pwc-archive/papers-with-abstracts huggingface dataset:
Distributed energy control in electric energy systems
Authors: Rupamathi Jaddivada, Marija D. Ilic
Date: 2021-11-23 00:00:00

The power interactions of any component in electric energy systems with the rest of the system happen naturally, as governed by the energy conservation principles. There may, however, occur instances when the rate at which power gets generated by one component through local energy conversion is not exactly the same as that absorbed by rest of the system. This is when instabilities get induced. To model and control such instabilities, this paper generalizes the notion of interaction variable used to characterize diverse system components in a unified manner. The same variable captures aggregate system-wide effects and sets reference points for multi-layered distributed output feedback control. It has a physical interpretation of instantaneous power and generalized reactive power. The higher layer design utilizes the interactive energy state-space model to derive intermediate reactive power control, which becomes a control command to the lower layer physical model. This command is implemented using either Feedback Linearizing Control (FBLC) or Sliding Mode Control (SMC), for which sufficient stability conditions are stated. This paper claims that the proposed design is fundamental to aligning dynamic interactions between components for stability and feasibility. Without loss of generality, we utilize a simple RLC circuit with a controllable voltage source for illustrations, which is a simplified representation of any controllable component in microgrids.
Energy control of HVAC units for provable ancillary service provision
Authors: Rupamathi Jaddivada, Marija D. Ilic, Mario Garcia- Sanz, Mirjana Marden, Sonja Glavaski
Date: 2021-11-23 00:00:00

In this paper, we consider the problem of controlling power consumption dynamics of residential heating, ventilation and air conditioning (HVAC) units so that they follow the grid-side power specifications. In order to do so, we design a novel dynamical energy controller which ensures regulation of the cumulative effects of power imbalances. For this, we derive a novel energy-based model that relates the HVAC physics-based dynamics to both real and reactive power balance at the point of interconnection with the grid. In contrast to several other approaches in the literature, we show that a limited number of HVAC units can meet the stringent performance metrics set by the ARPA-E/NODES program on following the frequency regulation signal, while maintaining consumer comfort. Theoretical and simulation-based model and control validation is provided by making use of real-world HVAC data.
Stability and Sensitivity Analysis of Multi-Vendor, Multi-Terminal HVDC Systems
Authors: Yicheng Liao, Heng Wu, Xiongfei Wang, Mario Ndreko, Robert Dimitrovski, Wilhelm Winter
Date: 2021-11-23 00:00:00
Tasks: Sensitivity

The stability of multi-vendor, multi-terminal HVDC systems can be analyzed in frequency domain by black-box impedance models using the generalized Nyquist stability criterion. Based on the impedance stability analysis, a multi-level sensitivity analysis approach using frequency-domain sensitivity functions is proposed to identify the root cause of potential instability. Case studies on a four-terminal HVDC system are carried out for stability and sensitivity analysis based on the impedance measurement in PSCAD. The analysis results are finally validated by electromagnetic transient simulations.
Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy
Authors: Si Zhang, Mingzhi Zhang, Rongxing Hu, David Lubkeman, Yunan Liu, Ning Lu
Date: 2021-11-23 00:00:00
Tasks: Ingenuity, reinforcement-learning, Reinforcement Learning (RL)

This paper develops a reinforcement learning (RL)approach to solve a cooperative, multi-agent Volt-Var Control (VVC) problem for high solar penetration distribution systems. The ingenuity of our RL method lies in a novel two-stage progressive training strategy that can effectively improve training speed and convergence of the machine learning algorithm. In Stage 1(individual training), while holding all the other agents inactive, we separately train each agent to obtain its own optimal VVC actions in the action space: {consume, generate, do-nothing}. In Stage 2 (cooperative training), all agents are trained again coordinatively to share VVC responsibility. Rewards and costs in our RL scheme include (i) a system-level reward (for taking an action), (ii) an agent-level reward (for doing-nothing), and(iii) an agent-level action cost function. This new framework allows rewards to be dynamically allocated to each agent based on their contribution while accounting for the trade-off between control effectiveness and action cost. The proposed methodology is tested and validated in a modified IEEE 123-bus system using realistic PV and load profiles. Simulation results confirm that the proposed approach is robust and computationally efficient; and it achieves desirable volt-var control performance under a wide range of operation conditions.
Reduced-order observer design for a robotic manipulator
Authors: Andrea Cristofaro, Alessandro De Luca
Date: 2021-11-23 00:00:00

This paper investigates the design of reduced-order observers for robotic manipulators. Observer stability conditions are obtained based on a Lyapunov analysis and the proposed observer is enhanced with a hybrid scheme that may adjust the gains to cope with possible unbounded velocities of the robot joints. Thanks to such hybrid strategy, the observer works accurately both for robots driven by open-loop controllers and by output feedback controllers. Numerical simulations illustrate the efficacy of the reduced-order observer in several scenarios, including a comparison with the performances of a classical full-order observer.
Techno-economic analysis of Power-to-Gas plants in a gas and electricity distribution network system with high renewable energy penetration
Authors: Gabriele Fambri, Cesar Diaz-Londono, Andrea Mazza, Marco Badami, Teemu Sihvonen, Robert Weiss
Date: 2021-11-23 00:00:00

Distributed generation, based on the exploitation of Renewable Energy Sources (RES), has increased in the last few decades to limit anthropogenic carbon dioxide emissions, and this trend will increase in the future. However, RES generation is not dispatchable, and an increasing share of RES may lead to inefficiencies and even problems for the electricity network. Flexible resources are needed to handle RES generation in order to support the delicate electricity generation and demand balance. Energy conversion technologies (P2X, Power to X) allow the flexibility of energy systems to be increased. These technologies make a connection between different energy sectors (e.g., electricity and gas) possible, and thus create new synergies within an overall multi energy system. This paper analyzes how the P2G technology can be used at the distribution network level (both gas and electricity) to optimize the use of RES. In fact, in order to coordinate P2X resources, it is necessary to take into account the whole multi energy scenario, and not just the electrical side: it therefore becomes fundamental to recognize the pros and cons that Balancing Service Providers (BSPs), composed of a number of P2G plants (representing the Balancing Responsible Providers, BRPs), may have when offering services to an electricity network. Moreover, the convenience of the decarbonization of the gas grid has been evaluated through the calculation of the levelized cost of Synthetic Natural Gas (LCSNG) for cost scenarios for the years 2030 and 2050, considering different assumptions about the cost of the surplus utilization of RES. The results show that LCSNG may vary from 47 to 319 EURO/MWh, according to the different configurations, i.e., only in the best case scenario is the SNG cost comparable with the cost of natural gas, and hence does the P2G technology result to be profitable
Robust Time-Varying Parameters Estimation Based on I-DREM Procedure
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2021-11-23 00:00:00
Tasks: regression

We consider a class of systems with time-varying parameters, which are written as linear regressions with bounded disturbances. The task is to estimate such parameters under the condition that the regressor is finitely exciting (FE). Considering such a problem statement, a new robust method is proposed to identify the time-varying parameters with bounded error, which could be reduced to the limit by the adjustment of such method parameters. For this purpose, the function of the system unknown parameters, which depends on time, is expanded into a Taylor series in order to turn the considered problem into the identification of the regression with piecewise-constant parameters. This results in the increase of the dimensionality of the problem to be solved. Then, the I-DREM procedure with exponential forgetting, resetting, and normalization of the regressor, which has been proposed earlier by the authors, is applied to the obtained regression. This allows one, in contrast to the known solutions, to get the dimensionality of the problem back to the initial one and provide the required exponential convergence of the parameter error to a bounded set with adjustable bound under the condition that the regressor is FE. In addition, this method guarantees that the parameter error is bounded beyond the regressor excitation interval. The above properties are proved analytically and shown via numerical simulations.
Strategic Competition of Electric Vehicle Charging Stations in a Regulated Retail Electricity Market
Authors: Reza Bayani, Arash Farokhi Soofi, Saeed D. Manshadi
Date: 2021-11-23 00:00:00

The increasing trend of transportation electrification presents investors the opportunity to provide charging services to Electric Vehicle (EV) owners via the energy purchased from the wholesale electricity market. This will benefit EV owners with the availability of competitive rates compared to the regulated utility time-of-use (TOU) rates. The fundamental questions addressed in this paper are 1) will EV owners benefit from the additional choice of Electric Vehicle Charging Stations (EVCSs) compared to home charging? 2) is there any profitable market opportunity for charging stations while the retail electricity market is regulated? To this end, the strategic bidding problem for EVCSs which purchase electricity from the Day-Ahead Electricity Market (DAM) and sell it to EV owners is presented. The strategic bidding problem is constrained by the market-clearing problem within the DAM as well as EVs' charging cost minimization problem. A bi-level optimization problem formulation and a solution method are presented to address this work's research questions. The effectiveness of the proposed structure in gaining profit for EVCSs is illustrated, and it is shown that EV owners also save on their charging cost with the presence of EVCSs as a choice.
Topology Identification under Spatially Correlated Noise
Authors: Mishfad Shaikh Veedu, Murti V. Salapaka
Date: 2020-12-08 00:00:00
Tasks: Time Series, Time Series Analysis

This article addresses the problem of reconstructing the topology of a network of agents interacting via linear dynamics, while being excited by exogenous stochastic sources that are possibly correlated across the agents, from time-series measurements alone. It is shown, under the assumption that the correlations are affine in nature, such network of nodal interactions is equivalent to a network with added agents, represented by nodes that are latent, where no corresponding time-series measurements are available; however, here all exogenous excitements are spatially (that is, across agents) uncorrelated. Generalizing affine correlations, it is shown that, under polynomial correlations, the latent nodes in the expanded networks can be excited by clusters of noise sources, where the clusters are uncorrelated with each other. The clusters can be replaced with a single noise source if the latent nodes are allowed to have non-linear interactions. Finally, using the sparse plus low-rank matrix decomposition of the imaginary part of the inverse power spectral density matrix (IPSDM) of the time-series data, the topology of the network is reconstructed. Under non conservative assumptions, the correlation graph is retrieved.
Drone flight data reveal energy and greenhouse gas emissions savings for small package delivery
Authors: Thiago A. Rodrigues, Jay Patrikar, Natalia L. Oliveira, H. Scott Matthews, Sebastian Scherer, Constantine Samaras
Date: 2021-11-22 00:00:00

The adoption of Uncrewed Aerial Vehicles (UAVs) for last-mile deliveries will affect the energy productivity of package delivery and require new methods to understand the associated energy consumption and greenhouse gas (GHG) emissions. Here we combine empirical testing of 187 quadcopter flights with first principles analysis to develop a usable energy model for drone package delivery. We develop a machine-learning algorithm to assess energy use across three different flight regimes: takeoff, cruise, and landing. Our model shows that, in the US, a small electric quadcopter drone with a payload of 1 kg would consume approximately 0.05 MJ/km and result in 41 g of CO$_{2}$e per package. The energy per package delivered by drones (0.19 MJ/package) can be up to 96\% lower than conventional transportation modes. Our open model and generalizable coefficients can assist stakeholders in understanding and improving the energy use of drone package delivery.
Cost Optimization of Water Distribution Networks: Model Refinement Is Better Than Problem-Specific Solving Techniques
Authors: Saumya Goyal, Om Damani, Ashutosh Mahajan
Date: 2021-11-22 00:00:00

Existing techniques for the cost optimization of water distribution networks either employ meta-heuristics, or try to develop problem-specific optimization techniques. Instead, we exploit recent advances in generic NLP solvers and explore a rich set of model refinement techniques. The networks that we study contain a single source and multiple demand nodes with residual pressure constraints. Indeterminism of flow values and flow direction in the network leads to non-linearity in these constraints making the optimization problem non-convex. While the physical network is cyclic, flow through the network is necessarily acyclic and thus enforces an acyclic orientation. We devise different strategies of finding acyclic orientations and explore the benefit of enforcing such orientations explicitly as a constraint. Finally, we propose a parallel link formulation that models flow in each link as two separate flows with opposing directions. This allows us to tackle numerical difficulties in optimization when flow in a link is near zero. We find that all our proposed formulations give results at par with least cost solutions obtained in the literature on benchmark networks. We also introduce a suite of large test networks since existing benchmark networks are small in size, and find that the parallel link approach outperforms all other approaches on these bigger networks, resulting in a more tractable technique of cost optimization.
Flight Control System Design for Autonomous Aerial Surveys of Volcanoes
Authors: Zhe Shen
Date: 2021-11-21 00:00:00

The controller for a quadrotor working in severe environment is developed in this study. Here, the severe environment indicates the temperature-varying air near the volcano. The controller overcomes the intensively changing temperature above the crater of the volcano which biases the nominal dynamics (25 Celcius). The target Volcano is picked as Satsuma-iojima located in a tiny insular South to the mainland of Japan. The temperature distribution is contributed from previous research by Geological Survey of Japan. To guarantee that the control signal is under the input saturation, a path planning method is developed. Picking the eigenvalue for a system with a moving reference is a novel topic; the method to develop a controller with specific requirement is created for the first time. This method might be referred as a standard way for designers/engineers in developing controller with a moving target/reference in further study. In controlling part, a state feedback controller is designed to stabilize the height of the quadrotor. The eigenvalue of the feedback controller is picked based on the method developed in Chapter Path Planning. And a PID controller is designed to control the attitude. The result of these are verified in a simulator written in MATLAB. At last, a Kalman filter is applied in height control to combine the measurement noise from IMU and laser scanner and the system noise caused by the changing temperature. Another reason for developing a Kalman filter is that IMU readout provides with acceleration. While the velocity is not achieved by sensor directly. The result of height control with a Kalman filter is verified in MATLAB Simulink.
Structure-based approach to identifying small sets of driver nodes in biological networks
Authors: Eli Newby, Jorge Gómez Tejda Zañudo, Réka Albert
Date: 2021-11-22 00:00:00
Tasks: Feedback Vertex Set (FVS)

In network control theory, driving all the nodes in the Feedback Vertex Set (FVS) forces the network into one of its attractors (long-term dynamic behaviors). The FVS is often composed of more nodes than can be realistically manipulated in a system; for example, only up to three nodes can be controlled in intracellular networks, while their FVS may contain more than 10 nodes. Thus, we developed an approach to rank subsets of the FVS on Boolean models of intracellular networks using topological, dynamics-independent measures. We investigated the use of seven topological prediction measures sorted into three categories -- centrality measures, propagation measures, and cycle-based measures. Using each measure every subset was ranked and then evaluated against two dynamics-based metrics that measure the ability of interventions to drive the system towards or away from its attractors: To Control and Away Control. After examining an array of biological networks, we found that the FVS subsets that ranked in the top according to the propagation metrics can most effectively control the network. This result was independently corroborated on a second array of different Boolean models of biological networks. Consequently, overriding the entire FVS is not required to drive a biological network to one of its attractors, and this method provides a way to reliably identify effective FVS subsets without knowledge of the network's dynamics.
Interactive Effects Panel Data Models with General Factors and Regressors
Authors: Bin Peng, Liangjun Su, Joakim Westerlund, Yanrong Yang
Date: 2021-11-22 00:00:00

This paper considers a model with general regressors and unobservable factors. An estimator based on iterated principal components is proposed, which is shown to be not only asymptotically normal and oracle efficient, but under certain conditions also free of the otherwise so common asymptotic incidental parameters bias. Interestingly, the conditions required to achieve unbiasedness become weaker the stronger the trends in the factors, and if the trending is strong enough unbiasedness comes at no cost at all. In particular, the approach does not require any knowledge of how many factors there are, or whether they are deterministic or stochastic. The order of integration of the factors is also treated as unknown, as is the order of integration of the regressors, which means that there is no need to pre-test for unit roots, or to decide on which deterministic terms to include in the model.
Semi-nonparametric Estimation of Operational Risk Capital with Extreme Loss Events
Authors: Heng Z. Chen, Stephen R. Cosslett
Date: 2021-11-22 00:00:00

Bank operational risk capital modeling using the Basel II advanced measurement approach (AMA) often lead to a counter-intuitive capital estimate of value at risk at 99.9% due to extreme loss events. To address this issue, a flexible semi-nonparametric (SNP) model is introduced using the change of variables technique to enrich the family of distributions to handle extreme loss events. The SNP models are proved to have the same maximum domain of attraction (MDA) as the parametric kernels, and it follows that the SNP models are consistent with the extreme value theory peaks over threshold method but with different shape and scale parameters from the kernels. By using the simulation dataset generated from a mixture of distributions with both light and heavy tails, the SNP models in the Frechet and Gumbel MDAs are shown to fit the tail dataset satisfactorily through increasing the number of model parameters. The SNP model quantile estimates at 99.9 percent are not overly sensitive towards the body-tail threshold change, which is in sharp contrast to the parametric models. When applied to a bank operational risk dataset with three Basel event types, the SNP model provides a significant improvement in the goodness of fit to the two event types with heavy tails, yielding an intuitive capital estimate that is in the same magnitude as the event type total loss. Since the third event type does not have a heavy tail, the parametric model yields an intuitive capital estimate, and the SNP model cannot provide additional improvement. This research suggests that the SNP model may enable banks to continue with the AMA or its partial use to obtain an intuitive operational risk capital estimate when the simple non-model based Basic Indicator Approach or Standardized Approach are not suitable per Basel Committee Banking Supervision OPE10 (2019).
Affirmative Action's Cumulative Fractional Assignments
Authors: Haydar Evren, Manshu Khanna
Date: 2021-11-23 00:00:00
Tasks: Vocal Bursts Valence Prediction

The Central Educational Institutions (Reservation in Teachers' Cadre) Act, 2019 provides for reserving teaching vacancies in India's central educational institutions for beneficiaries of its affirmative action policy. Reservation of teaching vacancies had been a contentious issue, and the act was introduced to resolve it after the Supreme Court's solution was met with protests from the Teachers' Union. Our paper demonstrates an impossibility result in the Supreme Court's solution and the act, which are flawed in reserving seats simultaneously at both the university and within its departments. To overcome this impossibility, we propose an alternative solution based on approximate implementation of fractional assignments, offering a promising middle-ground between the two disputed solutions practiced in India. This novel application demonstrates the practical relevance of the approximate implementation approach (Akbarpourand Nikzad(2020)) beyond the constraint structures examined in the literature.