Adversarial Training for a Continuous Robustness Control Problem in Power Systems

Source

• Raw Markdown: adversarial-training-power-systems-2020
• Rendered / retrieved PDF: paper_adversarial-training-power-systems-2020.pdf
• External source: https://arxiv.org/abs/2012.11390
• Official L2RPN reference list: https://l2rpn.chalearn.org/papers-references

Publication And Credibility

• Paper date: 2020-12-21; arXiv v3 on 2021-04-16.
• Venue/status: IEEE 2021 reference on the L2RPN page; arXiv preprint available.
• Credibility: Credible arXiv/IEEE-linked paper by L2RPN/RTE researchers. Older than one year; use as robustness benchmark context.

Core Claim

The paper frames grid robustness as a continuous control problem with adversarial line-disconnection disturbances.

L2RPN / Grid2Op Notes

It separates the operator agent from an adversarial disturbance process, making contingencies explicit exogenous events rather than hidden noise.

Action-Time-Series Notes

This source is useful when Grid2Op is treated as an action-conditioned graph time-series environment:

power-grid observations + topology / redispatch / storage control input + scenario context
  -> next grid observations + safety/cost outcome

The terminology distinction matters. Topology changes, redispatching, curtailment, and storage commands are actions or control inputs when an agent chooses them. Line failures, maintenance outages, weather-driven renewable shifts, and demand variation are events or exogenous variables unless they are deliberately controlled by the experimenter.

Foundation TSFM Relevance

Agenda slot	Verdict	Evidence	Missing pieces
Causal structure, counterfactuals, and control	partially closes	Important for TSFM world models because it stresses action consequences under rare but safety-critical exogenous events.	The paper evaluates agent robustness; it does not provide a reusable foundation-model training protocol for all grid dynamics.
Context interface: topology and channel context	partially closes	Power-grid state is naturally graph-structured and tied to physical assets, limits, and scenario metadata.	Needs a reusable schema that a general TSFM can consume across grids and non-grid operational systems.
Benchmark level	adjacent	L2RPN/Grid2Op provides simulator-backed trajectories with explicit controls and outcomes.	TSFM-ready comparisons require pinned environment versions, action sets, reward definitions, and train/test scenario splits.

Links Into The Wiki

• Grid2Op
• Learning to run a Power Network Challenge: a Retrospective Analysis
• Anticipating contingengies in power grids using fast neural net screening
• Optimization of computational budget for power system risk assessment
• Action-Conditioned Time-Series Datasets
• Time-Series Benchmark Hygiene
• World Models
• Foundation Time-Series Model Research Agenda