9 results on '"Wang, Le Yi"'
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2. Impact of Stochastic Generation/Load Variations on Distributed Optimal Energy Management in DC Microgrids for Transportation Electrification.
- Author
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Xie, Siyu, Nazari, Masoud H., Wang, Le Yi, Yin, George, and Chen, Wen
- Abstract
This paper studies the impact of stochastic load variations on distributed optimal load tracking and allocation (OLTA) problems in cyber-physical DC microgrids (MGs) for transportation electrification. Without load variations, the distributed optimization strategies developed in our earlier work can achieve convergence to global optimal solutions in a multi-objective optimization that balances fair load allocation and power loss reduction. Under persistent stochastic load variations, this paper develops distributed optimal strategies to track time-varying loads under noisy observations and establishes their convergence properties and error bounds. The limiting behavior of the errors characterizes the fundamental impact of the step size on irreducible errors due to conflict between attenuating observation noises and tracking load changes. Optimality conditions and algorithms for selecting the optimal step size are introduced to guide step size selection in practical applications. Simulation studies on real-world systems demonstrate the effectiveness of the proposed algorithms and validate the theoretical results. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. Stochastic Adaptive Optimization With Dithers.
- Author
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Xie, Siyu, Liang, Shu, Wang, Le Yi, Yin, George, and Chen, Wen
- Subjects
MATHEMATICAL optimization ,PARAMETER identification ,STOCHASTIC processes ,PARAMETER estimation ,NOISE measurement - Abstract
Optimization methods are essential and have been used extensively in a broad spectrum of applications. Most existing literature on optimization algorithms does not consider systems that involve unknown system parameters. This article studies a class of stochastic adaptive optimization problems in which identification of unknown parameters and search for the optimal solutions must be performed simultaneously. Due to a fundamental conflict between parameter identifiability and optimality in such problems, we introduce a method of adding stochastic dither signals into the system, which provide sufficient excitation for estimating the unknown parameters, leading to convergent adaptive optimization algorithms. Joint identification and optimization algorithms are developed and their simultaneous convergence properties of parameter estimation and optimization variable updates are proved. Under both noise-free and noisy observations, the corresponding convergence rates are established. The main results of this article reveal certain fundamental relationships and tradeoff among updating step sizes, dither magnitudes, parameter estimation errors, optimization accuracy, and convergence rates. Simulation case studies are used to illustrate the adaptive optimization algorithms and their main properties. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Dual Averaging Push for Distributed Convex Optimization Over Time-Varying Directed Graph.
- Author
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Liang, Shu, Wang, Le Yi, and Yin, George
- Subjects
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ALGORITHMS , *SUBGRADIENT methods , *DISTRIBUTED algorithms , *DIRECTED graphs , *NONSMOOTH optimization , *MULTIAGENT systems , *CONVEX functions - Abstract
Inspired by the subgradient push method developed recently by Nedić et al. we present a distributed dual averaging push algorithm for constrained nonsmooth convex optimization over time-varying directed graph. Our algorithm combines the dual averaging method with the push-sum technique and achieves an $O(1/ \sqrt{k})$ convergence rate. Compared with the subgradient push algorithm, our algorithm, first, addresses the constrained problems, and, second, has a faster convergence rate, and, third, simplifies the convergence analysis. We also generalize the proposed algorithm so that input variables of subgradient oracles have guaranteed convergence. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
5. Distributed Smooth Convex Optimization With Coupled Constraints.
- Author
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Liang, Shu, Wang, Le Yi, and Yin, George
- Subjects
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SUBGRADIENT methods , *NONSMOOTH optimization , *LINEAR programming , *CONVEX functions , *HEURISTIC algorithms , *DISTRIBUTED algorithms , *ALGORITHMS , *MATHEMATICAL equivalence - Abstract
This note develops a distributed algorithm to solve a convex optimization problem with coupled constraints. Both coupled equality and inequality constraints are considered, where functions in the equality constraints are affine and functions in the inequality constraints are convex. Different from primal-dual subgradient methods with decreasing stepsizes for nonsmooth optimizations, our algorithm focuses on smooth problems and uses a fixed stepsize to find the exact optimal solution. Convergence analysis is derived with rigorous proofs. Our result is also illustrated by simulations. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
6. Distributed Energy Management for Smart Grids With an Event-Triggered Communication Scheme.
- Author
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Ding, Lei, Wang, Le Yi, Yin, George Yin, Zheng, Wei Xing, and Han, Qing-Long
- Subjects
ELECTRON tube grids ,DISTRIBUTED algorithms ,CHARITIES - Abstract
This paper is concerned with distributed energy management and control issues of both generators and loads. It aims to maximize the total social welfare that balances generation-side expanses, user-side payments, and transmission line costs. A distributed control strategy with continuous information exchange among neighbors is first proposed. It is shown that this distributed algorithm achieves the global optimal power outputs on generators and the optimal electricity usage on loads asymptotically. To reduce communication resource consumptions, the distributed optimization algorithm is further expanded to incorporate event-triggered communication and control mechanism. In this new algorithm, an event-triggering condition for each generator and each load is employed to determine when its related state information should be sampled and transmitted to its neighbors. Compared with the standard periodic sampling and communication schemes, this new distributed and event-triggered algorithm can significantly reduce communication data flows while achieving the nearly identical control performance to that under continuous data communications. The theoretical results of this paper are validated by using a simulation case study with distributed generators and multiple loads on an IEEE 9-bus system. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
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7. Distributed Cooperative Optimal Control of DC Microgrids With Communication Delays.
- Author
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Ding, Lei, Han, Qing-Long, Wang, Le Yi, and Sindi, Eyad
- Abstract
One of the fundamental and challenging issues in microgrids is to guarantee fairness of load sharing while realizing voltage regulation of distributed generations. In order to address this issue, a new multiobjective optimization problem with tunable weighting coefficients is first formulated for dc microgrids. Second, a new distributed control scheme, which only requires local communications among neighbors, is proposed to solve the optimization problem. It is theoretically proved that the distributed control scheme can exponentially achieve the global optimal outputs of voltages and currents at distributed generations. Compared with a centralized control scheme, the proposed distributed control scheme provides remarkable advantages in improving reliability and scalability of microgrids. Third, the distributed control scheme is extended to accommodate a constant communication delay. The effects of the communication delay on the stability of microgrids are explicitly characterized. Finally, the performance of the proposed control schemes is evaluated by a modified six-bus microgrid with dc-powered trolleybus systems in terms of their convergence, robustness to load variations, plug-and-play functionality, tradeoff ability, and effects of communication delays. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
8. Probabilistic Per-Packet Real-Time Guarantees for Wireless Networked Sensing and Control.
- Author
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Chen, Yu, Zhang, Hongwei, Fisher, Nathan, Wang, Le Yi, and Yin, George
- Abstract
The mission-critical nature of wireless networked sensing and control (WSC) systems, such as the control of industrial plants, requires stringent real-time delivery of packets. Due to inherent dynamics and uncertainties in wireless communication, real-time communication guarantees are probabilistic in nature. In this paper, a probabilistic framework is therefore proposed for per-packet real-time delivery guarantee. The notion of real-time in this paper differs from the existing work in the sense that it ensures, in an execution history of arbitrary length, every packet is successfully delivered before its deadline with a probability no less than a user-specified threshold (e.g., 99%). The framework has several novel building blocks: First, “R3 (requirement-reliability-resource) mapping” translates the upper layer probabilistic real-time communication requirement, and the lower layer links reliability into the resource (i.e., optimal number of transmission opportunities) reserved for each packet. Second, “EDF (earliest deadline first) based real-time scheduling” as well as the “admission test” and “traffic load optimization” maximize system utility while satisfying per-packet real-time communication requirements. The proposed admission test is proved to be both sufficient and necessary, and the simulation results show that the proposed framework ensures probabilistic per-packet real-time communication. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
- Full Text
- View/download PDF
9. Optimal Power Management in DC Microgrids With Applications to Dual-Source Trolleybus Systems.
- Author
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Zhang, Di, Wang, Le Yi, Jiang, Jiuchun, and Zhang, Weige
- Abstract
This paper investigates optimal power management in dc microgrids, with its applications to dc dual-source trolleybus systems. High mobility of buses and their impact on power supply networks introduce challenging power management issues. This paper incorporates line power losses in power management strategies and introduces a new distributed optimal power management methodology. A multi-objective optimization model is developed. Using only neighborhood information exchange among feeder lines in the network, the new consensus-type control accommodates both feeder current allocation and power loss reduction with fast convergence. One critical finding of this paper is that our local recursive optimization algorithms achieve the global optimal solution asymptotically. Under random noise on information exchange, convergence and optimality of the proposed method are established rigorously. The power system configurations of the Beijing dual-source trolleybus system are used for simulation case studies on the new power management methods. Feasibility, accuracy, and comparison with global optimization results are demonstrated. [ABSTRACT FROM PUBLISHER]
- Published
- 2018
- Full Text
- View/download PDF
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