pwc-archive/papers-with-abstracts huggingface dataset:
Model Predictive Control Strategies for Electric Endurance Race Cars Accounting for Competitors Interactions
Authors: Jorn van Kampen, Mauro Moriggi, Francesco Braghin, Mauro Salazar
Date: 2024-03-11 00:00:00
Tasks: Decision Making, Model Predictive Control

This paper presents model predictive control strategies for battery electric endurance race cars accounting for interactions with the competitors. In particular, we devise an optimization framework capturing the impact of the actions of the ego vehicle when interacting with competitors in a probabilistic fashion, jointly accounting for the optimal pit stop decision making, the charge times and the driving style in the course of the race. We showcase our method for a simulated 1h endurance race at the Zandvoort circuit, using real-life data of internal combustion engine race cars from a previous event. Our results show that optimizing both the race strategy as well as the decision making during the race is very important, resulting in a significant 21s advantage over an always overtake approach, whilst revealing the competitiveness of e-race cars w.r.t. conventional ones.
Identifikation auslösender Umstände von SOTIF-Gefährdungen durch systemtheoretische Prozessanalyse
Authors: Robert Graubohm, Marvin Loba, Marcus Nolte, Markus Maurer
Date: 2024-03-11 00:00:00

Developers have to obtain a sound understanding of existing risk potentials already in the concept phase of driverless vehicles. Deductive as well as inductive SOTIF analyses of potential triggering conditions for hazardous behavior help to achieve this goal. In this regard, ISO 21448 suggests conducting a System-Theoretic Process Analysis (STPA). In this article, we introduce German terminology for SOTIF considerations and critically discuss STPA theory in the course of an example application, while also proposing methodological additions. -- -- Um bereits in der Konzeptphase autonomer Fahrzeuge einen fundierten Eindruck bestehender Risikopotenziale zu erhalten, werden im Zuge von deduktiven und induktiven SOTIF-Analysen m\"ogliche ausl\"osende Umst\"ande f\"ur gef\"ahrliches Verhalten untersucht. In diesem Zusammenhang wird in der ISO 21448 die Durchf\"uhrung einer systemtheoretischen Prozessanalyse (STPA) vorgeschlagen. In diesem Beitrag f\"uhren wir deutsche Terminologie f\"ur SOTIF-Betrachtungen ein und setzen uns im Zuge einer Anwendung kritisch mit der STPA-Theorie auseinander, wobei wir begleitend methodische Erg\"anzungen anregen.
Robust and fast backbone tracking via phase-locked loops
Authors: Patrick Hippold, Maren Scheel, Ludovic Renson, Malte Krack
Date: 2024-03-11 00:00:00

Phase-locked loops are commonly used for shaker-based backbone tracking of nonlinear structures. The state of the art is to tune the control parameters by trial and error. In the present work, an approach is proposed to make backbone tracking much more robust and faster. A simple PI controller is proposed, and closed-form expressions for the gains are provided that lead to an optimal settling of the phase transient. The required input parameters are obtained from a conventional shaker-based linear modal test, and an open-loop sine test at a single frequency and level. For phase detection, an adaptive filter based on the LMS algorithm is used, which is shown to be superior to the synchronous demodulation commonly used. Once the phase has locked, one can directly take the next step along the backbone, eliminating the hold times. The latter are currently used for recording the steady state, and to estimate Fourier coefficients in the post-process, which becomes unnecessary since the adaptive filter yields a highly accurate estimation at runtime.The excellent performance of the proposed approach is demonstrated for a doubly clamped beam undergoing bending-stretching coupling leading to a 20 percent shift of the lowest modal frequency. Even for fixed control parameters, designed for the linear regime, only about 100 periods are needed per backbone point, also in the nonlinear regime. This is much faster than what has been reported in the literature so far.
Aggregated distribution grid flexibilities in subtransmission grid operational management
Authors: Neelotpal Majumdar, Lutz Hofmann
Date: 2024-03-11 00:00:00
Tasks: Management

Aggregated flexibilities or PQ-capabilities (active and reactive power capabilities) are termed in literature as Feasible Operating Regions (FORs). The FORs from underlying active distribution grids can effectively contribute to the operational management at the HV grid level. The HV buses are allocated aggregated FORs from the underlying MV grids, which are inherently nonlinear and non-convex. Therefore, two approaches are proposed in the paper to apply the FOR constraints in the HV grid operational management. First, a mixed integer linear programming (MILP) based optimization approach for alleviating the HV grid constraint violations is proposed, which addresses the non-convexity of the FOR using piecewise segmentation. Furthermore, the MILP method is enhanced to consider the influence of the HV bus voltage on the underlying MV grid flexibilities resulting in a three dimensional PQ(V)-FOR. Second, a convexification approach is proposed, which uses a convex approximation of the non-convex 3D PQ(V)-FOR shape for implementation in a linear optimization method. Results reveal a robust utilization of the distribution flexibilities to maintain grid security and reliability at the HV grid level. Comparisons present increased computation times for the MILP method which are significantly improved using the convexification based approach.
AC/DC optimal power flow and techno-economic assessment for hybrid microgrids: TIGON CEDER demonstrator
Authors: Alejandro Martín-Crespo, Alejandro Hernández-Serrano, Óscar Izquierdo-Monge, Paula Peña-Carro, Ángel Hernández-Jiménez, Fernando Frechoso-Escudero, Enrique Baeyens
Date: 2024-03-11 00:00:00

In the recent years, the interest in electric direct current (DC) technologies (such as converters, batteries, electric vehicles, etc.) is increasing due to its potential on energy efficiency and sustainability. However, the vast majority of electric systems and networks are based on alternating current (AC), as they also have certain advantages regarding cost-effective transport and robustness. In this paper, an AC/DC optimal power flow method for hybrid microgrids and several key performance indicators (KPIs) for its techno-economic assessment are presented. The combination of both calculations allows users to clearly determine the viability of their hybrid microgrids. AC/DC networks have been modelled considering their most common elements. For the power flow method, a polynomial optimisation is formulated considering four different objective functions: the minimisation of energy losses, voltage deviation and operational costs, and also the maximisation of the microgrid generation. The power flow method and the techno-economic analysis have been implemented in Python and validated in the Centro de Desarrollo de Energ\'ias Renovables (CEDER) demonstrator for TIGON. The results show that the calculated power flow variables and the ones measured at CEDER are practically the same. In addition, the KPIs have been obtained and compared for four operating scenarios: baseline, no battery, battery flexibility and virtual battery (VB) flexibility. The last one result in the most profitable option.
Edge Information Hub: Orchestrating Satellites, UAVs, MEC, Sensing and Communications for 6G Closed-Loop Controls
Authors: Chengleyang Lei, Wei Feng, Peng Wei, Yunfei Chen, Ning Ge, Shiwen Mao
Date: 2024-03-11 00:00:00
Tasks: Edge-computing

An increasing number of field robots would be used for mission-critical tasks in remote or post-disaster areas. Due to the limited individual abilities, these robots usually require an edge information hub (EIH), with not only communication but also sensing and computing functions. Such EIH could be deployed on a flexibly-dispatched unmanned aerial vehicle (UAV). Different from traditional aerial base stations or mobile edge computing (MEC), the EIH would direct the operations of robots via sensing-communication-computing-control ($\textbf{SC}^3$) closed-loop orchestration. This paper aims to optimize the closed-loop control performance of multiple $\textbf{SC}^3$ loops, with constraints on satellite-backhaul rate, computing capability, and on-board energy. Specifically, the linear quadratic regulator (LQR) control cost is used to measure the closed-loop utility, and a sum LQR cost minimization problem is formulated to jointly optimize the splitting of sensor data and allocation of communication and computing resources. We first derive the optimal splitting ratio of sensor data, and then recast the problem to a more tractable form. An iterative algorithm is finally proposed to provide a sub-optimal solution. Simulation results demonstrate the superiority of the proposed algorithm. We also uncover the influence of $\textbf{SC}^3$ parameters on closed-loop controls, highlighting more systematic understanding.
Enhancing Industrial Flexibility and Market Participation in Cement Manufacturing Through Optimized Production Scheduling
Authors: Sebastián Rojas-Innocenti, Enrique Baeyens, Alejandro Martín-Crespo, Sergio Saludes-Rodil, Fernando Frechoso-Escudero
Date: 2024-03-11 00:00:00
Tasks: Scheduling

The growing share of variable renewable energy (VRE) sources in power systems is increasing the need for short term operational flexibility, particularly from large industrial electricity consumers. This study proposes a practical, two stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity balancing markets. In Stage 1, a mixed integer linear programming (MILP) model minimizes electricity procurement costs by optimally scheduling the raw milling subsystem. In Stage 2, a flexibility assessment model evaluates profitable deviations, targeting participation in Spain manual Frequency Restoration Reserve (mFRR) market. A real world case study in a Spanish cement plant (including PV and battery storage) shows that flexibility services can yield monthly revenues of up to 800 EUR and paybacks as short as six years. This framework offers a replicable pathway for industrial flexibility in energy intensive sectors.
Ride-pooling Electric Autonomous Mobility-on-Demand: Joint Optimization of Operations and Fleet and Infrastructure Design
Authors: Fabio Paparella, Karni Chauhan, Luc Koenders, Theo Hofman, Mauro Salazar
Date: 2024-03-11 00:00:00

This paper presents a modeling and design optimization framework for an Electric Autonomous Mobility-on-Demand system that allows for ride-pooling, i.e., multiple users can be transported at the same time towards a similar direction to decrease vehicle hours traveled by the fleet at the cost of additional waiting time and delays caused by detours. In particular, we first devise a multi-layer time-invariant network flow model that jointly captures the position and state of charge of the vehicles. Second, we frame the time-optimal operational problem of the fleet, including charging and ride-pooling decisions as a mixed-integer linear program, whereby we jointly optimize the placement of the charging infrastructure. Finally, we perform a case-study using Manhattan taxi-data. Our results indicate that jointly optimizing the charging infrastructure placement allows to decrease overall energy consumption of the fleet and vehicle hours traveled by approximately 1% compared to an heuristic placement. Most significantly, ride-pooling can decrease such costs considerably more, and up to 45%. Finally, we investigate the impact of the vehicle choice on the energy consumption of the fleet, comparing a lightweight two-seater with a heavier four-seater, whereby our results show that the former and latter designs are most convenient for low- and high-demand areas, respectively.
A prediction-based forward-looking vehicle dispatching strategy for dynamic ride-pooling
Authors: Xiaolei Wang, Chen Yang, Yuzhen Feng, Luohan Hu, Zhengbing He
Date: 2024-03-11 00:00:00

For on-demand dynamic ride-pooling services, e.g., Uber Pool and Didi Pinche, a well-designed vehicle dispatching strategy is crucial for platform profitability and passenger experience. Most existing dispatching strategies overlook incoming pairing opportunities, therefore suffer from short-sighted limitations. In this paper, we propose a forward-looking vehicle dispatching strategy, which first predicts the expected distance saving that could be brought about by future orders and then solves a bipartite matching problem based on the prediction to match passengers with partially occupied or vacant vehicles or keep passengers waiting for next rounds of matching. To demonstrate the performance of the proposed strategy, a number of simulation experiments and comparisons are conducted based on the real-world road network and historical trip data from Haikou, China. Results show that the proposed strategy outperform the baseline strategies by generating approximately 31\% more distance saving and 18\% less average passenger detour distance. It indicates the significant benefits of considering future pairing opportunities in dispatching, and highlights the effectiveness of our innovative forward-looking vehicle dispatching strategy in improving system efficiency and user experience for dynamic ride-pooling services.
Multi-gated perimeter flow control for monocentric cities: Efficiency and equity
Authors: Ruzanna Mat Jusoh, Konstantinos Ampountolas
Date: 2024-03-10 00:00:00

A control scheme for the multi-gated perimeter traffic flow control problem of cities is presented. The proposed scheme determines feasible and optimally distributed input flows for the various gates located at the periphery of a protected network. A parsimonious model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, the state-space model is augmented with additional state variables to account for vehicle queues at store-and-forward origin links at the periphery. The perimeter flow control problem is formulated as a convex optimisation problem with finite horizon, and constrained control and state variables. It aims to equalise the relative queues at origin links and to maintain the vehicle accumulation in the protected network around a desired set point, while the system's throughput is maximised. For real-time control, the optimal control problem is embedded in a rolling-horizon scheme using the current state of the system as the initial state as well as predicted demand flows at entrance links. Furthermore, practical flow allocation policies for single-region perimeter control without explicitly considering entrance link dynamics are presented. These policies allocate a global perimeter-ordered flow to candidate gates at the periphery of a protected network by taking into account the different geometric characteristics of origin links. The proposed flow allocation policies are then benchmarked against the multi-gated perimeter flow control. A study is carried out for a 2.5 square mile protected network area of San Francisco, CA, including fifteen gates of different geometric characteristics. The results have showed that the proposed scheme is able to manage excessive queues outside of the protected network and to optimally distribute the input flows, which confirms its efficiency and equity properties.
A Tunable Universal Formula for Safety-Critical Control
Authors: Ming Li, Zhiyong Sun, Patrick J. W. Koelewijn, Siep Weiland
Date: 2024-03-10 00:00:00
Tasks: Collision Avoidance

Sontag's universal formula is a widely used technique for stabilizing control through control Lyapunov functions. Recently, it has been extended to address safety-critical control by incorporating control barrier functions (CBFs). However, deriving a universal formula that satisfies requirements on essential properties, including safety, smoothness, and robustness against input disturbances, is still an open problem. To address this challenge, this paper introduces a novel solution - a tunable universal formula - by incorporating a (state-dependent) tunable term into Sontag's formula. This tunable term enables the regulation of safety-critical control performances, allowing the attainment of desired properties through a proper selection of tunable terms. Generally, the tunable universal formula can be seen as a controller that improves the quadratic program (QP)-synthesized controllers in terms of robustness and smoothness, while also reducing the conservatism (corresponding to robustness) in Sontag's formula. Furthermore, we extend the tunable universal formula to address safety-critical control problems with norm-bounded input constraints, showcasing its applicability across diverse control scenarios. Finally, we demonstrate the efficacy of our method through a two-link manipulator safe tracking example, investigating the essential properties including safety, smoothness, and robustness against input disturbances under various tunable terms.
Decentralized P2P Trading based on Blockchain for Retail Electricity Markets
Authors: Masoud H. Nazari, Antar Kumar Biswas
Date: 2024-03-10 00:00:00

This paper introduces peer to peer (P2P) trading mechanisms based on decentralized Blockchain to facilitate retail electricity market for ever-increasing distributed energy resources (DERs). The Blockchain network supports fast and secure retail trading among DERs and facilitates a sustainable local P2P trading platform. In this decentralized Blockchain architecture no single entity or organization has control over the entire system rather all users collectively maintain control. The effectiveness of the proposed automated market design and optimization is simulated using different use case scenarios in an open source Blockchain Simulator and MATLAB. The results show the efficacy of the trading mechanism in achieving demand response through strategies such as peak load shaving, load shifting, and integration of DERs.
Matrix Concentration Inequalities for Sensor Selection
Authors: Christopher I. Calle, Shaunak D. Bopardikar
Date: 2024-03-09 00:00:00
Tasks: State Estimation

In this work, we address the problem of sensor selection for state estimation via Kalman filtering. We consider a linear time-invariant (LTI) dynamical system subject to process and measurement noise, where the sensors we use to perform state estimation are randomly drawn according to a sampling with replacement policy. Since our selection of sensors is randomly chosen, the estimation error covariance of the Kalman filter is also a stochastic quantity. Fortunately, concentration inequalities (CIs) for the estimation error covariance allow us to gauge the estimation performance we intend to achieve when our sensors are randomly drawn with replacement. To obtain a non-trivial improvement on existing CIs for the estimation error covariance, we first present novel matrix CIs for a sum of independent and identically-distributed (i.i.d.) and positive semi-definite (p.s.d.) random matrices, whose support is finite. Next, we show that our guarantees generalize an existing matrix CI. Also, we show that our generalized guarantees require significantly fewer number of sampled sensors to be applicable. Lastly, we show through a numerical study that our guarantees significantly outperform existing ones in terms of their ability to bound (in the semi-definite sense) the steady-state estimation error covariance of the Kalman filter.
Efficient Fault Detection and Categorization in Electrical Distribution Systems Using Hessian Locally Linear Embedding on Measurement Data
Authors: Victor Sam Moses Babu K., Sidharthenee Nayak, Divyanshi Dwivedi, Pratyush Chakraborty, Chandrashekhar Narayan Bhende, Pradeep Kumar Yemula, Mayukha Pal
Date: 2024-03-09 00:00:00
Tasks: Clustering, Fault Detection

Faults on electrical power lines could severely compromise both the reliability and safety of power systems, leading to unstable power delivery and increased outage risks. They pose significant safety hazards, necessitating swift detection and mitigation to maintain electrical infrastructure integrity and ensure continuous power supply. Hence, accurate detection and categorization of electrical faults are pivotal for optimized power system maintenance and operation. In this work, we propose a novel approach for detecting and categorizing electrical faults using the Hessian locally linear embedding (HLLE) technique and subsequent clustering with t-SNE (t-distributed stochastic neighbor embedding) and Gaussian mixture model (GMM). First, we employ HLLE to transform high-dimensional (HD) electrical data into low-dimensional (LD) embedding coordinates. This technique effectively captures the inherent variations and patterns in the data, enabling robust feature extraction. Next, we perform the Mann-Whitney U test based on the feature space of the embedding coordinates for fault detection. This statistical approach allows us to detect electrical faults providing an efficient means of system monitoring and control. Furthermore, to enhance fault categorization, we employ t-SNE with GMM to cluster the detected faults into various categories. To evaluate the performance of the proposed method, we conduct extensive simulations on an electrical system integrated with solar farm. Our results demonstrate that the proposed approach exhibits effective fault detection and clustering across a range of fault types with different variations of the same fault. Overall, this research presents an effective methodology for robust fault detection and categorization in electrical systems, contributing to the advancement of fault management practices and the prevention of system failures.
Fault Classification in Electrical Distribution Systems using Grassmann Manifold
Authors: Victor Sam Moses Babu K., Sidharthenee Nayak, Divyanshi Dwivedi, Pratyush Chakraborty, Chandrashekhar Narayan Bhende, Pradeep Kumar Yemula, Mayukha Pal
Date: 2024-03-09 00:00:00
Tasks: Classification

Electrical fault classification is vital for ensuring the reliability and safety of power systems. Accurate and efficient fault classification methods are essential for timely and effective maintenance. In this paper, we propose a novel approach for effective fault classification through Grassmann manifolds, which is a non-Euclidean space that captures the intrinsic structure of high-dimensional data and offers a robust framework for feature extraction. We use simulated data for electrical distribution systems with various types of electrical faults. The proposed method involves transforming the measurement fault data into Grassmann manifold space using techniques from differential geometry. This transformation aids in uncovering the underlying fault patterns and reducing the computational complexity of subsequent classification steps. To achieve fault classification, we employ a machine learning technique optimized for the Grassmann manifold. The support vector machine classifier is adapted to operate within the Grassmann manifold space, enabling effective discrimination between different fault classes. The results illustrate the efficacy of the proposed Grassmann manifold-based approach for electrical fault classification which showcases its ability to accurately differentiate between various fault types.