pwc-archive/papers-with-abstracts huggingface dataset:
Humidity-Aware Model Predictive Control for Residential Air Conditioning: A Field Study
Authors: Elias N. Pergantis, Parveen Dhillon, Levi D. Reyes Premer, Alex H. Lee, Davide Ziviani, Kevin J. Kircher
Date: 2024-07-01 00:00:00
Tasks: Model Predictive Control

Model predictive control of residential air conditioning could reduce energy costs and greenhouse gas emissions while maintaining or improving occupants' thermal comfort. However, most approaches to predictive air conditioning control either do not model indoor humidity or treat it as constant. This simplification stems from challenges with modeling indoor humidity dynamics, particularly the high-order, nonlinear equations that govern heat and mass transfer between the air conditioner's evaporator coil and the indoor air. This paper develops a machine-learning approach to modeling indoor humidity dynamics that is suitable for real-world deployment at scale. This study then investigates the value of humidity modeling in four field tests of predictive control in an occupied house. The four field tests evaluate two different building models: One with constant humidity and one with time-varying humidity. Each modeling approach is tested in two different predictive controllers: One that focuses on reducing energy costs and one that focuses on constraining electric power below a utility-specified threshold. The two models lead to similar performance for reducing energy costs. Combining the results of this study and a prior heating study of the same house, the estimated year-round energy cost savings were $340-497 or 22-31% (95% confidence intervals); these savings were consistent across both humidity models. However, in the demand response tests, the simplifying assumption of constant humidity led to far more frequent and severe violations of the power constraint. These results suggest that accurate building models are important for nonlinear objectives, such as reducing or constraining peak demand, while for linear objectives such as reducing energy costs or emissions, model accuracy is less important.
Joint Design of Conventional Public Transport Network and Mobility on Demand
Authors: Xiaoyi Wu, Nisrine Mouhrim, Andrea Araldo, Yves Molenbruch, Dominique Feillet, Kris Braekers
Date: 2024-07-01 00:00:00
Tasks: Bilevel Optimization

Conventional Public Transport (PT) is based on fixed lines, running with routes and schedules determined a-priori. In low-demand areas, conventional PT is inefficient. Therein, Mobility on Demand (MoD) could serve users more efficiently and with an improved quality of service (QoS). The idea of integrating MoD into PT is therefore abundantly discussed by researchers and practitioners, mainly in the form of adding MoD on top of PT. Efficiency can be instead gained if also conventional PT lines are redesigned after integrating MoD in the first or last mile. In this paper we focus on this re-design problem. We devise a bilevel optimization problem where, given a certain initial design, the upper level determines stop selection and frequency settings, while the lower level routes a fleet of MoD vehicles. We propose a solution method based on Particle Swarm Optimization (PSO) for the upper level, while we adopt Large Neighborhood Search (LNS) in the lower level. Our solution method is computationally efficient and we test it in simulations with up to 10k travel requests. Results show important operational cost savings obtained via appropriately reducing the conventional PT coverage after integrating MoD, while preserving QoS.
C-MP: A decentralized adaptive-coordinated traffic signal control using the Max Pressure framework
Authors: Tanveer Ahmed, Hao liu, Vikash V. Gayah
Date: 2024-07-01 00:00:00
Tasks: Traffic Signal Control

Coordinated traffic signals seek to provide uninterrupted flow through a series of closely spaced intersections, typically using pre-defined fixed signal timings and offsets. Adaptive traffic signals dynamically change signal timings based on observed traffic conditions in a way that might disrupt coordinated movements, particularly when these decisions are made independently at each intersection. To alleviate this issue, this paper introduces a novel Max Pressure-based traffic signal framework that can provide coordination even under decentralized decision-making. The proposed Coordinated Max Pressure (C-MP) algorithm uses the space mean speeds of vehicles to explicitly detect freely flowing platoons of vehicles and prioritizes their movement along a corridor. Specifically, upstream platoons are detected and their weight in the MP framework increased to provide priority, while downstream platoons are detected and their weight reduced to ensure smooth traffic flow across corridors. The study analytically proves that C-MP maintains the desirable maximum stability property, while micro-simulation analyses conducted on an arterial network demonstrate its ability to achieve a larger stable region compared to benchmark MP control policies. Simulation results also reveal that the proposed control algorithm can effectively coordinate traffic signals in both directions along an arterial without explicitly assigned offsets or constraints. The results also reveal C-MP's superiority to benchmark coordination strategies in reducing travel time, and fuel consumption both at the corridor level and the network level by balancing the negative impact imparted to vehicles in the minor direction.
Hydraulic Parameter Estimation for District Heating Based on Laboratory Experiments
Authors: Felix Agner, Christian Møller Jensen, Anders Rantzer, Carsten Skovmose Kallesøe, Rafal Wisniewski
Date: 2024-07-01 00:00:00
Tasks: parameter estimation

In this paper we consider calibration of hydraulic models for district heating systems based on operational data. We extend previous theoretical work on the topic to handle real-world complications, namely unknown valve characteristics and hysteresis. We generate two datasets in the Smart Water Infrastructure laboratory in Aalborg, Denmark, on which we evaluate the proposed procedure. In the first data set the system is controlled in such a way to excite all operational modes in terms of combinations of valve set-points. Here the best performing model predicted volume flow rates within roughly 5 and 10 \% deviation from the mean volume flow rate for the consumer with the highest and lowest mean volume flow rates respectively. This performance was met in the majority of the operational region. In the second data set, the system was controlled in order to mimic real load curves. The model trained on this data set performed similarly well when evaluated on data in the operational range represented in the training data. However, the model performance deteriorated when evaluated on data which was not represented in the training data.
From Coupling to Resilience: Quantifying the Impact of Interconnection in Energy Carrier Grids
Authors: Rico Schrage, Astrid Nieße
Date: 2024-07-01 00:00:00

Due to the increasing share of renewable energy resources and the emergence of couplings of different energy carrier grids, which may support the electricity networks by providing additional flexibility, conducting research on the properties of multi-energy systems is necessary. Primarily to keep stable grid operation and provide efficient planning, the resilience of such systems against low-probability, high-impact events is central. Previous steady-state resilience studies of electricity grids also involved investigating the topological attributes from a complex network theory perspective. However, this work aims to determine the influence of complex topological attributes on the resilience of coupled energy grids. To achieve this, we set up a Monte Carlo simulation to calculate the load-shedding performance indicator for the grids when affected by high-impact events. This indicator is used to calculate resilience metrics, which express the influence of the grids on each other. The metrics are the base to search for correlations between centrality/vitality metrics and the resilience impact metric. We apply our method to a case study based on a benchmark electricity grid. Our results show that, first, our impact metric is feasible for determining the influences of the network on each other. Second, we show that increasing coupling densities can lead to lower resilience in single-carrier grids. Third, it is apparent that centrality influences the impact of the grid components' resilience.
An AI-based, Error-bounded Compression Scheme for High-frequency Power Quality Disturbance Data
Authors: Markus Stroot, Stefan Seiler, Philipp Lutat, Andreas Ulbig
Date: 2024-07-01 00:00:00
Tasks: Data Compression

The implementation of modern monitoring systems for power quality disturbances have the potential to generate substantial amounts of data, reaching a point where transmission and storage of high-frequency measurements become impractical. This research paper addresses this challenge by presenting a new, AI-based data compression method. It is based on existing, multi-level compression schemes; however, it uses state-of-the-art technologies, such as autoencoders, to improve the performance. Furthermore, it solves the problem that such algorithms usually cannot ensure an error bound. The scheme is tested on synthetically generated power quality disturbance samples. The evaluation is performed using different metrics such as final compression rate and overhead size. Compression rates between 5 and 68 were achieved depending on the error bound and noise level. Additionally, the impact of the compression on the performance of subsequent algorithms is determined by applying a classification algorithm to the decompressed data. The classification accuracy only declined by 0.8--11.9 \%, depending on the chosen error bound.
Effective Management of Airport Security Queues with Passenger Reassignment
Authors: Shangqing Cao, Aparimit Kasliwal, Masoud Reihanifar, Francesc Robuste, Mark Hansen
Date: 2024-07-01 00:00:00
Tasks: Management

Airport security queues often suffer from inefficiencies that result in long wait times and decreased throughput, especially at peak departure time, affecting both passengers and airlines. This work addresses the problem of reassigning passengers to specific time slots for crossing security, aiming to mitigate these inefficiencies. We frame this problem as a Minimum Cost Network Flow (MCNF) problem, enabling us to solve it exactly in polynomial time due to its linear programming structure. Our approach redistributes passenger demand across different time intervals. By optimizing the reassignment of passengers to sigma-minute time slots, we achieve significant improvements in throughput and reductions in waiting time. Preliminary results demonstrate the effectiveness of our method in enhancing operational efficiency and passenger satisfaction. The MCNF formulation offers a scalable and adaptable solution, providing long-term benefits for airport security management.
Fleet Size and Spill for UAM Operation under Uncertain Demand
Authors: Shangqing Cao, Xuan Jiang, Emin Burak Onat, Bo Zou, Mark Hansen, Raja Sengupta, Anjan Chakrabarty
Date: 2024-07-01 00:00:00

Variation and imbalance in demand poses significant challenges to Urban Air Mobility (UAM) operations, affecting strategic decisions such as fleet sizing. To study the implications of demand variation on UAM fleet operations, we propose a stochastic passenger arrival time generation model that uses real-world data to infer demand distributions, and two integer programs that compute the zero-spill fleet size and the spill-minimizing flight schedules and charging policies, respectively. Our numerical experiment on a two-vertiport network shows that spill in relatively inelastic to fleet size and that the driving factor behind spill is the imbalance in demand.
Safe Reinforcement Learning for Power System Control: A Review
Authors: Peipei Yu, Zhenyi Wang, Hongcai Zhang, Yonghua Song
Date: 2024-06-30 00:00:00
Tasks: energy management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

The large-scale integration of intermittent renewable energy resources introduces increased uncertainty and volatility to the supply side of power systems, thereby complicating system operation and control. Recently, data-driven approaches, particularly reinforcement learning (RL), have shown significant promise in addressing complex control challenges in power systems, because RL can learn from interactive feedback without needing prior knowledge of the system model. However, the training process of model-free RL methods relies heavily on random decisions for exploration, which may result in ``bad" decisions that violate critical safety constraints and lead to catastrophic control outcomes. Due to the inability of RL methods to theoretically ensure decision safety in power systems, directly deploying traditional RL algorithms in the real world is deemed unacceptable. Consequently, the safety issue in RL applications, known as safe RL, has garnered considerable attention in recent years, leading to numerous important developments. This paper provides a comprehensive review of the state-of-the-art safe RL techniques and discusses how these techniques can be applied to power system control problems such as frequency regulation, voltage control, and energy management. We then present discussions on key challenges and future research directions, related to convergence and optimality, training efficiency, universality, and real-world deployment.
An Application of Model Reference Adaptive Control for Multi-Agent Synchronization in Drone Networks
Authors: Miguel F. Arevalo-Castiblanco, Yejin Wi, Marzia Cescon and, Cesar A. Uribe
Date: 2024-06-30 00:00:00

This paper presents the application of a Distributed Model Reference Adaptive Control (DMRAC) strategy for robust multi-agent synchronization of a network of drones. The proposed approach enables the development of controllers capable of accommodating differences in real-life model parameters between agents, thereby enhancing overall network performance. We compare the performance of the adaptive control laws with classical PID controllers for the reference tracking task. Each follower drone has a model reference adaptive controller that continuously updates its parameters based on real-time feedback and reference model information. This adaptability ensures an adequate performance that, compared to conventional non-adaptive techniques, can reduce the amount of energy required and consequently increase the operating duration of the drones. The experimental results, particularly in vertical velocity control, underscore the effectiveness of the proposed approach in achieving synchronized behavior.
Data-Driven Control of Linear Parabolic Systems using Koopman Eigenstructure Assignment
Authors: J. Deutscher
Date: 2024-06-29 00:00:00

This paper considers the data-driven stabilization of linear boundary controlled parabolic PDEs by making use of the Koopman operator. For this, a Koopman eigenstructure assignment problem is solved, which amounts to determine a feedback of the Koopman open-loop eigenfunctionals assigning a desired finite set of closed-loop Koopman eigenvalues and eigenfunctionals to the closed-loop system. It is shown that the designed controller only needs a finite number of open-loop Koopman eigenvalues and modes of the state. They are determined by extending the classical Krylov-DMD to parabolic systems. For this, only a finite number of pointlike outputs and their temporal samples as well as temporal samples of the inputs are required resulting in a data-driven solution of the eigenstructure assignment problem. Exponential stability of the closed-loop system in the presence of small Krylov-DMD errors is verified. An unstable diffusion-reaction system demonstrates the new data-driven controller design technique for distributed-parameter systems.
Safe and Stable Filter Design Using a Relaxed Compatibitlity Control Barrier -- Lyapunov Condition
Authors: Han Wang, Kostas Margellos, Antonis Papachristodoulou
Date: 2024-06-29 00:00:00

In this paper, we propose a quadratic programming-based filter for safe and stable controller design, via a Control Barrier Function (CBF) and a Control Lyapunov Function (CLF). Our method guarantees safety and local asymptotic stability without the need for an asymptotically stabilizing control law. Feasibility of the proposed program is ensured under a mild regularity condition, termed relaxed compatibility between the CLF and CBF. The resulting optimal control law is guaranteed to be locally Lipschitz continuous. We also analyze the closed-loop behaviour by characterizing the equilibrium points, and verifying that there are no equilibrium points in the interior of the control invariant set except at the origin. For a polynomial system and a semi-algebraic safe set, we provide a sum-of-squares program to design a relaxed compatible pair of CLF and CBF. The proposed approach is compared with other methods in the literature using numerical examples, exhibits superior filter performance and guarantees safety and local stability.
Sparse Actuator Scheduling for Discrete-Time Linear Dynamical Systems
Authors: Krishna Praveen V. S. Kondapi, Chandrasekhar Sriram, Geethu Joseph, Chandra R. Murthy
Date: 2024-06-29 00:00:00
Tasks: Scheduling

We consider the control of discrete-time linear dynamical systems using sparse inputs where we limit the number of active actuators at every time step. We develop an algorithm for determining a sparse actuator schedule that ensures the existence of a sparse control input sequence, following the schedule, that takes the system from any given initial state to any desired final state. Since such an actuator schedule is not unique, we look for a schedule that minimizes the energy of sparse inputs. For this, we optimize the trace of the inverse of the resulting controllability Gramian, which is an approximate measure of the average energy of the inputs. We present a greedy algorithm along with its theoretical guarantees. Finally, we empirically show that our greedy algorithm ensures the controllability of the linear system with a small number of active actuators per time step without a significant average energy expenditure compared to the fully actuated system.
A Review of Safe Reinforcement Learning Methods for Modern Power Systems
Authors: Tong Su, Tong Wu, Junbo Zhao, Anna Scaglione, Le Xie
Date: 2024-06-29 00:00:00
Tasks: energy management, Reinforcement Learning (RL), Safe Reinforcement Learning, Scheduling

Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in operation and control. Conventional RL relies on trial-and-error interactions with the environment and reward feedback, which often leads to exploring unsafe operating regions and executing unsafe actions, especially when deployed in real-world power systems. To address these challenges, safe RL has been proposed to optimize operational objectives while ensuring safety constraints are met, keeping actions and states within safe regions throughout both training and deployment. Rather than relying solely on manually designed penalty terms for unsafe actions, as is common in conventional RL, safe RL methods reviewed here primarily leverage advanced and proactive mechanisms. These include techniques such as Lagrangian relaxation, safety layers, and theoretical guarantees like Lyapunov functions to rigorously enforce safety boundaries. This paper provides a comprehensive review of safe RL methods and their applications across various power system operations and control domains, including security control, real-time operation, operational planning, and emerging areas. It summarizes existing safe RL techniques, evaluates their performance, analyzes suitable deployment scenarios, and examines algorithm benchmarks and application environments. The paper also highlights real-world implementation cases and identifies critical challenges such as scalability in large-scale systems and robustness under uncertainty, providing potential solutions and outlining future directions to advance the reliable integration and deployment of safe RL in modern power systems.
Replication of filtered interferometer measurements in interstellar communications
Authors: William J. Crilly Jr
Date: 2024-06-29 00:00:00

Interstellar communication signals have been conjectured to be present, albeit difficult to identify. Experiments conducted since 2018 indicate an anomalous presence of a type of speculated interstellar signal, delta-t delta-f polarized pulse pairs, thought to be possibly sourced from a celestial direction near 5.25 hr Right Ascension and -7.6 deg. Declination. A recent experiment utilizing a radio interferometer identified anomalous pulse pairs associated with these celestial coordinates. The experiment is described in arXiv:2404.08994. Other experiments produced anomalous results, reported in arXiv:2105.03727, arXiv:2106.10168, arXiv:2202.12791 and arXiv:2203.10065. After the recent experiment was concluded, the interferometer antenna elements were modified to have increased aperture and reduction in radio interference-caused false positives. An experiment was conducted to attempt replication of the previously reported interferometer measurements. Observations are reported here. Apparent replicated falsification of an expected random white noise explanatory hypothesis compels the development and testing of alternate and auxiliary hypotheses.
Predictability of viral load dynamics in the early phases of SARS-CoV-2 through a model-based approach
Authors: Andrea Bondesan, Antonio Piralla, Elena Ballante, Antonino Maria Guglielmo Pitrolo, Silvia Figini, Fausto Baldanti, Mattia Zanella
Date: 2024-07-03 00:00:00

A pipeline to evaluate the evolution of viral dynamics based on a new model-driven approach has been developed in the present study. The proposed methods exploit real data and the multiscale structure of the infection dynamics to provide robust predictions of the epidemic dynamics. We focus on viral load kinetics whose dynamical features are typically available in the symptomatic stage of the infection. Hence, the epidemiological evolution is obtained by relying on a compartmental approach characterized by a varying infection rate to estimate early-stage viral load dynamics, of which few data are available. We test the proposed approach with real data of SARS-CoV-2 viral load kinetics collected from patients living in an Italian province. The considered database refers to early-phase infections, whose viral load kinetics have not been affected by the mass vaccination policies in Italy.
Solving the prisoner's dilemma trap in Hamilton's model of temporarily formed random groups
Authors: José F. Fontanari, Mauro Santos
Date: 2024-07-02 00:00:00

Explaining the evolution of cooperation in the strong altruism scenario, where a cooperator does not benefit from her contribution to the public goods, is a challenging problem that requires positive assortment among cooperators (i.e., cooperators must tend to associate with other cooperators) or punishment of defectors. The need for these drastic measures stems from the analysis of a group selection model of temporarily formed random groups introduced by Hamilton nearly fifty years ago to describe the fate of altruistic behavior in a population. Challenging conventional wisdom, we show analytically here that strong altruism evolves in Hamilton's original model in the case of biparental sexual reproduction. Moreover, when the cost of cooperation is small and the amplified contribution shared by group members is large, cooperation is the only stable strategy in equilibrium. Thus, our results provide a solution to the `problem of origination' of strong altruism, i.e. how cooperation can take off from an initial low frequency of cooperators. We discuss a possible reassessment of cooperation in cases of viral co-infection, as cooperation may even be favored in situations where the prisoner's dilemma applies.