Code underlying the publication: A Benchmark for the Application of Distributed Control Techniques to the Electricity Network of the European Economic Area
doi: 10.4121/d2c0d075-1c49-41af-8113-5e50c27ca97e
- Artificial Intelligence and Image Processing
- Mechanical Engineering
- Electrical and Electronic Engineering
- Data Format
- Other Engineering
- Computer Software
- Distributed Computing
- Information Systems
- Interdisciplinary Engineering
- Environmental Engineering
- Computation Theory and Mathematics
- Climate and Climate Change
- Engineering
- Information and Computing Sciences
- Environment
The European Economic Area Electricity Network Benchmark (EEA-ENB) is a multi-area power system representing the European network of transmission systems for electricity to facilitate the application of distributed control techniques. In the EEA-ENB we consider the Load Frequency Control (LFC) problem in the presence of renewable energy sources (RESs), and energy storage systems (ESSs). RESs are known to cause instability in power networks due to their inertia-less and intermittent characteristics, while ESSs are introduced as a resource to mitigate the problem. In the EEA-ENB, particular attention is dedicated to Distributed Model Predictive Control (DMPC), whose application is often limited to small and homogeneous test cases due to the lack of standardized large-scale scenarios for testing, and due to the large computation time required to obtain a centralized MPC action for performance comparison with DMPC strategies under consideration. The second problem is exacerbated when the scale of the system grows. To address these challenges and to provide a real-world-based and control-independent benchmark, the EEA-ENB has been developed. The benchmark includes a centralized MPC strategy providing performance and computation time metrics to compare distributed control within a repeatable and realistic simulation environment.
- 2024-03-27 first online, published, posted
- CLariNet (grant code 101018826) [more info...] European Research Council
DATA
To access the source code, use the following command:
git clone https://data.4tu.nl/v3/datasets/78170934-ebb2-4774-9cf9-ec584ad088a0.git