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465 results on '"Davoudi, Ali"'

1. A First-Order Numerical Algorithm without Matrix Operations

Author

Adil, Muhammad, Madani, Ramtin, Tavakkol, Sasan, and Davoudi, Ali

Subjects
Mathematics - Optimization and Control
Abstract

This paper offers a matrix-free first-order numerical method to solve large-scale conic optimization problems. Solving systems of linear equations pose the most computationally challenging part in both first-order and second-order numerical algorithms. Existing direct and indirect methods are either computationally expensive or compromise on solution accuracy. Alternatively, we propose an easy-to-compute decomposition method to solve sparse linear systems that arise in conic optimization problems. Its iterations are tractable, highly parallelizable, with closed-form solutions. This algorithm can be easily implemented on distributed platforms, such as graphics processing units, with orders-of-magnitude time improvement. The performance of the proposed solver is demonstrated on large-scale conic optimization problems and is compared with the state-of-the-art first-order solvers.

Published
2022

2. Enhanced Modeling of Contingency Response in Security-constrained Optimal Power Flow

Author

Altun, Tuncay, Madani, Ramtin, Atamturk, Alper, Baldick, Ross, and Davoudi, Ali

Subjects
Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

This paper provides an enhanced modeling of the contingency response that collectively reflects high-fidelity physical and operational characteristics of power grids. Integrating active and reactive power contingency responses into the security-constrained optimal power flow (SCOPF) problem is challenging, due to the nonsmoothness and nonconvexity of feasible sets in consequence of piece-wise curves representing generator characteristics. We introduce a continuously-differentiable model using functions that closely resemble PV/PQ switching and the generator contingency response. These models enforce physical and operational limits by optimally allocating active power imbalances among available generators and deciding the bus type to switch from the PV type to the PQ type. The efficacy of this method is numerically validated on the IEEE 30-bus, 300-bus, and 118-bus systems with 12, 10, and 100 contingencies, respectively.

Published
2021

6. A Scalable Semidefinite Relaxation Approach to Grid Scheduling

Author

Madani, Ramtin, Atamturk, Alper, and Davoudi, Ali

Subjects
Mathematics - Optimization and Control
Abstract

Determination of the most economic strategies for supply and transmission of electricity is a daunting computational challenge. Due to theoretical barriers, so-called NP-hardness, the amount of effort to optimize the schedule of generating units and route of power, can grow exponentially with the number of decision variables. Practical approaches to this problem involve legacy approximations and ad-hoc heuristics that may undermine the efficiency and reliability of power system operations, that are ever growing in scale and complexity. Therefore, the development of powerful optimization methods for detailed power system scheduling is critical to the realization of smart grids and has received significant attention recently. In this paper, we propose for the first time a computational method, which is capable of solving large-scale power system scheduling problems with thousands of generating units, while accurately incorporating the nonlinear equations that govern the flow of electricity on the grid. The utilization of this accurate nonlinear model, as opposed to its linear approximations, results in a more efficient and transparent market design, as well as improvements in the reliability of power system operations. We design a polynomial-time solvable third-order semidefinite programming (TSDP) relaxation, with the aim of finding a near globally optimal solution for the original NP-hard problem. The proposed method is demonstrated on the largest available benchmark instances from real-world European grid data, for which provably optimal or near-optimal solutions are obtained.

Published
2017

13. Distributed Assistive Control of DC Microgrids

Author

Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, Lewis, Frank L., Grimble, Michael J., Series editor, Johnson, Michael A., Series editor, Goodwin, Graham C, Editorial board, Harris, Thomas J., Editorial board, Lee, Tong Heng, Editorial board, Malik, Om P., Editorial board, Man, Kim-Fung, Editorial board, Olsson, Gustaf, Editorial board, Ray, Asok, Editorial board, Seborg, Dale E, Editorial board, Engell, Sebastian, Advisory editor, Yamamoto, Ikuo, Editorial board, Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, and Lewis, Frank L.

Published
2017
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14. Droop-Free Distributed Control of AC Microgrids

Author

Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, Lewis, Frank L., Grimble, Michael J., Series editor, Johnson, Michael A., Series editor, Goodwin, Graham C, Editorial board, Harris, Thomas J., Editorial board, Lee, Tong Heng, Editorial board, Malik, Om P., Editorial board, Man, Kim-Fung, Editorial board, Olsson, Gustaf, Editorial board, Ray, Asok, Editorial board, Seborg, Dale E, Editorial board, Engell, Sebastian, Advisory editor, Yamamoto, Ikuo, Editorial board, Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, and Lewis, Frank L.

Published
2017
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15. Cooperative Control for DC Microgrids

Author

Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, Lewis, Frank L., Grimble, Michael J., Series editor, Johnson, Michael A., Series editor, Goodwin, Graham C, Editorial board, Harris, Thomas J., Editorial board, Lee, Tong Heng, Editorial board, Malik, Om P., Editorial board, Man, Kim-Fung, Editorial board, Olsson, Gustaf, Editorial board, Ray, Asok, Editorial board, Seborg, Dale E, Editorial board, Engell, Sebastian, Advisory editor, Yamamoto, Ikuo, Editorial board, Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, and Lewis, Frank L.

Published
2017
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16. Multi-objective and Adaptive Distributed Control of AC Microgrids

Author

Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, Lewis, Frank L., Grimble, Michael J., Series editor, Johnson, Michael A., Series editor, Goodwin, Graham C, Editorial board, Harris, Thomas J., Editorial board, Lee, Tong Heng, Editorial board, Malik, Om P., Editorial board, Man, Kim-Fung, Editorial board, Olsson, Gustaf, Editorial board, Ray, Asok, Editorial board, Seborg, Dale E, Editorial board, Engell, Sebastian, Advisory editor, Yamamoto, Ikuo, Editorial board, Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, and Lewis, Frank L.

Published
2017
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17. Distributed Control of AC Microgrids

Author

Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, Lewis, Frank L., Grimble, Michael J., Series editor, Johnson, Michael A., Series editor, Goodwin, Graham C, Editorial board, Harris, Thomas J., Editorial board, Lee, Tong Heng, Editorial board, Malik, Om P., Editorial board, Man, Kim-Fung, Editorial board, Olsson, Gustaf, Editorial board, Ray, Asok, Editorial board, Seborg, Dale E, Editorial board, Engell, Sebastian, Advisory editor, Yamamoto, Ikuo, Editorial board, Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, and Lewis, Frank L.

Published
2017
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18. Introduction

Author

Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, Lewis, Frank L., Grimble, Michael J., Series editor, Johnson, Michael A., Series editor, Goodwin, Graham C, Editorial board, Harris, Thomas J., Editorial board, Lee, Tong Heng, Editorial board, Malik, Om P., Editorial board, Man, Kim-Fung, Editorial board, Olsson, Gustaf, Editorial board, Ray, Asok, Editorial board, Seborg, Dale E, Editorial board, Engell, Sebastian, Advisory editor, Yamamoto, Ikuo, Editorial board, Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, and Lewis, Frank L.

Published
2017
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19. Introduction to Multi-agent Cooperative Control

Author

Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, Lewis, Frank L., Grimble, Michael J., Series editor, Johnson, Michael A., Series editor, Goodwin, Graham C, Editorial board, Harris, Thomas J., Editorial board, Lee, Tong Heng, Editorial board, Malik, Om P., Editorial board, Man, Kim-Fung, Editorial board, Olsson, Gustaf, Editorial board, Ray, Asok, Editorial board, Seborg, Dale E, Editorial board, Engell, Sebastian, Advisory editor, Yamamoto, Ikuo, Editorial board, Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, and Lewis, Frank L.

Published
2017
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20. Control and Modeling of Microgrids

Author

Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, Lewis, Frank L., Grimble, Michael J., Series editor, Johnson, Michael A., Series editor, Goodwin, Graham C, Editorial board, Harris, Thomas J., Editorial board, Lee, Tong Heng, Editorial board, Malik, Om P., Editorial board, Man, Kim-Fung, Editorial board, Olsson, Gustaf, Editorial board, Ray, Asok, Editorial board, Seborg, Dale E, Editorial board, Engell, Sebastian, Advisory editor, Yamamoto, Ikuo, Editorial board, Bidram, Ali, Nasirian, Vahidreza, Davoudi, Ali, and Lewis, Frank L.

Published
2017
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21. Data-Driven Inverse Reinforcement Learning Control for Linear Multiplayer Games

Author

Lian, Bosen, Donge, Vrushabh S., Lewis, Frank L., Chai, Tianyou, and Davoudi, Ali

Abstract

This article proposes a data-driven inverse reinforcement learning (RL) control algorithm for nonzero-sum multiplayer games in linear continuous-time differential dynamical systems. The inverse RL problem in the games is solved by a learner reconstructing the unknown expert players’ cost functions from demonstrated expert’s optimal state and control input trajectories. The learner, thus, obtains the same control feedback gains and trajectories as the expert, only using data along system trajectories without knowing system dynamics. This article first proposes a model-based inverse RL policy iteration framework that has: 1) policy evaluation step for reconstructing cost matrices using Lyapunov functions; 2) state-reward weight improvement step using inverse optimal control (IOC); and 3) policy improvement step using optimal control. Based on the model-based policy iteration algorithm, this article further develops an online data-driven off-policy inverse RL algorithm without knowing any knowledge of system dynamics or expert control gains. Rigorous convergence and stability analysis of the algorithms are provided. It shows that the off-policy inverse RL algorithm guarantees unbiased solutions while probing noises are added to satisfy the persistence of excitation (PE) condition. Finally, two different simulation examples validate the effectiveness of the proposed algorithms.

Published
2024
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23. Data-Efficient Reinforcement Learning for Complex Nonlinear Systems

Author

Donge, Vrushabh S., Lian, Bosen, Lewis, Frank L., and Davoudi, Ali

Abstract

This article proposes a data-efficient model-free reinforcement learning (RL) algorithm using Koopman operators for complex nonlinear systems. A high-dimensional data-driven optimal control of the nonlinear system is developed by lifting it into the linear system model. We use a data-driven model-based RL framework to derive an off-policy Bellman equation. Building upon this equation, we deduce the data-efficient RL algorithm, which does not need a Koopman-built linear system model. This algorithm preserves dynamic information while reducing the required data for optimal control learning. Numerical and theoretical analyses of the Koopman eigenfunctions for dataset truncation are discussed in the proposed model-free data-efficient RL algorithm. We validate our framework on the excitation control of the power system.

Published
2024
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24. Accelerated Reinforcement Learning via Dynamic Mode Decomposition

Author

Donge, Vrushabh S., Lian, Bosen, Lewis, Frank L., and Davoudi, Ali

Abstract

This work applies the decomposition principle to discrete-time reinforcement learning (RL) to solve the optimal control problems for a network of subsystems. The control design is defined as a linear quadratic regulator graphical problem, where the performance function couples the subsystems' dynamics. We first present a model-free discrete-time RL algorithm based on online behaviors without using system dynamics. This could become a prohibitively long learning process for larger networks. To remedy this issue, we develop an efficient model-free RL algorithm based on dynamic mode decomposition. This decomposition method reduces the size of the measured data while the dynamic information of the original network is still retained. This algorithm is then implemented online. The proposed methodology is validated using examples of a consensus network and a power system network.

Published
2023
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41. Introduction

Author

Bidram, Ali, primary, Nasirian, Vahidreza, additional, Davoudi, Ali, additional, and Lewis, Frank L., additional

Published
2017
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