pwc-archive/papers-with-abstracts huggingface dataset:
Managing Renewable Energy Resources Using Equity-Market Risk Tools - the Efficient Frontiers
Authors: Haim Grebel, Divya Vikas, Jim Shi
Date: 2024-05-06 00:00:00
Tasks: Management

The energy market, and specifically the renewable sector carries volatility and risks, similar to the financial market. Here, we leverage on a well-established, return-risk approach, commonly used by equity portfolio-managers and apply it to energy resources. We visualize the relationship between the resources' costs and their risks in terms of efficient frontiers. We apply this analysis to publically available data for various US regions: Central, Eastern and Western coasts. Since risk management is contingent on costs, this approach sheds useful light in assessing dynamic pricing in modern electrical grids. By integrating geographical and temporal dimensions into our research, we aim at providing more nuanced and context-specific recommendations for energy resource allocation. This approach may help decision-makers in the renewable energy sector to make informed choices that account for regional variations, climatic conditions, and long-term performance trends.
On the constrained feedback linearization control based on the MILP representation of a ReLU-ANN
Authors: Huu-Thinh Do, Ionela Prodan
Date: 2024-05-06 00:00:00
Tasks: Model Predictive Control

In this work, we explore the efficacy of rectified linear unit artificial neural networks in addressing the intricate challenges of convoluted constraints arising from feedback linearization mapping. Our approach involves a comprehensive procedure, encompassing the approximation of constraints through a regression process. Subsequently, we transform these constraints into an equivalent representation of mixed-integer linear constraints, seamlessly integrating them into other stabilizing control architectures. The advantage resides in the compatibility with the linear control design and the constraint satisfaction in the model predictive control setup, even for forecasted trajectories. Simulations are provided to validate the proposed constraint reformulation.
How improving performance may imply losing consistency in event-triggered consensus
Authors: David Meister, Duarte J. Antunes, Frank Allgöwer
Date: 2024-05-06 00:00:00

Event-triggered control is often argued to lower the average triggering rate compared to time-triggered control while still achieving a desired control goal, e.g., the same performance level. However, this property, often called consistency, cannot be taken for granted and can be hard to analyze in many settings. In particular, although numerous decentralized event-triggered control schemes have been proposed in the past years, their performance properties with respect to time-triggered control remain mostly unexplored. In this paper, we therefore examine the performance properties of event-triggered control (relative to time-triggered control) for a single-integrator consensus problem with a level-triggering rule. We consider the long-term average quadratic deviation from consensus as a performance measure. For this setting, we show that enriching the information the local controllers use improves the performance of the consensus algorithm but renders a previously consistent event-triggered control scheme inconsistent. In addition, we do so while deploying optimal control inputs which we derive for both information cases and all triggering schemes. With this insight, we can furthermore explain the relationship between two contrasting consistency results from the literature on decentralized event-triggered control. We support our theoretical findings with simulation results.
Slicing for Dense Smart Factory Network: Current State, Scenarios, Challenges and Expectations
Authors: Regina Ochonu, Josep Vidal
Date: 2024-05-06 00:00:00

In the era of Industry 4.0, smart factories have emerged as a paradigm shift, redefining manufacturing with the integration of advanced digital technologies. Central to this transformation is the deployment of 5G networks, offering unprecedented levels of connectivity, speed, reliability, and ultra-low latency. Among the revolutionary features of 5G is network slicing, a technology that offers enhanced capabilities through the customization of network resources by allowing multiple logical networks (or slices) to run on top of a shared physical infrastructure. This capability is particularly crucial in the densely packed and highly dynamic environment of smart factories, where diverse applications - from robotic automation to real-time analytics - demand varying network requirements. In this paper, we present a comprehensive overview of the integration of slicing in smart factory networks, emphasizing its critical role in enhancing operational efficiency and supporting the diverse requirements of future manufacturing processes. We elaborate on the recent advances, and technical scenarios, including indoor factory propagation conditions, traffic characteristics, system requirements, slice-aware radio resource management, network elements, enabling technologies and current standardisation efforts. Additionally, we identify open research challenges as well as key technical issues stifling deployments. Finally, we speculate on the future trajectory of slicing-enabled smart factories, emphasizing the need for continuous adaptation to emerging technologies.
Experimental Investigation of Repurposed Kaplan Turbines as Variable-Speed Propellers for Maximizing Frequency Containment Reserve
Authors: Francesco Gerini, Elena Vagnoni, Martin Seydoux, Rachid Cherkaoui, Mario Paolone
Date: 2024-05-06 00:00:00

This study explores the practical viability of repurposing aging Kaplan turbines into variable-speed propellers by employing full-size frequency converters. The motivation behind this approach is to improve the provision of \emph{Frequency Containment Reserve} (FCR) while reducing fatigue in the Kaplan blades servomechanism. We evaluate the performance of these modified Kaplan turbines against the one of another hydro asset composed of the same Kaplan turbine hybridized with a \emph{Battery Energy Storage System} (BESS). Experiments are conducted on a one-of-its-kind reduced-scale model testing platform. Our findings reveal that Kaplan turbines repurposed as variable-speed propellers exhibit similar dynamic response characteristics compared to the standalone Kaplan operation, with the added benefit of effectively eliminating blade movements. Furthermore, the ability to control the speed increases the hydraulic efficiency for certain operating points. In summary, investment in variable speed technology emerges as a viable alternative to BESS-based hydropower hybridization.
Experiences with Sub-Arctic Sensor Network Deployment
Authors: Priyesh Pappinisseri Puluckul, Maarten Weyn
Date: 2024-05-05 00:00:00

This paper discusses the experiences gained from designing, deploying, and maintaining low-power Wireless Sensor Networks (WSN) in three geothermally active remote locations in Iceland. The network was deployed for environmental monitoring and real-time data collection to assist in investigating the impact of global warming on the (sub)Arctic climate and the resulting carbon release from the region. Functional networks with more than 50 sensor nodes from three sites with extreme weather conditions and hard-to-access terrain have been collecting data since 2021. The networks employ primary cell-powered wireless sensor nodes equipped with DASH7 Alliance Protocol (D7A) for low-power data transmission and solar-powered D7A-cellular gateways for the backend connection. The WSNs have so far achieved over three years of uptime with minimal maintenance required throughout this period. We present a detailed discussion of different network components, their architecture, and the networks' overall performance and reliability.
Probabilistic Tube-based Control Synthesis of Stochastic Multi-Agent Systems under Signal Temporal Logic
Authors: Eleftherios E. Vlahakis, Lars Lindemann, Pantelis Sopasakis, Dimos V. Dimarogonas
Date: 2024-05-05 00:00:00
Tasks: Scheduling

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical.
Model Predictive Control for Joint Ramping and Regulation-Type Service from Distributed Energy Resource Aggregations
Authors: Joel Mathias, Rajasekhar Anguluri, Oliver Kosut, Lalitha Sankar
Date: 2024-05-05 00:00:00
Tasks: Model Predictive Control

Distributed energy resources (DERs) such as grid-responsive loads and batteries can be harnessed to provide ramping and regulation services across the grid. This paper concerns the problem of optimal allocation of different classes of DERs, where each class is an aggregation of similar DERs, to balance net-demand forecasts. The resulting resource allocation problem is solved using model-predictive control (MPC) that utilizes a rolling sequence of finite time-horizon constrained optimizations. This is based on the concept that we have more accurate estimates of the load forecast in the short term, so each optimization in the rolling sequence of optimization problems uses more accurate short term load forecasts while ensuring satisfaction of capacity and dynamical constraints. Simulations demonstrate that the MPC solution can indeed reduce the ramping required from bulk generation, while mitigating near-real time grid disturbances.
Does Optimal Control Always Benefit from Better Prediction? An Analysis Framework for Predictive Optimal Control
Authors: Xiangrui Zeng, Cheng Yin, Zhouping Yin
Date: 2024-05-05 00:00:00

The ``prediction + optimal control'' scheme has shown good performance in many applications of automotive, traffic, robot, and building control. In practice, the prediction results are simply considered correct in the optimal control design process. However, in reality, these predictions may never be perfect. Under a conventional stochastic optimal control formulation, it is difficult to answer questions like ``what if the predictions are wrong''. This paper presents an analysis framework for predictive optimal control where the subjective belief about the future is no longer considered perfect. A novel concept called the hidden prediction state is proposed to establish connections among the predictors, the subjective beliefs, the control policies and the objective control performance. Based on this framework, the predictor evaluation problem is analyzed. Three commonly-used predictor evaluation measures, including the mean squared error, the regret and the log-likelihood, are considered. It is shown that neither using the mean square error nor using the likelihood can guarantee a monotonic relationship between the predictor error and the optimal control cost. To guarantee control cost improvement, it is suggested the predictor should be evaluated with the control performance, e.g., using the optimal control cost or the regret to evaluate predictors. Numerical examples and examples from automotive applications with real-world driving data are provided to illustrate the ideas and the results.
Risk Assessment for Nonlinear Cyber-Physical Systems under Stealth Attacks
Authors: Guang Chen, Zhicong Sun, Yulong Ding, Shuang-Hua Yang
Date: 2024-05-04 00:00:00

Stealth attacks pose potential risks to cyber-physical systems because they are difficult to detect. Assessing the risk of systems under stealth attacks remains an open challenge, especially in nonlinear systems. To comprehensively quantify these risks, we propose a framework that considers both the reachability of a system and the risk distribution of a scenario. We propose an algorithm to approximate the reachability of a nonlinear system under stealth attacks with a union of standard sets. Meanwhile, we present a method to construct a risk field to formally describe the risk distribution in a given scenario. The intersection relationships of system reachability and risk regions in the risk field indicate that attackers can cause corresponding risks without being detected. Based on this, we introduce a metric to dynamically quantify the risk. Compared to traditional methods, our framework predicts the risk value in an explainable way and provides early warnings for safety control. We demonstrate the effectiveness of our framework through a case study of an automated warehouse.
A Robust Data-Driven Iterative Control Method for Linear Systems with Bounded Disturbances
Authors: Kaijian Hu, Tao Liu
Date: 2024-05-04 00:00:00

This paper proposes a new robust data-driven control method for linear systems with bounded disturbances, where the system model and disturbances are unknown. Due to disturbances, accurately determining the true system becomes challenging using the collected dataset. Therefore, instead of designing controllers directly for the unknown true system, an available approach is to design controllers for all systems compatible with the dataset. To overcome the limitations of using a single dataset and benefit from collecting more data, multiple datasets are employed in this paper. Furthermore, a new iterative method is developed to address the challenges of using multiple datasets. Based on this method, this paper develops an offline and online robust data-driven iterative control method, respectively. Compared to the existing robust data-driven controller method, both proposed control methods iteratively utilize multiple datasets in the controller design process. This allows for the incorporation of numerous datasets, potentially reducing the conservativeness of the designed controller. Particularly, the online controller is iteratively designed by continuously incorporating online collected data into the historical data to construct new datasets. Lastly, the effectiveness of the proposed methods is demonstrated using a batch reactor.
Incremental Volt/Var Control for Distribution Networks via Chance-Constrained Optimization
Authors: Antonin Colot, Elisabetta Perotti, Mevludin Glavic, Emiliano Dall'Anese
Date: 2024-05-03 00:00:00

This paper considers an incremental Volt/Var control scheme for distribution systems with high integration of inverter-interfaced distributed generation (such as photovoltaic systems). The incremental Volt/Var controller is implemented with the objective of minimizing reactive power usage while maintaining voltages within safe limits sufficiently often. To this end, the parameters of the incremental Volt/Var controller are obtained by solving a chance-constrained optimization problem, where constraints are designed to ensure that voltage violations do not occur more often than a pre-specified probability. This approach leads to cost savings in a controlled, predictable way, while still avoiding significant over- or under-voltage issues. The proposed chance-constrained problem is solved using a successive convex approximation method. Once the gains are broadcast to the inverters, no additional communication is required since the controller is implemented locally at the inverters. The proposed method is successfully tested on a low-voltage single-phase 42-nodes network and on the three-phase unbalanced IEEE 123-node test system.
Low-cost sensors and circuits for plasma education: characterizing power and illuminance
Authors: Alessandro N. Vargas, Victor Miller, Ali Mesbah, Gabriele Neretti
Date: 2024-05-03 00:00:00

Industrial applications of plasma have significantly increased beyond semiconductor manufacturing in recent years. This necessitates training a skilled workforce in plasma science and technology. However, an essential challenge to this end stems from the high cost of plasma devices and diagnostics. The limited access to plasma devices has hindered plasma education, particularly in the least developed countries. To this end, this paper demonstrates how low-cost sensors and circuits can be developed to enable inexpensive plasma experiments in laboratory environments. In particular, we show how to measure high voltage, current, and power from a cold-atmospheric plasma discharge. Additionally, we develop a low-cost illuminance sensor and demonstrate how it can be used to estimate the corresponding plasma power. The low-cost sensors and electronics presented in this paper can aid educators in characterizing plasma power versus plasma illuminance.
Distributed Online Feedback Optimization for Real-time Distribution System Voltage Regulation
Authors: Sen Zhan, Nikolaos G. Paterakis, Wouter van den Akker, Anne van der Molen, Johan Morren, J. G. Slootweg
Date: 2024-05-03 00:00:00

We investigate the real-time voltage regulation problem in distribution systems employing online feedback optimization (OFO) with short-range communication between physical neighbours. OFO does not need an accurate grid model nor estimated consumption of non-controllable loads, affords fast calculations, and demonstrates robustness to uncertainties and disturbances, which render it particularly suitable for real-time distribution system applications. However, many OFO controllers require centralized communication, making them susceptible to single-point failures. This paper proposes a distributed OFO design based on a nested feedback optimization strategy and analyzes its convergence. Numerical study results demonstrate that the proposed design achieves effective voltage regulation and outperforms other distributed and local approaches.
Hybrid Lyapunov-based feedback stabilization of bipedal locomotion based on reference spreading
Authors: Riccardo Bertollo, Gianni Lunardi, Andrea Del Prete, Luca Zaccarian
Date: 2024-05-03 00:00:00
Tasks: Model Predictive Control, Position

We propose a hybrid formulation of the linear inverted pendulum model for bipedal locomotion, where the foot switches are triggered based on the center of mass position, removing the need for pre-defined footstep timings. Using a concept similar to reference spreading, we define nontrivial tracking error coordinates induced by our hybrid model. These coordinates enjoy desirable linear flow dynamics and rather elegant jump dynamics perturbed by a suitable extended class ${\mathcal K}_\infty$ function of the position error. We stabilize this hybrid error dynamics using a saturated feedback controller, selecting its gains by solving a convex optimization problem. We prove local asymptotic stability of the tracking error and provide a certified estimate of the basin of attraction, comparing it with a numerical estimate obtained from the integration of the closed-loop dynamics. Simulations on a full-body model of a real robot show the practical applicability of the proposed framework and its advantages with respect to a standard model predictive control formulation.
Obstacle Avoidance of Autonomous Vehicles: An LPVMPC with Scheduling Trust Region
Authors: Maryam Nezami, Dimitrios S. Karachalios, Georg Schildbach, Hossam S. Abbas
Date: 2024-05-03 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Model Predictive Control, Scheduling

Reference tracking and obstacle avoidance rank among the foremost challenging aspects of autonomous driving. This paper proposes control designs for solving reference tracking problems in autonomous driving tasks while considering static obstacles. We suggest a model predictive control (MPC) strategy that evades the computational burden of nonlinear nonconvex optimization methods after embedding the nonlinear model equivalently to a linear parameter-varying (LPV) formulation using the so-called scheduling parameter. This allows optimal and fast solutions of the underlying convex optimization scheme as a quadratic program (QP) at the expense of losing some performance due to the uncertainty of the future scheduling trajectory over the MPC horizon. Also, to ensure that the modeling error due to the application of the scheduling parameter predictions does not become significant, we propose the concept of scheduling trust region by enforcing further soft constraints on the states and inputs. A consequence of using the new constraints in the MPC is that we construct a region in which the scheduling parameter updates in two consecutive time instants are trusted for computing the system matrices, and therefore, the feasibility of the MPC optimization problem is retained. We test the method in different scenarios and compare the results to standard LPVMPC as well as nonlinear MPC (NMPC) schemes.
Multi-objective Optimal Trade-off Between V2G Activities and Battery Degradation in Electric Mobility-as-a-Service Systems
Authors: Fabio Paparella, Pim Labee, Steven Wilkins, Theo Hofman, Soora Rasouli, Mauro Salazar
Date: 2024-05-03 00:00:00

This paper presents optimization models for electric Mobility-as-a-Service systems, whereby electric vehicles not only provide on-demand mobility, but also perform charging and Vehicle-to-Grid (V2G) operations to enhance the fleet operator profitability. Specifically, we formulate the optimal fleet operation problem as a mixed-integer linear program, with the objective combining of operational costs and revenues generated from servicing requests and grid electricity sales. Our cost function explicitly captures battery price and degradation, reflecting their impact on the fleet total cost of ownership due to additional charging and discharging activities. Simulation results for Eindhoven, The Netherlands, show that integrating V2G activities does not compromise the number of travel requests being served. Moreover, we emphasize the significance of accounting for battery degradation, as the costs associated with it can potentially outweigh the revenues stemming from V2G operations.
A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving Cars
Authors: Cristian Tiriolo, Walter Lucia
Date: 2024-05-02 00:00:00
Tasks: Model Predictive Control, Self-Driving Cars

This paper proposes a novel real-time affordable solution to the trajectory tracking control problem for self-driving cars subject to longitudinal and steering angular velocity constraints. To this end, we develop a dual-mode Model Predictive Control (MPC) solution starting from an input-output feedback linearized description of the vehicle kinematics. First, we derive the state-dependent input constraints acting on the linearized model and characterize their worst-case time-invariant inner approximation. Then, a dual-mode MPC is derived to be real-time affordable and ensuring, by design, constraints fulfillment, recursive feasibility, and uniformly ultimate boundedness of the tracking error in an ad-hoc built robust control invariant region. The approach's effectiveness and performance are experimentally validated via laboratory experiments on a Quanser Qcar. The obtained results show that the proposed solution is computationally affordable and with tracking capabilities that outperform two alternative control schemes.