Xin Chen

Texas A&M University | TAMU · Department of Electrical and Computer Engineering

To facilitate effective decarbonization of the electric power sector, this paper introduces the generic Carbon-aware Optimal Power Flow (C-OPF) method for power system decision-making that considers demand-side carbon accounting and emission management. Built upon the classic optimal power flow (OPF) model, the C-OPF method incorporates carbon emis...

This paper presents a comprehensive framework for advancing research and policy development related to decarbonization in electric power systems. It encompasses three pivotal subjects, including carbon accounting, carbon-aware decision-making, and carbon-electricity market design. For each of these subjects, this paper outlines the key problems, ex...

With ubiquitous distributed energy resources (DERs) such as renewable energies, electric vehicles, and smart appliances, modern power systems experience a series of new challenges in operation and control including growing complexity, increasing uncertainty, and intensifying volatility. The upside is that more and more data are becoming available o...

This paper introduces a class of model-free feedback methods for solving generic constrained optimization problems where the specific mathematical forms of the objective and constraint functions are not available. The proposed methods, termed Projected Zeroth-Order (P-ZO) dynamics, incorporate projection maps into a class of continuous-time model-f...

In this paper, we propose a model-free feedback solution method to solve generic constrained optimization problems, without knowing the specific formulations of the objective and constraint functions. This solution method is termed projected primal-dual zeroth-order dynamics (P-PDZD) and is developed based on projected primal-dual gradient dynamics...

With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sen...

Single-point zeroth-order optimization (SZO) is useful in solving online black-box optimization and control problems in time-varying environments, as it queries the function value only once at each time step. However, the vanilla SZO method is known to suffer from a large estimation variance and slow convergence, which seriously limits its practica...

This paper studies the automated control method for regulating air conditioner (AC) loads in incentive-based residential demand response (DR). The critical challenge is that the customer responses to load adjustment are uncertain and unknown in practice. In this paper, we formulate the AC control problem in a DR event as a multi-period stochastic o...

In power distribution systems, the growing penetration of renewable energy resources brings new challenges to maintaining voltage safety, which is further complicated by the limited model information of distribution systems. To address these challenges, we develop a model-free optimal voltage control algorithm based on projected primal-dual gradie...

Adaptive robust optimization (ARO) is a well-known technique to deal with the parameter uncertainty in optimization problems. While the ARO framework can actually be borrowed to solve some special problems without uncertain parameters, such as the power flexibility aggregation problem studied in this paper. To effectively harness the significant fl...

With large-scale integration of renewable generation and distributed energy resources (DERs), modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, sm...

** This paper has been accepted for publication in IEEE Transaction on Smart Grid. The title of this paper has been modified as "Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges". ** Abstract: With large-scale integration of renewable generation and distributed energy resources, modern p...

This paper studies the automated control method for regulating air conditioner (AC)-type loads in incentive-based residential demand response (DR). The critical challenge is that the customer responses to load adjustment are uncertain and unknown in practice. In this paper, we formulate the AC control problem in a DR event as a Markov decision proc...

With the increasing penetration of renewable energy resources, power systems confront new challenges in maintaining power balance and the nominal frequency. This paper studies load-side frequency control to handle these challenges. In particular, a fully distributed automatic load control (ALC) algorithm, which only needs local measurement and loca...

Residential loads have great potential to enhance the efficiency and reliability of electricity systems via demand response (DR) programs. One major challenge in residential DR is how to learn and handle unknown and uncertain customer behaviors. In this paper, we consider the residential DR problem where the load service entity (LSE) aims to select...

Adaptive robust optimization (ARO) is a well-known technique to deal with the parameter uncertainty in optimization problems. While the ARO framework can actually be borrowed to solve some special problems without uncertain parameters, such as the power flexibility aggregation problem studied in this paper. To effectively harness the significant fl...

Residential load demands have huge potential to be exploited to enhance the efficiency and reliability of power system operation through demand response (DR) programs. This paper studies the strategies to select the right customers for residential DR from the perspective of load service entities (LSEs). One of the main challenges to implement resid...

This paper studies the online optimal control problem with time-varying convex stage costs for a time-invariant linear dynamical system, where a finite look-ahead window with accurate predictions of the stage costs is available at each time. We design online algorithms, Receding Horizon Gradient-based Control (RHGC), that utilizes the predictions t...

With a large-scale integration of distributed energy resources (DERs), distribution systems are expected to be capable of providing capacity support for the transmission grid. To effectively harness the collective flexibility from massive DER devices, this paper studies distribution-level power aggregation strategies for transmission-distribution i...

This paper studies the exponential stability of primal-dual gradient dynamics (PDGD) for solving convex optimization problems where constraints are in the form of Ax + By = d and the objective is min f (x) + g(y) with strongly convex smooth f but only convex smooth g. We show that when g is a quadratic function or when g and matrix B together satis...

With the increasing penetration of renewable energy resources, power systems face new challenges in balancing power supply and demand and maintaining the nominal frequency. This paper studies load control to handle these challenges. In particular, a fully distributed automatic load control algorithm, which only needs local measurement and local com...

To settle a large-scale integration of renewable distributed generations (DGs), it requires to assess the maximal DG hosting capacity of active distribution networks (ADNs). For fully exploiting the ability of ADNs to accommodate DG, this paper proposes a robust comprehensive DG capacity assessment method considering three-phase power flow modellin...

This paper proposes a data-driven method based on distributionally robust optimization to determine the maximum penetration level of distributed generation (DG) for active distribution networks (ADNs). In our method, the uncertain DG outputs and load demands are formulated as stochastic variables following some ambiguous distributions. In addition...

As large scale distributed energy resources are integrated into distribution networks, coordinated dynamic economic dispatch (DED) for integrated transmission and distribution networks is becoming essential. In this paper, we describe a transmission and distribution network coordinated DED model and propose an efficient decentralized method to solv...

Fluctuating outputs of distributed generations, time-varying load demands and estimation errors of loads bring substantial uncertainty risks to the active distribution network restoration, which becomes a challenge to the traditional deter-ministic algorithms. In this paper, a robust restoration approach is proposed to solve this issue, considering...

With the fact that more distributed generations (DGs) are used in network, the operation uncertainty of active distribution network is becoming significant. Restoration strategies are affected by these uncertainties, especially by the variation of load demand and DG output. In this paper, a robust optimization model using information gap decision t...