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Safe Reinforcement Learning for Grid Voltage Control [article]

Thanh Long Vu, Sayak Mukherjee, Renke Huang, Qiuhua Huang
2021 arXiv   pre-print
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
more » ... 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.
arXiv:2112.01484v1 fatcat:qvhi44punndopfgod3jeiwju5u

Quantifying Bounds of Model Gap for Synchronous Generators [article]

Peng Wang, Shaobu Wang, Renke Huang, Zhenyu Huang
2021 arXiv   pre-print
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]

Sayak Mukherjee, Renke Huang, Qiuhua Huang, Thanh Long Vu, Tianzhixi Yin
2021 arXiv   pre-print
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]

Qiuhua Huang, Renke Huang, Rui Fan, Jason Fuller, Trevor Hardy, Zhenyu Huang, Vijay Vittal
2017 arXiv   pre-print
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
more » ... 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.
arXiv:1711.02736v1 fatcat:z5hyuqfnkzbxbivyh3skpjntzu

Adaptive Power System Emergency Control using Deep Reinforcement Learning [article]

Qiuhua Huang, Renke Huang, Weituo Hao, Jie Tan, Rui Fan, Zhenyu Huang
2019 arXiv   pre-print
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]

Nicholas H. Stair, Renke Huang, Francesco A. Evangelista
2019 arXiv   pre-print
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
more » ... 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.
arXiv:1911.05163v1 fatcat:dtduxdaekfgyngrhcum74lqy7i

Safe Reinforcement Learning for Emergency LoadShedding of Power Systems [article]

Thanh Long Vu and Sayak Mukherjee and Tim Yin and Renke Huang and and Jie Tan and Qiuhua Huang
2020 arXiv   pre-print
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
more » ... 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.
arXiv:2011.09664v1 fatcat:pj3wk5i22velxgymlpnrjr6osu

A Reference Implementation of WECC Composite Load Model in Matlab and GridPACK [article]

Qiuhua Huang, Renke Huang, Bruce J. Palmer, Yuan Liu, Shuangshuang Jin, Ruisheng Diao, Yousu Chen, Yu Zhang
2017 arXiv   pre-print
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]

Renke Huang, Rui Fan, Tianzhixi Yin, Shaobu Wang, Zhenyu Tan
2019 arXiv   pre-print
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.
more » ... The accuracy and effectiveness of the proposed deep learning approach have been validated through extensive simulation and field data.
arXiv:1905.03172v1 fatcat:xb3c35mcp5f3xetdcyayptr7ey

Integrated Smart Grid Hierarchical Control

A.P. Meliopoulos, George J. Cokkinides, Renke Huang, Evangelos Farantatos
2012 2012 45th Hawaii International Conference on System Sciences  
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
more » ... 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.
doi:10.1109/hicss.2012.331 dblp:conf/hicss/MeliopoulosCHF12 fatcat:jx4aztp6pfd5thzobl5jzugkw4

Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression [article]

Tong Ma and Renke Huang and David Barajas-Solano and Ramakrishna Tipireddy and Alexandre M. Tartakovsky
2019 arXiv   pre-print
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)
more » ... 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.
arXiv:1910.03783v1 fatcat:rstn3uo42bhvrasnw4e6qsqcle

Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control [article]

Renke Huang, Yujiao Chen, Tianzhixi Yin, Xinya Li, Ang Li, Jie Tan, Wenhao Yu, Yuan Liu, Qiuhua Huang
2020 arXiv   pre-print
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
more » ... 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.
arXiv:2006.12667v2 fatcat:vhhl5ipzwvbfthk4regiolu63y

Smart Sampling for Reduced and Representative Power System Scenario Selection

Xueqing Sun, Xinya Li, Sohom Datta, Xinda Ke, Qiuhua Huang, Renke Huang, Z. Jason Hou
2021 IEEE Open Access Journal of Power and Energy  
(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]

Renke Huang, Yujiao Chen, Tianzhixi Yin, Qiuhua Huang, Jie Tan, Wenhao Yu, Xinya Li, Ang Li, Yan Du
2022 arXiv   pre-print
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)
more » ... 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.
arXiv:2101.05317v2 fatcat:55lmzubo5fhrfjsderg3csbfae

Modeling Single-Phase Inverter and Its Decentralized Coordinated Control by Using Feedback Linearization

Renke Han, Qiuye Sun, Dazhong Ma, Bonan Huang
2014 Mathematical Problems in Engineering  
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
more » ... 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.
doi:10.1155/2014/581323 fatcat:2gh3ebazpfeu7pozfdjedwygai
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