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Safe Reinforcement Learning for Grid Voltage Control
[article]
2021
arXiv
pre-print
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Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usually trips a massive amount of load inefficiently. Reinforcement learning (RL) has been adopted as a promising approach to circumvent the issues; however, RL approach usually cannot guarantee the safety of the systems under control. In this paper, we discuss a couple of novel safe RL approaches, namely constrained
arXiv:2112.01484v1
fatcat:qvhi44punndopfgod3jeiwju5u
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... zation approach and Barrier function-based approach, that can safely recover voltage under emergency events. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark are performed to demonstrate the effectiveness of the proposed safe RL emergency control.
Quantifying Bounds of Model Gap for Synchronous Generators
[article]
2021
arXiv
pre-print
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Peng Wang, Shaobu Wang, and Zhenyu Huang are with Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA. 99354. ...
arXiv:2102.02980v1
fatcat:4ddc4elc5jg6zjpzz3sbu2wd7i
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
[article]
2021
arXiv
pre-print
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Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning Sayak Mukherjee, Member, IEEE, Renke Huang, Senior Member, IEEE, Qiuhua Huang, Member, IEEE, Thanh Long Vu, Member ...
arXiv:2102.00077v1
fatcat:lcme6ztwpne5vbn2za2hmremii
A Comparative Study of Interface Techniques for Transmission and Distribution Dynamic Co-Simulation
[article]
2017
arXiv
pre-print
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Transmission and distribution dynamic co-simulation is a practical and effective approach to leverage existing simulation tools for transmission and distribution systems to simulate dynamic stability and performance of transmission and distribution systems in a systematic manner. Given that these tools are developed as stand-alone programs and there are inherent differences between them, interface techniques become critical to bridge them. Two important unsolved questions are: 1) which
arXiv:1711.02736v1
fatcat:z5hyuqfnkzbxbivyh3skpjntzu
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... technique is better and should be used, and 2) how the modeling and simulation capabilities in these tools that are available and can be exploited for co-simulation should be considered when selecting an interface technique. To address these questions, this paper presents a comparative study for different interface techniques that can be employed for T and D dynamic co-simulation. The study provides insights into the pros and cons of each interface technique, and helps researchers make informed decisions on choosing the interface techniques.
Adaptive Power System Emergency Control using Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
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Corresponding author: Renke Huang (renke.huang@pnnl.gov) Q. Huang, R. Huang, R. Fan and Z. ...
Huang are with Pacific Northwest National Laboratory, Richland, WA 99354, USA (e-mail: {qiuhua.huang, renke.huang, rui.fan, zhenyu.huang}@pnnl.gov). W. ...
arXiv:1903.03712v2
fatcat:tlnl7if7uvb25ir3oijhihysti
A Multireference Quantum Krylov Algorithm for Strongly Correlated Electrons
[article]
2019
arXiv
pre-print
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We introduce a multireference selected quantum Krylov (MRSQK) algorithm suitable for quantum simulation of many-body problems. MRSQK is a low-cost alternative to the quantum phase estimation algorithm that generates a target state as a linear combination of non-orthogonal Krylov basis states. This basis is constructed from a set of reference states via real-time evolution avoiding the numerical optimization of parameters. An efficient algorithm for the evaluation of the off-diagonal matrix
arXiv:1911.05163v1
fatcat:dtduxdaekfgyngrhcum74lqy7i
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... nts of the overlap and Hamiltonian matrices is discussed and a selection procedure is introduced to identify a basis of orthogonal references that ameliorates the linear dependency problem. Preliminary benchmarks on linear H_6, H_8, and BeH_2 indicate that MRSQK can predict the energy of these systems accurately using very compact Krylov bases.
Safe Reinforcement Learning for Emergency LoadShedding of Power Systems
[article]
2020
arXiv
pre-print
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The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid's security and resilience. In particular, the increased uncertainties and rapidly changing operational conditions in power systems have revealed outstanding issues in terms of either speed, adaptiveness, or scalability of the existing control methods for power systems. On the other hand, the availability of massive real-time data can provide a clearer picture of what is happening
arXiv:2011.09664v1
fatcat:pj3wk5i22velxgymlpnrjr6osu
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... n the grid. Recently, deep reinforcement learning(RL) has been regarded and adopted as a promising approach leveraging massive data for fast and adaptive grid control. However, like most existing machine learning (ML)-basedcontrol techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel method for safe RL-based load shedding of power systems that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.
A Reference Implementation of WECC Composite Load Model in Matlab and GridPACK
[article]
2017
arXiv
pre-print
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A Reference Implementation of WECC Composite Load Model in Matlab and GridPACK Qiuhua Huang, Member, IEEE, Renke Huang, Member, IEEE, Bruce J. ...
Huang, R. Huang, B. Palmer, Y. Liu, S. Jin, R. Diao, Y. Chen, Y. ...
arXiv:1708.00939v1
fatcat:zbfb2by77zgcpleomus46n7nsm
Parameters Calibration for Power Grid Stability Models using Deep Learning Methods
[article]
2019
arXiv
pre-print
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This paper presents a novel parameter calibration approach for power system stability models using automatic data generation and advanced deep learning technology. A PMU-measurement-based event playback approach is used to identify potential inaccurate parameters and automatically generate extensive simulation data, which are used for training a convolutional neural network (CNN). The accurate parameters will be predicted by the well-trained CNN model and validated by original PMU measurements.
arXiv:1905.03172v1
fatcat:xb3c35mcp5f3xetdcyayptr7ey
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... The accuracy and effectiveness of the proposed deep learning approach have been validated through extensive simulation and field data.
Integrated Smart Grid Hierarchical Control
2012
2012 45th Hawaii International Conference on System Sciences
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A previous paper presented a new smart grid infrastructure for active distribution systems that will allow continuous and accurate monitoring of distribution system operations and customer utilization of electric power. This paper presents the utilization of this system for the purpose of optimizing the operation of the system over a rolling planning horizon. Specifically, we propose the use of a hierarchical optimization method that optimizes the operation of the system by scheduling the
doi:10.1109/hicss.2012.331
dblp:conf/hicss/MeliopoulosCHF12
fatcat:jx4aztp6pfd5thzobl5jzugkw4
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... ion of various resources with storage capability. The hierarchical method has three levels: (a) distribution feeder optimization, (b) substation level optimization, and (c) system optimization. At the lowest level (feeder optimization) the problem is formulated as a quadratic optimization problem that is solved via barrier methods. At the higher levels the problem is formulated as a stochastic dynamic programming problem. The proposed method captures the uncertainty associated with many resources in the system resulting from the integration of renewables. Finally, a method is proposed for the business case analysis of the benefits of the proposed scheme and results for a hypothetical system are presented.
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
[article]
2019
arXiv
pre-print
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We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for efficient operation of power grid systems. In this work, we use a synthetic data set describing a power grid with 700 buses and 134 generators over a 365-days period with data synthetically generated at an hourly rate. The proposed approach for week-long forecasting is based on the Gaussian process regression (GPR)
arXiv:1910.03783v1
fatcat:rstn3uo42bhvrasnw4e6qsqcle
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... thod, with prior covariance matrices of the quantities of interest (QoI) computed from ensembles formed by up to twenty preceding weeks of QoI observations. Then, we use these covariances within the GPR framework to forecast the QoIs for the following week. We demonstrate that the the proposed ensemble GPR (EGPR) method is capable of accurately forecasting weekly total load demand and power generation profiles. The EGPR method is shown to outperform traditional forecasting methods including the standard GPR and autoregressive integrated moving average (ARIMA) methods.
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2006.12667v1 fatcat:w2av3m346vhmhpiyyt6uunkbcy
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. With increased uncertainties and rapidly changing operational conditions in power systems, existing methods have outstanding issues in terms of either speed, adaptiveness, or scalability. Deep reinforcement learning (DRL) was regarded and adopted as a promising approach for fast and adaptive grid stability control in recent years. However, existing DRL algorithms show two
arXiv:2006.12667v2
fatcat:vhhl5ipzwvbfthk4regiolu63y
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... tstanding issues when being applied to power system control problems: 1) computational inefficiency that requires extensive training and tuning time; and 2) poor scalability making it difficult to scale to high dimensional control problems. To overcome these issues, an accelerated DRL algorithm named PARS was developed and tailored for power system voltage stability control via load shedding. PARS features high scalability and is easy to tune with only five main hyperparameters. The method was tested on both the IEEE 39-bus and IEEE 300-bus systems, and the latter is by far the largest scale for such a study. Test results show that, compared to other methods including model-predictive control (MPC) and proximal policy optimization(PPO) methods, PARS shows better computational efficiency (faster convergence), more robustness in learning, excellent scalability and generalization capability.
Smart Sampling for Reduced and Representative Power System Scenario Selection
2021
IEEE Open Access Journal of Power and Energy
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(Corresponding authors: Renke Huang, Z. Jason Hou) X. Sun, X. Li, S. Datta, X. Ke, Q. Huang, R. Huang, Z. ...
doi:10.1109/oajpe.2021.3093278
fatcat:ys7pc7xprbam5elyc33r6szd3a
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
[article]
2022
arXiv
pre-print
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As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1)
arXiv:2101.05317v2
fatcat:55lmzubo5fhrfjsderg3csbfae
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... cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method and achieve superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.
Modeling Single-Phase Inverter and Its Decentralized Coordinated Control by Using Feedback Linearization
2014
Mathematical Problems in Engineering
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It is a very crucial problem to make a microgrid operated reasonably and stably. Considering the nonlinear mathematics model of inverter established in this paper, the input-output feedback linearization method is used to transform the nonlinear mathematics model of inverters to a linear tracking synchronization and consensus regulation control problem. Based on the linear mathematics model and multiagent consensus algorithm, a decentralized coordinated controller is proposed to make amplitudes
doi:10.1155/2014/581323
fatcat:2gh3ebazpfeu7pozfdjedwygai
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... and angles of voltages from inverters be consensus and active and reactive power shared in the desired ratio. The proposed control is totally distributed because each inverter only requires local and one neighbor's information with sparse communication structure based on multiagent system. The hybrid consensus algorithm is used to keep the amplitude of the output voltages following the leader and the angles of output voltage as consensus. Then the microgrid can be operated more efficiently and the circulating current between DGs can be effectively suppressed. The effectiveness of the proposed method is proved through simulation results of a typical microgrid system.
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