pwc-archive/papers-with-abstracts huggingface dataset:
An application of reinforcement learning to residential energy storage under real-time pricing
Authors: Eli Brock, Lauren Bruckstein, Patrick Connor, Sabrina Nguyen, Robert Kerestes, Mai Abdelhakim
Date: 2021-11-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

With the proliferation of advanced metering infrastructure (AMI), more real-time data is available to electric utilities and consumers. Such high volumes of data facilitate innovative electricity rate structures beyond flat-rate and time-of-use (TOU) tariffs. One such innovation is real-time pricing (RTP), in which the wholesale market-clearing price is passed directly to the consumer on an hour-by-hour basis. While rare, RTP exists in parts of the United States and has been observed to reduce electric bills. Although these reductions are largely incidental, RTP may represent an opportunity for large-scale peak shaving, demand response, and economic efficiency when paired with intelligent control systems. Algorithms controlling flexible loads and energy storage have been deployed for demand response elsewhere in the literature, but few studies have investigated these algorithms in an RTP environment. If properly optimized, the dynamic between RTP and intelligent control has the potential to counteract the unwelcome spikes and dips of demand driven by growing penetration of distributed renewable generation and electric vehicles (EV). This paper presents a simple reinforcement learning (RL) application for optimal battery control subject to an RTP signal.
Electric Vehicle Attack Impact on Power Grid Operation
Authors: Mohammad Ali Sayed, Ribal Atallah, Chadi Assi, Mourad Debbabi
Date: 2021-11-22 00:00:00

The increasing need for reducing greenhouse gas emissions and the drive for green cities have promoted the use of electric vehicles due to their environmental benefits. In fact, countries have set their own targets and are offering incentives for people to purchase EVs as opposed to traditional gasoline-powered cars. Manufacturers have been hastily deploying charging stations to meet the charging requirements of the EVs on the road. This rapid deployment has contributed to the EV ecosystem's lack of proper security measures, raising multiple questions related to the power grid security and vulnerability. In this paper, we offer a complete examination of the EV ecosystem from the vulnerability to the attacks and finally the solutions. We start by examining the existing vulnerabilities in the EV ecosystem that can be exploited to control the EV charging and launch attacks against the power grid. We then discuss the non-linear nature of the EV charging load and simulate multiple attacks that can be launched against the power grid using these EVs. EV loads have high reactive power demand which can have a larger impact on the grid compared to residential loads. We perform simulations on two power grids and demonstrate that while the grid can recover after a 48 MW attack utilizing traditional residential loads, a smaller 30 MW EV load attack can completely destabilize the system. Finally, we suggest several patches for the existing vulnerabilities and discuss two methods aimed at detecting EV attacks.
Prediction of Probabilistic Transient Stability Using Support Vector Machine
Authors: Umair Shahzad
Date: 2021-11-22 00:00:00
Tasks: Bayesian Optimization

Transient stability assessment is an integral part of dynamic security assessment of power systems. Traditional methods of transient stability assessment, such as time domain simulation approach and direct methods, are appropriate for offline studies and thus, cannot be applied for online transient stability prediction, which is a major requirement in modern power systems. This motivated the requirement to apply an artificial intelligence-based approach. In this regard, supervised machine learning is beneficial for predicting transient stability status, in the presence of uncertainties. Therefore, this paper examines the application of a binary support vector machine-based supervised machine learning, for predicting the transient stability status of a power system, considering uncertainties of various factors, such as load, faulted line, fault type, fault location and fault clearing time. The support vector machine is trained using a Gaussian Radial Basis function kernel and its hyperparameters are optimized using Bayesian optimization. Results obtained for the IEEE 14-bus test system demonstrated that the proposed method offers a fast technique for probabilistic transient stability status prediction, with an excellent accuracy. DIgSILENT PowerFactory and MATLAB was utilized for transient stability time-domain simulations (for obtaining training data for support vector machine) and for applying support vector machine, respectively.
Does Oversizing Improve Prosumer Profitability in a Flexibility Market? -- A Sensitivity Analysis using PV-battery System
Authors: Babu Kumaran Nalini, Michel Zade, Zhengjie You, Peter Tzscheutschler, Ulrich Wagner
Date: 2021-11-22 00:00:00
Tasks: Sensitivity

The possibilities to involve small-scale prosumers for ancillary services in the distribution grid through aggregators and local flexibility markets question whether it is profitable for prosumers to oversize proactively. In this analysis, a Python model is developed to identify the cost-optimal operation plan of the PV-battery system and to evaluate the device flexibility. An economic assessment is carried out to derive the cost-benefit of sizing based on the mean electricity price. A sensitivity analysis is performed with the above results to study the profitable sizing of PV-battery systems with flexibility services. The results show a promising advantage of oversizing although limitations prevail with the extent of flexibility services offered.
Extending the Dissipating Energy Flow Method to Flexible AC Transmission Systems
Authors: Kaustav Chatterjee, Sayan Samanta, Nilanjan Ray Chaudhuri
Date: 2021-11-22 00:00:00

In recent years, the dissipating energy flow (DEF) method has emerged as a promising tool for online localization of oscillation sources. In literature, the mathematical foundations of this method are well-studied for networks with synchronous generators. In this paper, we extend the analysis to flexible AC transmission systems (FACTS). To that end, we derive the DEF-expressions for a thyristor controlled series capacitor (TCSC) and a static synchronous compensator (STATCOM) operating with conventional control strategies. Analyzing their respective DEFs, we obtain the conditions for which these FACTS devices could behave as the sources of oscillation energy. Our findings are structured into propositions and are supported through numerical case studies on IEEE test systems.
Identification of Low Rank Vector Processes
Authors: Wenqi Cao, Giorgio Picci, Anders Lindquist
Date: 2021-11-21 00:00:00

We study modeling and identification of stationary processes with a spectral density matrix of low rank. Equivalently, we consider processes having an innovation of reduced dimension for which Prediction Error Methods (PEM) algorithms are not directly applicable. We show that these processes admit a special feedback structure with a deterministic feedback channel which can be used to split the identification in two steps, one of which can be based on standard algorithms while the other is based on a deterministic least squares fit. Identifiability of the feedback system is analyzed and a unique identifiable structure is characterized. Simulations show that the proposed procedure works well in some simple examples.
Development of Microcontroller Based Smart Grid Framework
Authors: Saurav Biswas
Date: 2021-11-21 00:00:00
Tasks: energy management, Management

Smart grid technology has been recognized as a promising solution for the next-generation energy efficient electric power systems to mitigate energy crisis. Smart grid provides highly consistent and reliable services, efficient energy management practices, smart metering integration, automation and precision decision support systems and self-healing facilities. The smart power grid introduces a sensing, monitoring, and control system that provides end users with the cost of energy at any moment through real-time pricing. The classical power system operation has no control over the loads except in an emergency situation when a portion of the loads can be dropped as needed to balance the power grid generation with its loads. Furthermore, the smart power grid supplies the platform for the use of renewable energy sources that acts as a safeguard against a complete blackout of the interconnected power grid. In this work, all the concepts involved in smart grid mechanism is implemented with PIC18F452 microcontroller and other supplementary components. A solar module with storage capacity is connected to the proposed system for minimizing grid energy consumption and plays a primary energy source for maximum utilization of green energy. This intelligent device may inherently reduce the consumption of electrical energy during peak hours and allow consumers to sell back electricity into grid using bi-directional technique. Barriers, challenges, benefits and future trends regarding the technologies and the role of users have also been discussed in this paper.
Automatic Generation of Ice Hockey Defensive Motion via Coverage Control and Control Barrier Functions
Authors: Kornvik Tanpipat, Takeshi Hatanaka, Masatoshi Hiroura
Date: 2021-11-21 00:00:00
Tasks: valid

A successful defensive strategy in ice hockey games is often designed empirically by an experienced professional. The majority of previous work on automating the strategy focuses on analyzing spatial data to decide the most optimal formation and action but cannot generalize the system to real games with real-time capabilities. We propose a novel control logic for generating real-time ice hockey defensive motion based on a control barrier function (CBF) and coverage control to extend our antecessors' logic that succeeds in duplicating ideal formations for specific scenes. To this end, we first present an ellipsoidal CBF to overcome the drawbacks of the existing line-based CBF of our antecessors. We also tune and add a novel density function to reflect real specifications more precisely than the previous work. The control logic is then demonstrated through simulations with offensive motion in real games. It is confirmed that the present logic generates valid defensive movements without specification to these specific scenes. We further exemplify that the logic generates proper motion under ice hockey's man-to-man and zone defense strategies and their intermediate strategies by tuning the logic. This would contribute to reducing the efforts of the practitioners to educate ice hockey players.
Velocity and Disturbance Robust Non-linear Estimator for Autonomous Surface Vehicles with Reduced Sensing Capabilities
Authors: Guillermo Bejarano, Sufiyan N-Yo, Luis Orihuela
Date: 2021-11-20 00:00:00

This paper presents a robust non-linear state estimator for autonomous surface vehicles, where the movement is restricted to the horizontal plane. It is assumed that only the vehicle position and orientation can be measured, being the former affected by bounded noises. Then, under some fair standard assumptions concerning the maximum velocities and acceleration rates of the vehicle, the estimator is able to reconstruct not only the velocities, but also the lumped generalised disturbances, that cluster external disturbances, non-linearities, and unmodelled dynamics. The observer is easily tunable by the user, with a set of four scalars, two of them related to the velocity of convergence of the estimator, and the other two parameters to set the desired trade-off between noise sensitivity and disturbance rejection. Several simulations with a well-known test-bed craft are provided to show how the proposed algorithm outperforms previous ones in the literature.
Passivity-based Analysis and Design for Population Dynamics with Conformity Biases
Authors: Shunya Yamashita, Kodai Irifune, Takeshi Hatanaka, Yasuaki Wasa, Kenji Hirata, Kenko Uchida
Date: 2021-11-20 00:00:00

This paper addresses mechanisms for boundedly rational decision makers in discrete choice problem. First, we introduce two mathematical models of population dynamics with conformity biases. We next analyze the models in terms of delta-passivity, and show that the conformity biases work to break passivity of decision makers. Based on the passivity perspective, we propose mechanisms so as to induce decision makers to a desired population state. Furthermore, we analyze a convergence property of designed mechanisms, and present parameter conditions to guarantee stable inducements.
Kalman filters as the steady-state solution of gradient descent on variational free energy
Authors: Manuel Baltieri, Takuya Isomura
Date: 2021-11-20 00:00:00
Tasks: Bayesian Inference, Decision Making

The Kalman filter is an algorithm for the estimation of hidden variables in dynamical systems under linear Gauss-Markov assumptions with widespread applications across different fields. Recently, its Bayesian interpretation has received a growing amount of attention especially in neuroscience, robotics and machine learning. In neuroscience, in particular, models of perception and control under the banners of predictive coding, optimal feedback control, active inference and more generally the so-called Bayesian brain hypothesis, have all heavily relied on ideas behind the Kalman filter. Active inference, an algorithmic theory based on the free energy principle, specifically builds on approximate Bayesian inference methods proposing a variational account of neural computation and behaviour in terms of gradients of variational free energy. Using this ambitious framework, several works have discussed different possible relations between free energy minimisation and standard Kalman filters. With a few exceptions, however, such relations point at a mere qualitative resemblance or are built on a set of very diverse comparisons based on purported differences between free energy minimisation and Kalman filtering. In this work, we present a straightforward derivation of Kalman filters consistent with active inference via a variational treatment of free energy minimisation in terms of gradient descent. The approach considered here offers a more direct link between models of neural dynamics as gradient descent and standard accounts of perception and decision making based on probabilistic inference, further bridging the gap between hypotheses about neural implementation and computational principles in brain and behavioural sciences.
A Novel Data Segmentation Method for Data-driven Phase Identification
Authors: Han Pyo Lee, Mingzhi Zhang, Mesut Baran, Ning Lu, PJ Rehm, Edmond Miller, Matthew Makdad
Date: 2021-11-20 00:00:00
Tasks: Clustering

This paper presents a smart meter phase identification algorithm for two cases: meter-phase-label-known and meter-phase-label-unknown. To improve the identification accuracy, a data segmentation method is proposed to exclude data segments that are collected when the voltage correlation between smart meters on the same phase are weakened. Then, using the selected data segments, a hierarchical clustering method is used to calculate the correlation distances and cluster the smart meters. If the phase labels are unknown, a Connected-Triple-based Similarity (CTS) method is adapted to further improve the phase identification accuracy of the ensemble clustering method. The methods are developed and tested on both synthetic and real feeder data sets. Simulation results show that the proposed phase identification algorithm outperforms the state-of-the-art methods in both accuracy and robustness.
Fuzzy Lifetime Analysis of a Fault-Tolerant Two-Phase Interleaved Converter
Authors: Tohid Rahimi, Masoud Farhadi, Ka Hong Loo, Josep Pou
Date: 2021-11-19 00:00:00
Tasks: Vocal Bursts Valence Prediction

Interleaved converters are used in photovoltaic (PV) applications to handle high power conditions with high reliability. To improve the reliability of these converters, redundant switch configuration can be employed which reduce the failure rates of the power switches significantly. However, evaluation reliability of the interleaved converters equipped with redundant switch configurations may be complex. This paper aims to simplify the reliability Analysis of interleaved converters considering mission profile and redundant switch configuration. Different possible configurations of the studied converter are shown as Markov chain states in the proposed method, which simplify the reliability and failure rates Analysis. The effect of different parameters such as the converter power level, switch configuration type, and operation modes are derived and a better insight into the effectiveness of the switch configuration on improving the reliability is provided.
Molecular Dynamics Analysis of Biomolecular Systems Including Nucleic Acids
Authors: Takeru Kameda, Akinori Awazu, Yuichi Togashi
Date: 2021-11-21 00:00:00

Along with recent progress in structural biology and genome biology, structural dynamics of molecular systems including nucleic acids has attracted attention in the context of gene regulation. Structure-function relationship is an important topic, where physicochemical properties of nucleotides are important as well as those of amino acids in proteins. Simulation is a useful tool for the analysis of molecular dynamics in detail, complementary to experiments. However, molecular simulation of nucleic acids has less developed than that of proteins yet, partly due to the physical nature of nucleic acids. In this review, we briefly describe the current situation and future directions of the field, hopefully as a guide to collaboration between experiment and computation.
Damped oscillations of the probability of random events followed by absolute refractory period: exact analytical results
Authors: A. V. Paraskevov, A. S. Minkin
Date: 2019-05-24 00:00:00

There are numerous examples of natural and artificial processes that represent stochastic sequences of events followed by an absolute refractory period during which the occurrence of a subsequent event is impossible. In the simplest case of a generalized Bernoulli scheme for uniform random events followed by the absolute refractory period, the event probability as a function of time can exhibit damped transient oscillations. Using stochastically-spiking point neuron as a model example, we present an exact and compact analytical description for the oscillations without invoking the standard renewal theory. The resulting formulas stand out for their relative simplicity, allowing one to analytically obtain the amplitude damping of the 2nd and 3rd peaks of the event probability.
Herd immunity under individual variation and reinfection
Authors: Antonio Montalbán, Rodrigo M. Corder, M. Gabriela M. Gomes
Date: 2020-07-31 00:00:00

We study a SEIR model considered by Gomes et al. \cite{Gomes2020} and Aguas et al. \cite{Aguas2020} where different individuals are assumed to have different levels of susceptibility or exposure to infection. Under this heterogeneity assumption, epidemic growth is effectively suppressed when the percentage of population having acquired immunity surpasses a critical level - the herd immunity threshold - that is lower than in homogeneous populations. We find explicit formulas to calculate herd immunity thresholds and stable configuration, and explore extensions of the model.
Orthogonal Policy Learning Under Ambiguity
Authors: Riccardo D'Adamo
Date: 2021-11-21 00:00:00

This paper studies the problem of estimating individualized treatment rules when treatment effects are partially identified, as it is often the case with observational data. By drawing connections between the treatment assignment problem and classical decision theory, we characterize several notions of optimal treatment policies in the presence of partial identification. Our unified framework allows to incorporate user-defined constraints on the set of allowable policies, such as restrictions for transparency or interpretability, while also ensuring computational feasibility. We show how partial identification leads to a new policy learning problem where the objective function is directionally -- but not fully -- differentiable with respect to the nuisance first-stage. We then propose an estimation procedure that ensures Neyman-orthogonality with respect to the nuisance components and we provide statistical guarantees that depend on the amount of concentration around the points of non-differentiability in the data-generating-process. The proposed methods are illustrated using data from the Job Partnership Training Act study.
Why Synthetic Control estimators are biased and what to do about it: Introducing Relaxed and Penalized Synthetic Controls
Authors: Oscar Engelbrektson
Date: 2021-11-21 00:00:00

This paper extends the literature on the theoretical properties of synthetic controls to the case of non-linear generative models, showing that the synthetic control estimator is generally biased in such settings. I derive a lower bound for the bias, showing that the only component of it that is affected by the choice of synthetic control is the weighted sum of pairwise differences between the treated unit and the untreated units in the synthetic control. To address this bias, I propose a novel synthetic control estimator that allows for a constant difference of the synthetic control to the treated unit in the pre-treatment period, and that penalizes the pairwise discrepancies. Allowing for a constant offset makes the model more flexible, thus creating a larger set of potential synthetic controls, and the penalization term allows for the selection of the potential solution that will minimize bias. I study the properties of this estimator and propose a data-driven process for parameterizing the penalization term.
Identifying Dynamic Discrete Choice Models with Hyperbolic Discounting
Authors: Taiga Tsubota
Date: 2021-11-21 00:00:00
Tasks: Discrete Choice Models

We study identification of dynamic discrete choice models with hyperbolic discounting. We show that the standard discount factor, present bias factor, and instantaneous utility functions for the sophisticated agent are point-identified from observed conditional choice probabilities and transition probabilities in a finite horizon model. The main idea to achieve identification is to exploit variation in the observed conditional choice probabilities over time. We present the estimation method and demonstrate a good performance of the estimator by simulation.
Optimized Inference in Regression Kink Designs
Authors: Majed Dodin
Date: 2021-11-21 00:00:00
Tasks: regression, valid

We propose a method to remedy finite sample coverage problems and improve upon the efficiency of commonly employed procedures for the construction of nonparametric confidence intervals in regression kink designs. The proposed interval is centered at the half-length optimal, numerically obtained linear minimax estimator over distributions with Lipschitz constrained conditional mean function. Its construction ensures excellent finite sample coverage and length properties which are demonstrated in a simulation study and an empirical illustration. Given the Lipschitz constant that governs how much curvature one plausibly allows for, the procedure is fully data driven, computationally inexpensive, incorporates shape constraints and is valid irrespective of the distribution of the assignment variable.