Bin Huang

Bin Huang

Phd Candidate at Southern Methodist University

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

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Blog Post number 1

less than 1 minute read

Published:

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awards

Awards list

  • China National Scholarship, Ministry of Education of China, September 2013. (0.2%)
  • China National Scholarship, Ministry of Education of China, September 2013. (0.2%)
  • Outstanding Graduate, Huazhong University of Science and Technology, June 2016.
  • Pan Jia Zheng Hydropower Scholarship, China Society for Hydropower Engineering, September 2014. (0.5%)
  • Scholarship for Excellence in Academics, South China University of Technology, September 2018.
  • Scholarship for Excellence in Academics, South China University of Technology, September 2017.
  • Scholarship for Excellence in Academics, South China University of Technology, September 2016.
  • Scholarship for Excellence in Academics, Huazhong University of Science and Technology, September 2014.
  • Scholarship for Excellence in Academics, Huazhong University of Science and Technology, March 2013.
  • Merit Student, Huazhong University of Science and Technology, September 2013. (1%)

portfolio

projects

Projects list

  • Machine Learning-power Battery Storage Modeling and Evaluation for Fast Frequency Regulation Service (11. 2019-present)

    Southwest Research Institute and SMU

    1. accurate model for battery based FFRS, which allows the integration of stationary/mobile batteries, renewable energy generation units as well as different energy demand patterns;
    2. battery safety control scheme, which can adopt different ancillary services and market mechanisms;
    3. fully data-driven real-time optimal control strategies based on Deep Reinforcement Learning which can self-adjust according to the system and market dynamics.

  • Hierarchical Multi-Objective Reactive Power Optimization and Decision Making for Large Scale Power Systems Considering the Resilience(11.2016-09.2018)

    Power Dispatch and Control Center of Guizhou Power Grid at China Southern Power Grid Corp.

    develop the software module of the network reduction, which can adaptively reduce the scale of the power system by eliminating the low voltage level buses. This module can scale to the practical power grid with more than 10,000 buses and has been launched in practice.

  • Key Technologies for Simulation and Visualization of Global Energy Internet(05.2017-06.2019)

    China Electric Power Research Institute

    research on the equivalence method of distributed generation in active distribution network considering uncertainty, which can not only significantly alleviate the computation and communication burden of the deregulated bulk system.

  • Basic Theory and Methodology of Planning and Operation of Integrated Energy System(09.2016-01.2017)

    Energy Research Institute at South China University of Technology

    research on the Benders decomposition method for the unit commitment in the integrated energy system, which enables nonlinear mixed integer programming by decomposing it into a master problem (mixed integer programming) and slave problem (nonlinear programming).

publications

A Modified Ward Equivalent Based on Sensitivity Matrices for Static Security Analysis

Published in IEEJ Transactions on Electrical and Electronic Engineering IEEJ Transactions on Electrical and Electronic Engineering, 2018

This letter proposes a modified Ward equivalent method (M-Ward) for the static security analysis of electric network. The M-Ward develops the external to boundary sensitivity matrices to track the change of the operation condition in the external network. Hence, the M-Ward can guarantee the accuracy of the equivalent network along with the condition change of the external network.

Recommended citation: Huang, Bin, et al. (2018). "A modified Ward equivalent based on sensitivity matrices for static security analysis." IEEJ Transactions on Electrical and Electronic Engineering. 13(11). http://binhuangscut.github.io/files/J1.pdf

An Equivalent Modeling Method for Multi-port Area Load Based on the Extended Generalized ZIP Load Model

Published in 2018 International Conference on Power System Technology (POWERCON 2018), 2018

To model the load area and simplify the electrical network, a multi-port area load equivalent modelling method (ALEEM) based on the extended generalized ZIP load model (EGZIP) is proposed. Different from the traditional ZIP load, the EGZIP load model incorporates the voltage magnitudes and voltage phase angles of all boundary buses, which can equivalently model the area load with multiple boundary buses more accurately.

Recommended citation: Huang, Bin, et al. (2018). "An Equivalent Modeling Method for Multi-port Area Load Based on the Extended Generalized ZIP Load Model." POWERCON 2018). Guangzhou, 6-8 Nov. 2018. http://binhuangscut.github.io/files/C1.pdf

Electrical Network Equivalent Modeling Method with Boundary Buses Interconnected

Published in 2019 IEEE PES GTD Grand International Conference and Exposition Asia (GTD Asia), 2019

This paper proposes an equivalent modeling method (EMM) based on system measurements to reduce the scale of electrical network. In this method, the equivalent network model adopts a novel framework with boundary buses interconnected to improve the accuracy of equivalent network while maintaining the simplicity of the network structure.

Recommended citation: Huang, Bin, et al. (2019). "Electrical Network Equivalent Modeling Method with Boundary Buses Interconnected." GTD Asia 2019). Bangkok, 19-23 March 2019. http://binhuangscut.github.io/files/C2.pdf

Probabilistic active distribution network equivalence with correlated uncertain injections for grid analysis

Published in IET Renewable Power Generation, 2020

Equivalent modelling for active distribution networks (ADNs) is essential for improving the efficiency of analysing transmission networks. Current equivalent modelling methods for ADNs neglect the probabilistic characteristics of renewable energy sources (RESs) and loads. To address this issue, this study proposes a probabilistic equivalent modelling method (PEMM) for ADNs considering the uncertainty of RESs and loads. The uncertainty of the RESs and loads is transferred to the equivalent boundary bus injection using the properties of cumulant and power transfer matrices. The PEMM is extended to incorporate the correlations of RESs through an orthogonal transformation. A sampling method using the Gaussian copula function is employed to generate the correlated samples and the joint cumulants, providing the input data for the PEMM. The comparative results of the case studies on two different test systems demonstrate the effectiveness of the PEMM. The equivalent model developed in this study is a practical solution for analysing the transmission network efficiently and taking the uncertainty of RESs and loads in the ADNs into account simultaneously.

Recommended citation: Huang, Bin, et al. (2020). "Probabilistic active distribution network equivalence with correlated uncertain injections for grid analysis." IET Renewable Power Generation. 14(11). http://binhuangscut.github.io/files/J2.pdf

An Evidential Reasoning Based Approach to Building Node Selection Criterion for Network Reduction

Published in arXiv, 2020

A reasonable node selection criterion (NSC) is crucial for the network reduction in power systems. In contrast to the previous works that only consider structure property, this paper proposes a comprehensive and quantitative NSC considering both structural and electrical properties. The proposed NSC is developed by employing the evidential reasoning approach, in which the quasi-one-hot encoding is used to determine the evaluation grades of different criteria or attributes. Then, different criteria are combined through the multi-evidence reasoning. Eventually, the utility evaluation is used to derive the quantitative NSC.

Recommended citation: Huang, Bin, et al. (2020). "An Evidential Reasoning Based Approach to Building Node Selection Criterion for Network Reduction." arXiv. http://binhuangscut.github.io/files/C3.pdf

Deep Reinforcement Learning-based Capacity Scheduling for PV-Battery Storage System

Published in IEEE Transactions on Smart Grid, 2020

Investor-owned photovoltaic-battery storage systems (PV-BSS) can gain revenue by providing stacked services, including PV charging and frequency regulation, and by performing energy arbitrage. Capacity scheduling (CS) is a crucial component of PV-BSS energy management, aiming to ensure the secure and economic operation of the PV-BSS. This paper proposes a Proximal Policy Optimization (PPO)-based deep reinforcement learning (DRL) agent to perform the CS of PV-BSS.

Recommended citation: Huang, Bin, et al. (2020). "Deep Reinforcement Learning-based Capacity Scheduling for PV-Battery Storage System." IEEE TRANSACTIONS ON SMART GRID. http://binhuangscut.github.io/files/J3.pdf

services

Reviewers

Journals

  • IEEE Trans on Neural Networks and Learning Systems
  • IEEE Trans on Smart Grid
  • IEEE Trans on Power Systems
  • IEEE Trans on Power Electronics
  • IEEE Trans on Energy Markets, Policy, and Regulation
  • IEEE Trans on Network Science and Engineering
  • IEEE Journal of Emerging and Selected Topics in Industrial Electronics
  • Renewable and Sustainable Energy Reviews
  • International Journal of Electrical Power and Energy Systems
  • Energy Reports
  • International Journal of Data Science and Analytics
  • International Transactions on Electrical Energy Systems
  • Expert Systems With Applications
  • Knowledge-Based Systems
  • Energy and AI

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.