pwc-archive/papers-with-abstracts huggingface dataset:
Structure Identifiability of an NDS with LFT Parametrized Subsystems
Authors: Tong Zhou
Date: 2020-05-27 00:00:00

Requirements on subsystems have been made clear in this paper for a linear time invariant (LTI) networked dynamic system (NDS), under which subsystem interconnections can be estimated from external output measurements. In this NDS, subsystems may have distinctive dynamics, and subsystem interconnections are arbitrary. It is assumed that system matrices of each subsystem depend on its (pseudo) first principle parameters (FPPs) through a linear fractional transformation (LFT). It has been proven that if in each subsystem, the transfer function matrix (TFM) from its internal inputs to its external outputs is of full normal column rank (FNCR), while the TFM from its external inputs to its internal outputs is of full normal row rank (FNRR), then the structure of the NDS is identifiable. Moreover, under some particular situations like there are no direct information transmission from an internal input to an internal output in each subsystem, a necessary and sufficient condition is established for NDS structure identifiability. A matrix valued polynomial (MVP) rank based equivalent condition is further derived, which depends affinely on subsystem (pseudo) FPPs and can be independently verified for each subsystem. From this condition, some necessary conditions are obtained for both subsystem dynamics and its (pseudo) FPPs, using the Kronecker canonical form (KCF) of a matrix pencil.
Economic MPC of Markov Decision Processes: Dissipativity in Undiscounted Infinite-Horizon Optimal Control
Authors: Sébastien Gros, Mario Zanon
Date: 2021-04-22 00:00:00
Tasks: Model Predictive Control

Economic Model Predictive Control (MPC) dissipativity theory is central to discussing the stability of policies resulting from minimizing economic stage costs. In its current form, the dissipativity theory for economic MPC applies to problems based on deterministic dynamics or to very specific classes of stochastic problems, and does not readily extend to generic Markov Decision Processes. In this paper, we clarify the core reason for this difficulty, and propose a generalization of the economic MPC dissipativity theory that circumvents it. This generalization focuses on undiscounted infinite-horizon problems and is based on nonlinear stage cost functionals, allowing one to discuss the Lyapunov asymptotic stability of policies for Markov Decision Processes in terms of the probability measures underlying their stochastic dynamics. This theory is illustrated for the stochastic Linear Quadratic Regulator with Gaussian process noise, for which a storage functional can be provided explicitly. For the sake of brevity, we limit our discussion to undiscounted Markov Decision Processes.
Abstraction-Based Output-Feedback Control with State-Based Specifications
Authors: Anne-Kathrin Schmuck, Mehrdad Zareian
Date: 2021-04-22 00:00:00

We consider abstraction-based design of output-feedback controllers for non-linear dynamical systems against specifications over state-based predicates in linear-time temporal logic (LTL). In this context, our contribution is two-fold: (I) we generalize feedback-refinement relations for abstraction-based output-feedback control to systems with arbitrary predicate and observation maps, and (II) we introduce a new algorithm for the synthesis of abstract output-feedback controllers w.r.t. LTL specifications over unobservable state-based predicates. Our abstraction-based output-feedback controller synthesis algorithm consists of two steps. First, we compute a finite state abstraction of the original system using existing techniques. This process typically leads to an abstract system with non-deterministic predicate and observation maps which are not necessarily related to each other. Second, we introduce an algorithm to compute an output-feedback controller for such abstract systems. Our algorithm is inspired by reactive synthesis under partial observation and utilizes bounded synthesis.
Towards Control of Dam and Reservoir Systems with Forward-Backward Stochastic Differential Equations Driven by Clustered Jumps
Authors: Hidekazu Yoshioka
Date: 2021-04-22 00:00:00
Tasks: Management

We deal with a new maximum principle-based stochastic control model for river management through operating a dam and reservoir system. The model is based on coupled forward-backward stochastic differential equations (FBSDEs) derived from jump-driven streamflow dynamics and reservoir water balance. A continuous-time branching process with immigration driven by a tempered stable subordinator efficiently describes clustered inflow streamflow dynamics. This is a completely new attempt in hydrology and control engineering. Applying a stochastic maximum principle to the dynamics based on an objective functional for designing cost-efficient control of dam and reservoir systems leads to the FBSDEs as a system of optimality equations. The FBSDEs under a linear-quadratic ansatz lead to a tractable model, while they are solved numerically in the other cases using a least-squares Monte-Carlo method. Optimal controls are found in the former, while only sub-optimal ones are computable in the latter due to a hard state constraint. Model parameters are successfully identified from a real data of a river in Japan having a dam and reservoir system. We also show that the linear-quadratic case can capture the real operation data of the system with underestimation of the outflow discharge. More complex cases with a realistic time horizon are analyzed numerically to investigate impacts of considering the environmental flows and seasonal operational purposes. Key challenges towards more sophisticated modeling and analysis with jump-driven FBSDEs are discussed as well.
A Conditional Value-at-Risk Based Planning Model for Integrated Energy System with Energy Storage and Renewables
Authors: Ang Xuan, Xinwei Shen, Qinglai Guo, Hongbin Sun
Date: 2021-04-22 00:00:00

Owing to the potential higher energy supply efficiency and operation flexibility, integrated energy system (IES), which usually includes electric power, gas and heating/cooling systems, is considered as one of the primary forms of energy carrier in the future. However, with the increasing complexity of multiple energy devices and systems integration, IES planning is facing a significant challenge in terms of risk assessment. To this end, an energy hub (EH) planning model considering renewable energy sources (RES) and energy storage system (ESS) integration is proposed in this paper, in which the risk is measured by Conditional Value-at-Risk (CVaR). The proposed IES planning model includes two stages: 1) investment planning on equipment types and capacity (e.g., energy converters, distributed RES and ESS) and 2) optimizing the potential risk loss in operation scenarios along with confidence level and risk preference. The problem solving is accelerated by Benders Decomposition and Improved Backward Scenario Reduction Method. The numerical results illustrate the effectiveness of proposed method in balancing the potential operation risk and investment cost. Moreover, the effectiveness of reducing potential operation risk by introducing ESS and RES are also verified.
Transient Stability of Hybrid Power Systems Dominated by Different Types of Grid-Forming Devices
Authors: Xiuqiang He, Hua Geng
Date: 2021-04-22 00:00:00

This paper investigates the transient stability of power systems co-dominated by different types of grid-forming (GFM) devices. Synchronous generators (SGs and VSGs) and droop-controlled inverters are typical GFM devices in modern power systems. SGs/VSGs are able to provide inertia while droop-controlled inverters are generally inertialess. The transient stability of power systems dominated by homogeneous GFM devices has been extensively studied. Regarding the hybrid system jointly dominated by heterogeneous GFM devices, the transient stability is rarely reported. This paper aims to fill this gap. It is found that the synchronization behavior of the hybrid system can be described by a second-order motion equation, resembling the swing equation of SGs. Moreover, two significant differences from conventional power systems are discovered. The first is that the droop control dramatically enhances the damping effect, greatly affecting the transient stability region. The second is that the frequency state variable exhibits a jump at the moment of fault disturbances, thus impacting the post-fault initial-state location and stability assessment. The underlying mechanism behind the two new characteristics is clarified and the impact on the transient stability performance is analyzed and verified. The findings provide new insights into transient stability of power systems hosting heterogeneous devices.
Build Smart Grids on Artificial Intelligence -- A Real-world Example
Authors: Shutang You, Yilu Liu, Hongyu Li, Shengyuan Liu, Kaiqi Sun, Yinfeng Zhao, Huangqing Xiao, Jiaojiao Dong, Yu Su, Weikang Wang, Yi Cui
Date: 2020-10-21 00:00:00

Power grid data are going big with the deployment of various sensors. The big data in power grids creates huge opportunities for applying artificial intelligence technologies to improve resilience and reliability. This paper introduces multiple real-world applications based on artificial intelligence to improve power grid situational awareness and resilience. These applications include event identification, inertia estimation, event location and magnitude estimation, data authentication, control, and stability assessment. These applications are operating on a real-world system called FNET-GridEye, which is a wide-area measurement network and arguably the world-largest cyber-physical system that collects power grid big data. These applications showed much better performance compared with conventional approaches and accomplished new tasks that are impossible to realized using conventional technologies. These encouraging results demonstrate that combining power grid big data and artificial intelligence can uncover and capture the non-linear correlation between power grid data and its stabilities indices and will potentially enable many advanced applications that can significantly improve power grid resilience.
Calculate Center-of-Inertia Frequency and System RoCoF Using PMU Data
Authors: Shutang You, Hongyu Li, Shengyuan Liu, Kaiqi Sun, Weikang Wang, Wei Qiu, Yilu Liu
Date: 2020-10-21 00:00:00

The power system frequency is important for the system overall stability. However, there does not exist a single measurement point of the system frequency due to the distributed nature of the system inertia and the small inconsistency of different generator rotor electrical speeds in one synchronized system. This paper proposed a new approach to calculate the system center-of-inertia (COI) frequency and the rate-of-change-of-frequency (RoCoF) more accurately using PMU data at multiple locations. The COI frequency and the RoCoF value were further used to assist fast estimation of the imbalance MW amount of a frequency event. Test results using actual measurements in the U.S. Eastern Interconnection system validated the effectiveness of the proposed method.
A Concise Guide on the Integration of Battery Electric Buses into Urban Bus Networks
Authors: Nicolas Dirks, Dennis Wagner, Maximilian Schiffer, Grit Walther
Date: 2021-04-21 00:00:00
Tasks: Decision Making

With the increasing market penetration of battery-electric buses into urban bus networks, practitioners face many novel planning problems. As a result, the interest in optimization-based decision-making for these planning problems increases but practitioners' requirements on planning solutions and current academic approaches often diverge. Against this background, this survey aims to provide a concise guide on optimization-based planning approaches for integrating battery-electric buses into urban bus networks for both practitioners and academics. First, we derive practitioners' requirements for integrating battery-electric buses from state-of-the-art specifications, project reports, and expert knowledge. Second, we analyze whether existing optimization-based planning models fulfill these practitioners' requirements. Based on this analysis, we carve out the existing gap between practice and research and discuss how to address these in future research.
Electrification of Commercial E-buses by Utilizing Stationary Battery Energy Storage Systems for Mass Transportation Network in Los Angeles
Authors: Amirhossein Ahmadian, Atharva Sunil Deo, Paawan Garg, Omar Elayyan
Date: 2021-04-21 00:00:00
Tasks: Management

The 2028 LA Olympics is approaching, and the mass transportation network requires to be expanded. The green electric bus transportation system is gaining increasing attention as an essential step to mitigate emission concerns. However, the introduction of a large electric bus fleet will increase the peak load demand, which will adversely affect the grid. This research addresses the issue of an increase in the peak load demand due to the introduction of the electric bus fleet. Therefore, the potential implementation of stationary battery energy storage systems is investigated to shave the peak load demand by providing energy during peak hours. Moreover, a fleet management software implementation has been demonstrated for a test case scenario in Los Angeles. As a result, based on specific energy demand values, battery hardware options, standard e-bus driving cycles and profiles, and local bus route characteristics, the optimal battery specifications, and power rates have been addressed. Moreover, techno-economic analysis has been carried out to demonstrate the cost-benefit analysis.
Feedforward-Feedback wake redirection for wind farm control
Authors: Steffen Raach, Bart Doekemeijer, Sjoerd Boersma, Jan-Willem van Wingerden, Po Wen Cheng
Date: 2021-04-21 00:00:00

This work presents a combined feedforward-feedback wake redirection framework for wind farm control. The FLORIS wake model, a control-oriented steady-state wake model is used to calculate optimal yaw angles for a given wind farm layout and atmospheric condition. The optimal yaw angles, which maximize the total power output, are applied to the wind farm. Further, the lidar-based closed-loop wake redirection concept is used to realize a local feedback on turbine level. The wake center is estimated from lidar measurements \unit[3]{D} downwind of the wind turbines. The dynamical feedback controllers support the feedforward controller and reject disturbances and adapt to model uncertainties. Altogether, the total framework is presented and applied to a nine turbine wind farm test case. In a high fidelity simulation study the concept shows promising results and an increase in total energy production compared to the baseline case and the feedforward-only case.
From Target Tracking to Targeting Track: A Data-Driven Yet Analytical Approach to Joint Target Detection and Tracking
Authors: Tiancheng Li, Yan Song, Hongqi Fan
Date: 2021-04-20 00:00:00

This paper addresses the problem of real-time detection and tracking of a non-cooperative target in the challenging scenario with almost no a-priori information about target birth, death, dynamics and detection probability. Furthermore, there are false and missing data at an unknown yet low rate in the measurements. The only information given in advance is about the target-measurement model and the constraint that there is no more than one target in the scenario. To solve these challenges, we model the movement of the target by using a polynomial trajectory function of time (T-FoT), which aims to estimate the continuous-time trajectory of the target rather than a series of discrete-time point estimates as is done in most existing filters/trackers. Data-driven T-FoT initiation and termination strategies are proposed for identifying the (re-)appearance and disappearance of the target. During the existence of the target, real target measurements are distinguished from clutter if the target indeed exists and is detected, in order to update the T-FoT at each scan for which we design a least-squares estimator. Overall, our approach is Markov-free, data-driven yet analytical. Simulations using either linear or nonlinear systems are conducted to demonstrate the effectiveness of our approach in comparison with the Bayes optimal Bernoulli filters. The results show that our approach is comparable to the perfectly modeled filters, even outperforms them in some cases while requiring much less a-priori information and computing much faster.
Power Efficient Communication for Low Signal to Noise Ratio Optical Links
Authors: Ravikiran Kakarla, Mikael Mazur, Jochen Schröder, Peter A. Andrekson
Date: 2021-04-20 00:00:00
Tasks: Position, Sensitivity

Receiver sensitivity is a particularly important metric in optical communication links operating at low signal-to-noise ratios (SNRs), for example in deep-space communication, since it directly limits the maximum achievable reach and data rate. Pulse position modulation (PPM) with direct detection photon-counting detectors are the most power-efficient solution known, however, the sensitivity gain comes at the expense of reduced spectral efficiency. We show that quadrature phase-shift keying (QPSK) modulation with a phase-sensitive ultralow noise pre-amplified coherent receiver outperforms other well-known power-efficient multi-dimensional coherent modulation formats, while simultaneously having higher spectral efficiency. It also results in better sensitivity than PPM for orders up to 64 with ideal direct detection using photon-counting receivers. This is because of the bit error rate characteristics favoring the QPSK format when forward error correction with a large overhead is considered.
Structural Beauty: A Structure-based Approach to Quantifying the Beauty of an Image
Authors: Bin Jiang, Chris de Rijke
Date: 2021-04-16 00:00:00

To say that beauty is in the eye of the beholder means that beauty is largely subjective so varies from person to person. While the subjectivity view is commonly held, there is also an objectivity view that seeks to measure beauty or aesthetics in some quantitative manners. Christopher Alexander has long discovered that beauty or coherence highly correlates to the number of subsymmetries or substructures and demonstrated that there is a shared notion of beauty - structural beauty - among people and even different peoples, regardless of their faiths, cultures, and ethnicities. This notion of structural beauty arises directly out of living structure or wholeness, a physical and mathematical structure that underlies all space and matter. Based on the concept of living structure, this paper develops an approach for computing the structural beauty or life of an image (L) based on the number of automatically derived substructures (S) and their inherent hierarchy (H). To verify this approach, we conducted a series of case studies applied to eight pairs of images including Leonardo da Vinci's Mona Lisa and Jackson Pollock's Blue Poles. We discovered among others that Blue Poles is more structurally beautiful than the Mona Lisa, and traditional buildings are in general more structurally beautiful than their modernist counterparts. This finding implies that goodness of things or images is largely a matter of fact rather than an opinion or personal preference as conventionally conceived. The research on structural beauty has deep implications on many disciplines, where beauty or aesthetics is a major concern such as image understanding and computer vision, architecture and urban design, humanities and arts, neurophysiology, and psychology. Keywords: Life; wholeness; figural goodness; head/tail breaks; computer vision
Investigating farming efficiency through a two stage analytical approach: Application to the agricultural sector in Northern Oman
Authors: Amar Oukil, Slim Zekri
Date: 2021-04-22 00:00:00

In this paper, we develop a two-stage analytical framework to investigate farming efficiency. In the first stage, data envelopment analysis is employed to estimate the efficiency of the farms and conduct slack and scale economies analyses. In the second stage, we propose a stochastic model to identify potential sources of inefficiency. The latter model integrates within a unified structure all variables, including inputs, outputs and contextual factors. As an application ground, we use a sample of 60 farms from the Batinah coastal region, an agricultural area representing more than 53 per cent of the total cropped area of Oman. The findings of the study lay emphasis on the inter-dependence of groundwater salinity, irrigation technology and operational efficiency of a farm, with as a key recommendation the necessity for more regulated water consumption and a readjustment of governmental subsidiary policies.
Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands
Authors: Sanmay Shelat, Oded Cats, Sander van Cranenburgh
Date: 2021-04-22 00:00:00
Tasks: Demand Forecasting

Public transport ridership around the world has been hit hard by the COVID-19 pandemic. Travellers are likely to adapt their behaviour to avoid the risk of transmission and these changes may even be sustained after the pandemic. To evaluate travellers' behaviour in public transport networks during these times and assess how they will respond to future changes in the pandemic, we conduct a stated choice experiment with train travellers in the Netherlands. We specifically assess behaviour related to three criteria affecting the risk of COVID-19 transmission: (i) crowding, (ii) exposure duration, and (iii) prevalent infection rate. Observed choices are analysed using a latent class choice model which reveals two, nearly equally sized traveller segments: 'COVID Conscious' and 'Infection Indifferent'. The former has a significantly higher valuation of crowding, accepting, on average 8.75 minutes extra waiting time to reduce one person on-board. Moreover, they demonstrate a strong desire to sit without anybody in their neighbouring seat and are quite sensitive to changes in the prevalent infection rate. By contrast, Infection Indifferent travellers' value of crowding (1.04 waiting time minutes/person) is only slightly higher than pre-pandemic estimates and they are relatively unaffected by infection rates. We find that older and female travellers are more likely to be COVD Conscious while those reporting to use the trains more frequently during the pandemic tend to be Infection Indifferent. Further analysis also reveals differences between the two segments in attitudes towards the pandemic and self-reported rule-following behaviour. The behavioural insights from this study will not only contribute to better demand forecasting for service planning but will also inform public transport policy decisions aimed at curbing the shift to private modes.