pwc-archive/papers-with-abstracts huggingface dataset:
Optimal activity and battery scheduling algorithm using load and solar generation forecast
Authors: Rui Yuan, Nam Trong Dinh, Yogesh Pipada, S. Ali Pourmouasvi
Date: 2021-12-06 00:00:00
Tasks: Missing Values, Scheduling, Time Series, Time Series Analysis, Time Series Forecasting

In this report, we provide a technical sequence on tackling the solar PV and demand forecast as well as optimal scheduling problem proposed by the IEEE-CIS 3rd technical challenge on predict + optimize for activity and battery scheduling. Using the historical data provided by the organizers, a simple pre-processing approach with a rolling window was used to detect and replace invalid data points. Upon filling the missing values, advanced time-series forecasting techniques, namely tree-based methods and refined motif discovery, were employed to predict the baseload consumption on six different buildings together with the power production on their associated solar PV panels. An optimization problem is then formulated to use the predicted values and the wholesale electricity prices to create a timetable for a set of activities, including the scheduling of lecture theatres and battery charging and discharging operation, for one month ahead. The valley-filling optimization was done across all the buildings with the objective of minimizing the total energy cost and achieving net-zero imported power from the grid.
Identification of Switched Linear Systems: Persistence of Excitation and Numerical Algorithms
Authors: Biqiang Mu, Tianshi Chen, Changming Cheng, Er-Wei Bai
Date: 2021-12-06 00:00:00

This paper investigates two issues on identification of switched linear systems: persistence of excitation and numerical algorithms. The main contribution is a much weaker condition on the regressor to be persistently exciting that guarantees the uniqueness of the parameter sets and also provides new insights in understanding the relation among different subsystems. It is found that for uniquely determining the parameters of switched linear systems, the minimum number of samples needed derived from our condition is much smaller than that reported in the literature. The secondary contribution of the paper concerns the numerical algorithm. Though the algorithm is not new, we show that our surrogate problem, relaxed from an integer optimization to a continuous minimization, has exactly the same solution as the original integer optimization, which is effectively solved by a block-coordinate descent algorithm. Moreover, an algorithm for handling unknown number of subsystems is proposed. Several numerical examples are illustrated to support theoretical analysis.
Voltage Stability Constrained Unit Commitment in High IBG-Penetrated Power Systems
Authors: Zhongda Chu, Fei Teng
Date: 2021-12-06 00:00:00
Tasks: Scheduling, Vocal Bursts Intensity Prediction

With the increasing penetration of renewable energy sources, power system operation has to be adapted to ensure the system stability and security while considering the distinguished feature of the Power Electronics (PE) interfaced generators. The static voltage stability which is mainly compromised by heavy loading conditions in conventional power systems, faces new challenges due to the large scale integration of PE-interfaced devices. This paper investigates the static voltage stability problem in high PE-penetrated system. The analytic criterion that ensures the voltage stability at the Inverter-Based Generator (IBG) buses are derived with the interaction of different IBGs being considered. Based on this, an optimal system scheduling model is proposed to minimize the overall system operation cost while maintaining the voltage stability during normal operation through dynamically optimizing the active and reactive power output from IBGs. The highly nonlinear voltage stability constraints are effectively converted into Second-Order-Cone (SOC) form, leading to an overall Mixed-Integer SOC Programming (MISOCP), together with the SOC reformulation of AC power flow and frequency constraints. The effectiveness of the proposed model and the impact of various factors on voltage stability are demonstrated in thorough case studies.
A new active disturbance controller based on an improved fraction-order extended state observer
Authors: Bolin Li, Lijun Zhu
Date: 2021-12-06 00:00:00

This paper proposes a new fraction-order active disturbance rejection controller based on an improved fraction-order extended state observer (IFESO) for a class of fraction-order systems. Applying the IFESO, the fraction-order system can be approximated as an integer-order integrator (1/s). The parameters that ensure the stability of the IFESO and closed-loop system are provided. The frequency-domain analysis shows that the IFADRC has a stronger disturbance estimation performance for the fraction-order system than an integer-order active disturbance rejection controller (IADRC). The simulation results demonstrates that the proposed IFADRC behaves better than the IADRC and the FADRC.
Generalized hybrid momentum maps and reduction by symmetries of forced mechanical systems with inelastic collisions
Authors: Leonardo J. Colombo, Manuel de León, María Emma Eyrea Irazú, Asier López-Gordón
Date: 2021-12-05 00:00:00

This paper discusses reduction by symmetries for autonomous and non-autonomous forced mechanical systems with inelastic collisions. In particular, we introduce the notion of generalized hybrid momentum map and hybrid constants of the motion to give general conditions on whether it is possible to perform symmetry reduction for Hamiltonian and Lagrangian systems subject to non-conservative external forces and non-elastic impacts, as well as its extension to time-dependent mechanical systems subject to time-dependent external forces and time-dependent inelastic collisions. We illustrate the applicability of the method with examples and numerical simulations.
Learning-Based Control Compensation for Multi-Axis Gimbal Systems Using Inverse and Forward Dynamics
Authors: Damla Leblebicioğlu, Ozgur Atesoglu, Anil E. Derinoz, Melih Cakmakci
Date: 2021-12-05 00:00:00

Unmanned aerospace vehicles usually carry sensors (i.e., electro-optical and/or infrared imaging cameras) as their primary payload. These sensors are used for image processing, target tracking, surveillance, mapping, and providing high-resolution imagery for environmental surveys. It is crucial to obtain a steady image in all these applications. This is typically accomplished by using multi-axis gimbal systems. This paper concentrates on the modeling and control of a multi-axis gimbal system. A novel and fully outlined procedure is proposed to derive the nonlinear and highly coupled Equations of Motion of the two-axis gimbal system. Different from the existing literature, Forward Dynamics of the two-axis gimbal system is modeled using multi-body dynamics modeling techniques. In addition to the Forward Dynamics model, the Inverse Dynamics model is developed to estimate the complementary torques associated with the state and mechanism-dependent, complex disturbances acting on the system. A disturbance compensator based on multilayer perceptron (MLP) structure is implemented to cope with external and internal disturbances and parameter uncertainties through the torque input channel. Our initial simulations and experimental work show that the new NN (neural network)-based controller is performs better in the full operational range without requiring any tuning or adjustment when compared with well-known controllers such as cascaded PID, ADRC (Active Disturbance Rejection Control), Inverse Dynamics based controllers.
Optimal Participation of Heterogeneous, RES-based Virtual Power Plants in Energy Markets
Authors: Oluwaseun Oladimeji, Alvaro Ortega, Lukas Sigrist, Luis Rouco, Pedro Sanchez-Martin, Enrique Lobato
Date: 2021-12-04 00:00:00

In this work, we present a detailed model of a Renewable Energy Source (RES)-based Virtual Power Plant (VPP) that participates in Day-Ahead Market (DAM) and Intra-Day Market (IDM) with dispatchable and non-dispatchable RESs and flexible demand assets. We propose a demand model with bi-level flexibility which are associated with the market sessions plus an improved solar thermal plant model with piece-wise linear formulation of efficiency. A network-constrained unit commitment model is used by the VPP to submit DAM auctions and consequently participates in IDM to correct for deviations. Finally, we validate our model by assessing its operation on different weather conditions of uncertainty.
NN Based Active Disturbance Rejection Controller for a Multi-Axis Gimbal System
Authors: Damla Leblebicioglu, Ozgur Atesoglu, Melih Cakmakci
Date: 2021-12-03 00:00:00

The increasing demand for target tracking, environmental surveys, surveillance and mapping requires multi-axis gimbal systems with high tracking and stabilization performance. In this paper, first, computed torque model is generated to estimate the complex disturbances acting on the system. Then, two different control strategies based on active disturbance rejection control (ADRC) and computed torque model are implemented on a two-axis gimbal system. The purpose is to improve the robustness, environmental adaptability and tracking accuracy of the system and reduce the tuning effort of ADRC by integrating a neural network (NN) based disturbance compensator (NN assisted ADRC). In the second control strategy, NN is replaced with a computed torque model (CTM assisted ADRC), whose inputs come from plant outputs. The simulation results show that, NN and CTM assisted ADRC structures can decrease mean tracking errors up to 85.4% and 40.8%, respectively.
BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale
Authors: Carl Poelking, Felix A. Faber, Bingqing Cheng
Date: 2021-12-04 00:00:00
Tasks: Benchmarking, Hyperparameter Optimization, regression

We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarking approach is to evaluate raw descriptor performance by limiting model complexity to simple regression schemes while enforcing best ML practices, allowing for unbiased hyperparameter optimization, and assessing learning progress through learning curves along series of synchronized train-test splits. The resulting models are intended as baselines that can inform future method development, next to indicating how easily a given dataset can be learnt. Through a comparative analysis of the training outcome across a diverse set of physicochemical, topological and geometric representations, we glean insight into the relative merits of these representations as well as their interrelatedness.
A biophysical threshold for biofilm formation
Authors: Jenna A. Ott, Selena Chiu, Daniel B. Amchin, Tapomoy Bhattacharjee, Sujit S. Datta
Date: 2021-12-05 00:00:00

Bacteria are ubiquitous in our daily lives, either as motile planktonic cells or as immobilized surface-attached biofilms. These different phenotypic states play key roles in agriculture, environment, industry, and medicine; hence, it is critically important to be able to predict the conditions under which bacteria transition from one state to the other. Unfortunately, these transitions depend on a dizzyingly complex array of factors that are determined by the intrinsic properties of the individual cells as well as those of their surrounding environments, and are thus challenging to describe. To address this issue, here, we develop a generally-applicable biophysical model of the interplay between motility-mediated dispersal and biofilm formation under positive quorum sensing control. Using this model, we establish a universal rule predicting how the onset and extent of biofilm formation depend collectively on cell concentration and motility, nutrient diffusion and consumption, chemotactic sensing, and autoinducer production. Our work thus provides a key step toward quantitatively predicting and controlling biofilm formation in diverse and complex settings.
Thermodynamics of Darwinian evolution in molecular replicators
Authors: Artemy Kolchinsky
Date: 2021-12-06 00:00:00

We consider the relationship between thermodynamics, fitness, and Darwinian evolution in autocatalytic molecular replicators. We uncover a thermodynamic bound that relates fitness, replication rate, and the Gibbs free energy dissipated per copy. This bound applies to a broad range of systems, including elementary and non-elementary autocatalytic reactions, polymer-based replicators, and certain kinds of autocatalytic sets. In addition, we show that the critical selection coefficient (the minimal fitness difference visible to selection) is bounded by the Gibbs free energy dissipated per replication. Our results imply fundamental thermodynamic bounds on the strength of selection in molecular evolution, complementary to other bounds that arise from finite population sizes and error thresholds. These bounds may be relevant for understanding thermodynamic constraints faced by early replicators at the origin of life. We illustrate our approach on several examples, including a classic model of replicators in a chemostat.
Prediction and compression of lattice QCD data using machine learning algorithms on quantum annealer
Authors: Boram Yoon, Chia Cheng Chang, Garrett T. Kenyon, Nga T. T. Nguyen, Ermal Rrapaj
Date: 2021-12-03 00:00:00
Tasks: BIG-bench Machine Learning, regression

We present regression and compression algorithms for lattice QCD data utilizing the efficient binary optimization ability of quantum annealers. In the regression algorithm, we encode the correlation between the input and output variables into a sparse coding machine learning algorithm. The trained correlation pattern is used to predict lattice QCD observables of unseen lattice configurations from other observables measured on the lattice. In the compression algorithm, we define a mapping from lattice QCD data of floating-point numbers to the binary coefficients that closely reconstruct the input data from a set of basis vectors. Since the reconstruction is not exact, the mapping defines a lossy compression, but, a reasonably small number of binary coefficients are able to reconstruct the input vector of lattice QCD data with the reconstruction error much smaller than the statistical fluctuation. In both applications, we use D-Wave quantum annealers to solve the NP-hard binary optimization problems of the machine learning algorithms.
Complexity and Persistence of Price Time Series of the European Electricity Spot Market
Authors: Chengyuan Han, Hannes Hilger, Eva Mix, Philipp C. Böttcher, Mark Reyers, Christian Beck, Dirk Witthaut, Leonardo Rydin Gorjão
Date: 2021-12-02 00:00:00
Tasks: Time Series, Time Series Analysis

The large variability of renewable power sources is a central challenge in the transition to a sustainable energy system. Electricity markets are central for the coordination of electric power generation. These markets rely evermore on short-term trading to facilitate the balancing of power generation and demand and to enable systems integration of small producers. Electricity prices in these spot markets show pronounced fluctuations, featuring extreme peaks as well as occasional negative prices. In this article, we analyse electricity price time series from the European EPEX market, in particular the hourly day-ahead, hourly intraday, and 15-min intraday market prices. We quantify the fluctuations, correlations, and extreme events and reveal different time scales in the dynamics of the market. The short-term fluctuations show remarkably different characteristics for time scales below and above 12 hours. Fluctuations are strongly correlated and persistent below 12 hours, which contributes to extreme price events and a strong multifractal behaviour. On longer time scales, they get anti-correlated and price time series revert to their mean, witnessed by a stark decrease of the Hurst coefficient after 12 hours. The long-term behaviour is strongly influenced by the evolution of a large-scale weather patterns with a typical time scale of four days. We elucidate this dependence in detail using a classification into circulation weather types. The separation in time scales enables a superstatistical treatment, which confirms the characteristic time scale of four days, and motivates the use of $q$-Gaussian distributions as the best fit to the empiric distribution of electricity prices.
Machine Learning in Nuclear Physics
Authors: Amber Boehnlein, Markus Diefenthaler, Cristiano Fanelli, Morten Hjorth-Jensen, Tanja Horn, Michelle P. Kuchera, Dean Lee, Witold Nazarewicz, Kostas Orginos, Peter Ostroumov, Long-Gang Pang, Alan Poon, Nobuo Sato, Malachi Schram, Alexander Scheinker, Michael S. Smith, Xin-Nian Wang, Veronique Ziegler
Date: 2021-12-04 00:00:00
Tasks: BIG-bench Machine Learning, Diversity

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.
Compartmental and cellular automaton $SEIRS$ epidemiology models for the COVID-19 pandemic with the effects of temporal immunity and vaccination
Authors: Jaroslav Ilnytskyi, Taras Patsahan
Date: 2021-12-05 00:00:00
Tasks: Epidemiology

We consider the $SEIRS$ epidemiology model with such features of the COVID-19 outbreak as: abundance of unidentified infected individuals, limited time of immunity and a possibility of vaccination. Within a compartmental realization of this model, we found the disease-free and the endemic stationary states. They exist in their respective restricted regions of the le via linear stability analysis. The expression for the basic reproductive number is obtained as well. The positions and heights of a first peak for the fractions of infected individuals are obtained numerically and are fitted to simple algebraic forms, that depend on model rates. Computer simulations of a lattice-based realization for this model was performed by means of the cellular automaton algorithm. These allowed to study the effect of the quarantine measures explicitly, via changing the neighbourhood size. The attempt is made to match both quarantine and vaccination measures aimed on balanced solution for effective suppression of the pandemic.
Visual Inference and Graphical Representation in Regression Discontinuity Designs
Authors: Christina Korting, Carl Lieberman, Jordan Matsudaira, Zhuan Pei, Yi Shen
Date: 2021-12-06 00:00:00
Tasks: regression

Despite the widespread use of graphs in empirical research, little is known about readers' ability to process the statistical information they are meant to convey ("visual inference"). We study visual inference within the context of regression discontinuity (RD) designs by measuring how accurately readers identify discontinuities in graphs produced from data generating processes calibrated on 11 published papers from leading economics journals. First, we assess the effects of different graphical representation methods on visual inference using randomized experiments. We find that bin widths and fit lines have the largest impacts on whether participants correctly perceive the presence or absence of a discontinuity. Our experimental results allow us to make evidence-based recommendations to practitioners, and we suggest using small bins with no fit lines as a starting point to construct RD graphs. Second, we compare visual inference on graphs constructed using our preferred method with widely used econometric inference procedures. We find that visual inference achieves similar or lower type I error (false positive) rates and complements econometric inference.
Increased Electrification of Heating and Weather Risk in the Nordic Power System
Authors: Ian M. Trotter, Torjus F. Bolkesjø, Eirik O. Jåstad, Jon Gustav Kirkerud
Date: 2021-12-06 00:00:00

Weather is one of the main drivers of both the power demand and supply, especially in the Nordic region which is characterized by high heating needs and a high share of renewable energy. Furthermore, ambitious decarbonization plans may cause power to replace fossil fuels for heating in the Nordic region, at the same time as large wind power expansions are expected, resulting in even greater exposure to weather risk. In this study, we quantify the increase in weather risk resulting from replacing fossil fuels with power for heating in the Nordic region, at the same time as variable renewable generation expands. First, we calibrate statistical weather-driven power consumption models for each of the countries Norway, Sweden, Denmark, and Finland. Then, we modify the weather sensitivity of the models to simulate different levels of heating electrification, and use 300 simulated weather years to investigate how differing weather conditions impact power consumption at each electrification level. The results show that full replacement of fossil fuels by power for heating in 2040 leads to an increase in annual consumption of 155 TWh (30%) compared to a business-as-usual scenario during an average weather year, but a 178 TWh (34%) increase during a one-in-twenty weather year. However, the increase in the peak consumption is greater: around 50% for a normal weather year, and 70% for a one-in-twenty weather year. Furthermore, wind and solar generation contribute little during the consumption peaks. The increased weather sensitivity caused by heating electrification causes greater total load, but also causes a significant increase in inter-annual, seasonal, and intra-seasonal variations. We conclude that heating electrification must be accompanied by an increase in power system flexibility to ensure a stable and secure power supply.
Cocoa pollination, biodiversity-friendly production, and the global market
Authors: Thomas Cherico Wanger, Francis Dennig, Manuel Toledo-Hernández, Teja Tscharntke, Eric F. Lambin
Date: 2021-12-06 00:00:00
Tasks: Management

Production of cocoa, the third largest trade commodity globally has experienced climate related yield stagnation since 2016, forcing farmers to expand production in forested habitats and to shift from nature friendly agroforestry systems to intensive monocultures. The goal for future large-scale cocoa production combines high yields with biodiversity friendly management into a climate adapted smart agroforestry system (SAS). As pollination limitation is a key driver of global production, we use data of more than 150,000 cocoa farms and results of hand pollination experiments to show that manually enhancing cocoa pollination (hereafter manual pollination) can produce SAS. Manual pollination can triple farm yields and double farmers annual profit in the major producer countries Ivory Coast, Ghana, and Indonesia, and can increase global cocoa supplies by up to 13%. We propose a win win scenario to mitigate negative long term price and socioeconomic effects, whereby manual pollination compensates only for yield losses resulting from climate and disease related decreases in production area and conversion of monocultures into agroforestry systems. Our results highlight that yields in biodiversity friendly and climate adapted SAS can be similar to yields currently only achieved in monocultures. Adoption of manual pollination could be achieved through wider implementation of ecocertification standards, carbon markets, and zero deforestation pledges.