Di Shi

New Mexico State University | NMSU · Department of Electrical and Computer Engineering

This paper proposes a voltage stability constrained optimal power flow (VSC-OPF) for an unbalanced distribution system with distributed generators (DGs) based on semidefinite programming (SDP). The AC optimal power flow (ACOPF) for unbalanced distribution systems is formulated as a chordal relaxation-based SDP model. The minimal singular value (MSV...

This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids. As one of the critical components for paving the way to smart grids' success, an intelligent and feasible non-intrusive load monitoring (NILM) algorithm is urgently needed. However, most recent researches on NIL...

The rapid development of Internet-of-Things (IoT) in smart grid has enabled millions of grid-connected Distributed Controllable Resources (DCR, e.g., electric vehicles , controllable loads) to provide service to the grid, such as frequency regulation and demand response. The integration of these DCRs may become a large virtual power plant (VPP) net...

With widespread deployment of renewables, the electric power grids are experiencing increasing dynamics and uncertainties, with its secure operation being threatened. Existing frequency control schemes based on day-ahead offline analysis and minute-level online sensitivity calculations are difficult to adapt to rapidly changing system states. In pa...

With widespread deployment of renewables, the electric power grids are experiencing increasing dynamics and uncertainties, with its secure operation being threatened. Existing frequency control schemes based on day-ahead offline analysis and minute-level online sensitivity calculations are difficult to adapt to rapidly changing system states. In pa...

We present a novel method for calculating Pad\'e approximants that is capable of eliminating spurious poles placed at the point of development and of identifying and eliminating spurious poles created by precision limitations and/or noisy coefficients. Information contained in in the eliminated poles is assimilated producing a reduced order Pad\'e...

Analytical battery models depend on a set of complex nonlinear equations that make them impractical to use in probabilistic analyses (e.g., reliability evaluation) of power systems. Machine-learning algorithms have the potential to reduce or even avoid the computational complexities of incorporating actual battery characteristics in probabilistic a...

Developing effective strategies to arrest grid frequency drop in case of severe contingencies is an important requirement. While distributed responsive loads for primary frequency control have been studied in the literature, the coordination of the distributed loads relies on high-speed communication or centralized frequency threshold setting. They...

Modern power systems are experiencing larger fluctuations and more uncertainties caused by increased penetration of renewable energy sources (RESs) and power electronics equipment. Therefore, fast and accurate corrective control actions in real time are needed to ensure the system security and economics. This paper presents a novel method to derive...

Few studies have focused on assessing the transient and steady-state voltage stability status of dynamic systems simultaneously. This motivated us to propose a new concept referred to as joint voltage stability assessment (JVSA). Towards this end, this paper proposes a novel data-driven JVSA method considering load uncertainty. It combines multiple...

This paper proposes a neural-network-based state estimation (NNSE) method that aims to achieve higher time efficiency, improved robustness against noise, and extended observability when compared with the conventional weighted least squares (WLS) state estimation method. NNSE consists of two parts, the linear state estimation neural network (LSE-net...

Accurate load modeling is critical for credible power system stability analysis. Because of the increasing complexity in modern system load, establishing a more comprehensive load model and performing accurate parameter estimation are the two biggest challenges in composite load modeling. In this paper, an induction motor with variable frequency dr...

Existing power system resilience enhancement methods, such as proactive generation rescheduling, movable sources dispatch, and network topology reconfiguration, do not explore the capability and flexibility of shunts to maintain voltage stability during and after disrupting events. Besides, existing methods rely on accurate system models that are n...

Besides being new loads that would call for further capacity from fossil-fuel based Conventional Sources (CS), managing Electric Vehicles (EVs) smartly i.e. using Vehicle-to-Grid (V2G), holds the opportunity to be used for Load Frequency Control (LFC). However, there exists uncertainty in available dispatch capacity and time delay linked to chargin...

Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain.In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering.To resolve observed issues, we pro...

Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid. This paper presents a novel artificial intelligence (AI) based methodology to achieve multi-objective real-time power grid control for real-world implementation. State-of-the-art off-poli...

Accurate mode identification and effective preventive control strategy of low frequency oscillation (LFO) are vital to improve the small signal stability of power system. This paper proposes a novel data-driven method based on Convolutional Neural Network (CNN) to identify the low frequency modes. The application of feature selection and feature fu...

High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, i...

The system equivalent inertia of the power grid is gradually decreased with the increasing penetration of renewable energy resources, which leads to a higher risk of frequency fluctuation after a major fault. For the frequency emergency control in the scenario of a high‐voltage direct current (HVDC) line block fault, a novel criterion for enabling...

Energy Disaggregation at substations (EDS) is challenging because measurements are mostly aggregated over multiple types of loads, and the existence of some loads such as behind-the-meter solar is unknown to the operator. This paper for the first time addresses this so-called partial labels issue in energy disaggregation and develops a model-free E...

With more data-driven applications introduced in wide-area monitoring systems (WAMS), data quality of phasor measurement units (PMUs) becomes one of the fundamental requirements for ensuring reliable WAMS applications. This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation, which can: ① identify...

With the increasing penetration of renewable energy, power grid operators are observing both fast and large fluctuations in power and voltage profiles on a daily basis. Fast and accurate control actions derived in real time are vital to ensure system security and economics. To this end, solving alternating current (AC) optimal power flow (OPF) with...

Maintaining good quality of transient stability models for power system planning and operational analysis is of great importance. Identification and calibration of bad parameters using PMU measurements that work well for multiple events remains a challenging problem. In this letter, we present a novel parameter calibration method based on off-polic...

This paper presents a machine learning based time-series classification method for using synchrophasor measurements to locate the source of forced oscillation (FO) for fast disturbance removal. First, multivariate time series (MTS) matrices are constructed by the most informative measurements selected by sequential feature selection from each power...

The power system is under increasing threat of terrorist attacks, and it is important to develop efficient methods to improve the power system resiliency for defending against the attacks. In this paper, the defense strategy of the transmission system in case of multi-period attacks considering uncertainties is investigated. The problem formulation...

Fast and accurate load parameter identification has a large impact on power systems operation and stability analysis. This paper proposes a novel Imitation and Transfer Q-learning (ITQ)-based method to identify parameters of composite constant impedance-current-power (ZIP) and induction motor (IM) load models. Firstly, an imitation learning process...

The concept of peer-to-peer (P2P) trading, or transactive energy (TE), is gaining momentum as a future grid restructure. It has the potentials to utilize distributed energy resources (DERs), proactive demand side management (DSM), and the infusion in information and communication technologies (e.g., blockchain and internet of things (IoT)) for prom...

Non-intrusive load monitoring (NILM) is a critical technique for advanced smart grid management due to the convenience of monitoring and analysing individual appliances’ power consumption in a non-intrusive fashion. Inspired by emerging machine learning technologies, many recent non-intrusive load monitoring studies have adopted artificial neural n...

Composite load model of Western Electricity Coordinating Council (WECC) is a newly developed load model that has drawn great interest from the industry. To analyze its dynamic characteristics with both mathematical and engineering rigors, a detailed mathematical model is needed. Although composite load model of WECC is available in commercial softw...

Power transfer limits or transfer capability (TC) directly relate to the system operation and control as well as electricity markets. As a consequence, their assessment has to comply with static constraints, such as line thermal limits, and dynamic constraints, such as transient stability limits, voltage stability limits and small-signal stability...

Modelling and simulation of a high voltage‐level modular multilevel converter (MMC) is challenging due to large amount of semiconductor switches, various submodule (SM) circuits, and different operating conditions. Several equivalent models have been proposed for modelling and simulation in system level, which did not consider an industrial or digi...

In this paper, the real-time non-intrusive load monitoring (NILM) problem with limited measurements, i.e., low sampling rate data, is investigated. NILM is a technique to identify the various types of appliances by analyzing the voltage and current features collected by sensors such as smart outlets. It is one of the most important topics in smart...

Ensuring the stability of power systems is gaining more attention today than ever before due to the rapid growth of uncertainties in load and increased renewable energy penetration. Lately, wide-area measurement system (WAMS)-based centralized controlling techniques are offering flexibility and more robust control to keep the system stable. WAMS-ba...

Load frequency control (LFC) has been considered as one of the most important frequency regulation mechanisms in modern power system. One of the inevitable problems involved in LFC over a wide area is communication delay. Not only can the delay deteriorate the system performance but also cause system instability. In this paper, an alternative desig...

As an Internet-of-Things (IoT) device for smart homes, smart plugs have been pervasive in households, which enable users to monitor and control their electrical appliances remotely and automatically. It is promising that, the networks of smart plugs in the power system will enable autonomous demand response for optimal grid operation. This benefits...

The complexity of modern power grids keeps increasing due to the expansion of renewable energy resources and the requirement of fast demand responses, which results in a great challenge for conventional power grid control systems. Existing autonomous control approaches for the power grid requires an accurate system model and a powerful computationa...

Forced oscillations are caused by sustained cyclic disturbances. This paper presents a machine learning (ML) based time-series classification method that uses the synchrophasor measurements to locate the sources of forced oscillations for fast disturbance removal. Sequential feature selection is used to identify the most informative measurements of...

With the increasing complexity of modern power system, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the Western Electricity Coordinating Council Composite Load Model (WECC CLM) has shown to effectively capture the dynamic lo...

Solving AC optimal power flow (OPF) with operational security constraints remains an important but challenging optimization problem for secure and economic operations of the power grid. With the ever-increasing penetration of renewable energy, fast and large fluctuations in power and voltage profiles are observed on a daily basis; thus, fast and ac...

Power system oscillations under a large disturbance often exhibit distorted waveforms as captured by increasingly deployed phasor measurement units. One cause is the occurrence of a near-resonance condition among several dominant modes that are influenced by nonlinear transient dynamics of generators. This paper proposes an Extended Prony Analysis...

What has become known as Stahl's Theorem in power-engineering circles has been used to justify a convergence guarantee of the Holomorphic Embedding Method (HEM) as it applies to the power-flow problem. In this, the second part of a two-part paper, we examine implications to numerical convergence of HEM and the numerical properties of a Pad\'e appro...

What has become known as Stahl's Theorem in power engineering circles has been used to justify a convergence guarantee of the Holormorphic Embedding Method (HEM) as it applies to the power flow (PF) problem. In this two-part paper, we examine in more detail the implications of Stahl's theorems to both theoretcial and numerical convergence for a wid...

Growing model complexities in load modeling have created high dimensionality in parameter estimations, and thereby substantially increasing associated computational costs. In this paper, a tensor-based method is proposed for identifying composite load modeling (CLM) parameters and for conducting a global sensitivity analysis. Tensor format and Fokk...

Developing effective strategies to rapidly support grid frequency while minimizing loss in case of severe contingencies is an important requirement in power systems. While distributed responsive load demands are commonly adopted for frequency regulation, it is difficult to achieve both rapid response and global accuracy in a practical and cost-effe...

Ensuring the stability of power systems is gaining more attraction today than ever before, due to the rapid growth of uncertainties in load and renewable energy penetration. Lately, wide area measurement system-based centralized controlling techniques started providing a more flexible and robust control to keep the system stable. But, such a modern...

Growing model complexities in load modeling have created high dimensionality in parameter estimations, and thereby substantially increasing associated computational costs. In this paper, a tensor-based method is proposed for identifying composite load modeling (CLM) parameters and for conducting a global sensitivity analysis. Tensor format and Fokk...

This paper proposes a measurement-based voltage stability assessment method considering VAR limits of generators. Traditional measurement-based methods for monitoring a load bus or a load area often simplify the external system as a simple constant electromotive force (e.m.f.) behind a Thevenin impedance, which may give inaccurate voltage stability...

This paper presents a novel AI-based approach for maximizing time-series available transfer capabilities (ATCs) via autonomous topology control considering various practical constraints and uncertainties. Several AI techniques including supervised learning and deep reinforcement learning (DRL) are adopted and improved to train effective AI agents f...

The ever-increasing penetration of centralized and distributed renewable energy, power electronics-based transmission equipment and loads, advanced protection and control systems, storage devices and new power market rules all contribute to the growing dynamics and stochastic behaviors being observed in today’s grid operation. Understanding operati...

With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load model (WECC CLM) has shown to effectively capture the dynamic load responses over traditional load...

Accurate estimation of customer baseline load (CBL) is a key factor in the successful implementation of demand response (DR). CBL technologies implemented at utilities currently are primarily designed for large industrial and commercial customers. The U.S. Federal Energy Regulatory Commission (FERC) order 745 states that DR owners, including reside...

In this paper, the building thermal dynamic characteristics are introduced in the community microgrid (MG) planning model. The proposed planning model is formulated as a mixed integer linear programming (MILP) which seeks to determine the optimal deployment strategy for various distributed energy resources (DER). The objective is to minimize the an...

In this letter, a novel autonomous control framework “Grid Mind” is proposed for secure operation of power grids based on cutting-edge artificial intelligence (AI) technologies. The proposed platform provides a data-driven, model-free and closed-loop control agent trained using deep reinforcement learning (DRL) algorithms by interacting with massiv...

The stochastic and dynamic nature of renewable energy sources and power electronic devices are creating unique challenges for modern power systems. One such challenge is that the conventional mathematical systems models-based optimal active power dispatch (OAPD) method is limited in its ability to handle uncertainties caused by renewables and other...