A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
PhI-GPR improves power grid state estimation and forecasting.
Power system studies require the topological structures of real-world power networks; however, such data is confidential due to important security concerns. Thus, power grid synthesis (PGS), i.e., creating realistic power grids that imitate actual power networks, has gained significant attention. In this letter, we cas…
Paper proposes a method to locate power grid recordings using ENF sequences.
Survey of RL methods for optimizing power grid topologies.
Power grids are one of the most important components of infrastructure in today's world. Every nation is dependent on the security and stability of its own power grid to provide electricity to the households and industries. A malfunction of even a small part of a power grid can cause loss of productivity, revenue and i…
GP CC-OPF solves uncertain power grid optimization with Gaussian Process.
This paper bounds errors in data-driven power grid models using Rademacher complexity.
In this paper we propose a quadratic programming model that can be used for calculating the term structure of electricity prices while explicitly modeling startup costs of power plants. In contrast to other approaches presented in the literature, we incorporate the startup costs in a mathematically rigorous manner with…
A predictor improves power grid frequency forecasts up to one hour.
Distribution grid is the medium and low voltage part of a large power system. Structurally, the majority of distribution networks operate radially, such that energized lines form a collection of trees, i.e. forest, with a substation being at the root of any tree. The operational topology/forest may change from time to …
WindDragon forecasts wind power with deep learning.
GraPhyR uses GNNs to optimize power grid reconfiguration in real-time.
The topology of a power grid affects its dynamic operation and settlement in the electricity market. Real-time topology identification can enable faster control action following an emergency scenario like failure of a line. This article discusses a graphical model framework for topology estimation in bulk power grids (…
Paper tackles RL for power grid topology optimization.
Paper introduces reinforcement learning for managing power grids.
System identifies power grid location from media recordings.
The segmentation of large scale power grids into zones is crucial for control room operators when managing the grid complexity near real time. In this paper we propose a new method in two steps which is able to automatically do this segmentation, while taking into account the real time context, in order to help them ha…
Statistical depth metrics help identify risky power grid scenarios.
Study finds cherry-picking load shaping strategies outperforms others in reducing grid CO2 emissions.
Paper proposes model to assess financial risk of grid-ignited wildfires.
PowRL uses RL to manage power grids robustly, reducing overloads and maintaining power reliability.
Due to limited metering infrastructure, distribution grids are currently challenged by observability issues. On the other hand, smart meter data, including local voltage magnitudes and power injections, are communicated to the utility operator from grid buses with renewable generation and demand-response programs. This…
We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully. We call our architeture LEAP net, for Latent Encoding of Atypical Perturbation. Our method implements…
Graph neural networks improve topology control of power grids.
Paper uses Gaussian processes to solve AC-OPF with renewable uncertainty.
AI model enhances grid monitoring with synchro-waveform tech.
We address the problem of maintaining high voltage power transmission networks in security at all time. This requires that power flowing through all lines remain below a certain nominal thermal limit above which lines might melt, break or cause other damages. Current practices include enforcing the deterministic "N-1" …
New AI assistant for power grid operators simplifies complex decision-making.
Novel method improves load estimation in power grids using anomaly and change point detection.
Insights in power grid pixel maps (PGPMs) refer to important facility operating states and unexpected changes in the power grid. Identifying insights helps analysts understand the collaboration of various parts of the grid so that preventive and correct operations can be taken to avoid potential accidents. Existing sol…
New AI model improves grid planning efficiency and reliability.
Locational Marginal Pricing aims to free UK power markets.
Optimal sampling reduces power grid data analysis costs.
Random Forest outperforms other IDS algorithms in smart grids.
Distribution grids currently lack comprehensive real-time metering. Nevertheless, grid operators require precise knowledge of loads and renewable generation to accomplish any feeder optimization task. At the same time, new grid technologies, such as solar photovoltaics and energy storage units are interfaced via invert…
Machine learning predicts CO2 emissions in power grids, reducing uncertainty.
Trading affects grid frequency fluctuations, making them more extreme.
For power grid operations, a large body of research focuses on using generation redispatching, load shedding or demand side management flexibilities. However, a less costly and potentially more flexible option would be grid topology reconfiguration, as already partially exploited by Coreso (European RSC) and RTE (Frenc…
The grid integration of intermittent Renewable Energy Sources (RES) causes costs for grid operators due to forecast uncertainty and the resulting production schedule mismatches. These so-called profile service costs are marginal cost components and can be understood as an insurance fee against RES production schedule u…
The growing integration of distributed energy resources (DERs) in urban distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With a large-scale DER penetration, traditional outage detection methods, which rely on customers making phone calls and smart meters' "last gasp" si…
New method identifies distribution grid outages using smart meter data.
Paper examines adversarial attacks on event cause analysis in power grids.
Study examines barriers to grid-connected battery systems in Spain, finding high cycle cost remains main obstacle.
Framework detects anomalies in real-time PMU data.
In this paper, we develop an online method that leverages machine learning to obtain feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps on extremely fast timescales (e.g., milliseconds), bypassing solving an AC OPF altogether. This is motivated by the fact that as the power gr…
Study on Matérn covariance approximations on grids, finding issues with high-frequency aliasing.
DNN policies improve stochastic AC OPF for power grid optimization.
Grid security and open markets are two major smart grid goals. Transparency of market data facilitates a competitive and efficient energy environment, yet it may also reveal critical physical system information. Recovering the grid topology based solely on publicly available market data is explored here. Real-time ener…