis for stability of the power system through reconfiguration.
  • It thus also allows for preparing load shedding and system restoration / re-configuration schedules for system failures.

Framework

The results of the project includes a framework with:

  1. dynamic profiles and agent models for energy consumers and resource providers together with the technology designed to this purpose
  2. decentralised clustering algorithms as the basis for coordination: criteria, objectives, and boundary conditions
  3. negotiation markets and negotiation strategies for cluster and SLA determination/reconfiguration
  4. forecasting mechanisms and strategies for different types of loads and markets together with the algorithms and models designed and implemented.

The systems are to be evaluated by distributed simulation and emulation during their design, with both simulated and live data.

Adaptation, clustering, decentralized multi-issue negotiation, load profiling, and forecasting thus provide the basis for stability of the power system through optimal reconfiguration and local management of S/D balances.

Hoofdaanvrager

 

  • Uitvoerders: Dhr. A.G. Casaca de Rocha Vaz MSc
    Mw. S. Causevic MSc
Looptijd

01/01/2015 tot 01/01/2019

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