pwc-archive/papers-with-abstracts huggingface dataset:
Data-driven Coordinated AC/DC Control Strategy for Frequency Safety
Authors: Qianni Cao, Chen Shen
Date: 2024-05-21 00:00:00

With high penetrations of renewable energy and power electronics converters, less predictable operating conditions and strong uncertainties in under-frequency events pose challenges for emergency frequency control (EFC). On the other hand, the fast adjustability of converter-based sources presents opportunities to reduce economic losses from traditional load shedding for EFC. By integrating DC power emergency support, a data-driven coordinated AC/DC control strategy for frequency safety - Coordinated Emergency Frequency Control (CEFC) - has been designed. CEFC coordinates both the initiation and control amount of emergency DC power support (EDCPS) and traditional load shedding. Based on real-time power system response data, CEFC ensures system frequency safety at a minimum control cost under non-envisioned operating conditions and large power deficits. A sufficient condition where data-driven modeling errors do not affect the precision of the control strategy for power system frequency is rigorously provided. Simulation results demonstrate CEFC's adaptability, prediction accuracy, and control effectiveness.
Efficient Economic Model Predictive Control of Water Treatment Process with Learning-based Koopman Operator
Authors: Minghao Han, Jingshi Yao, Adrian Wing-Keung Law, Xunyuan Yin
Date: 2024-05-21 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

Used water treatment plays a pivotal role in advancing environmental sustainability. Economic model predictive control holds the promise of enhancing the overall operational performance of the water treatment facilities. In this study, we propose a data-driven economic predictive control approach within the Koopman modeling framework. First, we propose a deep learning-enabled input-output Koopman modeling approach, which predicts the overall economic operational cost of the wastewater treatment process based on input data and available output measurements that are directly linked to the operational costs. Subsequently, by leveraging this learned input-output Koopman model, a convex economic predictive control scheme is developed. The resulting predictive control problem can be efficiently solved by leveraging quadratic programming solvers, and complex non-convex optimization problems are bypassed. The proposed method is applied to a benchmark wastewater treatment process. The proposed method significantly improves the overall economic operational performance of the water treatment process. Additionally, the computational efficiency of the proposed method is significantly enhanced as compared to benchmark control solutions.
Evaluation of Connected Vehicle Identification-Aware Mixed Traffic Freeway Cooperative Merging
Authors: Haoji Liu, Fatemeh Jahedinia, Zeyu Mu, B. Brian Park
Date: 2024-05-21 00:00:00

Cooperative on-ramp merging control for connected automated vehicles (CAVs) has been extensively investigated. However, they did neglect the connected vehicle identification process, which is a must for CAV cooperations. In this paper, we introduced a connected vehicle identification system (VIS) into the on-ramp merging control process for the first time and proposed an evaluation framework to assess the impacts of VIS on on-ramp merging performance. First, the mixed-traffic cooperative merging problem was formulated. Then, a real-world merging trajectory dataset was processed to generate dangerous merging scenarios. Aiming at resolving the potential collision risks in mixed traffic where CAVs and traditional human-driven vehicles (THVs) coexist, we proposed on-ramp merging strategies for CAVs in different mixed traffic situations considering the connected vehicle identification process. The performances were evaluated via simulations. Results indicated that while safety was assured for all cases with CAVs, the cases with VIS had delayed initiation of cooperation, limiting the range of cooperative merging and leading to increased fuel consumption and acceleration variations.
Reach and hold flexibility characterization and trade-off analysis for aggregations of thermostatically controlled loads
Authors: Mazen Elsaadany, Mads R. Almassalkhi
Date: 2024-05-21 00:00:00

Thermostatically controlled loads (TCLs) have the potential to be flexible and responsive loads to be used in demand response (DR) schemes. With increasing renewable penetration, DR is playing an increasingly important role in enhancing power grid reliability. The aggregate demand of a population of TCLs can be modulated by changing their temperature setpoint. When and/or what proportion of the population sees the setpoint change determines the change in aggregate demand. However, since the TCL population is finite, not all changes in aggregate demand can be maintained for arbitrarily long periods of time. In this paper, the dynamic behavior of a TCL fleet is modeled and used to characterize the set possible changes in aggregate demand that can be reached and the corresponding time for which the demand change can be held, for a given change in setpoint. This set is referred to, in this paper, as the reach and hold set of a TCL fleet. Furthermore, the effect of the setpoint change and ambient temperature on the reach and hold are analyzed. The characterized set is then validated through simulation using both the population TCL models and individual TCL micro-models.
Design and Analysis of a Detuned Series-Series IPT System with Solenoid Coil Structure for Drone Charging Applications
Authors: Elias Nadi, Hua Zhang
Date: 2024-05-20 00:00:00

This paper proposes a new coil configuration that uses solenoid ferrites on the receiver side instead of planar ferrite coils that are employed in existing wireless charging systems.The solenoid ferrites are used in the drone legs to help mount the receiver on a moving truck while the two parts of the transmitter are placed on the truck.To validate this idea a detuned transmitter series-series (SS) compensated IPT prototype system has been investigated to charge the onboard battery of a drone.The proposed detuned design helps to limit the system output power and transmitter side current at even zero coupling condition and tolerates misalignment positions. Experimental results demonstrate that the system switching frequency is 245 kHz and the primary side resonates at 223.5 kHz, which is a detuned design The transmitting current is limited to 4.5 A at zero coupling condition with a low power loss of only 4.2 W at zero coupling condition. The target achievable power of the system is 50 W with a 10 mm air gap and horizontal and vertical misalignment in the range of [0, 50 mm].This system has been validated by experiments to charge an 11.1V battery at 48.2W with 88.2% efficiency.
Locational marginal burden: Quantifying the equity of optimal power flow solutions
Authors: Samuel Talkington, Amanda West, Rabab Haider
Date: 2024-05-20 00:00:00

Fair distribution of benefits in electric power systems is a pertinent energy policymaking problem; however, these efforts cannot be easily quantified in power system engineering studies. Therefore, we propose locational marginal burden (LMB) to provide an interface between a well-studied measure of energy pricing equity, energy burden, with an optimal power flow problem (OPF). This is achieved by investigating the intrinsic link between the dual optimal solution of an OPF problem and the electricity prices, which are used to calculate the energy burden. By applying results from the field of differentiable optimization, locational marginal prices (LMPs) associated with an OPF solution can be differentiated with respect to demand. This enables electricity retail prices, and thereby, energy burden itself, to be differentiated, resulting in the proposed LMB. Simulation of a synthetic Hawaii network interfaced with real-world socioeconomic data shows how the LMB provides new insights into how the operation of the electricity network affects the equity of energy prices.
Robust VAR Capability Curve of DER with Uncertain Renewable Generation
Authors: Aditya Shankar Kar, Kiran Kumar Challa, Alok Kumar Bharati, Ankit Singhal, Venkataramana Ajjarapu
Date: 2024-05-20 00:00:00
Tasks: Decision Making

Active distribution system with high penetration of inverter based distributed energy resources (DER) can be utilized for VAR-related ancillary services. To utilize the DER flexibility, transmission system operator (TSO) must be presented with the aggregated DER flexibility of distribution system. However, the uncertainty in renewable generation questions the credibility of aggregated capability curve in practice. In this paper, we incorporate the uncertainty into aggregation process to develop a robust capability curve while preserving the real physics (unbalance and lossy nature) of distribution system. Statistical inference method is employed to quantify uncertainty in solar generation and quantified uncertainty is integrated into a chance constrained optimal power flow (OPF). It provides the grid operator with the dispatchable aggregated reactive power capability. The resulting capability curve with the associated probability can be harnessed by the TSO for decision making for both planning and operation.
Design, Control, and Motion-Planning for a Root-Perching Rotor-Distributed Manipulator
Authors: Takuzumi Nishio, Moju Zhao, Kei Okada, Masayuki Inaba
Date: 2024-05-20 00:00:00
Tasks: Motion Planning

Manipulation performance improvement is crucial for aerial robots. For aerial manipulators, the baselink position and attitude errors directly affect the precision at the end effector. To address this stability problem, fixed-body approaches such as perching on the environment using the rotor suction force are useful. Additionally, conventional arm-equipped multirotors, called rotor-concentrated manipulators (RCMs), find it difficult to generate a large wrench at the end effector due to joint torque limitations. Using distributed rotors to each link, the thrust can support each link weight, decreasing the arm joints' torque. Based on this approach, rotor-distributed manipulators (RDMs) can increase feasible wrench and reachability of the end-effector. This paper introduces a minimal configuration of a rotor-distributed manipulator that can perch on surfaces, especially ceilings, using a part of their body. First, we design a minimal rotor-distributed arm considering the flight and end-effector performance. Second, a flight controller is proposed for this minimal RDM along with a perching controller adaptable for various types of aerial robots. Third, we propose a motion planning method based on inverse kinematics (IK), considering specific constraints to the proposed RDMs such as perching force. Finally, we evaluate flight and perching motions and \revise{confirm} that the proposed manipulator can significantly improve the manipulation performance.
The Case for DeepSOH: Addressing Path Dependency for Remaining Useful Life
Authors: Hamidreza Movahedi, Andrew Weng, Sravan Pannala, Jason B. Siegel, Anna G. Stefanopoulou
Date: 2024-05-20 00:00:00
Tasks: State Estimation

The battery state of health (SOH) based on capacity fade and resistance increase is not sufficient for predicting Remaining Useful life (RUL). The electrochemical community blames the path-dependency of the battery degradation mechanisms for our inability to forecast the degradation. The control community knows that the path-dependency is addressed by full state estimation. We show that even the electrode-specific SOH (eSOH) estimation is not enough to fully define the degradation states by simulating infinite possible degradation trajectories and remaining useful lives (RUL) from a unique eSOH. We finally define the deepSOH states that capture the individual contributions of all the common degradation mechanisms, namely, SEI, plating, and mechanical fracture to the loss of lithium inventory. We show that the addition of cell expansion measurement may allow us to estimate the deepSOH and predict the remaining useful life.
Optimal Privacy-Aware Stochastic Sampling
Authors: Chuanghong Weng, Ehsan Nekouei
Date: 2024-05-20 00:00:00
Tasks: Gaussian Processes

This paper presents a stochastic sampling framework for privacy-aware data sharing, where a sensor observes a process correlated with private information. A sampler determines whether to retain or discard sensor observations, balancing the tradeoff between data utility and privacy. Retained samples are shared with an adversary who may attempt to infer the private process, with privacy leakage quantified using mutual information. The sampler design is formulated as an optimization problem with two objectives: $\left(\romannumeral1\right)$ minimizing the reconstruction error of the observed process using the sampler's output, $\left(\romannumeral2\right)$ reducing the privacy leakages. For a general class of processes, we show that the optimal reconstruction policy is deterministic and derive the optimality conditions for the sampling policy using a dynamic decomposition method, which enables the sampler to control the adversary's belief about private inputs. For linear Gaussian processes, we propose a simplified design by restricting the sampling policy to a specific collection, providing analytical expressions for the reconstruction error, belief state, and sampling objectives based on conditional means and covariances. Additionally, we develop a numerical optimization algorithm to optimize the sampling and reconstruction policies, wherein the policy gradient theorem for the optimal sampling design is derived based on the implicit function theorem. Simulations demonstrate the effectiveness of the proposed method in achieving accurate state reconstruction, privacy protection, and data size reduction.
A Simulation Tool for V2G Enabled Demand Response Based on Model Predictive Control
Authors: Cesar Diaz-Londono, Stavros Orfanoudakis, Pedro P. Vergara, Peter Palensky, Fredy Ruiz, Giambattista Gruosso
Date: 2024-05-20 00:00:00
Tasks: energy management, Management, Model Predictive Control

Integrating electric vehicles (EVs) into the power grid can revolutionize energy management strategies, offering both challenges and opportunities for creating a more sustainable and resilient grid. In this context, model predictive control (MPC) emerges as a powerful tool for addressing the complexities of Grid-to-vehicle (G2V) and vehicle-to-grid (V2G) enabled demand response management. By leveraging advanced optimization techniques, MPC algorithms can anticipate future grid conditions and dynamically adjust EV charging and discharging schedules to balance supply and demand while minimizing operational costs and maximizing flexibility. However, no standard tools exist to evaluate novel energy management strategies based on MPC approaches. Our research focuses on harnessing the potential of MPC in G2V and V2G applications, by providing a simulation tool that allows to maximize EV flexibility and support demand response initiatives while mitigating the impact on EV battery health. In this paper, we propose an open-source MPC controller for G2V and V2G-enabled demand response management. The proposed approach is capable of tackling the uncertainties inherent in demand response operations. Through extensive simulation and analysis, we demonstrate the efficacy of our approach in maximizing the benefits of G2V and V2G while assessing the impact on the longevity and reliability of EV batteries. Specifically, our controller enables Charge Point Operators (CPOs) to optimize EV charging and discharging schedules in real-time, taking into account fluctuating energy prices, grid constraints, and EV user preferences.
A Flat Dual-Polarized Millimeter-Wave Luneburg Lens Antenna Using Transformation Optics with Reduced Anisotropy and Impedance Mismatch
Authors: Yuanyan Su, Teng Li, Wei Hong, Zhi Ning Chen, Anja K. Skrivervik
Date: 2024-05-20 00:00:00

In this paper, a compact wideband dual-polarized Luneburg lens antenna (LLA) with reduced anisotropy and improved impedance matching is proposed in Ka band with a wide 2D beamscanning capability. Based on transformation optics, the spherical Luneburg lens is compressed into a cylindrical one, while the merits of high gain, broad band, wide scanning, and free polarization are preserved. A trigonometric function is employed to the material property of the flattened Luneburg lens with reduced anisotropy, thus effectively alleviates the strong reflection, the high sidelobes and back radiation with a free cost on the antenna weight and volume. Furthermore, a light thin wideband 7-by-1 metasurface phased array is studied as the primary feed for the LLA. The proposed metantenna, shorted for metamaterial-based antenna, has a high potential for B5G, future wireless communication and radar sensing as an onboard system.
Measurement and Control of Solenoid Stroke using Its Electrical Characteristics
Authors: Junichi Akita
Date: 2024-05-20 00:00:00
Tasks: Position

In this paper, we describe the algorithm to measure the stroke of solenoid using the electric characteristics of the solenoid, without mechanical attachments. We also describe the experimental results of controlling the solenoid stroke at intermediate position.
Multi-Objective Learning Model Predictive Control
Authors: Siddharth H. Nair, Charlott Vallon, Francesco Borrelli
Date: 2024-05-19 00:00:00
Tasks: model, Model Predictive Control

Multi-Objective Learning Model Predictive Control is a novel data-driven control scheme which improves a linear system's closed-loop performance with respect to several convex control objectives over iterations of a repeated task. At each task iteration, collected system data is used to construct terminal components of a Model Predictive Controller. The formulation presented in this paper ensures that closed-loop control performance improves between successive iterations with respect to each objective. We provide proofs of recursive feasibility and performance improvement, and show that the converged policy is Pareto optimal. Simulation results demonstrate the applicability of the proposed approach.
SmartAntenna: Enhancing Wireless Range with Autonomous Orientation
Authors: Michael Swann, Pedro Machado, Isibor Kennedy Ihianle, Salisu Yahaya, Farbod Zorriassatine, Andreas Oikonomou
Date: 2024-05-18 00:00:00

The SmartAntenna proposes a novel approach to extend wireless communication, focusing on autonomous orientation to extend range and optimize performance. Through meticulous evaluation, various aspects of its functionality were assessed, revealing both strengths and areas for improvement. Notably, the antenna tracking mechanism exhibited remarkable efficacy. The SmartAntenna demonstrated robust functionality throughout extensive testing, underscoring its reliability even amidst complex operational scenarios. However, challenges emerged during target tracking, particularly evident in 360-degree sweeps, necessitating further refinement to enhance accuracy. Despite reliance on the HC-12 module, LoRa, performance limitations surfaced, prompting concerns regarding its suitability for production systems, especially within noisy frequency bands. Nevertheless, the SmartAntenna's adaptability across various wireless technologies holds promise, opening avenues for extended communication ranges and diverse applications. SmartAntenna research contributes valuable insights into optimizing wireless communication systems, paving the way for enhanced performance and expanded capabilities in diverse operational environments.
Optimal control barrier functions for RL based safe powertrain control
Authors: Habtamu Hailemichael, Beshah Ayalew, Andrej Ivanco
Date: 2024-05-18 00:00:00
Tasks: Reinforcement Learning (RL)

Reinforcement learning (RL) can improve control performance by seeking to learn optimal control policies in the end-use environment for vehicles and other systems. To accomplish this, RL algorithms need to sufficiently explore the state and action spaces. This presents inherent safety risks, and applying RL on safety-critical systems like vehicle powertrain control requires safety enforcement approaches. In this paper, we seek control-barrier function (CBF)-based safety certificates that demarcate safe regions where the RL agent could optimize the control performance. In particular, we derive optimal high-order CBFs that avoid conservatism while ensuring safety for a vehicle in traffic. We demonstrate the workings of the high-order CBF with an RL agent which uses a deep actor-critic architecture to learn to optimize fuel economy and other driver accommodation metrics. We find that the optimized high-order CBF allows the RL-based powertrain control agent to achieve higher total rewards without any crashes in training and evaluation while achieving better accommodation of driver demands compared to previously proposed exponential barrier function filters and model-based baseline controllers.
Combined film and pulse heating of lithium ion batteries to improve performance in low ambient temperature
Authors: Habtamu Hailemichael, Beshah Ayalew
Date: 2024-05-18 00:00:00
Tasks: Reinforcement Learning (RL)

Low ambient temperatures significantly reduce Lithium ion batteries' (LIBs') charge/discharge power and energy capacity, and cause rapid degradation through lithium plating. These limitations can be addressed by preheating the LIB with an external heat source or by exploiting the internal heat generation through the LIB's internal impedance. Fast external heating generates large temperature gradients across the LIB due to the low thermal conductivity of the cell, while internal impedance heating (usually through AC or pulse charge/discharging) tends to be relatively slow, although it can achieve more uniform temperature distribution. This paper investigates the potential of combining externally sourced resistive film heating with bidirectional pulse heating to achieve fast preheating without causing steep temperature gradients. The LIB is modeled with the Doyle Fuller Newman (DFN) electrochemical model and 1D thermal model, and reinforcement learning (RL) is used to optimize the pulse current amplitude and film voltage concurrently. The results indicate that the optimal policy for maximizing the rate of temperature rise while limiting temperature gradients has the film heating dominate the initial phases and create the ideal conditions for pulse heating to take over. In addition, the pulse component shares the heating load and reduces the energy rating of the auxiliary power source.
Control-Aware Transmit Power Allocation for 6G In-Factory Subnetwork Control Systems
Authors: Daniel Abode, Pedro Maia de Sant Ana, Alexander Artemenko, Ramoni Adeogun, Gilberto Berardinelli
Date: 2024-05-18 00:00:00
Tasks: Bayesian Optimization

In this paper, we develop a novel power control solution for subnetworks-enabled distributed control systems in factory settings. We propose a channel-independent control-aware (CICA) policy based on the logistic model and learn the parameters using Bayesian optimization with a multi-objective tree-structured Parzen estimator. The objective is to minimize the control cost of the plants, measured as a finite horizon linear quadratic regulator cost. The proposed policy can be executed in a fully distributed manner and does not require cumbersome measurement of channel gain information, hence it is scalable for large-scale deployment of subnetworks for distributed control applications. With extensive numerical simulation and considering different densities of subnetworks, we show that the proposed method can achieve competitive stability performance and high availability for large-scale distributed control plants with limited radio resources.