Article
Computer Science, Information Systems
Mostafa Bazzaz, Ali Hoseinghorban, Farimah Poursafaei, Alireza Ejlali
Summary: The study shows that using emerging non-volatile memories to improve the memory subsystem of embedded systems is feasible. In addition, the proposed instruction memory architecture reduces effective memory access latency, does not increase the worst case execution time of the system, and improves both average case execution time and energy consumption of the system.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2021)
Article
Automation & Control Systems
Yunsong Xu, Zhengen Zhao, Shen Yin, Zhiqiang Long
Summary: This article investigates real-time levitation performance optimization of the EML system with an imperfectly known model. The system is first modeled and an equivalent demonstration benchmark is developed. Then, a structure for levitation performance optimization is presented on top of the coprime factorization technique. Furthermore, a real-time levitation performance optimization algorithm is developed, utilizing only the input and output data.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Engineering, Electrical & Electronic
Teng Long, Qing-Shan Jia, Gongming Wang, Yu Yang
Summary: This paper presents an efficient and scalable real-time scheduling method for handling the charging demands of plug-in electric vehicles (PEV), demonstrating through simulations that the proposed method provides high computation efficiency and scalability while reducing operating costs for charging stations. Compared to existing methods, it outperforms in terms of charging policy search capabilities and performance guarantee.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Energy & Fuels
F. J. Vivas, F. Segura, J. M. Andujar, A. J. Calderon, F. Isorna
Summary: Battery-based energy storage systems play a crucial role in renewable energy sources-based microgrids, and a suitable energy management strategy and charging control are important for maximizing efficiency and lifespan. This paper presents a new charging algorithm that improves the operation of the energy storage system and microgrid.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Energy & Fuels
Min Gyung Yu, Gregory S. Pavlak
Summary: This study introduces an uncertainty-aware transactive control framework for coordinating thermal energy storage assets of multiple buildings, aiming to achieve energy cost savings and increased system flexibility.
Article
Construction & Building Technology
Jin Hou, Xin Li, Hang Wan, Qin Sun, Kaijun Dong, Gongsheng Huang
Summary: Real-time optimal control is a critical tool for improving energy efficiency in HVAC systems, but obtaining a reliable and accurate model for optimization is challenging. This paper investigates the impact of model accuracy on optimization actions and demonstrates that event-based control can reduce negative rewards compared to time-based control.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Energy & Fuels
Yi Guo, Xuejiao Han, Xinyang Zhou, Gabriela Hug
Summary: In this paper, a two-stage electricity market framework is proposed to explore the involvement of distributed energy resources (DERs) in both day-ahead (DA) and real-time (RT) markets. The optimal bidding strategies of the aggregated DERs in the DA market are determined using distributionally robust optimization, while online incentive signals are generated for DER-owners to optimize social-welfare in consideration of network operational constraints. A bi-level time-varying optimization problem is proposed in the RT market to design the online incentives for balancing services, taking into account the RT imbalance penalty for distribution system operators (DSOs) and the costs of individual DER-owners. Simulation results demonstrate the necessity and robustness of this two-stage design for network operations.
Article
Chemistry, Physical
S. Ida Evangeline, P. Rathika
Summary: The study proposes a real-time multiple objective optimal power flow framework to address the inconsistency issue of wind energy, and develops an optimization method to ensure feasibility and performance. The proposed approach is validated using different test systems, demonstrating its effectiveness.
JOURNAL OF POWER SOURCES
(2021)
Article
Green & Sustainable Science & Technology
Agota Banyai, Tamas Banyai
Summary: This study proposes a novel approach for managing maintenance strategies based on a real-time maintenance policy model and optimization algorithm using digital twin simulation. The results show that the real-time maintenance policy optimization can reduce energy consumption and greenhouse gas emissions.
Article
Automation & Control Systems
Lloyd MacKinnon, Christopher L. E. Swartz
Summary: Real-time optimization (RTO) is a valuable tool for economic optimization of chemical process systems. This paper extends the formulation of closed-loop dynamic RTO (CL-DRTO) to include uncertainty handling. A robust multi-scenario CL-DRTO scheme is introduced to model the dynamic behavior of the plant and its MPC system under uncertainty, and its performance is evaluated in nonlinear case studies.
JOURNAL OF PROCESS CONTROL
(2023)
Article
Computer Science, Hardware & Architecture
Kevin Zagalo, Yasmina Abdeddaim, Avner Bar-Hen, Liliana Cucu-Grosjean
Summary: In this paper, it is proven that a mean system utilization smaller than one is a necessary condition for the feasibility of real-time systems. Stable systems, which have two distinct states, a transient state and a steady-state, are defined as systems where the same distribution of response times is repeated infinitely for each task. The Liu and Layland theorem is proved to hold for stable probabilistic real-time systems with implicit deadlines, and an analytical approximation of response times for each of those two states is provided, along with a bound of the instant when a real-time system becomes steady.
IEEE TRANSACTIONS ON COMPUTERS
(2023)
Article
Mathematics
Anna Glazunova, Evgenii Semshikov, Michael Negnevitsky
Summary: This paper discusses the calculation of the flexibility of an electric power system with high wind energy penetration, providing the ability to maintain balance under irregular active power variations during a specified time. It relies on a deterministic method to assess the system's readiness for changes in load and ensures accurate determination of the actual value.
Article
Thermodynamics
Wenfa Kang, Minyou Chen, Wei Lai, Yanyu Luo
Summary: In this paper, a virtual energy storage system (VESS) is proposed for active distribution networks (ADN), which consists of battery energy storage system (BESS), renewable energy sources (RES), and flexible loads (FL). Dynamic pricing strategies based on system voltage condition are designed for voltage regulation in VESS. A distributed real-time power management model containing dynamic pricing strategies is proposed for voltage regulation and economic power sharing in VESS, with a focus on time-varying unbalanced directed networks.
Article
Engineering, Electrical & Electronic
Xu Han, Xiaofei Pan, Shiqi Wang, Shanshe Wang, Wen Gao
Summary: This paper presents an improved hybrid CPU + GPU accelerated framework for AVS3 decoding, and provides a detailed description of its design and implementation. Experimental results show that the proposed decoder achieves high frame rates and performance in processing 4K and 8K videos.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Construction & Building Technology
Hailing Zhu, Khmaies Ouahada
Summary: This paper investigates energy storage sharing among cooperative households with integrated renewable generations in a grid-connected microgrid under dynamic electricity pricing. A distributed real-time sharing control algorithm is proposed to optimize energy management, where each household independently solves a simple convex optimization problem in each time slot, leading to improved cost savings and renewable energy utilization.
ENERGY AND BUILDINGS
(2021)
Article
Engineering, Electrical & Electronic
Johannes J. Brust, Mihai Anitescu
Summary: This article analyzes the convergence conditions for a fixed point iteration method used in optimal power flow problems with chance constraints, and presents numerical experiments including for large IEEE networks.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Daniel Adrian Maldonado, Emil M. Constantinescu, Hong Zhang, Vishwas Rao, Mihai Anitescu
Summary: This work introduces a novel technique to compute extreme trajectories of power system dynamic simulations using trust-region optimization algorithm and second-order trajectory sensitivities. The method overcomes limitations of previous sensitivity-based techniques in approximating bounds of trajectories in nonlinear scenarios, showing accuracy and scalability in numerical experiments.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Anirudh Subramanyam, Jacob Roth, Albert Lam, Mihai Anitescu
Summary: This paper proposes a reliability-aware approach for power dispatch, which aims to reduce the probability of cascades by considering the failure potential. By utilizing a probability model, the proposed method can effectively decrease the risk of blackouts in power transmission systems.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dimosthenis Pasadakis, Christie Louis Alappat, Olaf Schenk, Gerhard Wellein
Summary: The study introduces a novel direct multiway spectral clustering algorithm based on p-norm, using a nonlinear reformulation of the spectral clustering method to achieve improved numerical benefits within a certain range. By recasting the problem and promoting sparser solution vectors, it aims to achieve optimal graph cuts as p approaches one.
Article
Engineering, Electrical & Electronic
Bendong Tan, Junbo Zhao, Nan Duan, Daniel Adrian Maldonado, Yingchen Zhang, Hongming Zhang, Mihai Anitescu
Summary: This paper proposes a distributed frequency divider to estimate power system bus frequency with a limited number of PMUs while considering the inertial contributions from DFIGs. The proposed method reformulates the original frequency divider by modeling the contributions of DFIGs, allowing estimation of local bus frequencies in a distributed manner. The numerical results on real power systems demonstrate the effectiveness and robustness of the proposed method.
IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS
(2022)
Article
Physics, Multidisciplinary
Puspa Upreti, Matthew Krogstad, Charlotte Haley, Mihai Anitescu, Vishwas Rao, Lekh Poudel, Omar Chmaissem, Stephan Rosenkranz, Raymond Osborn
Summary: The paper investigates structural correlations in the quasiskutterudites using single crystal x-ray diffraction and finds temperature-independent local atomic displacements below the transition, which persist to well above the transition, indicating order-disorder behavior.
PHYSICAL REVIEW LETTERS
(2022)
Article
Mathematics, Applied
Sungho Shin, Mihai Anitescu, Victor M. Zavala
Summary: This study investigates the solution sensitivity of nonlinear programs based on graph structures and presents the exponential decay result of solution sensitivity with respect to distance. This result holds under the strong second-order sufficiency condition and the linear independence constraint qualification, and is illustrated with numerical examples.
SIAM JOURNAL ON OPTIMIZATION
(2022)
Article
Computer Science, Software Engineering
Sen Na, Mihai Anitescu, Mladen Kolar
Summary: In this paper, we propose a stochastic algorithm based on sequential quadratic programming (SQP) for solving nonlinear optimization problems with a stochastic objective and deterministic equality constraints. We introduce a differentiable exact augmented Lagrangian as the merit function and incorporate a stochastic line search procedure to adaptively select the random stepsizes. The global almost sure convergence for both non-adaptive and adaptive SQP methods is established. Numerical experiments demonstrate the superiority of the adaptive algorithm.
MATHEMATICAL PROGRAMMING
(2023)
Article
Computer Science, Theory & Methods
Christie Alappat, Georg Hager, Olaf Schenk, Gerhard Wellein
Summary: The researchers improved the performance of sparse matrix polynomial multiplication with a dense vector by using recursive algebraic coloring engine (RACE) and achieving efficient multithreaded parallelization. The implementation showed significant speedups compared to the baseline approach.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Lisa Gaedke-Merzhaeuser, Janet van Niekerk, Olaf Schenk, Havard Rue
Summary: This work presents parallelization strategies for the methodology of integrated nested Laplace approximations (INLA) to meet the growing demand of larger-scale Bayesian inference tasks. The introduced approach leverages nested thread-level parallelism, robust regression and state-of-the-art sparse linear solver PARDISO, resulting in significant speedups in various real-world applications. The improved parallelization scheme is already integrated into the open-source R-INLA package for convenient use.
STATISTICS AND COMPUTING
(2023)
Article
Automation & Control Systems
Sen Na, Mihai Anitescu
Summary: We have developed an online, lag-L, model predictive control (MPC) algorithm that solves discrete-time, equality-constrained, nonlinear dynamic programs with one Newton step per horizon. Based on recent sensitivity analysis results, we have proven that this approach exhibits a behavior called superconvergence, where the tracking error with respect to the full-horizon solution is stable for successive horizon shifts and decreases with increasing shift order to a minimum value that decays exponentially in the length of the receding horizon. We have shown that the algorithmic error achieved quadratic convergence through Newton's method, while the perturbation error decayed exponentially with the lag between consecutive receding horizons. Overall, this approach induces local exponential convergence in terms of the receding horizon length for suitable values of L. Numerical experiments have validated our theoretical findings.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Operations Research & Management Science
Francois Pacaud, Sungho Shin, Michel Schanen, Daniel Adrian Maldonado, Mihai Anitescu
Summary: The interior-point method (IPM) is widely used in nonlinear programming, and its performance depends on the linear solver employed to solve the Karush-Kuhn-Tucker (KKT) system. We propose a novel reduced-space IPM algorithm that condenses the KKT system into a dense matrix proportional to the number of degrees of freedom. Two variants of the reduced-space method are derived, and computations are accelerated on GPUs. Extensive numerical results show that the reduced-space algorithms outperform Knitro and a hybrid full-space IPM algorithm when the relative number of degrees of freedom decreases.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Sen Na, Mihai Anitescu, Mladen Kolar
Summary: We propose an active-set stochastic sequential quadratic programming (StoSQP) algorithm for solving nonlinear optimization problems with a stochastic objective and deterministic constraints. The algorithm uses a differentiable exact augmented Lagrangian as the merit function and adaptively selects penalty parameters. It has been shown to have global convergence and outperforms previous work in terms of nonlinear inequality constraints and sample complexity.
MATHEMATICAL PROGRAMMING
(2023)
Article
Computer Science, Artificial Intelligence
Dimosthenis Pasadakis, Matthias Bollhoefer, Olaf Schenk
Summary: This paper introduces two algorithms for learning M-matrices, which can be used for graph Laplacian matrix estimation. The first method is an unconstrained approach that uses post processing to learn an M-matrix, and the second method is a constrained approach based on sequential quadratic programming. Experimental results demonstrate the effectiveness, accuracy, and performance of both algorithms.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Energy & Fuels
Amanda Lenzi, Julie Bessac, Mihai Anitescu
Summary: This study proposes statistical models for anticipating disturbances in the frequency response at multiple locations simultaneously and issues real-time event recommendations based on predefined loss functions. The case study shows that the proposed spatiotemporal model better captures uncertainty and increases the probability of identifying large frequency excursions, reducing risks for the system operator.
IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY
(2022)
