pwc-archive/papers-with-abstracts huggingface dataset:
Real-time optimization of thermoelectric coolers' performance based on energy and exergy analysis
Authors: Alireza Amiri-Margavi, Reza Jamali, Seyed Aria Hosseini, Farschad Torabi
Date: 2024-03-03 00:00:00

New strategy is presented to optimize the performance of Thermoelectric (TE) coolers. This approach breaks optimizing TE coolers free from traditional methods of controlling temperature or engineering materials and the structural properties of the junctions. We introduced a dimensionless figure, {\gamma}, that shows the ratio of the unavailable cooling capacity to the available cooling capacity. This parameter relates the TE coolers' coefficient of performance (COP) to the COP of the reversible cycle (second law of thermodynamics efficiency) for a given electrical current. The theoretical description of the model is presented, and it is shown that controlling {\gamma} during the TE performance minimizes entropy generation and energy loss, which leads to the maximum pumped heat. We validated this model against a designed TE cooler. In this cooler, contrary to conventional TE coolers, where the temperature of the cold space is generally controlled at a specific temperature, and the performance of the cooler overlooked, the entropy generation and heat loss are engineered, and the electrical current is tuned to minimize {\gamma} by the controller so that the TE cooler works near to its optimum performance at any time.
Implementation of Linear Parameter Varying System to Investigate the Impact of Varying Flow Rate on the Lithium-ion Batteries Thermal Management System Performance
Authors: Pedram Rabiee, Mohammad Hassan Saidi
Date: 2024-03-02 00:00:00
Tasks: Management, Scheduling

Battery thermal management system is an indispensable part of the electric vehicles working with Lithium-ion batteries. Accordingly, lithium-ion batteries modeling, battery heat generation, and thermal management are the main focus of researchers and car manufacturers. To fulfill the need of manufacturers in the design process, a faster model than time-consuming Computational Fluid Dynamics models (CFD) is required. Reduced Order Models (ROM) address this requirement to maintain the accuracy of CFD models while could be compiled faster. Linear Time Invariant (LTI) reduced order model has been used in the literature; however, due to the limitation of LTI system, considering the constant flow rate for the cooling fluid, a Linear Parameter Varying system with three scheduling parameters was developed in this study. It is shown that LPV system results could fit accurately to CFD results in conditions that LTI system cannot maintain accuracy. Moreover, it is shown that applying varying water flow rates could result in a smoother temperature profile.
Quantifying Maximum Actuator Degradation for a Given $H_2/H_{\infty}$ Performance with Full-State Feedback Control
Authors: Hrishav Das, Eliot Nychka, Raktim Bhattacharya
Date: 2024-03-02 00:00:00

In this paper, we address the issue of quantifying maximum actuator degradation in linear time-invariant dynamical systems. We present a new unified framework for computing the state-feedback controller gain that meets a user-defined closed-loop performance criterion while also maximizing actuator degradation. This degradation is modeled as a first-order filter with additive noise. Our approach involves two novel convex optimization formulations that concurrently determine the controller gain, maximize actuator degradation, and maintain the desired closed-loop performance in both the $H_2$ and $H_{\infty}$ system norms. The results are limited to open-loop stable systems. We demonstrate the application of our results through the design of a full-state feedback controller for a model representing the longitudinal motion of the F-16 aircraft.
Secure and Scalable Network Slicing with Plug-and-Play Support for Power Distribution System Communication Networks
Authors: Jian Zhong, Chen Chen, Yuqi Qian, Yiheng Bian, Yuxiong Huang, Zhaohong Bie
Date: 2024-03-02 00:00:00

With the rapid development of power distribution systems (PDSs), the number of terminal devices and the types of delivered services involved are constantly growing. These trends make the operations of PDSs highly dependent on the support of advanced communication networks, which face two related challenges. The first is to provide sufficient flexibility, resilience, and security to meet varying demands and ensure the proper operation of gradually diversifying network services. The second is to realize the automatic identification of terminal devices, thus reducing the network maintenance burden. To solve these problems, this paper presents a novel multiservice network integration and device authentication slice-based network slicing scheme. In this scheme, the integration of PDS communication networks enables network resource sharing, and recovery from communication interruption is achieved through network slicing in the integrated network. Authentication servers periodically poll terminal devices, adjusting network slice ranges based on authentication results, thereby facilitating dynamic network slicing. Additionally, secure plug-and-play support for PDS terminal devices and network protection are achieved through device identification and dynamic adjustment of network slices. On this basis, a network optimization and upgrading methodology for load balancing and robustness enhancement is further proposed. This approach is designed to improve the performance of PDS communication networks, adapting to ongoing PDS development and the evolution of PDS services. The simulation results show that the proposed schemes endow a PDS communication network with favorable resource utilization, fault recovery, terminal device plug-and-play support, load balancing, and improved network robustness.
Resilient Microgrid Formation Considering Communication Interruptions
Authors: Jian Zhong, Chen Chen, Young-Jin Kim, Yuxiong Huang, Mengjie Teng, Yiheng Bian, Zhaohong Bie
Date: 2024-03-02 00:00:00

Distribution system (DS) communication failures following extreme events often degrade monitoring and control functions, thus preventing the acquisition of complete global DS component state information, on which existing post-disaster DS restoration methods are based. This letter proposes methods of inferring the states of DS components in the case of incomplete component state information. By using the known DS information, the operating states of unobservable DS branches and buses can be inferred, providing complete information for DS performance restoration before full communication recovery
Strategic SDN-based Microgrid Formation for Managing Communication Failures in Distribution System Restoration
Authors: Jian Zhong, Chen Chen, Zhaohong Bie, Mohammad Shahidehpour
Date: 2024-03-02 00:00:00

Grid modernization has increased the reliance of power networks on cyber networks within distribution systems (DSs), heightening their vulnerability to disasters. Communication network failures significantly impede DS load recovery by diminishing observation and control. Prior research has largely ignored the need for integrated recovery of DS power and cyber networks' centralized control. Indeed, communication network restoration is critical for speedy load recovery through DS automation based microgrid formation. This paper exploits the data routing capabilities of software-defined networking (SDN) to enhance centralized control recovery in DS communication networks, incorporating it into a comprehensive DS restoration model. This model, tailored to the control requirements of load restoration, strategically allocates limited communication resources to re-establish connections between the operation center and terminal devices. Subsequently, DS automation is employed to orchestrate DS microgrid formation for power resupply. Additionally, we introduce a cyclic algorithm designed to optimize the load recovery via a multi-step, cooperative process. The efficacy of the proposed method is demonstrated on IEEE 33-node and IEEE 123-node test feeders.
Resilient Mobile Energy Storage Resources Based Distribution Network Restoration in Interdependent Power-Transportation-Information Networks
Authors: Jian Zhong, Chen Chen, Qiming Yang, Dafu Liu, Wentao Shen, Chenlin Ji, Zhaohong Bie
Date: 2024-03-02 00:00:00

The interactions between power, transportation, and information networks (PTIN), are becoming more profound with the advent of smart city technologies. Existing mobile energy storage resource (MESR)-based power distribution network (PDN) restoration schemes often neglect the interdependencies among PTIN, thus, efficient PDN restoration cannot be achieved. This paper outlines the interacting factors of power supply demand, traffic operation efficiency, communication coverage, electric vehicle (EV) deployment capability, and PDN controllability among PTIN and further develops a PTIN-interacting model to reflect the chained recovery effect of the MESR-based restoration process. On this basis, a two-stage PDN restoration scheme is proposed that utilizes three emergency resources, including EVs, mobile energy storage systems (MESSs), and unmanned aerial vehicles (UAVs), to restore the power supply and communication of PDNs. This scheme first improves the distribution automation function, EV deployment capability, and traffic operation efficiency by prioritizing the recovery of communication network (CN) and urban traffic network (UTN) loads. Then, EVs and MESSs are further scheduled to achieve a better PDN restoration effect with the support of the restored CNs and UTNs. Case studies on a PDN, CN, and UTN integrated test system are conducted to verify the effectiveness of the proposed scheme. The results show that the prioritized load recovery operation for CN and UTN facilities in this scheme greatly improves the PDN restoration effect.
Real-time hybrid controls of energy storage and load shedding for integrated power and energy systems of ships
Authors: Linh Vu, Thai-Thanh Nguyen, Bang Le-Huy Nguyen, Md Isfakul Anam, Tuyen Vu
Date: 2024-03-02 00:00:00
Tasks: Computational Efficiency, energy management, Management

This paper presents an original energy management methodology to enhance the resilience of ship power systems. The integration of various energy storage systems (ESS), including battery energy storage systems (BESS) and super-capacitor energy storage systems (SCESS), in modern ship power systems poses challenges in designing an efficient energy management system (EMS). The EMS proposed in this paper aims to achieve multiple objectives. The primary objective is to minimize shed loads, while the secondary objective is to effectively manage different types of ESS. Considering the diverse ramp-rate characteristics of generators, SCESS, and BESS, the proposed EMS exploits these differences to determine an optimal long-term schedule for minimizing shed loads. Furthermore, the proposed EMS balances the state-of-charge (SoC) of ESS and prioritizes the SCESS's SoC levels to ensure the efficient operation of BESS and SCESS. For better computational efficiency, we introduce the receding horizon optimization method, enabling real-time EMS implementation. A comparison with the fixed horizon optimization (FHO) validates its effectiveness. Simulation studies and results demonstrate that the proposed EMS efficiently manages generators, BESS, and SCESS, ensuring system resilience under generation shortages. Additionally, the proposed methodology significantly reduces the computational burden compared to the FHO technique while maintaining acceptable resilience performance.
Distribution-Free Guarantees for Systems with Decision-Dependent Noise
Authors: Heling Zhang, Lillian J. Ratliff, Roy Dong
Date: 2024-03-02 00:00:00
Tasks: Conformal Prediction

In many real-world dynamical systems, obtaining precise models of system uncertainty remains a challenge. It may be difficult to estimate noise distributions or robustness bounds, especially when the distributions/robustness bounds vary with different control inputs in unknown ways. Addressing this challenge, this paper presents a novel iterative method tailored for systems with decision-dependent noise without prior knowledge of the distributions. Our approach finds the open-loop control law that minimizes the worst-case loss, given that the noise induced by this control lies in its $(1 - p)$-confidence set for a predetermined $p$. At each iteration, we use a quantile method inspired by conformal prediction to empirically estimate the confidence set shaped by the preceding control law. These derived confidence sets offer distribution-free guarantees on the system's noise, guiding a robust control formulation that targets worst-case loss minimization. Under specific regularity conditions, our method is shown to converge to a near-optimal open-loop control. While our focus is on open-loop controls, the adaptive, data-driven nature of our approach suggests its potential applicability across diverse scenarios and extensions.
Assessing the Potential for Building Sector Retrofits to Mitigate ERCOT Electricity Shortfalls During Winter Storm Uri
Authors: Matthew J. Skiles, Joshua D. Rhodes, Michael E. Webber
Date: 2024-03-01 00:00:00

This analysis investigates energy performance of the residential and commercial building sectors in the Electric Reliability Council of Texas (ERCOT) during Winter Storm Uri. ERCOT electricity demand was modeled for the ERCOT baseline building stock as well as for the baseline building stock retrofitted with an efficiency upgrade package, an electrification upgrade package, and an efficiency + electrification upgrade package. The electrification scenario that retrofitted buildings with air-source heat pumps (ASHPs) would have lowered ERCOT daily peak electricity demand relative to the baseline scenario for every day of the year, except during the week of Winter Storm Uri. As the mean outdoor temperature dropped below -5{\deg}C (23{\deg}F), diminishing ASHP efficiency would have resulted in electrification scenario demand exceeding the two distinct baseline scenario daily demand peaks on February 15th and 16th (87.3 GW and 88.7 GW) to hit 111.8 GW and 117.5 GW. The efficiency package would have lowered daily peak demand on these days to 67.0 GW and 68.0 GW. The efficiency + electrification package would have lowered peak demand on these days to 81.5 GW and 85.6 GW. When electricity shortfall profiles were produced by comparing modeled electricity demand to actual ERCOT electricity generation during the storm, the results indicate that the electrification scenario electricity shortfall (1741 GWh) would have been larger than for the baseline scenario (1225 GWh) and the electricity shortfalls for the efficiency scenario (347 GWh) and efficiency + electrification scenario (704 GWh) would have been lower than the baseline. The efficiency, electrification, and efficiency + electrification scenarios would all have lowered summer daily peak demand due to improvements in building cooling efficiency and would have lowered annual electricity consumption by 5.9%, 6.8%, and 11.9%, respectively.
A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning
Authors: Fulong Yao, Wanqing Zhao, Matthew Forshaw, Yang song
Date: 2024-03-01 00:00:00
Tasks: Deep Reinforcement Learning, energy management

The global energy landscape is undergoing a transformation towards decarbonization, sustainability, and cost-efficiency. In this transition, microgrid systems integrated with renewable energy sources (RES) and energy storage systems (ESS) have emerged as a crucial component. However, optimizing the operational control of such an integrated energy system lacks a holistic view of multiple environmental, infrastructural and economic considerations, not to mention the need to factor in the uncertainties from both the supply and demand. This paper presents a holistic datadriven power optimization approach based on deep reinforcement learning (DRL) for microgrid control considering the multiple needs of decarbonization, sustainability and cost-efficiency. First, two data-driven control schemes, namely the prediction-based (PB) and prediction-free (PF) schemes, are devised to formulate the control problem within a Markov decision process (MDP). Second, a multivariate objective (reward) function is designed to account for the market profits, carbon emissions, peak load, and battery degradation of the microgrid system. Third, we develop a Double Dueling Deep Q Network (D3QN) architecture to optimize the power flows for real-time energy management and determine charging/discharging strategies of ESS. Finally, extensive simulations are conducted to demonstrate the effectiveness and superiority of the proposed approach through a comparative analysis. The results and analysis also suggest the respective circumstances for using the two control schemes in practical implementations with uncertainties.
Event-Triggered Robust Cooperative Output Regulation for a Class of Linear Multi-Agent Systems with an Unknown Exosystem
Authors: Yangyang Qian, Lu Liu
Date: 2024-03-01 00:00:00

This paper investigates the robust cooperative output regulation problem for a class of heterogeneous uncertain linear multi-agent systems with an unknown exosystem via event-triggered control (ETC). By utilizing the internal model approach and the adaptive control technique, a distributed adaptive internal model is constructed for each agent. Then, based on this internal model, a fully distributed ETC strategy composed of a distributed event-triggered adaptive output feedback control law and a distributed dynamic event-triggering mechanism is proposed, in which each agent updates its control input at its own triggering time instants. It is shown that under the proposed ETC strategy, the robust cooperative output regulation problem can be solved without requiring either the global information associated with the communication topology or the bounds of the uncertain or unknown parameters in each agent and the exosystem. A numerical example is provided to illustrate the effectiveness of the proposed control strategy.
Distributed MPC for autonomous ships on inland waterways with collaborative collision avoidance
Authors: Hoang Anh Tran, Tor Arne Johansen, Rudy R. Negenborn
Date: 2024-03-01 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase navigational safety for autonomous ships. Our approach allows for modifying traffic rules without changing the collision avoidance algorithm, and is based on a novel formulation of model predictive control (MPC) for collision avoidance of ships. This MPC formulation is designed for inland waterway traffic and can handle complex scenarios. The alternating direction method of multipliers is used as a scheme for exchanging and negotiating intentions among ships. Simulation results show that the proposed algorithm can comply with traffic rules. Furthermore, the proposed algorithm can safely deviate from traffic rules when necessary to increase efficiency in complex scenarios.
Optimization of the Energy-Comfort Trade-Off of HVAC Systems in Electric City Buses Based on a Steady-State Model
Authors: Fabio Widmer, Stijn van Dooren, Christopher H. Onder
Date: 2024-03-01 00:00:00

The electrification of public transport vehicles offers the potential to relieve city centers of pollutant and noise emissions. Furthermore, electric buses have lower life-cycle greenhouse gas (GHG) emissions than diesel buses, particularly when operated with sustainably produced electricity. However, the heating, ventilation, and air-conditioning (HVAC) system can consume a significant amount of energy, thus limiting the achievable driving range. In this paper, we address the HVAC system in an electric city bus by analyzing the trade-off between the energy consumption and the thermal comfort of the passengers. We do this by developing a dynamic thermal model for the bus, which we simplify by considering it to be in steady state. We introduce a method that is able to quickly optimize the steady-state HVAC system inputs for a large number of samples representative of a year-round operation. A comparison between the results from the steady-state optimization approach and a dynamic simulation reveals small deviations in both the HVAC system power demand and achieved thermal comfort. Thus, the approximation of the system performance with a steady-state model is justified. We present two case studies to demonstrate the practical relevance of the approach. First, we show how the method can be used to compare different HVAC system designs based on a year-round performance evaluation. Second, we show how the method can be used to extract setpoints for online controllers that achieve close-to-optimal performance without any predictive information. In conclusion, this study shows that a steady-state analysis of the HVAC systems of an electric city bus is a valuable approach to evaluate and optimize its performance.
Data-Based Control of Continuous-Time Linear Systems with Performance Specifications
Authors: Victor G. Lopez, Matthias A. Müller
Date: 2024-03-01 00:00:00

The design of direct data-based controllers has become a fundamental part of control theory research in the last few years. In this paper, we consider three classes of data-based state feedback control problems for linear systems. These control problems are such that, besides stabilization, some additional performance requirements must be satisfied. First, we formulate and solve a trajectory-reference control problem, on which desired closed-loop trajectories are known and a controller that allows the system to closely follow those trajectories is computed. Then, the solution of the LQR problem for continuous-time systems is presented. Finally, we consider the case in which the precise position of the desired poles of the closed-loop system is known, and introduce a data-based variant of a robust pole-placement procedure. The applicability of the proposed methods is tested using numerical simulations.
For time-invariant delay systems, global asymptotic stability does not imply uniform global attractivity
Authors: Antoine Chaillet, Fabian Wirth, Andrii Mironchenko, Lucas Brivadis
Date: 2024-03-01 00:00:00

Adapting a counterexample recently proposed by J.L. Mancilla-Aguilar and H. Haimovich, we show here that, for time-delay systems, global asymptotic stability does not ensure that solutions converge uniformly to zero over bounded sets of initial states. Hence, the convergence might be arbitrarily slow even if initial states are confined to a bounded set.
Mode Consensus Algorithms With Finite Convergence Time
Authors: Chao Huang, Hyungbo Shim, Siliang Yu, Brian D. O. Anderson
Date: 2024-03-01 00:00:00
Tasks: Attribute

This paper studies the distributed mode consensus problem in a multi-agent system, in which the agents each possess a certain attribute and they aim to agree upon the mode (the most frequent attribute owned by the agents) via distributed computation. Three algorithms are proposed. The first one directly calculates the frequency of all attributes at every agent, with protocols based on blended dynamics, and then returns the most frequent attribute as the mode. Assuming knowledge at each agent of a lower bound of the mode frequency as a priori information, the second algorithm is able to reduce the number of frequencies to be computed at every agent if the lower bound is large. The third algorithm further eliminates the need for this information by introducing an adaptive updating mechanism. The algorithms find the mode in finite time, and estimates of convergence time are provided. The proposed first and second algorithms enjoy the plug-and-play property with a dwell time.