pwc-archive/papers-with-abstracts huggingface dataset:
How to Leverage High Altitude Platforms in Green Computing?
Authors: Wiem Abderrahim, Osama Amin, Basem Shihada
Date: 2023-05-08 00:00:00
Tasks: Management, Vocal Bursts Intensity Prediction

Terrestrial data centers suffer from a growing carbon footprint that could contribute with $14\%$ to global CO2 emissions by 2040. High Altitude Platform (HAP) is a promising airborne technology that can unleash the computing frontier in the stratospheric range by hosting a flying data center. HAP systems can endorse the sustainable green operation of data centers thanks to the naturally low atmospheric temperature that saves cooling energy and its large surface that can host solar panels covering energy requirements. Throughout this article, we define the operation limitations of this innovative solution and study the energy-efficiency-related trade-offs. Then, we shed light on the significance of the scalability of the data center-enabled HAP architecture by investigating potential bottlenecks and proposing different deployment scenarios to avoid network congestion. We also highlight the importance of the management agility of the data center-enabled HAP system by defining effective management techniques that yield high-performing data centers. Our results demonstrate that deploying a single data center-enabled HAP can save $12\%$ of the electricity costs.
Power Distribution Grid Enhancement via Online Feedback Optimization
Authors: Jonas G. Matt, Lukas Ortmann, Saverio Bolognani, Florian Dörfler
Date: 2023-05-08 00:00:00

The rise in residential photovoltaics and other distributed energy sources poses unprecedented challenges for the operation of power distribution grids. When high amounts of active power are injected into the grid by such power sources, the overall power flow is often limited because of voltages reaching their upper acceptable limits. Volt/VAr control aims to raise this power flow limit by controlling the voltage using reactive power. This way, more active power can be transmitted safely without physically reinforcing the grid. In this paper, we use real consumption and generation data on a low-voltage CIGR\'E grid model and an experiment on a real distribution grid feeder to analyze how different Volt/VAr methods can enhance grid capacity, i.e., by how much they can improve the grid's capability to transmit active power without building new lines. We show that droop control enhances the grid but vastly underutilizes the reactive power resources. We discuss how the effectiveness of droop control can be partially improved by employing machine-learning techniques to tune the droop coefficients, but we demonstrate that local control laws are inherently unable to achieve optimal grid enhancement. In contrast, methods that coordinate the use of reactive power resources across the grid, such as Online Feedback Optimization (OFO), can enhance the grid to its full potential. A numerical study performed on data from an entire year using a realistic grid model suggests that OFO can enable another 9\% of maximum active power injections compared to droop control. To achieve that, OFO only requires voltage magnitude measurements, minimal model knowledge, and communication with the reactive power sources. A real-life experiment provides a demonstration of the practical feasibility of the proposed approach and enhanced the grid by another 10.5\% compared to droop control.
Strategic Planning of Carbon-Neutral Heating Demand Coverage Under Uncertainty in a Coupled Multi-Energy Grid
Authors: Marwan Mostafa, Davood Babazadeh, Christian Becker
Date: 2023-05-08 00:00:00
Tasks: Decision Making

Integrating the gas and district heating with the electrical grid in a multi-energy grid has been shown to provide flexibility and prevent bottlenecks in the operation of electrical distribution grids. This integration assumes a top-down grid planning approach and a perfect knowledge of consumer behaviour. In reality, consumers decides whether to adopt a heating technology based on costs and government regulation. This behavior is highly uncertain and depends on fluctuations in heating technology costs and energy prices. The uncertainty associated with consumer behavior increases the risk of investment in grid expansion. In response to this challenge, this paper proposes an approach with the consumer at the center of the planning method. Robust optimization is used to model the uncertainty in prices to reduce the risk of investment in grid expansion. The uncertainty in energy prices is modeled using interval uncertainty with a proportional deviation. This allows planners, operators and regulators to predict the adoption rate of certain heating technology in different geographical areas and prioritize the expansion of specific grids where they are required. By minimizing a cost function subject to robust constraints, the strategy ensures robustness against uncertainties in energy prices. This robust optimization approach is applied to Hamburg as a case study. The result of the optimization represents the consumer's decision. The impact of the consumer's decision on the electrical grid is analzed on different benchmark distribution grids. The study concludes that district heating expansion in high-density areas is a low-risk investment for carbon neutrality. In less dense areas, electrification supports decentralized heat pumps. Meanwhile, hydrogen gas grids are viable where electric expansion is impractical. Increased uncertainty leads to more conservative solutions.
Distributed Coordination of Multi-Microgrids in Active Distribution Networks for Provisioning Ancillary Services
Authors: Arghya Mallick, Abhishek Mishra, Ashish R. Hota, Prabodh Bajpai
Date: 2023-05-08 00:00:00
Tasks: Distributed Optimization

With the phenomenal growth in renewable energy generation, the conventional synchronous generator-based power plants are gradually getting replaced by renewable energy sources-based microgrids. Such transition gives rise to the challenges of procuring various ancillary services from microgrids. We propose a distributed optimization framework that coordinates multiple microgrids in an active distribution network for provisioning passive voltage support-based ancillary services while satisfying operational constraints. Specifically, we exploit the reactive power support capability of the inverters and the flexibility offered by storage systems available with microgrids for provisioning ancillary service support to the transmission grid. We develop novel mixed-integer inequalities to represent the set of feasible active and reactive power exchange with the transmission grid that ensures passive voltage support. The proposed alternating direction method of multipliers-based algorithm is fully distributed, and does not require the presence of a centralized entity to achieve coordination among the microgrids. We present detailed numerical results on the IEEE 33-bus distribution test system to demonstrate the effectiveness of the proposed approach and examine the scalability and convergence behavior of the distributed algorithm for different choice of hyperparameters and network sizes.
Robust Power Allocation for UAV-aided ISAC Systems with Uncertain Location Sensing Errors
Authors: Junchang Sun, Shuai Ma, Ruixin Yang, Yang Tingting, Shiyin Li
Date: 2023-05-08 00:00:00
Tasks: Integrated sensing and communication, ISAC, Robust Design

Unmanned aerial vehicle (UAV) holds immense potential in integrated sensing and communication (ISAC) systems for the Internet of Things (IoT). In this paper, we propose a UAV-aided ISAC framework and investigate three robust power allocation schemes. First, we derive an explicit expression of the Cram\'er-Rao bound (CRB) based on time-of-arrival (ToA) estimation, which serves as the performance metric for location sensing. Then, we analyze the impact of the location sensing error (LSE) on communications, revealing the inherent coupling relationship between communication and sensing. Moreover, we formulate three robust communication and sensing power allocation problems by respectively characterizing the LSE as an ellipsoidal distributed model, a Gaussian distributed model, and an arbitrary distributed model. Notably, the optimization problems seek to minimize the CRB, subject to data rate and total power constraints. However, these problems are non-convex and intractable. To address the challenges related to the three aforementioned LSE models, we respectively propose to use the ${\cal{S}}$-Procedure and alternating optimization (${\cal{S}}$-AO) method, Bernstein-type inequality and successive convex approximation (BI-SCA) method, and conditional value-at-risk (CVaR) and AO (CVaR-AO) method to solve these problems. Finally, simulation results demonstrate the robustness of our proposed UAV-aided ISAC system against the LSE by comparing with the non-robust design, and evaluate the trade-off between communication and sensing in the ISAC system.
A Deep Reinforcement Learning-based Reserve Optimization in Active Distribution Systems for Tertiary Frequency Regulation
Authors: Mukesh Gautam, Rakib Hossain, Mohammad MansourLakouraj, Narayan Bhusal, Mohammed Benidris, Hanif Livani
Date: 2023-05-07 00:00:00
Tasks: Deep Reinforcement Learning

Federal Energy Regulatory Commission (FERC) Orders 841 and 2222 have recommended that distributed energy resources (DERs) should participate in energy and reserve markets; therefore, a mechanism needs to be developed to facilitate DERs' participation at the distribution level. Although the available reserve from a single distribution system may not be sufficient for tertiary frequency regulation, stacked and coordinated contributions from several distribution systems can enable them participate in tertiary frequency regulation at scale. This paper proposes a deep reinforcement learning (DRL)-based approach for optimization of requested aggregated reserves by system operators among the clusters of DERs. The co-optimization of cost of reserve, distribution network loss, and voltage regulation of the feeders are considered while optimizing the reserves among participating DERs. The proposed framework adopts deep deterministic policy gradient (DDPG), which is an algorithm based on an actor-critic method. The effectiveness of the proposed method for allocating reserves among DERs is demonstrated through case studies on a modified IEEE 34-node distribution system.
Koopman-type inverse operator for linear non-minimum phase systems with disturbances
Authors: Yuhan Li, Xiaoqiang Ji
Date: 2023-05-07 00:00:00
Tasks: parameter estimation, Vocal Bursts Type Prediction

In this paper, a novel Koopman-type inverse operator for linear time-invariant non-minimum phase systems with stochastic disturbances is proposed. This operator employs functions of the desired output to directly calculate the input. Furthermore, it can be applied as a data-driven approach for systems with unknown parameters yet a known relative degree, which is a departure from the majority of existing data-driven methods that are only applicable to minimum phase systems. Based on this foundation, we use the Monte Carlo approach to develop an improved Koopman-type method for addressing the issue of inaccurate parameter estimation in data-driven systems with large disturbances. The simulation results justify the tracking accuracy of Koopman-type operator.
A Novel Decentralized Algorithm for Coordinating the Optimal Power and Traffic Flows with EVs based on Variable Inner Loop Selection
Authors: Santosh Sharma, QiFeng Li
Date: 2023-05-06 00:00:00
Tasks: Distributed Optimization

The electric power distribution network (PDN) and the transportation network (TN) are generally operated/coordinated by different entities. However, they are coupled with each other due to electric vehicle charging stations (EVCSs). This paper proposes to coordinate the operation of the two systems via a fully decentralized framework where the PDN and TN operators solve their own operation problems by sharing only limited information. Nevertheless, the operation problems generally are in mixed-integer programming (MIP) form. To the best of our knowledge, the most existing decentralized/distributed optimization algorithms, such as the alternating direction method of multipliers (ADMM), are not always guaranteed to converge for such MIP problems. Therefore, a novel fully decentralized optimization algorithm is proposed, whose contributions include: 1) it is applicable to MIP problems with convergence and optimality guaranteed for mild assumptions, 2) it only requires limited information exchange between PDN and TN operators, which will help preserve the privacy of the two systems and reduce the investment in building communication channels, and 3) it is fully decentralized so that all the computations are carried out by PDN operator and TN coordinator only. Simulations on the test cases show the efficiency and efficacy of the proposed framework and algorithm.
Hybrid Feedback for Affine Nonlinear Systems with Application to Global Obstacle Avoidance (Extended Version)
Authors: Miaomiao Wang, Abdelhamid Tayebi
Date: 2023-05-06 00:00:00

This paper explores the design of hybrid feedback for a class of affine nonlinear systems with topological constraints that prevent global asymptotic stability. A new hybrid control strategy is introduced, which differs conceptually from the commonly used synergistic hybrid approaches. The key idea involves the construction of a generalized synergistic Lyapunov function whose switching variable can either remain constant or dynamically change between jumps. Based on this new hybrid mechanism, a generalized synergistic hybrid feedback control scheme, endowed with global asymptotic stability guarantees, is proposed. This hybrid control scheme is then improved through a smoothing mechanism that eliminates discontinuities in the feedback term. Moreover, the smooth hybrid feedback is further extended to a larger class of systems through the integrator backstepping approach. The proposed hybrid feedback schemes are applied to solve the global obstacle avoidance problem using a new concept of synergistic navigation functions. Finally, numerical simulation results are presented to illustrate the performance of the proposed hybrid controllers.
Causal, Stochastic MPC for Wave Energy Converters
Authors: Connor H. Ligeikis, Jeffrey T. Scruggs
Date: 2023-05-06 00:00:00
Tasks: Model Predictive Control

We implement a causal model predictive control (MPC) strategy to maximize power generation from a wave energy converter (WEC) system, for which the power take-off (PTO) systems have both hard stroke (i.e., displacement) limits and force ratings. The approach models the WEC dynamics in discrete-time, in a manner that exactly preserves energy-flow quantities, and assumes a stationary stochastic disturbance model for the incident wave force. The control objective is to maximize the expected power generation in stationarity, while accounting for parasitic losses in the power train. PTO stroke measurements are assumed to be available for real-time feedback, as well as the free-surface elevation of the waves at a designated location relative to the WEC, and the open-loop dynamics of the WEC are assumed to be linear and time-invariant. Mean-square stability of the MPC algorithm is proven. The methodology is illustrated in a simulation example pertaining to a heaving cylindrical buoy.
Simulation of Non-inductive Vector Control of Permanent Magnet Synchronous Motor Based on Sliding Mode Observer
Authors: Caiyue Zhang, Zipin Liu, Bowen Xu
Date: 2023-05-06 00:00:00
Tasks: Position

Permanent magnet synchronous motors (PMSM) are widely used due to their numerous benefits. It is critical to get rotor position and speed information in order to operate the motor accurately. Sensorless control techniques have emerged as a popular study area both at home and overseas. The sliding mode observer (SMO) may indirectly detect rotor position and has the benefits of easy implementation and efficient algorithms. In this study, a mathematical model for sensorless control of a PMSM is developed using SMO, vector control, and other techniques. With a surface-mounted PMSM as the study object, a mathematical model for sensorless control of PMSM is developed. PMSM's sliding mode observer model is built in the matlab/simulink environment. Experiments demonstrate that the system can track the rotor position and speed of the motor precisely and fulfill the requirements of sensorless vector control of PMSM.
Semantically Optimized End-to-End Learning for Positional Telemetry in Vehicular Scenarios
Authors: Neelabhro Roy, Samie Mostafavi, James Gross
Date: 2023-05-05 00:00:00

End-to-end learning for wireless communications has recently attracted much interest in the community, owing to the emergence of deep learning-based architectures for the physical layer. Neural network-based autoencoders have been proposed as potential replacements of traditional model-based transmitter and receiver structures. Such a replacement primarily provides an unprecedented level of flexibility, allowing to tune such emerging physical layer network stacks in many different directions. The semantic relevance of the transmitted messages is one of those directions. In this paper, we leverage a specific semantic relationship between the occurrence of a message (the source), and the channel statistics. Such a scenario could be illustrated for instance, in vehicular communications where the distance is to be conveyed between a leader and a follower. We study two autoencoder approaches where these special circumstances are exploited. We then evaluate our autoencoders, showing through the simulations that the semantic optimization can achieve significant improvements in the BLERs (up till 93.6%) and RMSEs (up till 87.3%) for vehicular communications leading to considerably reduced risks and needs for message re-transmissions.
Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles
Authors: Sean Vaskov, Rien Quirynen, Marcel Menner, Karl Berntorp
Date: 2023-05-05 00:00:00
Tasks: Autonomous Vehicles, Friction, Model Predictive Control

This paper addresses the trajectory-tracking problem under uncertain road-surface conditions for autonomous vehicles. We propose a stochastic nonlinear model predictive controller (SNMPC) that learns a tire--road friction model online using standard automotive-grade sensors. Learning the entire tire--road friction model in real time requires driving in the nonlinear, potentially unstable regime of the vehicle dynamics, using a prediction model that may not have fully converged. To handle this, we formulate the tire-friction model learning in a Bayesian framework, and propose two estimators that learn different aspects of the tire--road friction. The estimators output the estimate of the tire-friction model as well as the uncertainty of the estimate, which expresses the confidence in the model for different driving regimes. The SNMPC exploits the uncertainty estimate in its prediction model to take proper action when the uncertainty is large. We validate the approach in an extensive Monte-Carlo study using real vehicle parameters and in CarSim. The results when comparing to various MPC approaches indicate a substantial reduction in constraint violations, as well as a reduction in closed-loop cost. We also demonstrate the real-time feasibility in automotive-grade processors using a dSPACE MicroAutoBox-II rapid prototyping unit, showing a worst-case computation time of roughly 40ms.
Koopman System Approximation Based Optimal Control of Multiple Robots -- Part II: Simulations and Evaluations
Authors: Qianhong Zhao, Gang Tao
Date: 2023-05-05 00:00:00

This report presents the results of a simulation study of the linear model and bilinear model approximations of the Koopman system model of the nonlinear utility functions in optimal control of a 3-robot system. In such a control problem, the nonlinear utility functions are maximized to achieve the control objective of moving the robots to their target positions and avoiding collisions. With the linear and bilinear model approximations of the utility functions, the optimal control problem is solved, based on the approximate model state variables rather than the original nonlinear utility functions. This transforms the original nonlinear game theory problem to a linear optimization problem. This report studies both the centralized and decentralized implementations of the approximation model based control signals for the 3-robot system control problem. The simulation results show that the maximum value of the posteriori estimation error of the bilinear approximation model is several thousand times less than the linear approxiamtion model. This indicates that the bilinear model has more capacity to approximate the nonlinear utility functions. Both the centralized and decentralized bilinear approximation model based control signals can achieve the control objective of moving the robots to their target positions. Based on the analysis of the simulation time, the bilinear model based optimal control solution is fast enough for real-time control implementation.
Koopman System Approximation Based Optimal Control of Multiple Robots -- Part I: Concepts and Formulations
Authors: Gang Tao, Qianhong Zhao
Date: 2023-05-05 00:00:00

This paper presents a study of the Koopman operator theory and its application to optimal control of a multi-robot system. The Koopman operator, while operating on a set of observation functions of the state vector of a nonlinear system, produces a set of dynamic equations which, through a dynamic transformation, form a new dynamic system. As an operator, it has a rich spectrum of mathematical properties, and as a tool for dynamic system analysis and control design, it has a unique collection of practical meanings. The Koopman system technique is then applied to the development of a linear or bilinear model approximation of nonlinear utility functions for optimal control of a system of multiple (mobile) robots, by selecting the utility functions as the Koopman system state variables and expressing the set of Koopman variables as the state variables of a linear or bilinear system whose parameters are determined through optimization. An iterative (online) algorithm is developed for adaptively estimating the parameters of the approximation model of the robot system with nonlinear utility functions. Finally, the control problems based on a linear or bilinear model for the nonlinear utility functions are formulated for optimal control of the multi-robot system, by transforming the nonlinear programming problem to a linear programming problem.
Combining physics-based and machine learning methods to accelerate innovation in sustainable transportation and beyond: a control perspective
Authors: Gabriele Pozzato, Simona Onori
Date: 2023-05-04 00:00:00

Lithium-ion batteries are playing a key role in the sustainable energy transition. To fully exploit the potential of this technology, a variety of modeling, estimation, and prediction problems need to be addressed to enhance its design and optimize its utilization. Batteries are complex electrochemical systems whose behavior drastically changes as a function of aging, temperature, C-rate, and state of charge, posing unique modeling and control research questions. In this tutorial paper, we provide insights into three battery modeling methodologies, namely first principle, machine learning, and hybrid modeling. Each approach has its own strengths and weaknesses, and by means of three case studies we describe main characteristics and challenges of each of the three methods.