Extreme Scenario Selection in Day-Ahead Power Grid Operational Planning

• URL: http://arxiv.org/abs/2309.11067v1
• Date: Wed, 20 Sep 2023 05:09:09 GMT
• Title: Extreme Scenario Selection in Day-Ahead Power Grid Operational Planning
• Authors: Guillermo Terr\'en-Serrano and Michael Ludkovski
• Abstract summary: We propose and analyze the application of statistical functional depth metrics for the selection of extreme scenarios in day-ahead grid planning. Our primary motivation is screening of probabilistic scenarios for realized load and renewable generation, in order to identify scenarios most relevant for operational risk mitigation. We investigate a range of functional depth measures, as well as a range of operational risks, including load shedding, operational costs, reserves shortfall and variable renewable energy curtailment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose and analyze the application of statistical functional depth metrics for the selection of extreme scenarios in day-ahead grid planning. Our primary motivation is screening of probabilistic scenarios for realized load and renewable generation, in order to identify scenarios most relevant for operational risk mitigation. To handle the high-dimensionality of the scenarios across asset classes and intra-day periods, we employ functional measures of depth to sub-select outlying scenarios that are most likely to be the riskiest for the grid operation. We investigate a range of functional depth measures, as well as a range of operational risks, including load shedding, operational costs, reserves shortfall and variable renewable energy curtailment. The effectiveness of the proposed screening approach is demonstrated through a case study on the realistic Texas-7k grid.

Related papers

• Extraction of Typical Operating Scenarios of New Power System Based on Deep Time Series Aggregation [9.058570505828103]
  Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system. This study proposes a novel deep time series aggregation scheme (DTSAs) to generate typical operational scenarios. Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-screening feature methods.
  arXiv  Detail & Related papers  (2024-08-23T08:39:25Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• Exploration via Planning for Information about the Optimal Trajectory [67.33886176127578]
  We develop a method that allows us to plan for exploration while taking the task and the current knowledge into account. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines.
  arXiv  Detail & Related papers  (2022-10-06T20:28:55Z)
• A Survey of Risk-Aware Multi-Armed Bandits [84.67376599822569]
  We review various risk measures of interest, and comment on their properties. We consider algorithms for the regret minimization setting, where the exploration-exploitation trade-off manifests. We conclude by commenting on persisting challenges and fertile areas for future research.
  arXiv  Detail & Related papers  (2022-05-12T02:20:34Z)
• Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators [62.997667081978825]
  We present a specialized scenario generation that utilizes forecast information to generate scenarios for the particular usage in day-ahead scheduling problems. In particular, we use normalizing flows to generate wind power generation scenarios by sampling from a conditional distribution that uses day-ahead wind speed forecasts to tailor the scenarios to the specific day. We apply the generated scenarios in a simple day-ahead bidding problem of a wind electricity producer and run a statistical analysis focusing on whether the scenarios yield profitable and reliable decisions.
  arXiv  Detail & Related papers  (2022-04-05T14:27:25Z)
• Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior [135.78858513845233]
  STRIVE is a method to automatically generate challenging scenarios that cause a given planner to produce undesirable behavior, like collisions. To maintain scenario plausibility, the key idea is to leverage a learned model of traffic motion in the form of a graph-based conditional VAE. A subsequent optimization is used to find a "solution" to the scenario, ensuring it is useful to improve the given planner.
  arXiv  Detail & Related papers  (2021-12-09T18:03:27Z)
• Risk Conditioned Neural Motion Planning [14.018786843419862]
  Risk-bounded motion planning is an important yet difficult problem for safety-critical tasks. We propose an extension of soft actor critic model to estimate the execution risk of a plan through a risk critic. We show the advantage of our model in terms of both computational time and plan quality, compared to a state-of-the-art mathematical programming baseline.
  arXiv  Detail & Related papers  (2021-08-04T05:33:52Z)
• Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation [23.43175124406634]
  Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks. We propose a flow-based multimodal safety-critical scenario generator for evaluating decisionmaking algorithms. We evaluate six Reinforcement Learning algorithms with our generated traffic scenarios and provide empirical conclusions about their robustness.
  arXiv  Detail & Related papers  (2020-09-16T15:16:43Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)
• Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method [20.280573307366627]
  We propose a generative framework to create safety-critical scenarios for evaluating task algorithms. We demonstrate that the proposed framework generates safety-critical scenarios more efficiently than grid search or human design methods.
  arXiv  Detail & Related papers  (2020-03-02T21:26:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.