pwc-archive/papers-with-abstracts huggingface dataset:
Pricing for Multi-modal Pickup and Delivery Problems with Heterogeneous Users
Authors: Mark Beliaev, Negar Mehr, Ramtin Pedarsani
Date: 2023-03-17 00:00:00

In this paper, we study the pickup and delivery problem with multiple transportation modalities, and address the challenge of efficiently allocating transportation resources while price matching users with their desired delivery modes. More precisely, we consider that orders are demanded by a heterogeneous population of users with varying trade-offs between price and latency. To capture how prices affect the behavior of heterogeneous selfish users choosing between multiple delivery modes, we construct a congestion game taking place over a form of star network, where each source-sink pair is composed of parallel links connecting users with their preferred delivery method. Using the unique geometry of this network, we prove that one can set prices explicitly to induce any desired network flow, i.e, given a desired allocation strategy, we have a closed-form solution for the delivery prices. We conclude by performing a case study on a meal delivery problem with multiple courier modalities using data from real world instances.
Quantifying Space-Time Load Shifting Flexibility in Electricity Markets
Authors: Weiqi Zhang, Victor M. Zavala
Date: 2023-03-17 00:00:00
Tasks: Sensitivity

The power grid is undergoing significant restructuring driven by the adoption of wind/solar power and the incorporation of new flexible technologies that can shift load in space and time (e.g., data centers, battery storage, and modular manufacturing). Load shifting is needed to mitigate space-time fluctuations associated with wind/solar power and other disruptions (e.g., extreme weather). The impact of load shifting on electricity markets is typically quantified via sensitivity analysis, which aims to assess impacts in terms of price volatility and total welfare. This sensitivity approach does not explicitly quantify operational flexibility (e.g., range or probability of feasible operation). In this work, we present a computational framework to enable this; specifically, we quantify operational flexibility by assessing how much uncertainty in net loads (which capture uncertain power injections/withdrawals) can be tolerated by the system under varying levels of load shifting capacity. The proposed framework combines optimization formulations that quantify operational flexibility with power grid models that capture load shifting in the form of virtual links (pathways that transfer load across space-time). Our case studies reveal that adding a single virtual link that shifts load in either space or time can lead to dramatic improvements in system-wide flexibility; this is because shifting relieves space-time congestion that results from transmission constraints and generator ramping constraints. Our results provide insights into how the incorporation of flexible technologies can lead to non-intuitive, system-wide gains in flexibility.
A matching principle for power transfer in Stochastic Thermodynamics
Authors: Olga Movilla Miangolarra, Amirhossein Taghvaei, Tryphon T. Georgiou
Date: 2023-03-17 00:00:00

Gradients in temperature and particle concentration fuel many processes in the physical and biological world. In the present work we study a thermodynamic engine powered by anisotropic thermal excitation (that may be due to e.g., a temperature gradient), and draw parallels with the well-known principle of impedance matching in circuit theory, where for maximal power transfer, the load voltage needs to be half of that of the supplying power source. We maximize power output of the thermodynamic engine at steady-state and show that the optimal reactive force is precise half of that supplied by the anisotropy.
Design and validation of a state-dependent Riccati equation filter for state of charge estimation in a latent thermal storage device
Authors: Michael Shanks, Uduak Inyang-Udoh, Neera Jain
Date: 2023-03-17 00:00:00
Tasks: Management, State Estimation

Latent thermal energy storage (TES) devices could enable advances in many thermal management applications, including peak load shifting for reducing energy demand and cost of HVAC or providing supplemental heat rejection in transient thermal management systems. However, real-time feedback control of such devices is currently limited by the absence of suitable state of charge estimation techniques, given the nonlinearities associated with phase change dynamics. In this paper we design and experimentally validate a state-dependent Riccati equation (SDRE) filter for state of charge estimation in a phase change material (PCM)-based TES device integrated into a single-phase thermal-fluid loop. The advantage of the SDRE filter is that it does not require linearization of the nonlinear finite-volume model; instead, it uses a linear parameter-varying system model which can be quickly derived using graph-based methods. We leverage graph-based methods to prove that the system model is uniformly detectable, guaranteeing that the state estimates are bounded. Using measurements from five thermocouples embedded in the PCM of the TES and two thermocouples measuring the fluid temperature at the inlet and outlet of the device, the state estimator uses a reduced-order finite-volume model to determine the temperature distribution inside the PCM and in turn, the state of charge of the device. We demonstrate the state estimator in simulation and on experimental data collected from a thermal management system testbed to show that the state estimation error converges near zero and remains bounded.
Data-driven Dissipativity Analysis of Linear Parameter-Varying Systems
Authors: Chris Verhoek, Julian Berberich, Sofie Haesaert, Frank Allgöwer, Roland Tóth
Date: 2023-03-17 00:00:00
Tasks: Scheduling

We derive direct data-driven dissipativity analysis methods for Linear Parameter-Varying (LPV) systems using a single sequence of input-scheduling-output data. By means of constructing a semi-definite program subject to linear matrix inequality constraints based on this data-dictionary, direct data-driven verification of $(Q,S,R)$-type of dissipativity properties of the data-generating LPV system is achieved. Multiple implementation methods are proposed to achieve efficient computational properties and to even exploit structural information on the scheduling, e.g., rate bounds. The effectiveness and trade-offs of the proposed methodologies are shown in simulation studies of academic and physically realistic examples.
Counter-example guided inductive synthesis of control Lyapunov functions for uncertain systems
Authors: Daniele Masti, Filippo Fabiani, Giorgio Gnecco, Alberto Bemporad
Date: 2023-03-17 00:00:00
Tasks: global-optimization

We propose a counter-example guided inductive synthesis (CEGIS) scheme for the design of control Lyapunov functions and associated state-feedback controllers for linear systems affected by parametric uncertainty with arbitrary shape. In the CEGIS framework, a learner iteratively proposes a candidate control Lyapunov function and a tailored controller by solving a linear matrix inequality (LMI) feasibility problem, while a verifier either falsifies the current candidate by producing a counter-example to be considered at the next iteration, or it certifies that the tentative control Lyapunov function actually enjoys such feature. We investigate the Lipschitz continuity of the objective function of the global optimization problem solved by the verifier, which is key to establish the convergence of our method in a finite number of iterations. Numerical simulations confirm the effectiveness of the proposed approach.
Optimizing EV Chargers Location via Integer Programming
Authors: Seungmo Kim, Yeonho Jeong, Jae-Won Nam
Date: 2023-03-17 00:00:00

There is no question to the fact that electric vehicles (EVs) are the most viable solution to the climate change that the planet has long been combating. Along the same line, it is a salient subject to expand the availability of charging infrastructure, which quintessentially necessitates the optimization of the charger's locations. This paper proposes to formulate the optimal EV charger location problem into a facility location problem (FLP). As an effort to find an efficient method to solve the well-known nonpolynomial deterministic (NP)-hard problem, we present a comparative quantification among several representative solving techniques.
Active Learning-based Model Predictive Coverage Control
Authors: Rahel Rickenbach, Johannes Köhler, Anna Scampicchio, Melanie N. Zeilinger, Andrea Carron
Date: 2023-03-17 00:00:00
Tasks: Active Learning, model, Model Predictive Control

The problem of coverage control, i.e., of coordinating multiple agents to optimally cover an area, arises in various applications. However, coverage applications face two major challenges: (1) dealing with nonlinear dynamics while respecting system and safety critical constraints, and (2) performing the task in an initially unknown environment. We solve the coverage problem by using a hierarchical framework, in which references are calculated at a central server and passed to the agents' local model predictive control (MPC) tracking schemes. Furthermore, to ensure that the environment is actively explored by the agents a probabilistic exploration-exploitation trade-off is deployed. In addition, we derive a control framework that avoids the hierarchical structure by integrating the reference optimization in the MPC formulation. Active learning is then performed drawing inspiration from Upper Confidence Bound (UCB) approaches. For all developed control architectures, we guarantee closed-loop constraint satisfaction and convergence to an optimal configuration. Furthermore, all methods are tested and compared on hardware using a miniature car platform.
Downing a Rogue Drone with a Team of Aerial Radio Signal Jammers
Authors: Savvas Papaioannou, Panayiotis Kolios, Georgios Ellinas
Date: 2023-03-17 00:00:00

This work proposes a novel distributed control framework in which a team of pursuer agents equipped with a radio jamming device cooperate in order to track and radio-jam a rogue target in 3D space, with the ultimate purpose of disrupting its communication and navigation circuitry. The target evolves in 3D space according to a stochastic dynamical model and it can appear and disappear from the surveillance area at random times. The pursuer agents cooperate in order to estimate the probability of target existence and its spatial density from a set of noisy measurements in the presence of clutter. Additionally, the proposed control framework allows a team of pursuer agents to optimally choose their radio transmission levels and their mobility control actions in order to ensure uninterrupted radio jamming to the target, as well as to avoid the jamming interference among the team of pursuer agents. Extensive simulation analysis of the system's performance validates the applicability of the proposed approach.
Model Reference Gaussian Process Regression: Data-Driven State Feedback Controller
Authors: Hyuntae Kim, Hamin Chang, Hyungbo Shim
Date: 2023-03-17 00:00:00
Tasks: GPR, regression

This paper proposes a data-driven state feedback controller that enables reference tracking for nonlinear discrete-time systems. The controller is designed based on the identified inverse model of the system and a given reference model, assuming that the identification of the inverse model is carried out using only the system's state/input measurements. When its results are provided, we present conditions that guarantee a certain level of reference tracking performance, regardless of the identification method employed for the inverse model. Specifically, when Gaussian process regression (GPR) is used as the identification method, we propose sufficient conditions for the required data by applying some lemmas related to identification errors to the aforementioned conditions, ensuring that the Model reference-GPR (MR-GPR) controller can guarantee a certain level of reference tracking performance. Finally, an example is provided to demonstrate the effectiveness of the MR-GPR controller.
A Spatio-temporal Decomposition Method for the Coordinated Economic Dispatch of Integrated Transmission and Distribution Grids
Authors: Qi Wang, Wenchuan Wu, Chenhui Lin, Bin Wang
Date: 2023-03-17 00:00:00
Tasks: Computational Efficiency

With numerous distributed energy resources (DERs) integrated into the distribution networks (DNs), the coordinated economic dispatch (C-ED) is essential for the integrated transmission and distribution grids. For large scale power grids, the centralized C-ED meets high computational burden and information privacy issues. To tackle these issues, this paper proposes a spatio-temporal decomposition algorithm to solve the C-ED in a distributed and parallel manner. In the temporal dimension, the multi-period economic dispatch (ED) of transmission grid (TG) is decomposed to several subproblems by introducing auxiliary variables and overlapping time intervals to deal with the temporal coupling constraints. Besides, an accelerated alternative direction method of multipliers (A-ADMM) based temporal decomposition algorithm with the warm-start strategy, is developed to solve the ED subproblems of TG in parallel. In the spatial dimension, a multi-parametric programming projection based spatial decomposition algorithm is developed to coordinate the ED problems of TG and DNs in a distributed manner. To further improve the convergence performance of the spatial decomposition algorithm, the aggregate equivalence approach is used for determining the feasible range of boundary variables of TG and DNs. Moreover, we prove that the proposed spatio-temporal decomposition method can obtain the optimal solution for bilevel convex optimization problems with continuously differentiable objectives and constraints. Numerical tests are conducted on three systems with different scales, demonstrating the high computational efficiency and scalability of the proposed spatio-temporal decomposition method.
Measurement Optimization under Uncertainty using Deep Reinforcement Learning
Authors: Amin Jabini, Erik A. Johnson
Date: 2023-03-17 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Optimal sensor placement enhances the efficiency of a variety of applications for monitoring dynamical systems. It has been established that deterministic solutions to the sensor placement problem are insufficient due to the many uncertainties in system input and parameters that affect system response sensor measurements. Accounting for the uncertainties in this typically expensive optimization is challenging due to computational intractability. This study proposes a stochastic environment in the form of a Markov Decision Process and a sensor placement agent that aims to maximize the information gain from placing a particular number of sensors of different types within the system. The agent is trained to maximize its reward based on an information-theoretic reward function. To verify the efficacy, the approach is applied to place a set of heterogeneous sensors in a shear building model. This methodology can be used to accommodate uncertainties in the sensor placement problem in real-world systems.
Mobile Energy Storage in Power Network: Marginal Value and Optimal Operation
Authors: Utkarsha Agwan, Junjie Qin, Kameshwar Poolla, Pravin Varaiya
Date: 2023-03-17 00:00:00

This paper examines the marginal value of mobile energy storage, i.e., energy storage units that can be efficiently relocated to other locations in the power network. In particular, we formulate and analyze the joint problem for operating the power grid and a fleet of mobile storage units. We use two different storage models: rapid storage, which disregards travel time and power constraints, and general storage, which incorporates them. By explicitly connecting the marginal value of mobile storage to locational marginal prices (LMPs), we propose efficient algorithms that only use LMPs and transportation costs to optimize the relocation trajectories of the mobile storage units. Furthermore, we provide examples and conditions under which the marginal value of mobile storage is strictly higher, equal to, or strictly lower than the sum of marginal value of corresponding stationary storage units and wires. We also propose faster algorithms to approximate the optimal operation and relocation for our general mobile storage model, and illustrate our results with simulations of an passenger EV and a heavy-duty electric truck in the PJM territory.
Neural Lyapunov Control for Nonlinear Systems with Unstructured Uncertainties
Authors: Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami
Date: 2023-03-16 00:00:00

Stabilizing controller design and region of attraction (RoA) estimation are essential in nonlinear control. Moreover, it is challenging to implement a control Lyapunov function (CLF) in practice when only partial knowledge of the system is available. We propose a learning framework that can synthesize state-feedback controllers and a CLF for control-affine nonlinear systems with unstructured uncertainties. Based on a regularity condition on these uncertainties, we model them as bounded disturbances and prove that a CLF for the nominal system (estimate of the true system) is an input-to-state stable control Lyapunov function (ISS-CLF) for the true system when the CLF's gradient is bounded. We integrate the robust Lyapunov analysis with the learning of both the control law and CLF. We demonstrate the effectiveness of our learning framework on several examples, such as an inverted pendulum system, a strict-feedback system, and a cart-pole system.
Universality and Control of Fat Tails
Authors: Michael Chertkov
Date: 2023-03-16 00:00:00
Tasks: Position

Motivated by applications in hydrodynamics and networks of thermostatically-control loads in buildings we study control of linear dynamical systems driven by additive and also multiplicative noise of a general position. Utilizing mathematical theory of stochastic multiplicative processes we present a universal way to estimate fat, algebraic tails of the state vector probability distributions. This prompts us to introduce and analyze mean-q-power stability criterion, generalizing the mean-square stability criterion, and then juxtapose it to other tools in control.
Revisiting Nyquist-Like Impedance-Based Criteria for Converter-Based AC Systems
Authors: Chongbin Zhao, Qirong Jiang, Yixin Guo
Date: 2023-03-16 00:00:00

Multiple types of Nyquist-like impedance-based criteria are utilized for the small-signal stability analysis of converter-based AC systems. It is usually considered that the determinant-based criterion can determine the overall stability of a system while the eigenvalue-based criterion can give more insights into the mechanism of the instability. This paper specifies such understandings starting with the zero-pole calculation of impedance matrices obtained by state-spaces with the Smith-McMillan form, then clarifying the absolute reliability of determinant-based criterion with the common assumption for impedance-based analysis that each subsystem can stably operate before the interconnection. However, ambiguities do exist for the eigenvalue-based criterion when an anticlockwise encirclement around the origin is observed in the Nyquist plot. To this end, a logarithmic derivative-based criterion to directly identify the system modes using the frequency responses of loop impedances is proposed, which owns a solid theoretical basis of the Schur complement of transfer function matrices. The theoretical analysis is validated using a PSCAD simulation of a grid-connected two-level voltage source converter.
Methodology for Capacity Credit Evaluation of Physical and Virtual Energy Storage in Decarbonized Power System
Authors: Ning Qi, Peng Li, Lin Cheng, Ziyi Zhang, Wenrui Huang, Weiwei Yang
Date: 2023-03-16 00:00:00
Tasks: energy management

Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization. Although ES and VES have been proven to deliver various types of grid services, little work has so far provided a systematical framework for quantifying their adequacy contribution and credible capacity value while incorporating human and market behavior. Therefore, this manuscript proposed a novel evaluation framework to evaluate the capacity credit (CC) of ES and VES. To address the system capacity inadequacy and market behavior of storage, a two-stage coordinated dispatch is proposed to achieve the trade-off between day-ahead self-energy management of resources and efficient adjustment to real-time failures. And we further modeled the human behavior with storage operations and incorporate two types of decision-independent uncertainties (DIUs) (operate state and self-consumption) and one type of decision-dependent uncertainty (DDUs) (available capacity) into the proposed dispatch. Furthermore, novel reliability and CC indices (e.g., equivalent physical storage capacity (EPSC)) are introduced to evaluate the practical and theoretical adequacy contribution of ES and VES, as well as the ability to displace generation and physical storage while maintaining equivalent system adequacy. Exhaustive case studies based on the IEEE RTS-79 system and real-world data verify the significant consequence (10%-70% overestimated CC) of overlooking DIUs and DDUs in the previous works, while the proposed method outperforms other and can generate a credible and realistic result. Finally, we investigate key factors affecting the adequacy contribution of ES and VES, and reasonable suggestions are provided for better flexibility utilization of ES and VES in decarbonized power system.
Probing the coverage of nanoparticles by biomimetic membranes through nanoplasmonics
Authors: Jacopo Cardellini, Andrea Ridolfi, Melissa Donati, Valentina Giampietro, Mirko Severi, Marco Brucale, Francesco Valle, Paolo Bergese, Costanza Montis, Lucrezia Caselli, Debora Berti
Date: 2023-03-17 00:00:00

Although promising for biomedicine, the clinical translation of inorganic nanoparticles (NPs) is limited by low biocompatibility and stability in biological fluids. A common strategy to circumvent this drawback consists in disguising the active inorganic core with a lipid bilayer coating, reminiscent of the structure of the cell membrane to redefine the chemical and biological identity of NPs. While recent reports introduced membrane coating procedures for NPs, a robust and accessible method to quantify the integrity of the bilayer coverage is not yet available. To fill this gap, we prepared SiO2 nanoparticles (SiO2NPs) with different membrane coverage degrees and monitored their interaction with AuNPs by combining microscopic, scattering, and optical techniques. The membrane-coating on SiO2NPs induces spontaneous clustering of AuNPs, whose extent depends on the coating integrity. Remarkably, we discovered a linear correlation between the membrane coverage and a spectral descriptor for the AuNPs plasmonic resonance, spanning a wide range of coating yields. These results provide a fast and cost-effective assay to monitor the compatibilization of NPs with biological environments, essential for bench tests and scale-up. In addition, we introduce a robust and scalable method to prepare SiO2NPsAuNPs hybrids through spontaneous self assembly, with a high fidelity structural control mediated by a lipid bilayer.