arXiv subjects

Bertrand Travacca

Publications and source records attributed to Bertrand Travacca.

5 recordsLinked to original sources

arXiv2026

ev-flow: A Reproducible, NHTS-Grounded Generator of Synthetic Plug-in Electric Vehicle Charging Behavior for Eight U.S. Regions

Electric-vehicle grid-integration studies need large, behaviorally realistic populations of individual charging profiles, but real charging telemetry is scarce and privacy-restricted, and the existing open generators are calibrated to non-U.S. mobility surveys or flatten the regional, seasonal, and equipment heterogeneity that drives aggregate demand. We present \texttt{ev-flow} (import name \texttt{pev\_synth}), an open-source, MIT-licensed Python package that generates synthetic plug-in electric vehicle charging behavior for eight U.S. regions, grounded in 2017 National Household Travel Survey (NHTS) microdata and regional sales-mix models. A deterministic nine-stage pipeline (M1--M9) carries each vehicle from survey records to a time-stamped charging profile: it stitches survey person-days into donor-matched 365-day travel calendars with a temperature-dependent winter energy uplift, samples behavioral plug-in start times from the published SPEECh K=16 Gaussian-mixture parameterization, evaluates a three-layer Bernoulli plug-in model, propagates a continuous-time state-of-charge ledger with an explicit PHEV gasoline range-extension term, and rasterizes plug status to 15-minute and hourly grids. The package generates residential and workplace profile types with descriptive EVSE brand and connector enrichment; every output is UTC-stored, timezone-aware, and bit-reproducible from a single master seed. A validation runner compares the generated distributions against published bounds and classifies every divergence with literature provenance: the reference \texttt{bay\_area} residential profile rolls up to 11 PASS, 0 unexplained FAIL, 6 explained failures, and 4 explained skips across 21 applicable checks. \texttt{ev-flow} fills a U.S.-focused, NHTS-grounded niche complementary to European generators such as emobpy and VencoPy and to charging simulators such as datafev and ACN-Sim.

eess.SY↗

arXiv2020

Implicit Deep Learning

Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks. Such rules are based on the solution of a fixed-point equation involving a single vector of hidden features, which is thus only implicitly defined. The implicit framework greatly simplifies the notation of deep learning, and opens up many new possibilities, in terms of novel architectures and algorithms, robustness analysis and design, interpretability, sparsity, and network architecture optimization.

cs.LG↗

arXiv2019

Inducing Human Behavior to Alleviate Overstay at PEV Charging Station

As the plug-in electric vehicle (PEV) market expands worldwide, PEV penetration has out-paced public PEV charging accessibility. In addition to charging infrastructure deployment, charging station operation is another key factor for improving charging service accessibility. In this paper, we propose a mathematical framework to optimally operate a PEV charging station, whose service capability is constrained by the number of available chargers. This mathematical framework specifically exploits human behavioral modeling to alleviate the "overstaying" issue that occurs when a vehicle is fully charged. Our behavioral model effectively captures human decision-making when humans are exposed to multiple charging product options, which differ in both price and quality-of-service. We reformulate the associated non-convex problem to a multi-convex problem via the Young-Fenchel transform. We then apply the Block Coordinate Descent algorithm to efficiently solve the optimization problem. Numerical experiments illustrate the performance of the proposed method. Simulation results show that a station operator who leverages optimally priced charging options could realize benefits in three ways: (i) net profits gains, (ii) overstay reduction, and (iii) increased quality-of-service.

eess.SY↗

arXiv2019

Hopfield Neural Network Flow: A Geometric Viewpoint

We provide gradient flow interpretations for the continuous-time continuous-state Hopfield neural network (HNN). The ordinary and stochastic differential equations associated with the HNN were introduced in the literature as analog optimizers, and were reported to exhibit good performance in numerical experiments. In this work, we point out that the deterministic HNN can be transcribed into Amari's natural gradient descent, and thereby uncover the explicit relation between the underlying Riemannian metric and the activation functions. By exploiting an equivalence between the natural gradient descent and the mirror descent, we show how the choice of activation function governs the geometry of the HNN dynamics. For the stochastic HNN, we show that the so-called "diffusion machine", while not a gradient flow itself, induces a gradient flow when lifted in the space of probability measures. We characterize this infinite dimensional flow as the gradient descent of certain free energy with respect to a Wasserstein metric that depends on the geodesic distance on the ground manifold. Furthermore, we demonstrate how this gradient flow interpretation can be used for fast computation via recently developed proximal algorithms.

math.OC↗

arXiv2018

Market based embedded Real Time Operation for Distributed Resources and Flexibility

We build upon previous work out of UC Berkeley's energy, controls, and applications laboratory (eCal) that developed a model for price prediction of the energy day-ahead market (DAM) and a stochastic load scheduling for distributed energy resources (DER) with DAM based objective\cite{Travacca}. In similar fashion to the work of Travacca et al., in this project we take the standpoint of a DER aggregator pooling a large number of electricity consumers - each of which have an electric vehicle and solar PV panels - to bid their pooled energy resources into the electricity markets. The primary contribution of this project is the optimization of an aggregated load schedule for participation in the California Independent System Operator (CAISO) real time (15-minute) electricity market. The goal of the aggregator is to optimally manage its pool of resources, particularly the flexible resources, in order to minimize its cost in the real time market. We achieves this through the use of a model predictive control scheme. A critical difference between the prior work in \cite{Travacca} is that the structure of the optimization problem is drastically different. Based upon our review of the current and public literature, no similar approaches exist. The main objective of this project were building methods. Nevertheless, to illustrate a simulation with 100 prosumers was realized. The results should therefore be taken with a grain of salt. We find that the Real Time operation does not substantially decrease or increase the total cost the aggregator faces in the RT market, but this is probably due to parameters that need further tuning and data, that need better processing.

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