pwc-archive/papers-with-abstracts huggingface dataset:
Techno-economic analysis of PV-battery systems in Switzerland
Authors: Xuejiao Han, Jared Garrison, Gabriela Hug
Date: 2021-03-30 00:00:00

This paper presents a techno-economic optimization model to analyze the economic viability of a PV-battery system for different customer groups in Switzerland clustered based on their annual electricity consumption, rooftop size, annual irradiation and location. The simulations for a static investment model are carried out for years 2020-2050 and a comprehensive sensitivity analysis is conducted to investigate the impacts of individual parameters such as costs, load profiles, electricity prices and tariffs, etc. Results show that while combining PV with batteries already results in better net present values than PV alone for some customer groups today, the payback periods fluctuate between 2020 and 2035 due to the mixed effects of policy changes, costs and electricity price developments. The optimal PV and battery sizes increase over time and in 2050 the PV investment is mostly limited by the rooftop size. The economic viability of PV-battery system investments varies between different customer groups and the most attractive investment (i.e., that has the shortest payback period) is mostly accessible to customer groups with higher annual irradiation and electricity demand. In addition, investment decisions are highly sensitive to payback periods, future costs, electricity prices and tariff developments. Lastly, the grid impact of the PV-battery system deployments are investigated by analyzing the residual Swiss system load profiles. The dynamics of residual load profiles caused by the seasonal, daily and hourly patterns of the solar generation emphasizes the need for flexible resources with fast ramping capabilities.
Floating photovoltaic systems: photovoltaic cable submersion and impacts analysis
Authors: Ricardo Rebelo, Luís Fialho, Maria Helena Novais
Date: 2021-03-30 00:00:00

Floating photovoltaics (FPV) is an emerging technology that is gaining attention worldwide. However, little information is still available on its possible impacts in the aquatic ecosystems, as well as on the durability of its components. Therefore, this work intends to provide a contribution to this field, analysing possible obstacles that can compromise the performance of this technology, adding to an increase of its reliability and assessing possible impacts. The problem under study is related to the potential submersion of photovoltaic cables, that can lead to a degradation of its electrical insulation capabilities and, consequently, higher energy production losses and water contamination. In the present study, the submersion of photovoltaic cables (with two different insulation materials) in freshwater and artificial seawater was tested, in order to replicate real life conditions, when FPV systems are located in reservoirs or in the marine environment. Electrical insulation tests were carried out weekly to assess possible cable degradation, the physical-chemical characteristics of the water were also periodically monitored, complemented by analysis to detect traces of copper and microplastics in the water. The results showed that the submersion of photovoltaic cables with rubber sheath in saltwater can lead to a cable accelerated degradation, with reduction of its electrical insulation and, consequently, copper release into the aquatic environment.
Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems
Authors: Chris Verhoek, Roland Tóth, Sofie Haesaert, Anne Koch
Date: 2021-03-30 00:00:00
Tasks: LEMMA

Based on the Fundamental Lemma by Willems et al., the entire behaviour of a Linear Time-Invariant (LTI) system can be characterised by a single data sequence of the system as long the input is persistently exciting. This is an essential result for data-driven analysis and control. In this work, we aim to generalise this LTI result to Linear Parameter-Varying (LPV) systems. Based on the behavioural framework for LPV systems, we prove that one can obtain a result similar to Willems'. Based on an LPV representation, i.e., embedding, of nonlinear systems, this allows the application of the Fundamental Lemma for systems beyond the linear class.
Parametric Tracking of Electrical Currents Components Using Gradient Descent Algorithm
Authors: Marouane Frini, Vincent Choqueuse, François Auger
Date: 2021-03-30 00:00:00
Tasks: Fault Detection

In the last few years, Motor Current Signature Analysis (MCSA) has proven to be an effective method for electrical machines condition monitoring. Indeed, many mechanical and electrical faults manifest as side-band spectral components generated around the fundamental frequency component of the motor current. These components are called interharmonics and they are a major focus of fault detection using MCSA. However, the main drawback of this approach is that the interference of other more prevalent components can obstruct the effect of interharmonics in the spectrum and may therefore impede fault detection accuracy. Thus, we propose in this paper an alternative approach that decomposes the different current components based on the Vandermonde model and implements the tracking of each distinct component in time and spectral domains. This is achieved by estimating their respective relevant parameters using the Gradient Descent algorithm. The results of this work prove to be promising and establish the parametric tracking of the electrical current components using the Gradient Descent algorithm as a reliable monitoring approach.
Data-Driven Predictive Control for Linear Parameter-Varying Systems
Authors: Chris Verhoek, Hossam S. Abbas, Roland Tóth, Sofie Haesaert
Date: 2021-03-30 00:00:00
Tasks: LEMMA, Scheduling

Based on the extension of the behavioral theory and the Fundamental Lemma for Linear Parameter-Varying (LPV) systems, this paper introduces a Data-driven Predictive Control (DPC) scheme capable to ensure reference tracking and satisfaction of Input-Output (IO) constraints for an unknown system under the conditions that (i) the system can be represented in an LPV form and (ii) an informative data-set containing measured IO and scheduling trajectories of the system is available. It is shown that if the data set satisfies a persistence of excitation condition, then a data-driven LPV predictor of future trajectories of the system can be constructed from the IO data set and online measured data. The approach represents the first step towards a DPC solution for nonlinear and time-varying systems due to the potential of the LPV framework to represent them. Two illustrative examples, including reference tracking control of a nonlinear system, are provided to demonstrate that the data-based LPV-DPC scheme, achieves similar performance as LPV model-based predictive control.
Impact of Local Energy Markets on the Distribution Systems: A Comprehensive Review
Authors: Viktorija Dudjak, Diana Neves, Tarek Alskaif, Shafi Khadem, Alejandro Pena-Bello, Pietro Saggese, Benjamin Bowler, Merlinda Andoni, Marina Bertolini, Yue Zhou, Blanche Lormeteau, Mustafa A. Mustafa, Yingjie Wang, Christina Francis, Fairouz Zobiri, David Parra, Antonios Papaemmanouil
Date: 2021-03-30 00:00:00
Tasks: energy trading

In recent years extensive research has been conducted on the development of different models that enable energy trading between prosumers and consumers due to expected high integration of distributed energy resources. Some of the most researched mechanisms include Peer-to-Peer energy trading, Community Self-Consumption and Transactive Energy Models. Here, we present an exhaustive review of existing research around Local Energy Market integration into distribution systems, in particular benefits and challenges that the power grid can expect from integrating these models. We also present a detailed overview of methods that are used to integrate physical network constraints into the market mechanisms, their advantages, drawbacks, and scaling potential. We find that financial energy transactions do not directly reflect the physical energy flows imposed by the constraints of the installed electrical infrastructure. Therefore, ensuring the stable and reliable delivery of electricity, it is vital to understand the impact of these transactions on the grid infrastructure, e.g., impacts on the control, operation, and planning of electricity distribution systems.
Fast Synthesis for Symbolic Self-triggered Control under Right-recursive LTL Specifications
Authors: Sasinee Pruekprasert, Clovis Eberhart, Jérémy Dubut
Date: 2021-03-30 00:00:00

We extend previous work on symbolic self-triggered control for non-deterministic continuous-time nonlinear systems without stability assumptions to a larger class of specifications. Our goal is to synthesise a controller for two objectives: the first one is modelled as a right-recursive LTL formula, and the second one is to ensure that the average communication rate between the controller and the system stays below a given threshold. We translate the control problem to solving a mean-payoff parity game played on a discrete graph. Apart from extending the class of specifications, we propose a heuristic method to shorten the computation time. Finally, we illustrate our results on the example of a navigating nonholonomic robot with several specifications.
Reduced Dynamics and Control for an Autonomous Bicycle
Authors: Jiaming Xiong, Bo Li, Ruihan Yu, Daolin Ma, Wei Wang, Caishan Liu
Date: 2021-03-30 00:00:00

In this paper, we propose the reduced model for the full dynamics of a bicycle and analyze its nonlinear behavior under a proportional control law for steering. Based on the Gibbs-Appell equations for the Whipple bicycle, we obtain a second-order nonlinear ordinary differential equation (ODE) that governs the bicycle's controlled motion. Two types of equilibrium points for the governing equation are found, which correspond to the bicycle's uniform straight forward and circular motions, respectively. By applying the Hurwitz criterion to the linearized equation, we find that the steer coefficient must be negative, consistent with the human's intuition of turning toward a fall. Under this condition, a critical angular velocity of the rear wheel exists, above which the uniform straight forward motion is stable, and slightly below which a pair of symmetrical stable uniform circular motions will occur. These theoretical findings are verified by both numerical simulations and experiments performed on a powered autonomous bicycle.
Empirical Analysis of Capacity Investment Solution in Distribution Grids
Authors: Luis Lopez, Alvaro Gonzalez-Castellanos, David Pozo
Date: 2021-03-30 00:00:00
Tasks: Clustering

This paper presents an analysis of the stability and quality of the distributed generation planning problem's investment solution. The entry of distributed generators power based on non-conventional energy sources has been extensively promoted in distribution grids. In this paper, a two-stage stochastic programming model is used to find the optimal distributed generators' installed capacities. We emphasize the design of scenarios to represent the stochasticity of power production on renewable sources. In the scenario generation, a method is proposed based on the clustering of real measurements of meteorological variables. We measure the quality and stability of the investment solution as a function of the number of scenarios. The results show that a reduced selection of scenarios can give an inadequate solution to distributed generators' investment strategy.
Feature-Based Compliance Control for Peg-in-Hole Assembly with Clearance or Interference Fit
Authors: Yuhang Gai, Jiuming Guo, Dan Wu, Ken Chen
Date: 2021-03-30 00:00:00

This paper aims at solving mass precise peg-in-hole assembly. First, a feature space and a response space are constructed according to the relative pose and equivalent forces and moments. Then the contact states are segmented in the feature space and the segmentation boundaries are mapped into the response space. Further, a feature-based compliance control (FBCC) algorithm is proposed based on boundary mapping. In the FBCC algorithm, a direction matrix is designed to execute accurate adjustment and an integrator is applied to eliminate the residual responses. Finally, the simulations and experiments demonstrate the superiority, robustness, and generalization ability of the FBCC.
Localized and Distributed H2 State Feedback Control
Authors: Jing Yu, Yuh-Shyang Wang, James Anderson
Date: 2020-10-06 00:00:00

Distributed linear control design is crucial for large-scale cyber-physical systems. It is generally desirable to both impose information exchange (communication) constraints on the distributed controller, and to limit the propagation of disturbances to a local region without cascading to the global network (localization). Recently proposed System Level Synthesis (SLS) theory provides a framework where such communication and localization requirements can be tractably incorporated in controller design and implementation. In this work, we derive a solution to the localized and distributed H2 state feedback control problem without resorting to Finite Impulse Response (FIR) approximation. Our proposed synthesis algorithm allows a column-wise decomposition of the resulting convex program, and is therefore scalable to arbitrary large-scale networks. We demonstrate superior cost performance and computation time of the proposed procedure over previous methods via numerical simulation.
Guidance Mechanism for Flexible Wing Aircraft Using Measurement-Interfaced Machine Learning Platform
Authors: Mohammed Abouheaf, Nathaniel Mailhot, Wail Gueaieb, Davide Spinello
Date: 2021-03-29 00:00:00
Tasks: Autonomous Navigation, BIG-bench Machine Learning

The autonomous operation of flexible-wing aircraft is technically challenging and has never been presented within literature. The lack of an exact modeling framework is due to the complex nonlinear aerodynamic relationships governed by the deformations in the flexible-wing shape, which in turn complicates the controls and instrumentation setup of the navigation system. This urged for innovative approaches to interface affordable instrumentation platforms to autonomously control this type of aircraft. This work leverages ideas from instrumentation and measurements, machine learning, and optimization fields in order to develop an autonomous navigation system for a flexible-wing aircraft. A novel machine learning process based on a guiding search mechanism is developed to interface real-time measurements of wing-orientation dynamics into control decisions. This process is realized using an online value iteration algorithm that decides on two improved and interacting model-free control strategies in real-time. The first strategy is concerned with achieving the tracking objectives while the second supports the stability of the system. A neural network platform that employs adaptive critics is utilized to approximate the control strategies while approximating the assessments of their values. An experimental actuation system is utilized to test the validity of the proposed platform. The experimental results are shown to be aligned with the stability features of the proposed model-free adaptive learning approach.
Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes
Authors: Zewen Yang, Stefan Sosnowski, Qingchen Liu, Junjie Jiao, Armin Lederer, Sandra Hirche
Date: 2021-03-29 00:00:00
Tasks: Gaussian Processes, regression

In this paper, a distributed learning leader-follower consensus protocol based on Gaussian process regression for a class of nonlinear multi-agent systems with unknown dynamics is designed. We propose a distributed learning approach to predict the residual dynamics for each agent. The stability of the consensus protocol using the data-driven model of the dynamics is shown via Lyapunov analysis. The followers ultimately synchronize to the leader with guaranteed error bounds by applying the proposed control law with a high probability. The effectiveness and the applicability of the developed protocol are demonstrated by simulation examples.
Intermittent non-pharmaceutical strategies to mitigate the COVID-19 epidemic in a network model of Italy via constrained optimization
Authors: Marco Coraggio, Shihao Xie, Francesco De Lellis, Giovanni Russo, Mario di Bernardo
Date: 2021-03-25 00:00:00
Tasks: Model Predictive Control, Unity

This paper is concerned with the design of intermittent non-pharmaceutical strategies to mitigate the spread of the COVID-19 epidemic exploiting network epidemiological models. Specifically, by studying a variational equation for the dynamics of the infected in a network model of the epidemic spread, we derive, using contractivity arguments, a condition that can be used to guarantee that, in epidemiological terms, the effective reproduction number is less than unity. This condition has three advantages: (i) it is easily computable; (ii) it is directly related to the model parameters; (iii) it can be used to enforce a scalability condition that prohibits the amplification of disturbances within the network system. We then include satisfaction of such a condition as a constraint in a Model Predictive Control problem so as to mitigate (or suppress) the spread of the epidemic while minimizing the economic impact of the interventions. A data-driven model of Italy as a network of three macro-regions (North, Center, and South), whose parameters are identified from real data, is used to illustrate and evaluate the effectiveness of the proposed control strategy.
Low complexity model to study scale dependence of phytoplankton dynamics in the tropical Pacific
Authors: Jozef Skakala, Paolo Lazzari
Date: 2020-09-08 00:00:00

We demonstrate that a simple model based on reaction-diffusion-advection (RDA) equation forced by realistic surface velocities and nutrients is skilled in reproducing the distributions of the surface phytoplankton chlorophyll in the tropical Pacific. We use the low-complexity RDA model to investigate the scale-relationships in the impact of different drivers (turbulent diffusion, mean and eddy advection, primary productivity) on the phytoplankton chlorophyll concentrations. We find that in the 1/4{\deg} (~25km) model, advection has a substantial impact on the rate of primary productivity, whilst the turbulent diffusion term has a fairly negligible impact. Turbulent diffusion has an impact on the phytoplankton variability, with the impact being scale-propagated and amplified by the larger scale surface currents. We investigate the impact of a surface nutrient decline and some changes to mesoscale eddy kinetic energy (climate change projections) on the surface phytoplankton concentrations. The RDA model suggests that unless mesoscale eddies radically change, phytoplankton chlorophyll scales sub-linearly with the nutrients, and it is relatively stable with respect to the nutrient concentrations. Furthermore we explore how a white multiplicative Gaussian noise introduced into the RDA model on its resolution scale propagates across spatial scales through the non-linear model dynamics under different sets of phytoplankton drivers. The unifying message of this work is that the low complexity (e.g. RDA) models can be successfully used to realistically model some specific aspects of marine ecosystem dynamics and by using those models one can explore many questions that would be beyond computational affordability of the higher-complexity ecosystem models.
On a Standard Method for Measuring the Natural Rate of Interest
Authors: Daniel Buncic
Date: 2021-03-30 00:00:00

I show that Holston, Laubach and Williams' (2017) implementation of Median Unbiased Estimation (MUE) cannot recover the signal-to-noise ratio of interest from their Stage 2 model. Moreover, their implementation of the structural break regressions which are used as an auxiliary model in MUE deviates from Stock and Watson's (1998) formulation. This leads to spuriously large estimates of the signal-to-noise parameter $\lambda _{z}$ and thereby an excessive downward trend in other factor $z_{t}$ and the natural rate. I provide a correction to the Stage 2 model specification and the implementation of the structural break regressions in MUE. This correction is quantitatively important. It results in substantially smaller point estimates of $\lambda _{z}$ which affects the severity of the downward trend in other factor $z_{t}$. For the US, the estimate of $\lambda _{z}$ shrinks from $0.040$ to $0.013$ and is statistically highly insignificant. For the Euro Area, the UK and Canada, the MUE point estimates of $\lambda _{z}$ are \emph{exactly} zero. Natural rate estimates from HLW's model using the correct Stage 2 MUE implementation are up to 100 basis points larger than originally computed.
Technological Leapfrogging and Manufacturing Value-added in sub-Saharan African (1990-2018)
Authors: Segun Michael Ojo, Edward Oladipo Ogunleye
Date: 2021-03-30 00:00:00

This study examines the impact of technological leapfrogging on manufacturing value-added in SSA. The study utilizes secondary data spanning 1990 to 2018. The data is analyzed using cross-sectional autoregressive distributed lags (CS-ARDL) and cross-sectional distributed lags (CS-DL) techniques. The study found that technological leapfrogging is a positive driver of manufacturing value-added in SSA. This implies that SSA can copy the foreign technologies and adapt them for domestic uses, rather than going through the evolutionary process of the old technologies that are relatively less efficient. If the governments of SSA could reinforce their absorptive capacity and beef up productivity through proper utilization of the existing technology. The productive activities of the domestic firms will stir new innovations and discoveries that will eventually translate into indigenous technology
The lattice of worker-quasi-stable matchings
Authors: Agustin G. Bonifacio, Nadia Guinazu, Noelia Juarez, Pablo Neme, Jorge Oviedo
Date: 2021-03-30 00:00:00
Tasks: Blocking

In a many-to-one matching model in which firms' preferences satisfy substitutability, we study the set of worker-quasi-stable matchings. Worker-quasi-stability is a relaxation of stability that allows blocking pairs involving a firm and an unemployed worker. We show that this set has a lattice structure and define a Tarski operator on this lattice that models a re-equilibration process and has the set of stable matchings as its fixed points.
The Formation of Global Free Trade Agreement
Authors: Akira Okada, Yasuhiro Shirata
Date: 2021-03-30 00:00:00

We investigate the formation of Free Trade Agreement (FTA) in a competing importers framework with $n$ countries. We show that (i) FTA formation causes a negative externality to non-participants, (ii) a non-participant is willing to join an FTA, and (iii) new participation may decrease the welfare of incumbent participants. A unique subgame perfect equilibrium of a sequential FTA formation game does not achieve global free trade under an open-access rule where a new applicant needs consent of members for accession, currently employed by many open regionalism agreements including APEC. We further show that global FTA is a unique subgame perfect equilibrium under an open-access rule without consent.