Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.

PID Control of Planar Nonlinear Uncertain Systems in the Presence of Actuator Saturation

This paper investigates PID control design for a class of planar nonlinear uncertain systems in the presence of actuator saturation.Based on the bounds on the growth rates of the nonlinear uncertain function in the system model,the system is placed in a linear differential inclusion.Each vertex system of the linear differential inclusion is a linear system subject to actuator saturation.By placing the saturated PID control into a convex hull formed by the PID controller and an auxiliary linear feedback law,we establish conditions under which an ellipsoid is contractively invariant and hence is an estimate of the domain of attraction of the equilibrium point of the closed-loop system.The equilibrium point corresponds to the desired set point for the system output.Thus,the location of the equilibrium point and the size of the domain of attraction determine,respectively,the set point that the output can achieve and the range of initial conditions from which this set point can be reached.Based on these conditions,the feasible set points can be determined and the design of the PID control law that stabilizes the nonlinear uncertain system at a feasible set point with a large domain of attraction can then be formulated and solved as a constrained optimization problem with constraints in the form of linear matrix inequalities(LMIs).Application of the proposed design to a magnetic suspension system illustrates the design process and the performance of the resulting PID control law.

Design of switched linear systems in the presence of actuator saturation and L-infinity disturbances

This paper considers the problem of disturbance tolerance/rejection of a switched system resulting from a family of linear systems subject to actuator saturation and E-infinity disturbances. For a given set of linear feedback gains, a given switching scheme and a given bound on the E-infinity norm of the disturbances, conditions are established, in terms of linear or bilinear matrix inequalities, under which a set of a certain form is invariant for a given switched linear system in the presence of actuator saturation and E-infinity disturbances, and the closed-loop system possesses a certain level of disturbance rejection capability. With these conditions, the design of feedback gains and switching scheme can be formulated and solved as constrained optimization problems. Disturbance tolerance is measured by the largest bound on the disturbances for which the trajectories starting from a given set remain bounded. Disturbance rejection is measured either by the E-infinity norm of the system output or by the system＇s ability to steer its state into and/or keep it within a small neighborhood of the origin. In the event that all systems in the family are identical, the switched system reduces to a single system under a switching feedback law. Simulation results show that such a single system under a switching feedback law could have stronger disturbance tolerance/rejection capability than a single linear feedback law can.

Linear systems toolkit in Matlab :structural decompositions and their applications

This paper presents a brief description of the software toolbox, linear systems toolkit, developed in Matlab environment. The toolkit contains 66 m-functious, including structural decompositions of linear autonomous systems, unforced/uuseused systems, proper systems, and singular systems, along with their applications to system factorizations, sensor/actuator selection, H-two and H-infinity control, and disturbance decoupling problems.

Development of a Bias Compensating Q-Learning Controller for a Multi-Zone HVAC Facility

We present the development of a bias compensating reinforcement learning(RL)algorithm that optimizes thermal comfort(by minimizing tracking error)and control utilization(by penalizing setpoint deviations)in a multi-zone heating,ventilation,and air-conditioning(HVAC)lab facility subject to unmeasurable disturbances and unknown dynamics.It is shown that the presence of unmeasurable disturbance results in an inconsistent learning equation in traditional RL controllers leading to parameter estimation bias(even with integral action support),and in the extreme case,the divergence of the learning algorithm.We demonstrate this issue by applying the popular Q-learning algorithm to linear quadratic regulation(LQR)of a multi-zone HVAC environment and showing that,even with integral support,the algorithm exhibits bias issue during the learning phase when the HVAC disturbance is unmeasurable due to unknown heat gains,occupancy variations,light sources,and outside weather changes.To address this difficulty,we present a bias compensating learning equation that learns a lumped bias term as a result of disturbances(and possibly other sources)in conjunction with the optimal control parameters.Experimental results show that the proposed scheme not only recovers the bias-free optimal control parameters but it does so without explicitly learning the dynamic model or estimating the disturbances,demonstrating the effectiveness of the algorithm in addressing the above challenges.

Distributed Cooperative Control of Battery Energy Storage Systems in DC Microgrids

The control of battery energy storage systems(BESSs)plays an important role in the management of microgrids.In this paper,the problem of balancing the state-ofcharge(SoC)of the networked battery units in a BESS while meeting the total charging/discharging power requirement is formulated and solved as a distributed control problem.Conditions on the communication topology among the battery units are established under which a control law is designed for each battery unit to solve the control problem based on distributed average reference power estimators and distributed average unit state estimators.Two types of estimators are proposed.One achieves asymptotic estimation and the other achieves finite time estimation.We show that,under the proposed control laws,SoC balancing of all battery units is achieved and the total charging/discharging power of the BESS tracks the desired power.A simulation example is shown to verify the theoretical results.

Recent Advances on Networked Systems and Intelligent Control--Dedicated to a Visionary Leader:Prof.Lihua Xie's 60th Birthday

It is with great pleasure and admiration that we celebrate the 60th birthday of ProfessorLihua Xie, a distinguished researcher and visionary leader in the field of robust control andestimation. Prof. Xie’s remarkable journey, marked by outstanding achievements and groundbreaking contributions, has left an indelible mark on the world of engineering and academia.

Stabilization of discrete-time linear systems by delay independent truncated predictor feedback

For a discrete-time linear system with input delay, the predictor feedback law is the product of a feedback gain matrix with the predicted state at a future time instant ahead of the current time instant by the amount of the delay, which is the sum of the zero in put solution and the zero state solutio n of the system. The zero state solution is a finite summation that involves past in put, requiring considerable memory in the digital implementation of the predictor feedback law. The truncated predictor feedback, which results from discarding the finite summat沁n part of the predictor feedback law, reduces implementation complexity. The delay independent truncated predictor feedback law further discards the delay dependent transition matrix in the truncated predictor feedback law and is thus robust to unknown delays. It is known that such a delay independent truncated predictor feedback law stabilizes a discrete-time linear system with all its poles at z = 1 or inside the unit circle no matter how large the delay is. In this paper, we first construct an example to show that the delay independent truncated predictor feedback law cannot compensate too large a delay if the open loop system has poles on the unit circle at z ≠ 1. Then, a delay bound is provided for the stabilizability of a general linear system by the delay independent truncated predictor feedback.

STRUCTURAL DECOMPOSITION AND ITS PROPERTIES OF LINEAR MULTIVARIABLE SINGULAR SYSTEMS

我们在这篇论文在场为线性 multivariablesingular 系统的结构的分解。如此的分解有捕获并且显示所有结构的性质的一个不同特征，例如有限、无限给定的系统的零结构， invertibility 结构，和冗余的动力学。作为它为非退化的系统的对应物，我们相信技术是在为单个系统解决控制问题的一个强大的工具。

Semi-global weighted output average tracking of discrete-time heterogeneous multi-agent systems subject to input saturation and external disturbances

In this paper,we revisit the semi-global weighted output average tracking problem for a discrete-time multi-agent system subject to input saturation and external disturbances.The multi-agent system consists of multiple heterogeneous linear systems as leader agents and multiple heterogeneous linear systems as follower agents.We design both the state feedback and output feedback control protocols for each follower agent.In particular,a distributed state observer is designed for each follower agent that estimates the state of each leader agent.In the output feedback case,state observer is also designed for each follower agent to estimate its own state.With these estimates,we design low gain-based distributed control protocols,parameterized in a scalar low gain parameter.It is shown that,for any bounded set of the initial conditions,these control protocols cause the follower agents to track the weighted average of the outputs of the leader agents as long as the value of the low gain parameter is tuned sufficiently small.Simulation results illustrate the validity of the theoretical results.

Control of a flexible rotor active magnetic bearing test rig: a characteristic model based all-coefficient adaptive control approach

Active magnetic bearings （AMBs） have found a wide range of applications in high-speed rotating machinery industry. The instability and nonlinearity of AMBs make controller designs difficult, and when AMBs are coupled with a flexible rotor, the resulting complex dynamics make the problems of stabilization and disturbance rejection, which are critical for a stable and smooth operation of the rotor AMB system, even more difficult. Proportional-integral-derivative （PID） control dominates the current AMB applications in the field. Even though PID controllers are easy to implement, there are critical performance limitations associated with them that prevent the more advanced applications of AMBs, which usually require stronger robustness and performance offered by modern control methods such as H-infinity control and if-synthesis. However, these advanced control designs rely heavily on the relatively accurate plant models and uncertainty characterizations, which are sometimes difficult to obtain. In this paper, we explore and report on the use of the characteristic model based all-coefficient adaptive control method to stabilize a flexible rotor AMB test rig. In spite of the simple structure of such a characteristic model based all-coefficient adaptive controller, both simulation and experimental results show its strong performance.

On robustness of an AMB suspended energy storage flywheel platform under characteristic model based all-coefficient adaptive control laws

A characteristic model based all-coefficient adaptive control law was recently implemented on an experimental test rig for high-speed energy storage flywheels suspended on magnetic bearings. Such a control law is an intelligent control law, as its design does not rely on a pre-established mathematical model of a plant but identifies its characteristic model while the plant is being controlled. Extensive numerical simulations and experimental results indicated that this intelligent control law outperforms a μ-synthesis control law, originally designed when the experimental platform was built in terms of their ability to suppress vibration on the high-speed test rig. We further establish, through an extensive simulation, that this intelligent control law possesses considerable robustness with respect to plant uncertainties, external disturbances, and time delay.