pwc-archive/papers-with-abstracts huggingface dataset:
Dispatchable Region for Active Distribution Networks Using Approximate Second-Order Cone Relaxation
Authors: Zhigang Li, Wenjing Huang, J. H. Zheng, Q. H. Wu
Date: 2021-07-01 00:00:00
Tasks: Computational Efficiency

Uncertainty in distributed renewable generation threatens the security of power distribution systems. The concept of the dispatchable region was developed to assess the ability of power systems to accommodate renewable generation at a given operating point. Although DC and linearized AC power flow equations are typically used to model dispatchable regions for transmission systems, these equations are rarely suitable for distribution networks. To achieve a suitable trade-off between accuracy and efficiency, this paper proposes a dispatchable region formulation for distribution networks using tight convex relaxation. Second-order cone relaxation is adopted to reformulate the AC power flow equations, which are then approximated by a polyhedron to improve tractability. Further, an efficient adaptive constraint generation algorithm is employed to construct the proposed dispatchable region. Case studies on distribution systems of various scales validate the computational efficiency and accuracy of the proposed method.
Performance evaluation of gust load alleviation systems for flexible aircraft via optimal control
Authors: Pierre Vuillemin, David Quero Martin, Charles Poussot-Vassal
Date: 2021-07-01 00:00:00

The dynamical response of an aircraft subject to gust perturbations is a key element in a preliminary design phase. In particular, the loads induced by gusts along the wing should not exceed some limit values and should even ideally be decreased. Active control is one lever to address this problem. However, evaluating the benefit that active control may bring considering some actuators characteristics or some delay in the loop is a difficult task, especially in the early design phase. This problem is addressed in this paper with an open-loop optimal control framework and more specifically with a direct transcription method resulting in a linear optimisation problem. The approach is illustrated on a realistic aeroelastic aircraft model built with a coupled fluid-structure solver which order is reduced to decrease the number of optimisation variables.
Uncertain Multi-Agent Systems with Distributed Constrained Optimization Missions and Event-Triggered Communications: Application to Resource Allocation
Authors: Mohammad Saeed Sarafraz, Mohammad Saleh Tavazoei
Date: 2020-04-03 00:00:00
Tasks: Distributed Optimization

This paper deals with solving distributed optimization problems with equality constraints by a class of uncertain nonlinear heterogeneous dynamic multi-agent systems. It is assumed that each agent with an uncertain dynamic model has limited information about the main problem and limited access to the information of the state variables of the other agents. A distributed algorithm that guarantees cooperative solving of the constrained optimization problem by the agents is proposed. Via this algorithm, the agents do not need to continuously broadcast their data. It is shown that the proposed algorithm can be useful in solving resource allocation problems.
Output Feedback Consensus for Networked Heterogeneous Nonlinear Negative-Imaginary Systems with Free Body Motion
Authors: Kanghong Shi, Ian R. Petersen, Igor G. Vladimirov
Date: 2020-11-30 00:00:00

This paper provides a protocol to address the robust output feedback consensus problem for networked heterogeneous nonlinear negative-imaginary (NI) systems with free body dynamics. We extend the definition of nonlinear NI systems to allow for systems with free body motion. A new stability result is developed for the interconnection of a nonlinear NI system and a nonlinear output strictly negative-imaginary (OSNI) system. Also, a class of networked nonlinear OSNI controllers is proposed to achieve output feedback consensus for heterogeneous networked nonlinear NI systems. We show that in this control framework, the system outputs converge to the same limit trajectory. This consensus protocol is illustrated by a numerical example.
Hierarchical Control of Utility-Scale Solar PV Plants for Mitigation of Generation Variability and Ancillary Service Provision
Authors: Simon Julien, Amirhossein Sajadi, Bri-Mathias Hodge
Date: 2021-07-01 00:00:00
Tasks: Management

Renewable energy technologies including solar and wind inevitably play a leading role in meeting the growing demand for a decarbonized and clean power grid. However, these technologies are highly dependent of meteorological conditions of power plant site and the challenge remains on how to cope with their short-term and momentarily variability. This paper presents a hierarchical control system to provide ancillary services from a solar PV power plant to the grid without the need for additional non-solar resources. With coordinated management of each inverter in the system, the control system commands the power plant to proactively curtail a fraction of its instantaneous maximum power potential, which gives the plant enough headroom to ramp up or down power production from the overall power plant, for a service such as regulation reserve, even under changing cloud cover conditions. A case study from a site in Hawaii with one-second resolution solar irradiance data is used to verify the efficacy of the proposed control system. The algorithm is subsequently compared with an alternative control technology from the literature, the grouping control algorithm; the results show that the proposed hierarchical control system is over 10 times more effective in reducing generator mileage to support power fluctuations from solar PV power plants.
Intelligent Anomaly Mitigation in Cyber-Physical Inverter-based Systems
Authors: Asad Ali Khan, Omar A. Beg, Sara Ahmed
Date: 2021-06-30 00:00:00

The distributed cooperative controllers for inverter-based systems rely on communication networks that make them vulnerable to cyber anomalies. In addition, the distortion effects of such anomalies may also propagate throughout inverter-based cyber-physical systems due to the cooperative cyber layer. In this paper, an intelligent anomaly mitigation technique for such systems is presented utilizing data driven artificial intelligence tools that employ artificial neural networks. The proposed technique is implemented in secondary voltage control of distributed cooperative control-based microgrid, and results are validated by comparison with existing distributed secondary control and real-time simulations on real-time simulator OPAL-RT.
Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systems
Authors: Martin Braquet, Efstathios Bakolas
Date: 2021-06-30 00:00:00

We propose a decentralized auction-based algorithm for the solution of dynamic task allocation problems for spatially distributed multi-agent systems. In our approach, each member of the multi-agent team is assigned to at most one task from a set of spatially distributed tasks, while several agents can be allocated to the same task. The task assignment is dynamic since it is updated at discrete time stages (iterations) to account for the current states of the agents as the latter move towards the tasks assigned to them at the previous stage. Our proposed methods can find applications in problems of resource allocation by intelligent machines such as the delivery of packages by a fleet of unmanned or semi-autonomous aerial vehicles. In our approach, the task allocation accounts for both the cost incurred by the agents for the completion of their assigned tasks (e.g., energy or fuel consumption) and the rewards earned for their completion (which may reflect, for instance, the agents' satisfaction). We propose a Greedy Coalition Auction Algorithm (GCAA) in which the agents possess bid vectors representing their best evaluations of the task utilities. The agents propose bids, deduce an allocation based on their bid vectors and update them after each iteration. The solution estimate of the proposed task allocation algorithm converges after a finite number of iterations which cannot exceed the number of agents. Finally, we use numerical simulations to illustrate the effectiveness of the proposed task allocation algorithm (in terms of performance and computation time) in several scenarios involving multiple agents and tasks distributed over a spatial 2D domain.
QuickFlex: a Fast Algorithm for Flexible Region Construction for the TSO-DSO Coordination
Authors: Luis Lopez, Alvaro Gonzalez-Castellanos, David Pozo, Mardavij Roozbehani, Munther Dahleh
Date: 2021-06-30 00:00:00

Most of the new technological changes in power systems are expected to take place in distribution grids. The enormous potential for distribution flexibility could meet the transmission system's needs, changing the paradigm of generator-centric energy and ancillary services provided to a demand-centric one, by placing more importance on smaller resources, such as flexible demands and electric vehicles. For unlocking such capabilities, it is essential to understand the aggregated flexibility that can be harvested from the large population of new technologies located in distribution grids. Distribution grids, therefore, could provide aggregated flexibility at the transmission level. To date, most computational methods for estimating the aggregated flexibility at the interface between distribution grids and transmission grids have the drawback of requiring significant computational time, which hinders their applicability. This paper presents a new algorithm, coined as QuickFlex} for constructing the flexibility domain of distribution grids. Contrary to previous methods, a priory flexibility domain accuracy can be selected. Our method requires few iterations for constructing the flexibility region. The number of iterations needed is mainly independent of the distribution grid's input size and flexible elements. Numerical experiments are performed in four grids ranging from 5 nodes to 123 nodes. It is shown that QuickFlex outperforms existing proposals in the literature in both speed and accuracy.
Transit-Gym: A Simulation and Evaluation Engine for Analysis of Bus Transit Systems
Authors: Ruixiao Sun, Rongze Gui, Himanshu Neema, Yuche Chen, Juliette Ugirumurera, Joseph Severino, Philip Pugliese, Aron Laszka, Abhishek Dubey
Date: 2021-06-30 00:00:00

Public-transit systems face a number of operational challenges: (a) changing ridership patterns requiring optimization of fixed line services, (b) optimizing vehicle-to-trip assignments to reduce maintenance and operation codes, and (c) ensuring equitable and fair coverage to areas with low ridership. Optimizing these objectives presents a hard computational problem due to the size and complexity of the decision space. State-of-the-art methods formulate these problems as variants of the vehicle routing problem and use data-driven heuristics for optimizing the procedures. However, the evaluation and training of these algorithms require large datasets that provide realistic coverage of various operational uncertainties. This paper presents a dynamic simulation platform, called Transit-Gym, that can bridge this gap by providing the ability to simulate scenarios, focusing on variation of demand models, variations of route networks, and variations of vehicle-to-trip assignments. The central contribution of this work is a domain-specific language and associated experimentation tool-chain and infrastructure to enable subject-matter experts to intuitively specify, simulate, and analyze large-scale transit scenarios and their parametric variations. Of particular significance is an integrated microscopic energy consumption model that also helps to analyze the energy cost of various transit decisions made by the transportation agency of a city.
Super Twisting based Lyapunov Redesign for Uncertain Linear Delay Systems
Authors: Marco A. Gomez, Christopher D. Cruz-Ancona, Leonid Fridman
Date: 2021-06-30 00:00:00

We present a new continuous Lyapunov Redesign (LR) methodology for the robust stabilization of a class of uncertain time-delay systems that is based on the so-called Super Twisting Algorithm. The main feature of the proposed approach is that allows one to simultaneously adjust the chattering effect and achieve asymptotic stabilization of the uncertain system, which is lost when continuous approximation of the unit control is considered. At the basis of the Super Twisting based LR methodology is a class of Lyapunov-Krasovskii functionals, whose particular form of its time derivative allows one to define a delay-free sliding manifold on which some class of smooth uncertainties are compensated.
A uniform reaching phase strategy in adaptive sliding mode control
Authors: Christopher D. Cruz-Ancona, Leonid Fridman, Hussein Obeid, Salah Laghrouche
Date: 2021-06-30 00:00:00

In adaptive sliding mode control methods, an updating gain strategy associated with finite-time convergence to the sliding set is essential to deal with matched bounded perturbations with unknown upper-bound. However, the estimation of the finite time of any adaptive design is a complicated task since it depends not only on the upper-bound of unknown perturbation but also on the size of initial conditions. This brief proposes a uniform adaptive reaching phase strategy (ARPS) within a predefined reaching-time. Moreover, as a case of study, the barrier function approach is extended for perturbed MIMO systems with uncertain control matrix. The usage of proposed ARPS in the MIMO case solves simultaneously two issues: giving a uniform reaching phase with a predefined reaching-time and adapting to the perturbation norm while in a predefined vicinity of the sliding manifold.
Proposed new dynamic power insertion method for stabilized power generation based on battery energy storage system
Authors: Amirhossein Khosravipour
Date: 2021-06-29 00:00:00
Tasks: Management

The solar energy is clean and future energy for electricity generation. Its energy has enormous potential but do not optimal to utilized caused by intermittent energy. The intermittent radiance and ambient temperature influence the energy produced fluctuates and unstable. These power fluctuations affect system stability and frequency. To overcome this problem, several methods for PV power stabilization have been developed. One of them is the Stabilized Power Generation where PV output power is to a certain power value. The result of SPG method is reducing in PV power fluctuations but still unstable. For reaching the stable condition of PV Power output, the Dynamic Power Insertion method is proposed which is a modification of the SPG method with energy storage batteries. DPI improve the SPG method and make PV power stable with active power management with charge and discharge action. The battery is using as energy storage when PV power is larger than Power limit. On the other hand battery as an energy source for active power insertion at PV power is smaller than Power limit. Thus the output power in each condition can be maintained as Power PV equals to P limit. For test this method, DPI modules are built on ARM lpc1768 NXP and monitored with the Thing speak webserver (simulated with Simulink MatLab Software). The experimental results of DPI can stabilize PV power fluctuation at its setting power with an error of 5 percent.
Adaptive 3D descattering with a dynamic synthesis network
Authors: Waleed Tahir, Hao Wang, Lei Tian
Date: 2021-07-01 00:00:00
Tasks: Denoising, Mixture-of-Experts

Deep learning has been broadly applied to imaging in scattering applications. A common framework is to train a descattering network for image recovery by removing scattering artifacts. To achieve the best results on a broad spectrum of scattering conditions, individual "expert" networks need to be trained for each condition. However, the expert's performance sharply degrades when the testing condition differs from the training. An alternative brute-force approach is to train a "generalist" network using data from diverse scattering conditions. It generally requires a larger network to encapsulate the diversity in the data and a sufficiently large training set to avoid overfitting. Here, we propose an adaptive learning framework, termed dynamic synthesis network (DSN), which dynamically adjusts the model weights and adapts to different scattering conditions. The adaptability is achieved by a novel "mixture of experts" architecture that enables dynamically synthesizing a network by blending multiple experts using a gating network. We demonstrate the DSN in holographic 3D particle imaging for a variety of scattering conditions. We show in simulation that our DSN provides generalization across a continuum of scattering conditions. In addition, we show that by training the DSN entirely on simulated data, the network can generalize to experiments and achieve robust 3D descattering. We expect the same concept can find many other applications, such as denoising and imaging in scattering media. Broadly, our dynamic synthesis framework opens up a new paradigm for designing highly adaptive deep learning and computational imaging techniques.
Feasible Implied Correlation Matrices from Factor Structures
Authors: Wolfgang Schadner
Date: 2021-07-01 00:00:00

Forward-looking correlations are of interest in different financial applications, including factor-based asset pricing, forecasting stock-price movements or pricing index options. With a focus on non-FX markets, this paper defines necessary conditions for option implied correlation matrices to be mathematically and economically feasible and argues, that existing models are typically not capable of guaranteeing so. To overcome this difficulty, the problem is addressed from the underlying factor structure and introduces two approaches to solve it. Under the quantitative approach, the puzzle is reformulated into a nearest correlation matrix problem which can be used either as a stand-alone estimate or to re-establish positive-semi-definiteness of any other model's estimate. From an economic approach, it is discussed how expected correlations between stocks and risk factors (like CAPM, Fama-French) can be translated into a feasible implied correlation matrix. Empirical experiments are carried out on monthly option data of the S\&P 100 and S\&P 500 index (1996-2020).
Political and legal aspects of the COVID-19 pandemic impact on world transport systems
Authors: Alexey Gubin, Valeri Lipunov, Mattia Masolletti
Date: 2021-07-01 00:00:00

The authors of the article analyze the impact of the global COVID-19 pandemic on the transport and logistics sector. The research is interdisciplinary in nature. The purpose of the study is to identify and briefly characterize new trends in the field of transport and cargo transportation in post-COVID conditions.
Choice of a Mentor: A Subjective Evaluation of Expectations, Experiences and Feedbacks
Authors: Kaibalyapati Mishra
Date: 2021-06-30 00:00:00

Recent trends in academics show an increase in enrollment levels in higher education Predominantly in Doctoral programmes where individual scholars institutes and supervisors play the key roles The human factor at receiving end of academic excellence is the scholar having a supervisor at the facilitating end In this paper I try to establish the role of different factors and availability of information about them in forming the basic choice set in a scholars mind After studying three different groups of individuals who were subjected to substitutive choices we found that scholars prefer an approachable, moderately intervening and frequently interacting professor as their guide
Auction Design with Data-Driven Misspecifications
Authors: Philippe Jehiel, Konrad Mierendorff
Date: 2021-07-01 00:00:00

We consider auction environments in which at the time of the auction bidders observe signals about their ex-post value. We introduce a model of novice bidders who do not know know the joint distribution of signals and instead build a statistical model relating others' bids to their own ex post value from the data sets accessible from past similar auctions. Crucially, we assume that only ex post values and bids are accessible while signals observed by bidders in past auctions remain private. We consider steady-states in such environments, and importantly we allow for correlation in the signal distribution. We first observe that data-driven bidders may behave suboptimally in classical auctions such as the second-price or first-price auctions whenever there are correlations. Allowing for a mix of rational (or experienced) and data-driven (novice) bidders results in inefficiencies in such auctions, and we show the inefficiency extends to all auction-like mechanisms in which bidders are restricted to submit one-dimensional (real-valued) bids.
Fiscal policy and inequality in a model with endogenous positional concerns
Authors: Kirill Borissov, Nigar Hashimzade
Date: 2021-07-01 00:00:00

We investigate the dynamics of wealth inequality in an economy where households have positional preferences, with the strength of the positional concern determined endogenously by inequality of wealth distribution in the society. We demonstrate that in the long run such an economy converges to a unique egalitarian steady-state equilibrium, with all households holding equal positive wealth, when the initial inequality is sufficiently low. Otherwise, the steady state is characterised by polarisation of households into rich, who own all the wealth, and poor, whose wealth is zero. A fiscal policy with government consumption funded by taxes on labour income and wealth can move the economy from any initial state towards an egalitarian equilibrium with a higher aggregate wealth.
Collective Progress: Dynamics of Exit Waves
Authors: Doruk Cetemen, Can Urgun, Leeat Yariv
Date: 2021-07-01 00:00:00

We study a model of collective search by teams. Discoveries beget discoveries and correlated search results are governed by a Brownian path. Search results' variation at any point -- the search scope -- is jointly controlled. Agents individually choose when to cease search and implement their best discovery. We characterize equilibrium and optimal policies. Search scope is constant and independent of search outcomes as long as no member leaves. It declines after departures. A simple drawdown stopping boundary governs each agent's search termination. We show the emergence of endogenous exit waves, whereby possibly heterogeneous agents cease search all at once.
Image Restoration for Remote Sensing: Overview and Toolbox
Authors: Benhood Rasti, Yi Chang, Emanuele Dalsasso, Loïc Denis, Pedram Ghamisi
Date: 2021-07-01 00:00:00
Tasks: Image Restoration

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.