Predictability and Fairness in Load Aggregation with Deadband
- URL: http://arxiv.org/abs/2305.17725v2
- Date: Wed, 09 Oct 2024 10:17:32 GMT
- Title: Predictability and Fairness in Load Aggregation with Deadband
- Authors: F. V. Difonzo, M. Roubalik, J. Marecek,
- Abstract summary: We consider the effects of losses in the alternating current model and the deadband in the controller.
We show that Filippov invariant measures enable reasoning about predictability and fairness.
- Score: 0.0
- License:
- Abstract: Virtual power plants and load aggregation are becoming increasingly common. There, one regulates the aggregate power output of an ensemble of distributed energy resources (DERs). Marecek et al. [Automatica, Volume 147, January 2023, 110743, arXiv:2110.03001] recently suggested that long-term averages of prices or incentives offered should exist and be independent of the initial states of the operators of the DER, the aggregator, and the power grid. This can be seen as predictability, which underlies fairness. Unfortunately, the existence of such averages cannot be guaranteed with many traditional regulators, including the proportional-integral (PI) regulator with or without deadband. Here, we consider the effects of losses in the alternating current model and the deadband in the controller. This yields a non-linear dynamical system (due to the non-linear losses) exhibiting discontinuities (due to the deadband). We show that Filippov invariant measures enable reasoning about predictability and fairness while considering non-linearity of the alternating-current model and deadband.
Related papers
- Fair CoVariance Neural Networks [34.68621550644667]
We propose Fair coVariance Neural Networks (FVNNs), which perform graph convolutions on the covariance matrix for both fair and accurate predictions.
We prove that FVNNs are intrinsically fairer than analogous PCA approaches thanks to their stability in low sample regimes.
arXiv Detail & Related papers (2024-09-13T06:24:18Z) - A Non-negative VAE:the Generalized Gamma Belief Network [49.970917207211556]
The gamma belief network (GBN) has demonstrated its potential for uncovering multi-layer interpretable latent representations in text data.
We introduce the generalized gamma belief network (Generalized GBN) in this paper, which extends the original linear generative model to a more expressive non-linear generative model.
We also propose an upward-downward Weibull inference network to approximate the posterior distribution of the latent variables.
arXiv Detail & Related papers (2024-08-06T18:18:37Z) - Mind the Graph When Balancing Data for Fairness or Robustness [73.03155969727038]
We define conditions on the training distribution for data balancing to lead to fair or robust models.
Our results show that, in many cases, the balanced distribution does not correspond to selectively removing the undesired dependencies.
Overall, our results highlight the importance of taking the causal graph into account before performing data balancing.
arXiv Detail & Related papers (2024-06-25T10:16:19Z) - Forecasting with an N-dimensional Langevin Equation and a Neural-Ordinary Differential Equation [0.0]
We develop a framework to systematically model and forecast non-stationary electricity-price time series.
Our findings reveal that the NODE nicely complements the LE, providing a comprehensive strategy to tackle both stationary and non-stationary electricity-price behavior.
arXiv Detail & Related papers (2024-05-12T18:45:30Z) - Scalable Optimal Design of Incremental Volt/VAR Control using Deep
Neural Networks [2.018732483255139]
We propose a scalable solution by reformulating Optimal Rule Design (ORD) as training a deep neural network (DNN)
We put forth a scalable solution by reformulating ORD as training a deep neural network (DNN)
Analytical findings and numerical tests corroborate that the proposed ORD solution can be neatly adapted to single/multi-phase feeders.
arXiv Detail & Related papers (2023-01-04T04:19:12Z) - Distributed Nonlinear State Estimation in Electric Power Systems using
Graph Neural Networks [1.1470070927586016]
This paper introduces an original graph neural network based SE implementation over the augmented factor graph of the nonlinear power system SE.
The proposed regression model has linear computational complexity during the inference time once trained, with a possibility of distributed implementation.
arXiv Detail & Related papers (2022-07-23T08:54:24Z) - Learning to Estimate Without Bias [57.82628598276623]
Gauss theorem states that the weighted least squares estimator is a linear minimum variance unbiased estimation (MVUE) in linear models.
In this paper, we take a first step towards extending this result to non linear settings via deep learning with bias constraints.
A second motivation to BCE is in applications where multiple estimates of the same unknown are averaged for improved performance.
arXiv Detail & Related papers (2021-10-24T10:23:51Z) - Predictability and Fairness in Load Aggregation and Operations of
Virtual Power Plants [3.8113588584597187]
In power systems, one wishes to regulate the aggregate demand of an ensemble of distributed energy resources.
We suggest a notion of predictability and fairness, which suggests that the long-term averages of prices or incentives offered should be independent of the initial states of the operators of the DER.
arXiv Detail & Related papers (2021-10-06T18:20:07Z) - Principal Component Density Estimation for Scenario Generation Using
Normalizing Flows [62.997667081978825]
We propose a dimensionality-reducing flow layer based on the linear principal component analysis (PCA) that sets up the normalizing flow in a lower-dimensional space.
We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013 to 2015.
arXiv Detail & Related papers (2021-04-21T08:42:54Z) - Stochastically forced ensemble dynamic mode decomposition for
forecasting and analysis of near-periodic systems [65.44033635330604]
We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system.
We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony.
Results are presented for a test case using load data from an electrical grid.
arXiv Detail & Related papers (2020-10-08T20:25:52Z) - Sparse Identification of Nonlinear Dynamical Systems via Reweighted
$\ell_1$-regularized Least Squares [62.997667081978825]
This work proposes an iterative sparse-regularized regression method to recover governing equations of nonlinear systems from noisy state measurements.
The aim of this work is to improve the accuracy and robustness of the method in the presence of state measurement noise.
arXiv Detail & Related papers (2020-05-27T08:30:15Z)
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.