Background

In modern energy systems, the integration of renewable energy sources such as wind and solar power plays a pivotal role. However, these energy sources are weather-dependent and therefore volatile. To ensure grid stability, supply and demand must always be balanced. This is where flexibilities come into play.

Flexibilities refer to the capability of a system or resource to adjust its output or consumption (e.g., electricity consumption or generation) in the short term. Examples of flexible resources include battery storage systems, heat pumps, or electric vehicles. Such flexibilities can be used to shift peak loads, absorb excess renewable energy, or alleviate grid constraints.

Why Aggregation?
Individual small flexibilities, such as those provided by a household battery, often lack the capacity to participate independently in energy markets or redispatch processes. By aggregating many small flexibilities into a larger, coordinated asset pool, these barriers can be overcome. An aggregator combines the flexibilities of its assigned agents and offers them collectively on markets or to grid operators.

Why Disaggregation?
When grid operators request flexibility (e.g., to mitigate congestion), the aggregator must forward and allocate this request to its agents (disaggregation). This allocation must consider costs, technical constraints, and the availability of individual agents.

1. Familiarization with the topic (understanding the basics of flexibility, familiarization with multi-agent systems in energy systems, research on aggregation and disaggregation mechanisms in the context of redispatch and energy markets, introduction to non-linear optimizations and their applications) 
2. Modeling (mathematical formulation of the aggregation and disaggregation problem with consideration of constraints)
   Implementation (implementation of the model in a programming language (preferably Python) using optimization libraries)
3. Evaluation (analyzing the model performance by examining metrics such as scalability and by comparing it with alternative aggregation and disaggregation methods provided)

Interest in energy systems, optimization and decentralized systems

Optional:

• Basic knowledge of mathematical optimization
• Programming skills, preferably Python
• Initial experience with optimization libraries such as Pyomo