ARPA-E Grid Optimization Challenge 1 Synthetic Power System Models

ARPA-E Grid Optimization Challenge 1 data from 2018-2019 provides synthetic power system network models for the Security Constrained AC Optimal Power Flow problem. The collection includes Real-Time and Online datasets with operating scenarios defining instantaneous power demand, renewable generation, and component availability. It was used for a competition requiring solvers to compute a base case operating point and verify feasibility across contingencies.

Use Cases

• Train optimization models to solve the base Security Constrained AC Optimal Power Flow problem using network models and operating scenarios.
• Benchmark solver performance on the Real-Time datasets which include starting-point information for the optimization.
• Analyze the feasibility of power flow solutions across all contingencies as defined by the Solver 2 step of the challenge.
• Compare synthetic network models generated by the GRID DATA program with the few industry-provided scenarios used in the Final Event.

Strengths

• Dataset covers a defined competition period from 2018 to 2019, providing temporal context.
• Includes two distinct dataset types (Real-Time and Online) with specific operational contexts.
• Contains power system network models of different sizes, allowing for scalability analysis.

Limitations

• Most data is synthetic, which may not fully capture the complexity of real-world power grids.
• Industry scenario data used in the Final Event is not included in the public release.
• Initial trial events revealed computational challenges, with some codes running over 90 hours, indicating problem difficulty.

Provenance

Source

Department of Energy ARPA-E GRID DATA program.

Collection Method

Synthetically generated network models and scenarios, with a few from industry (not public). Created for the GO Competition Challenge 1.

Time Range

2018 to 2019.

Freshness

Last updated in September 2024.