pwc-archive/papers-with-abstracts huggingface dataset:
Outdoor Characterization of Solar Cells with Micro-structured Anti-Reflective Coating in a Concentrator Photovoltaic Module
Authors: Arnaud J. K. Leoga, Arnaud Ritou, Mathieu Blanchard, Lysandre Dirand, Yanis Prunier, Philippe St-Pierre, David Chuet, Philippe-Olivier Provost, Maite Volatier, Vincent Aimez, Gwenaelle Hamon, Abdelatif Jaouad, Christian Dubuc, Maxime Darnon
Date: 2023-01-17 00:00:00
Tasks: ARC

Micro-structured anti reflective coatings (ARC) have been identified as a promising solution to reduce optical losses in Concentrator Photovoltaics modules (CPV). We fabricated and tested in field a CPV modules made of 4 sub-modules with a concentration factor of 250x, that embed either solar cells with micro-structured encapsulating ARC or solar cells with multilayer ARC as a reference. The micro-structured encapsulating ARC was made of semi-buried silica beads in polydimethylsiloxane (PDMS). The module was in operation for 1 year in the severe climatic conditions of Sherbrooke, Quebec, Canada, before extracting the sub-modules performance under Concentrator Standard Operating Condition (CSOC). An acceptance angle of +/-0.78 degree was determined for all sub-modules, demonstrating that improving angular collection at the cell level has no significant impact on the angle of acceptance at the module level. We report an increase of 12 to 14% of the short-circuit current and of 15 to 19% of maximum power at CSOC for solar cells with a micro structured encapsulating ARC compared to the reference. Despite a sub-optimal module design, we report a sub-module efficiency of 29.7% at CSOC for a cell with micro-structured encapsulating ARC. This proves the potential of micro-structured encapsulating ARC to improve CPV system performance and shows promise of reliability for sumi-buried microbeads in PDMS as encapsulating ARC.
A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles
Authors: Ivan Masmitja, Mario Martin, Kakani Katija, Spartacus Gomariz, Joan Navarro
Date: 2023-01-17 00:00:00
Tasks: Autonomous Vehicles, Reinforcement Learning (RL)

Underwater target localization using range-only and single-beacon (ROSB) techniques with autonomous vehicles has been used recently to improve the limitations of more complex methods, such as long baseline and ultra-short baseline systems. Nonetheless, in ROSB target localization methods, the trajectory of the tracking vehicle near the localized target plays an important role in obtaining the best accuracy of the predicted target position. Here, we investigate a Reinforcement Learning (RL) approach to find the optimal path that an autonomous vehicle should follow in order to increase and optimize the overall accuracy of the predicted target localization, while reducing time and power consumption. To accomplish this objective, different experimental tests have been designed using state-of-the-art deep RL algorithms. Our study also compares the results obtained with the analytical Fisher information matrix approach used in previous studies. The results revealed that the policy learned by the RL agent outperforms trajectories based on these analytical solutions, e.g. the median predicted error at the beginning of the target's localisation is 17% less. These findings suggest that using deep RL for localizing acoustic targets could be successfully applied to in-water applications that include tracking of acoustically tagged marine animals by autonomous underwater vehicles. This is envisioned as a first necessary step to validate the use of RL to tackle such problems, which could be used later on in a more complex scenarios
Planning of Fast Charging Infrastructure for Electric Vehicles in a Distribution System and Prediction of Dynamic Price
Authors: K. Victor Sam Moses Babu, Pratyush Chakraborty, Mayukha Pal
Date: 2023-01-17 00:00:00
Tasks: Time Series, Time Series Analysis

The increasing number of electric vehicles (EVs) has led to the need for installing public electric vehicle charging stations (EVCS) to facilitate ease of use and to support users who do not have the option of residential charging. The public electric vehicle charging infrastructures (EVCIs) must be equipped with a good number of EVCSs, with fast charging capability, to accommodate the EV traffic demand, which would otherwise lead to congestion at the charging stations. The location of these fast-charging infrastructures significantly impacts the distribution system (DS). We propose the optimal placement of fast-charging EVCIs at different locations in the distribution system, using multi-objective particle swarm optimization (MOPSO), so that the power loss and voltage deviations are kept at a minimum. Time-series analysis of the DS and EV load variations are performed using MATLAB and OpenDSS. We further analyze the cost benefits of the EVCIs under real-time pricing conditions and employ an autoregressive integrated moving average (ARIMA) model to predict the dynamic price. The simulated test system without any EVCI has a power loss of 164.36 kW and squared voltage deviations of 0.0235 p.u. Using the proposed method, the results obtained validate the optimal location of 5 EVCIs (each having 20 EVCSs with a 50kWh charger rating) resulting in a minimum power loss of 201.40 kW and squared voltage deviations of 0.0182 p.u. in the system. Significant cost benefits for the EVCIs are also achieved, and an R-squared value of dynamic price predictions of 0.9999 is obtained. This would allow the charging station operator to make promotional offers for maximizing utilization and increasing profits.
Valuing Distributed Energy Resources for Non-Wires Alternatives
Authors: Nicholas D. Laws, Michael E. Webber
Date: 2023-01-16 00:00:00
Tasks: Bilevel Optimization

Distributed energy resources (DER) as non-wires alternatives, regardless of owner, have the potential to reduce system operating costs and delay system upgrades. However, it is difficult to determine the appropriate economic signal to incentivize DER investors to install capacity that will benefit both the DER investors and the system operator. In an attempt to determine this co-optimal price signal, we present a bilevel optimization framework for determining the least cost solution to distribution system over-loads. A key output of the framework is a spatiotemporal price signal to DER owners that simultaneously guarantees the DER owners' required rate of return and minimizes the system operation costs. The framework is demonstrated with a case by which the system operator considers utility owned battery energy storage systems, traditional system upgrades, and energy purchase from DER owners. The results show that by valuing DER for non-wires alternatives the utility owned storage system sizes can be reduced, less hardware upgrades are necessary, and upfront capital costs as well as operating costs are reduced.
Finite Dimensional Koopman Form of Polynomial Nonlinear Systems
Authors: Lucian Cristian Iacob, Maarten Schoukens, Roland Tóth
Date: 2023-01-16 00:00:00
Tasks: Form

The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process, using so-called observable functions. While there is an extensive theory on infinite dimensional representations in the operator sense, there are few constructive results on how to select the observables to realize them. When it comes to the possibility of finite Koopman representations, which are highly important form a practical point of view, there is no constructive theory. Hence, in practice, often a data-based method and ad-hoc choice of the observable functions is used. When truncating to a finite number of basis, there is also no clear indication of the introduced approximation error. In this paper, we propose a systematic method to compute the finite dimensional Koopman embedding of a specific class of polynomial nonlinear systems in continuous-time such that, the embedding, without approximation, can fully represent the dynamics of the nonlinear system.
Data-Driven Encoding: A New Numerical Method for Computation of the Koopman Operator
Authors: Jerry Ng, Haruhiko Harry Asada
Date: 2023-01-16 00:00:00

This paper presents a data-driven method for constructing a Koopman linear model based on the Direct Encoding (DE) formula. The prevailing methods, Dynamic Mode Decomposition (DMD) and its extensions are based on least squares estimates that can be shown to be biased towards data that are densely populated. The DE formula consisting of inner products of a nonlinear state transition function with observable functions does not incur this biased estimation problem and thus serves as a desirable alternative to DMD. However, the original DE formula requires knowledge of the nonlinear state equation, which is not available in many practical applications. In this paper, the DE formula is extended to a data-driven method, Data-Driven Encoding (DDE) of Koopman operator, in which the inner products are calculated from data taken from a nonlinear dynamic system. An effective algorithm is presented for the computation of the inner products, and their convergence to true values is proven. Numerical experiments verify the effectiveness of DDE compared to Extended DMD. The experiments demonstrate robustness to data distribution and the convergent properties of DDE, guaranteeing accuracy improvements with additional sample points. Furthermore, DDE is applied to deep learning of the Koopman operator to further improve prediction accuracy.
Optimal Coordination and Discount Allocation in Residential Renewable Energy Communities with Smart Home Appliances
Authors: Francesco Conte, Federico Silvestro, Andrea Vinci, Anna Rita Di Fazio
Date: 2023-01-16 00:00:00
Tasks: Management

This paper proposes an optimal management strategy for a Renewable Energy Community defined according to the Italian legislation. The specific case study is composed by a set of houses equipped with smart appliances, that share a PV plant. The objective is to minimize the cost of electrical energy use for each member of the community, taking into account the discount achievable from government incentives with proper shaping of the community daily consumption. Such incentives are indeed proportional to the shared energy, i.e. the portion of the renewable energy consumed at each hour by community members. The management algorithm allows an optimal coordination of houses power demands, according to the degree of flexibility granted by users. Moreover, a policy to fairly distribute the obtained discount is introduced. Simulation results show the potentialities of the approach.
Assessment of frequency stability behaviour regarding inertia reduction due to high renewable integration in the Iberian system
Authors: Francisco de Sousa Fernandes, Joao Pecas Lopes
Date: 2023-01-16 00:00:00
Tasks: parameter estimation

The progressive integration of electronic converter based Renewable Energy Sources will lead to a reduction of the system synchronous inertia that can endanger the system frequency stability behavior, especially in the case of isolated or poorly interconnected systems, such as the case of Iberian Peninsula system. This work focuses on the study of the impact that the synchronous inertia reduction can have on the Iberian system frequency stability for different critical operation scenarios and disturbances. For this purpose, an equivalent dynamic model of the Iberian system interconnected with the central European network was first developed using the Matlab Simulink graphical programming environment. The parameters of this model are identified using a parameter estimation approach that explores the recorded behavior of the frequency following an outage of a nuclear unit in Spain. This model was used afterwards to perform simulations for different generation outages and multiple operation scenarios between 2020 and 2040.
Analyze the Effects of COVID-19 on Energy Storage Systems: A Techno-Economic Approach
Authors: Nhat Le, Alexis Plasencia Leos, Juan Henriquez, Anh Phuong Ngo, Hieu T. Nguyen
Date: 2023-01-16 00:00:00
Tasks: energy trading

During the COVID-19 pandemic, the U.S. power sector witnessed remarkable electricity demand changes in many geographical regions. these changes were evident in population-dense cities. This paper incorporates a techno-economic analysis of energy storage systems to investigate the pandemic's influence on ESS development, In particular, we employ a linear program-based revenue maximization model to capture the revenues of ESS from participating in the electricity market, by performing arbitrage on energy trading, and regulation market, by providing regulation services to stabilize the grid's frequency. We consider five dominant energy storage technologies in the U.S., namely, Lithium-ion, Advanced Lead Acid, Flywheel, Vanadium Redox Flow, and Lithium-Iron Phosphate storage technologies. Extensive numerical results conducted on the case of New York City allow us to highlight the negative impact that COVID-19 had on the NYC power sector.
Extended Adaptive Observer for Linear Systems with Overparametrization
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2023-01-15 00:00:00

Exponentially stable extended adaptive observer is proposed for a class of linear time-invariant systems with unknown parameters and overparameterization. It allows one to reconstruct unmeasured states and bounded external disturbance produced by a known linear exosystem with unknown initial conditions if a weak requirement of regressor finite excitation is met. In contrast to the existing solutions, the proposed observer reconstructs the original (physical) states of the system rather than the virtual one of its observer canonical form. Simulation results to validate the developed theory are presented.
Linear Mean-Field Games with Discounted Cost
Authors: Naci Saldi
Date: 2023-01-15 00:00:00

In this paper, we introduce discrete-time linear mean-field games subject to an infinite-horizon discounted-cost optimality criterion. The state space of a generic agent is a compact Borel space. At every time, each agent is randomly coupled with another agent via their dynamics and one-stage cost function, where this randomization is generated via the empirical distribution of their states (i.e., the mean-field term). Therefore, the transition probability and the one-stage cost function of each agent depend linearly on the mean-field term, which is the key distinction between classical mean-field games and linear mean-field games. Under mild assumptions, we show that the policy obtained from infinite population equilibrium is $\varepsilon(N)$-Nash when the number of agents $N$ is sufficiently large, where $\varepsilon(N)$ is an explicit function of $N$. Then, using the linear programming formulation of MDPs and the linearity of the transition probability in mean-field term, we formulate the game in the infinite population limit as a generalized Nash equilibrium problem (GNEP) and establish an algorithm for computing equilibrium with a convergence guarantee.
Machine Learning for Process Control of (Bio)Chemical Processes
Authors: Andreas Himmel, Janine Matschek, Rudolph Kok, Bruno Morabito, Hoang Hai Nguyen, Rolf Findeisen
Date: 2023-01-15 00:00:00

The control of manufacturing processes must satisfy high quality and efficiency requirements while meeting safety requirements. A broad spectrum of monitoring and control strategies, such as model- and optimization-based controllers, are utilized to address these issues. Driven by rising demand for flexible yet energy and resource-efficient operations existing approaches are challenged due to high uncertainties and changes. Machine learning algorithms are becoming increasingly important in tackling these challenges, especially due to the growing amount of available data. The ability for automatic adaptation and learning from human operators offer new opportunities to increase efficiency yet provide flexible operation. Combining machine learning algorithms with safe or robust controls offers novel reliable operation methods. This chapter highlights ways to fuse machine learning and control for the safe and improved operation of chemical and biochemical processes. We outline and summarize both - learning models for control and learning the control components. We offer a general overview, including a literature review, to provide a guideline for utilizing machine learning techniques in control structures.
Efficient anomaly detection method for rooftop PV systems using big data and permutation entropy
Authors: Sahand Karimi-Arpanahi, Ali Pourmousavi
Date: 2023-01-15 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

The number of rooftop photovoltaic (PV) systems has significantly increased in recent years around the globe, including in Australia. This trend is anticipated to continue in the next few years. Given their high share of generation in power systems, detecting malfunctions and abnormalities in rooftop PV systems is essential for ensuring their high efficiency and safety. In this paper, we present a novel anomaly detection method for a large number of rooftop PV systems installed in a region using big data and a time series complexity measure called weighted permutation entropy (WPE). This efficient method only uses the historical PV generation data in a given region to identify anomalous PV systems and requires no new sensor or smart device. Using a real-world PV generation dataset, we discuss how the hyperparameters of WPE should be tuned for the purpose. The proposed PV anomaly detection method is then tested on rooftop PV generation data from over 100 South Australian households. The results demonstrate that anomalous systems detected by our method have indeed encountered problems and require a close inspection. The detection and resolution of potential faults would result in better rooftop PV systems, longer lifetimes, and higher returns on investment.
Day-Ahead PV Power Forecasting Based on MSTL-TFT
Authors: XueTao Jiang, Meiyu Jiang, Qingguo Zhou
Date: 2023-01-14 00:00:00
Tasks: Prediction

In recent years, renewable energy resources have accounted for an increasing share of electricity energy.Among them, photovoltaic (PV) power generation has received broad attention due to its economic and environmental benefits.Accurate PV generation forecasts can reduce power dispatch from the grid, thus increasing the supplier's profit in the day-ahead electricity market.The power system of a PV site is affected by solar radiation, PV plant properties and meteorological factors, resulting in uncertainty in its power output.This study used multiple seasonal-trend decomposition using LOESS (MSTL) and temporal fusion transformer (TFT) to perform day-ahead PV prediction on the desert knowledge Australia solar centre (DKASC) dataset.We compare the decomposition algorithms (VMD, EEMD and VMD-EEMD) and prediction models (BP, LSTM and XGBoost, etc.) which are commonly used in PV prediction presently.The results show that the MSTL-TFT method is more accurate than the aforementioned methods, which have noticeable improvement compared to other recent day-ahead PV predictions on desert knowledge Australia solar centre (DKASC).
Distributed Optimal Formation Control for an Uncertain Multiagent System in the Plane
Authors: Clinton Enwerem, John Baras, Danilo Romero
Date: 2023-01-14 00:00:00

In this paper, we present a distributed optimal multiagent control scheme for quadrotor formation tracking under localization errors. Our control architecture is based on a leader-follower approach, where a single leader quadrotor tracks a desired trajectory while the followers maintain their relative positions in a triangular formation. We begin by modeling the quadrotors as particles in the YZ-plane evolving under dynamics with uncertain state information. Next, by formulating the formation tracking task as an optimization problem -- with a constraint-augmented Lagrangian subject to dynamic constraints -- we solve for the control law that leads to an optimal solution in the control and trajectory error cost-minimizing sense. Results from numerical simulations show that for the planar quadrotor model considered -- with uncertainty in sensor measurements modeled as Gaussian noise -- the resulting optimal control is able to drive each agent to achieve the desired global objective: leader trajectory tracking with formation maintenance. Finally, we evaluate the performance of the control law using the tracking and formation errors of the multiagent system.
Multi-Agent Coordination Fluid Flow Modeling and Experimental Evaluation
Authors: Harshvardhan Uppaluru, Mohammad Ghuran, Hossein Rastgoftar
Date: 2023-01-14 00:00:00

Reliability is a critical aspect of multi-agent system coordination as it ensures that the system functions correctly and consistently. If one agent in the system fails or behaves unexpectedly, it can negatively impact the performance and effectiveness of the entire system. Therefore, it is important to design and implement multi-agent systems with a high level of reliability to ensure that they can operate safely and move smoothly in the presence of unforeseen agent failure or lack of communication with some agent teams moving in a shared motion space. This paper presents a novel fluid flow navigation model that, in an ideal fluid flow, divides agents into cooperative (non-singular) and noncooperative (singular) agents, with cooperative agents sliding along streamlines safely enclosing noncooperative agents in a shared motion space. A series of flight experiments utilizing crazyflie quadcopters will experimentally validate the suggested model.
Analysis of November 21, 2021, Kaua`i Island Power System 18-20 Hz Oscillations
Authors: Shuan Dong, Bin Wang, Jin Tan, Cameron J. Kruse, Brad W. Rockwell, Anderson Hoke
Date: 2023-01-13 00:00:00

This letter discusses the 18-20 Hz oscillation event at 05:30 am on November 21, 2021, in Kaua`i's power system following the trip of an oil power plant. As far as the authors are aware, this is the first report of a transmission system-wide subsynchronous oscillation driven by inverter-based resources (though the system in question is relatively small). In this letter, we leverage two data-based methods-the dissipating energy flow method and the sub/super-synchronous power flow method-to locate the sources of the oscillation. Also, we build an electromagnetic transient model of the Kaua`i power system and replay the 18-20 Hz oscillation. Finally, we propose two mitigation methods and validate their effectiveness via numerical simulation.
Learning Near-Optimal Intrusion Responses Against Dynamic Attackers
Authors: Kim Hammar, Rolf Stadler
Date: 2023-01-11 00:00:00

We study automated intrusion response and formulate the interaction between an attacker and a defender as an optimal stopping game where attack and defense strategies evolve through reinforcement learning and self-play. The game-theoretic modeling enables us to find defender strategies that are effective against a dynamic attacker, i.e. an attacker that adapts its strategy in response to the defender strategy. Further, the optimal stopping formulation allows us to prove that optimal strategies have threshold properties. To obtain near-optimal defender strategies, we develop Threshold Fictitious Self-Play (T-FP), a fictitious self-play algorithm that learns Nash equilibria through stochastic approximation. We show that T-FP outperforms a state-of-the-art algorithm for our use case. The experimental part of this investigation includes two systems: a simulation system where defender strategies are incrementally learned and an emulation system where statistics are collected that drive simulation runs and where learned strategies are evaluated. We argue that this approach can produce effective defender strategies for a practical IT infrastructure.