Background

In modern energy systems, flexibilities play a critical role in maintaining grid stability despite the volatility of renewable energy sources such as wind and solar power. In the currently developed system design for implementing market-based Redispatch 3.0, flexibilities are calculated decentrally by individual agents but are centrally aggregated and distributed by an aggregator. This centralized approach enables coordinated participation in the redispatch market but may limit the scalability and efficiency of the system.

A self-organized aggregation offers an alternative approach: Instead of aggregating flexibilities centrally, agents could interact and cooperate in a distributed manner to create offers collectively. Such a decentralized approach has the potential to increase the amount of flexibility offered, as individual constraints can be more directly accounted for. However, this introduces challenges, as new algorithms for coordination, negotiation, and consensus-building are required.

Objective

The objective of this thesis is to design, implement, and evaluate a self-organized approach for the aggregation and disaggregation of flexibilities in a multi-agent system. The developed algorithms will be compared to a provided centralized aggregation method to evaluate their potential in terms of flexibility, scalability, and efficiency.

1. Familiarization with the topic (understanding the basics of flexibilities, familiarization with multi-agent systems in energy systems, research on aggregation and disaggregation mechanisms in the context of redispatch and energy markets, introduction to self-organized systems and algorithms for cooperative decision-making) 
2. Conception (development of a concept for the self-organized aggregation and disaggregation of flexibilities, definition of the communication and decision-making mechanisms between the agents)
3. Implementation (implementation of the concept in a suitable programming language (e.g. Python) and use of the agent framework mango)
4. Evaluation (comparison of self-organized and centralized aggregation in terms of efficiency, scalability and flexibility potential using simulation studies)

Interest in decentralized energy systems, multi-agent systems and optimization algorithms

Optional:

• Knowledge of algorithmic design and programming, preferably in Python
• Initial experience with multi-agent systems or optimization algorithms