pwc-archive/papers-with-abstracts huggingface dataset:
An Open Source Representation for the NYS Electric Grid to Support Power Grid and Market Transition Studies
Authors: M. Vivienne Liu, Bo Yuan, Zongjie Wang, Jeffrey A. Sward, K. Max Zhang, C. Lindsay Anderson
Date: 2021-12-13 00:00:00

Under the increasing need to decarbonize energy systems, there is coupled acceleration in connection of distributed and intermittent renewable resources in power grids. To support this transition, researchers and other stakeholders are embarking on detailed studies and analyses of the evolution of this complex system, which require a validated representation of the essential characteristics of the power grid that is accurate for a specific region of interest. For example, the Climate Leadership and Community Protection Act (CLCPA) in New York State (NYS) sets ambitious targets for the transformation of the energy system, opening many interesting research and analysis questions. To provide a platform for these analyses, this paper presents an overview of the current NYS power grid and develops an open-source (https://github.com/AndersonEnergyLab-Cornell/NYgrid) baseline model using publicly available data. The proposed model is validated with real data for power flow and Locational Marginal Prices (LMPs), demonstrating the feasibility, functionality, and consistency of the model. The model is easily adjustable and customizable for various analyses of future configurations and scenarios that require spatiotemporal information about the NYS power grid with data access to all the available historical data and serves as a practical system for general methods and algorithms testing.
Long-Term Benefits of Network Boosters for Renewables Integration and Corrective Grid Security
Authors: Amin Shokri Gazafroudi, Elisabeth Zeyen, Martha Frysztacki, Fabian Neumann, Tom Brown
Date: 2021-12-13 00:00:00

The preventative strategies for $N-1$ network security dominant in European networks mean that network capacity is kept free in case a line fails. If instead fast corrective actions are used to overcome network overloading when single lines fail, this has the potential to free up network capacity that is otherwise underused in preventive $N-1$ security strategies. In this paper, we investigate the impact on renewable integration of a corrective network security strategy, whereby storage or other flexibility assets are used to correct overloading shortly after line outages. In this way, we find significant cost savings for the integration of renewable energy of up to 2.4 billion euros per year in an aggregated 50-bus model of the German power system utilizing these flexibility assets, so-called network boosters (NB). This offers a role for storage beyond energy arbitrage or ancillary services like frequency control. While previous literature has focused on the potential savings of NB in the short-term operation, we focus on the long-term benefits in systems with high shares of renewable energy sources, where the capacities and dispatch of generation and NB are optimised. We demonstrate the benefits of NB for various shares of renewable energy, NB and flexibility costs, as well as different allowed levels of temporary overloading the lines in both (i) a sequential model, where long-run generation investments are optimised separately from the NB capacities, and (ii) a simultaneous model, where generation is co-optimised with NB investment so that mixed preventive-corrective approaches are possible.
Consensus-Based Distributed Filtering with Fusion Step Analysis
Authors: Jiachen Qian, Peihu Duan, Zhisheng Duan, Guanrong Chen, Ling Shi
Date: 2021-12-13 00:00:00
Tasks: State Estimation

For consensus on measurement-based distributed filtering (CMDF), through infinite consensus fusion operations during each sampling interval, each node in the sensor network can achieve optimal filtering performance with centralized filtering. However, due to the limited communication resources in physical systems, the number of fusion steps cannot be infinite. To deal with this issue, the present paper analyzes the performance of CMDF with finite consensus fusion operations. First, by introducing a modified discrete-time algebraic Riccati equation and several novel techniques, the convergence of the estimation error covariance matrix of each sensor is guaranteed under a collective observability condition. In particular, the steady-state covariance matrix can be simplified as the solution to a discrete-time Lyapunov equation. Moreover, the performance degradation induced by reduced fusion frequency is obtained in closed form, which establishes an analytical relation between the performance of the CMDF with finite fusion steps and that of centralized filtering. Meanwhile, it provides a trade-off between the filtering performance and the communication cost. Furthermore, it is shown that the steady-state estimation error covariance matrix exponentially converges to the centralized optimal steady-state matrix with fusion operations tending to infinity during each sampling interval. Finally, the theoretical results are verified with illustrative numerical experiments.
Dynamic Exploitation Gaussian Bare-Bones Bat Algorithm for Optimal Reactive Power Dispatch to Improve the Safety and Stability of Power System
Authors: Zhaoyang Qu, Yunchang Dong, Sylvère Mugemanyi, Tong Yu, Xiaoyong Bo, Huashun Li, Yang Li, François Xavier Rugema, Christophe Bananeza
Date: 2021-12-13 00:00:00
Tasks: continuous-control, Continuous Control

In this paper, a novel Gaussian bare-bones bat algorithm (GBBBA) and its modified version named as dynamic exploitation Gaussian bare-bones bat algorithm (DeGBBBA) are proposed for solving optimal reactive power dispatch (ORPD) problem. The optimal reactive power dispatch (ORPD) plays a fundamental role in ensuring stable, secure, reliable as well as economical operation of the power system. The ORPD problem is formulated as a complex and nonlinear optimization problem of mixed integers including both discrete and continuous control variables. Bat algorithm (BA) is one of the most popular metaheuristic algorithms which mimics the echolocation of the microbats and which has also outperformed some other metaheuristic algorithms in solving various optimization problems. Nevertheless, the standard BA may fail to balance exploration and exploitation for some optimization problems and hence it may often fall into local optima. The proposed GBBBA employs the Gaussian distribution in updating the bat positions in an effort to mitigate the premature convergence problem associated with the standard BA. The GBBBA takes advantages of Gaussian sampling which begins from exploration and continues to exploitation. DeGBBBA is an advanced variant of GBBBA in which a modified Gaussian distribution is introduced so as to allow the dynamic adaptation of exploitation and exploitation in the proposed algorithm. Both GBBBA and DeGBBBA are used to determine the optimal settings of generator bus voltages, tap setting transformers and shunt reactive sources in order to minimize the active power loss, total voltage deviations and voltage stability index. Simulation results show that GBBBA and DeGBBBA are robust and effective in solving the ORPD problem.
Time-of-use Pricing for Energy Storage Investment
Authors: Dongwei Zhao, Hao Wang, Jianwei Huang, Xiaojun Lin
Date: 2021-12-13 00:00:00

Time-of-use (ToU) pricing is widely used by the electricity utility to shave peak load. Such a pricing scheme provides users with incentives to invest in behind-the-meter energy storage and to shift peak load towards low-price intervals. However, without considering the implication on energy storage investment, an improperly designed ToU pricing scheme may lead to significant welfare loss, especially when users over-invest the storage, which leads to new energy consumption peaks. In this paper, we will study how to design a social-optimum ToU pricing scheme by explicitly considering its impact on storage investment. We model the interactions between the utility and users as a two-stage optimization problem. To resolve the challenge of asymmetric information due to users' private storage cost, we propose a ToU pricing scheme based on different storage types and the aggregate demand per type. Each user does not need to reveal his private cost information. We can further compute the optimal ToU pricing with only a linear complexity. Simulations based on real-world data show that the suboptimality gap of our proposed ToU pricing, compared with the social optimum achieved under complete information, is less than 5%.
Knowledge-based optimal irrigation scheduling of agro-hydrological systems
Authors: Soumya R. Sahoo, Bernard T. Agyeman, Sarupa Debnath, Jinfeng Liu
Date: 2021-12-12 00:00:00
Tasks: Model Predictive Control, Scheduling

The typical agricultural irrigation scheduler provides information on how much to irrigate and when to irrigate. The accurate and effective scheduler decision for a large agricultural field is still an open research problem. In this work, we address the high dimensionality of the agricultural field and propose a systematic approach to provide optimum irrigation amount and irrigation time for three-dimensional agro-hydrological systems. The water dynamics of the agro-hydrological system are represented using a cylindrical three-dimensional Richards Equation. We introduce a structure-preserving model reduction technique to decrease the dimension of the system model. Using the reduced model, the optimization-based closed-loop scheduler is designed in model predictive control (MPC) environment. The closed-loop approach can handle weather disturbances and provide improved yield and water conservation. The primary objective of the proposed scheduler is to ensure maximum yield, minimum water consumption and maximize the time between the two irrigation events, which results in less electricity usage. The proposed approach is applied to three different scenarios to show the effectiveness and superiority of the proposed framework.
LSTM-based model predictive control with discrete inputs for irrigation scheduling
Authors: Bernard T. Agyeman, Soumya R. Sahoo, Jinfeng Liu, Sirish L. Shah
Date: 2021-12-12 00:00:00
Tasks: Computational Efficiency, Model Predictive Control, Scheduling

The development of well-devised irrigation scheduling methods is desirable from the perspectives of plant quality and water conservation. In this article, a model predictive control (MPC) with discrete actuators is developed for irrigation scheduling, where a long short-term memory (LSTM) model of the soil-water-atmosphere system is used to evaluate the objective of ensuring optimal water uptake in crops while minimizing total water consumption and irrigation costs. A heuristic method involving a sigmoid function is used in this framework to enhance the computational efficiency of the scheduler. The scheduling scheme is applied to homogeneous and spatially variable fields and the results indicate that the LSTM-based MPC with discrete actuators is able to prescribe optimal or near-optimal irrigation schedules that are typical of irrigation practice.
The Role of Distributed Energy Resources in Distribution System Restoration
Authors: Noah Rhodes, Line Roald
Date: 2021-12-12 00:00:00

With increasing levels of distributed energy resources (DERs) connected to the grid, it is important to understand the role that DERs can play in post-disaster restoration. In this paper, we propose a two-step optimization method to identify and implement an optimal restoration schedule under different DER operating scenarios. We investigate how the presence and geographical distribution of DERs change the optimal restoration order, and assess the impacts on customers with and without DERs. In our case study using the IEEE 123 single phase distribution system, we find that optimal restoration order changes significantly when DERs are concentrated in one part of the grid. We also observe that the presence of DERs generally reduces the energy not served across all customers and can help prioritize grid reconnection of customers without DERs.
Parallelized Robust Distributed Model Predictive Control in the Presence of Coupled State Constraints
Authors: Adrian Wiltz, Fei Chen, Dimos V. Dimarogonas
Date: 2021-12-11 00:00:00
Tasks: Model Predictive Control

In this paper, we present a robust distributed model predictive control (DMPC) scheme for dynamically decoupled nonlinear systems which are subject to state constraints, coupled state constraints and input constraints. In the proposed control scheme, all subsystems solve their local optimization problem in parallel and neighbor-to-neighbor communication suffices. The approach relies on consistency constraints which define a neighborhood around each subsystem's reference trajectory where the state of the subsystem is guaranteed to stay in. Reference trajectories and consistency constraints are known to neighboring subsystems. Contrary to other relevant approaches, the reference trajectories are improved consecutively. The presented approach allows the formulation of convex optimization problems for systems with linear dynamics even in the presence of non-convex state constraints. Additionally, we employ tubes in order to ensure the controller's robustness against bounded uncertainties. In the end, we briefly comment on an iterative extension of the DMPC scheme. The effectiveness of the proposed DMPC scheme and its iterative extension are demonstrated with simulations.
Applying a System Dynamics Approach for the Pharmaceutical Industry: Simulation and Optimization of the Quality Control Process
Authors: Evripidis P. Kechagias, Dimitrios M. Miloulis, Georgios Chatzistelios, Sotiris P. Gayialis, Georgios A. Papadopoulos
Date: 2021-12-11 00:00:00
Tasks: Philosophy

As countries interact more and more, technology gains a decisive role in facilitating today's increased need for interconnection. At the same time, systems, becoming more advanced as technology progresses, feed each other and can produce highly complex and unpredictable results. However, with this ever-increasing need for interconnected operations, complex problems arise that need to be effectively tackled. This need extends far beyond the scientific and mechanical fields, covering every aspect of life. Systemic Thinking Philosophy and the System Dynamics methodology now seem to be more relevant than ever and their practical implementation in real-life industrial cases has started to become a trend. Companies that decide to implement such approaches can achieve significant improvements to the effectiveness of their operations and gain a competitive advantage. This research, influenced by the Systemic Thinking Philosophy, applies a System Dynamics approach in practice by improving the quality control process of a pharmaceutical company. The process is modeled, simulated, analyzed, and improvements are performed to achieve more effective and efficient operations. The results show that all these steps led to a successful identification and optimization of the critical factors, and a significant process improvement was achieved.
Cascode Cross-Coupled Stage High-Speed Dynamic Comparator in 65 nm CMOS
Authors: Komala Krishna, Nandakumar Nambath
Date: 2021-12-11 00:00:00

Dynamic comparators are the core of high-speed, high-resolution analog-to-digital converters (ADCs) used for communication applications. Most of the dynamic comparators attain high-speed operation only for sufficiently high input difference voltages. The comparator performance degrades at small input difference voltages due to a limited pre-amplifier gain, which is undesirable for high-speed, high-resolution ADCs. To overcome this drawback, a cascode cross-coupled dynamic comparator is presented. The proposed comparator improves the differential gain of the pre-amplifier and reduces the common-mode voltage seen by the latch, which leads to a much faster regeneration at small input difference voltages. The proposed comparator is designed, simulated, and compared with the state-of-the-art techniques in 65 nm CMOS technology. The results demonstrate that the proposed comparator achieves a delay of 46.5 ps at 1 mV input difference, and a supply of 1.1 V.
Simultaneous Localization and Mapping: Through the Lens of Nonlinear Optimization
Authors: Amay Saxena, Chih-Yuan Chiu, Joseph Menke, Ritika Shrivastava, Shankar Sastry
Date: 2021-12-11 00:00:00
Tasks: Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) algorithms perform visual-inertial estimation via filtering or batch optimization methods. Empirical evidence suggests that filtering algorithms are computationally faster, while optimization methods are more accurate. This work presents an optimization-based framework that unifies these approaches, and allows users to flexibly implement different design choices, e.g., the number and types of variables maintained in the algorithm at each time. We prove that filtering methods correspond to specific design choices in our generalized framework. We then reformulate the Multi-State Constrained Kalman Filter (MSCKF), implement the reformulation on challenging image sequence datasets in simulation, and contrast its performance with that of sliding window based filters. Using these results, we explain the relative performance characteristics of these two classes of algorithms in the context of our algorithm. Finally, we illustrate that under different design choices, the empirical performance of our algorithm interpolates between those of state-of-the-art approaches.
Economic MPC-based planning for marine vehicles: Tuning safety and energy efficiency
Authors: Haojiao Liang, Huiping Li, Jian Gao, Rongxin Cui, Demin Xu
Date: 2021-12-10 00:00:00
Tasks: Model Predictive Control, Motion Planning

Energy efficiency and safety are two critical objectives for marine vehicles operating in environments with obstacles, and they generally conflict with each other. In this paper, we propose a novel online motion planning method of marine vehicles which can make trade-offs between the two design objectives based on the framework of economic model predictive control (EMPC). Firstly, the feasible trajectory with the most safety margin is designed and utilized as tracking reference. Secondly, the EMPC-based receding horizon motion planning algorithm is designed, in which the practical consumed energy and safety measure (i.e., the distance between the planning trajectory and the reference) are considered. Experimental results verify the effectiveness and feasibility of the proposed method.
Artificial selection of communities drives the emergence of structured interactions
Authors: Jules Fraboul, Giulio Biroli, Silvia De Monte
Date: 2021-12-13 00:00:00

Species-rich communities, such as the microbiota or microbial ecosystems, provide key functions for human health and climatic resilience. Increasing effort is being dedicated to design experimental protocols for selecting community-level functions of interest. These experiments typically involve selection acting on populations of communities, each of which is composed of multiple species. If numerical simulations started to explore the evolutionary dynamics of this complex, multi-scale system, a comprehensive theoretical understanding of the process of artificial selection of communities is still lacking. Here, we propose a general model for the evolutionary dynamics of communities composed of a large number of interacting species, described by disordered generalised Lotka-Volterra equations. Our analytical and numerical results reveal that selection for scalar community functions leads to the emergence, along an evolutionary trajectory, of a low-dimensional structure in an initially featureless interaction matrix. Such structure reflects the combination of the properties of the ancestral community and of the selective pressure. Our analysis determines how the speed of adaptation scales with the system parameters and the abundance distribution of the evolved communities. Artificial selection for larger total abundance is thus shown to drive increased levels of mutualism and interaction diversity. Inference of the interaction matrix is proposed as a method to assess the emergence of structured interactions from experimentally accessible measures.
Stabilization of Microbial Communities by Responsive Phenotypic Switching
Authors: Pierre A. Haas, Maria A. Gutierrez, Nuno M. Oliveira, Raymond E. Goldstein
Date: 2021-12-12 00:00:00

Clonal microbes can switch between different phenotypes and recent theoretical work has shown that stochastic switching between these subpopulations can stabilize microbial communities. This phenotypic switching need not be stochastic, however, but could also be in response to environmental factors, both biotic and abiotic. Here, motivated by the bacterial persistence phenotype, we explore the ecological effects of such responsive switching by analyzing phenotypic switching in response to competing species. We show that the stability of microbial communities with responsive switching differs generically from that of communities with stochastic switching only. To understand the mechanisms by which responsive switching stabilizes coexistence, we go on to analyze simple two-species models. Combining exact results and numerical simulations, we extend the classical stability results for the competition of two species without phenotypic variation to the case in which one species switches, stochastically and responsively, between two phenotypes. In particular, we show that responsive switching can stabilize coexistence even when stochastic switching on its own does not affect the stability of the community.
Large-scale GPU-based network analysis of the human T-cell receptor repertoire
Authors: Paul Richter
Date: 2021-12-13 00:00:00
Tasks: GPU

Understanding the structure of the human T-cell receptor repertoire is a crucial precondition to understand the ability of the immune system to recognize and respond to antigens. T-cells are often compared via the complementarity determining region 3 (CDR3) of their respective T-cell receptor beta chains. Nevertheless, previous studies often simply compared if CDR3beta sequences were equal, while network theory studies were usually limited to several thousand sequences due to the high computational effort of constructing the network. To overcome that hurdle, we introduce the GPU-based algorithm TCR-NET to construct large-scale CDR3beta similarity networks using model-generated and empirical sequence data with up to 800,000 CDR3beta sequences on a normal computer for the first time. Using network analysis methods we study the structural properties of these networks and conclude that (i) the fraction of public TCRs depends on the size of the TCR repertoire, along with the exact (not unified) definition of "public" sequences, (ii) the TCR network is assortative with the average neighbor degree being proportional to the squareroot of the degree of a node and (iii) the repertoire is robust against losses of TCRs. Moreover, we analyze the networks of antigen-specific TCRs for different antigen families and find differing clustering coefficients and assortativities. TCR-NET offers better access to assess large-scale TCR repertoire networks, opening the possibility to quantify their structure and quantitatively distinguish their ability to react to antigens, which we anticipate to become a useful tool in a time of increasingly large amounts of repertoire sequencing data becoming available.