Distributed Control Methods and Cyber Security Issues in Microgrids

By Xiaoyu Wang

Distributed Control and Cyber Security Issues in Microgrids presents a thorough treatment of distributed control methods and cyber security issues for power system researchers and engineers. With the help of mathematical tools, this reference gives a deep understanding of microgrids and new research directions, addressing emerging concepts, methodologies and applications of monitoring, control and protection in smart microgrids with large-scale renewables. With the integration of more distributed or aggregated renewables and the wide utilization of power electronic devices, the smart microgrid is facing new stability and security challenges.

• Includes global case studies to demonstrate distributed control success stories
• Offers detailed illustrations and flowcharts to address challenges and technical solutions for those working in power systems in utilities and industry
• Showcases new challenges faced in the stability and security of smart microgrids

• Language: English
• Publisher: Academic Press
• Release date: Mar 22, 2020
• ISBN: 9780128169476

Book preview

Distributed Control Methods and Cyber Security Issues in Microgrids

First Edition

Wenchao Meng

Xiaoyu Wang

Shichao Liu

Table of Contents

Cover image

Title page

Copyright

Contributors

Preface

Part I: Frequency and voltage control

Chapter 1: Sliding mode control of grid-connected power converters for microgrid applications

Abstract

1 Introduction of sliding mode control

2 Mathematical models of power converters

3 SMC for power converters

4 Conclusion

Chapter 2: Distributed voltage restoration and power allocation control in islanded DC microgrids

Abstract

1 Introduction

2 Problem formulation and objectives

3 Distributed secondary control for voltage restoration

4 Simulation results

5 Experimental validation

6 Conclusion

Chapter 3: Optimal distributed secondary control for an islanded microgrid

Abstract

1 Introduction

2 Preliminaries

3 Proposed optimal distributed control strategy

4 Simulation analysis

5 Conclusion

Chapter 4: Distributed power control of flexible loads in microgrids

Abstract

1 Coordinated active power dispatch control for a microgrid

2 Demand response load following control of smart grids

Chapter 5: False data injection attacks on inverter-based microgrid in autonomous mode

Abstract

1 Introduction

2 Inverter-based microgrid structure

3 System dynamic model

4 System performance under FDI attacks

5 Simulation examples

6 Conclusion

Part II: Energy management

Chapter 6: Distributed finite-time control of aggregated energy storage systems for frequency regulation in multiarea microgrids

Abstract

1 Introduction

2 Proposed frequency control scheme

3 Proposed disturbance observer

4 Distributed finite-time control of ESA

5 Results and discussions

6 Conclusion

Chapter 7: Distributed optimization algorithm for economic dispatch: A bisectional approach

Abstract

1 Introduction

2 System modeling

3 Introduction to consensus-like algorithm

4 Distributed bisection algorithm: Design and analysis

5 Numerical examples

6 Conclusion and discussion

Chapter 8: Scheduling of EV battery swapping in microgrids*

Abstract

1 Introduction

2 Problem formulation

3 Centralized solution

4 Distributed solutions

5 Numerical results

6 Concluding remarks

Appendix: Proof of Theorem 1

Chapter 9: Dispatch strategy of energy bank system with hybrid energy storage for multiple microgrids

Abstract

1 Introduction

2 EBS model structure

3 Trading model

4 Listed-energy and listed-price (LELP) model

5 Deposit system of energy

6 Case study

7 Conclusion

Chapter 10: False data injection attacks and countermeasures in smart microgrid systems

Abstract

1 Introduction

2 Preliminaries and problem formulation

3 Main results

4 Simulation

5 Conclusions

Index

Copyright

Academic Press is an imprint of Elsevier

125 London Wall, London EC2Y 5AS, United Kingdom

525 B Street, Suite 1650, San Diego, CA 92101, United States

50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States

The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom

Copyright © 2020 Elsevier Inc. All rights reserved.

No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions.

This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

Library of Congress Cataloging-in-Publication Data

A catalog record for this book is available from the Library of Congress

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library

ISBN 978-0-12-816946-9

For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Joe Hayton

Acquisitions Editor: Lisa Reading

Editorial Project Manager: Joanna Collet

Production Project Manager: Kamesh Ramajogi

Cover Designer: Matthew Limbert

Typeset by SPi Global, India

Contributors

Numbers in parentheses indicate the pages on which the authors’ contributions begin.

Jiming Chen (263)     College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

Peng Cheng (263)     College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

Ruilong Deng (263)

College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

School of Computer Science and Engineering, Nanyang Technological University, Singapore

Zhao Yang Dong (243)     School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, Australia

Minyue Fu (177)     School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW, Australia

Yabin Gao (3)     Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, People’s Republic of China

Fanghong Guo (29)     Department of Automatic Control, Zhejiang University of Technology, Hangzhou, Zhejiang, People’s Republic of China

Jianqiang Hu (83)     Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, and School of Mathematics, Southeast University, Zhejiang, People’s Republic of China

Zhiyun Lin (177)     School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, People’s Republic of China

Jianxing Liu (3)     Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, People’s Republic of China

Mengxiang Liu (263)     College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

Wensheng Luo (3)     Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, People’s Republic of China

Wenchao Meng (125)     Department of Control Science and Engineering, Zhejiang University, Hangzhou, China

Junjian Qi (125)     Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, United States

Jing Qiu (149, 243)     School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia

Guanghui Sun (3)     Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, People’s Republic of China

Lingling Sun (243)     School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, Australia

Lei Wang (29)     School of Electrical Engineering and Computing, University of Newcastle, Newcastle, NSW, Australia

Wenhai Wang (263)     College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

Xiaoyu Wang (125)     Department of Electronics, Carleton University, Ottawa, ON, Canada

Yu Wang (149)     Nanyang Technological University, Singapore

Changyun Wen (29)     School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore

Hao Xing (177)     School of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, People’s Republic of China

Yan Xu (149)     Nanyang Technological University, Singapore

Yinliang Xu (59)     Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Shenzhen, People’s Republic of China

Zhongkai Yi (59)     Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Shenzhen, People’s Republic of China

Yunfei Yin (3)     Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, People’s Republic of China

Pengcheng You (203)     Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States

Heng Zhang (125)     School of Science, Jiangsu Ocean University, Lianyungang, Jiangsu, People’s Republic of China

Chengcheng Zhao (263)     College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

Wei Xing Zheng (125)     School of Computing, Engineering and Mathematics, Western Sydney University, Sydney, NSW, Australia

Preface

Recent years have witnessed a growing interest in microgrids, since they can provide increased reliability and can facilitate the effective integration of distributed generators (DGs). However, the traditional centralized methods cannot guarantee the reliability and effective integration of DGs because they are subject to the well-known single point of failure and not flexible. As a result, distributed methods have been investigated in recent years. Meanwhile, microgrids integrate cyber components such as information and communication technologies and computer processing into physical components, which raises cyber security concerns such as Denial-of-Service, data integrity attacks, and replay attacks, and these cyber attacks can influence physical infrastructures because of their tight coupling. In order to improve the grid's reliability and resilience, robust defense methods must be studied. In addition, the increasing sensing abilities of microgrids have also raised privacy concerns.

Therefore, in this book, we investigate distributed control solutions and cyber security issues for microgrids. In Chapters 1-4, the distributed frequency control approaches and distributed voltage control approaches are studied. These methods are distributed in the sense that each participant only needs local information instead of the global information. In Chapter 5, the influence of false data injection on the distributed frequency control is investigated. In Chapter 6, the energy storage is used for frequency regulation in microgrids. In Chapters 7-9, distributed energy management including the economic dispatch, scheduling of EV battery swapping, and dispatch strategy for energy bank system is studied. In Chapter 10, the false data injection and its countermeasures in smart DC microgrid systems are investigated.

Part I

Frequency and voltage control

Chapter 1

Sliding mode control of grid-connected power converters for microgrid applications

Jianxing Liu; Wensheng Luo; Yabin Gao; Yunfei Yin; Guanghui Sun    Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, People’s Republic of China

Abstract

In this chapter, we focus on output feedback control and observer design for two types of commonly used microgrid power converters, that is, three-phase two-level AC/DC power converters and DC/DC converters. As is well known, the sliding mode control (SMC) is naturally well suited for the control of variable structure systems. Since power converters inherently include switching devices, making them belong to variable structure systems; therefore, it is straightforward to apply SMC that yields a discontinuous control law. Moreover, given that power converters are usually modeled using the state space averaging method, SMC forms an efficient analysis and design tool for the control of switched mode power converters, because it offers excellent large-signal handling capability.

Conventional linear control is small signal based; it only allows one to optimally operate the converters for a specific range of operating conditions and often fails to achieve satisfactory performance under large parameter/load variations, that is, large signal operating condition. SMC as a kind of nonlinear control method is suitable for controlling the power converters, which is able to achieve better regulation and dynamical performance for a wider range of operating conditions. The main reason is that there is no need to have a linear model of the power converter for nonlinear controller design. However, the main obstacle associated with the application of SMC is its variable frequency nature, which makes the design of output filter difficult. Nonetheless, if this problem is properly handled, SMC is a powerful control design method for power converters and has huge potential in industrial applications.

The three-phase two-level ACDC power converter acts as a rectifier or voltage source inverter (VSI). When acting as a rectifier, it is the primary interface for a wind turbine (WT) generator, converting the WT source AC voltage to a varying output DC voltage following the maximum power point tracking (MPPT) control. As a microgrid is normally an AC microgrid, the VSI is the most important module of the converters, which works as a DC/AC converter. VSI interfaces among the renewable energy source (RES) DC-link output, energy storage system (ESS) DC-link output, and the microgrid. It converts DC voltage to AC voltage with the microgrid voltage magnitude and frequency, in order to inject the active and reactive power to the microgrid. The control objectives of AC/DC converter are: (1) DC-link voltage regulation; and (2) AC current tracking with the possible lowest harmonic distortion.

The DC/DC converter converts a source of direct current from one voltage level to the fixed or adjustable one. In a microgrid, a DC/DC converter is used in the following scenarios: (1) working as a primary interface for photovoltaic panels, which converts the PV source DC voltage to an adjustable output DC voltage based on the MPPT control; (2) for a DC microgrid, the DC/DC converter acts as an interface among the microgrid, DC loads, RES DC-link output, and the ESS, such as batteries and flywheels. In this scenario, the DC/DC output voltage is a fixed one. To control the DC/DC converter, great control effort should be made to regulate the output voltage to the desired value, due to input voltage disturbances and abrupt load variation. From a control point of view, the control design for boost converters is more difficult than the buck type, because its standard model is a nonminimum phase system. Traditionally, the control problems of the DC/DC converters are solved by using pulse width modulation (PWM) techniques, in which an external high-frequency signal is used to modulate a low-frequency desired function to be tracked. In view of practical implementation, SMC is much easier than a PWM control, since the maximum frequency of commercially available switching elements increases higher and higher.

In this chapter, a novel observer-based control is proposed for both three-phase two-level grid-connected power converters and DC/DC buck converters. The proposed control technique forces the input currents to track the desired values, which can indirectly regulate the output voltage while achieving a user-defined power factor. The presented approach has two control loops. A current control loop based on the sliding mode technique and a DC-link voltage regulation loop which consists of an extended state observer (ESO) plus SMC are adopted. The load connected to the DC-link capacitors is considered as an external disturbance. An ESO is used to reject asymptotically this external disturbance. Therefore, its design is considered in the control law derivation to achieve a high performance.

SMC is known for the low sensitivity to disturbances and parameter variations, making it an effective method to deal with the nonlinear behavior of complex systems. It has been demonstrated to be a highly promising solution for power electronic systems, such as switching DC/DC power converters, grid-connected power converters, and motor drives. The sliding mode approach is expected to become increasingly popular in the field of power converter control. This chapter will be organized as follows. Section 1 begins with an introduction to the fundamental theory and methodology of SMC to familiarize readers with the main principle and background of SMC. Section 2 will discuss the mathematical models of three-phase grid-connected AC/DC power converters and DC/DC buck converters. Next, in Section 3, the design of the SMC and output feedback control for aforementioned power converters will be presented, and the simulation results comparing the performance of the proposed control with the conventional proportional-integral controller will be discussed. Finally, some conclusions are drawn in Section 4.

Keywords

Sliding mode control; Super twisting algorithm; Power converters; Cascade control

1 Introduction of sliding mode control

Sliding mode control (SMC) was first proposed and elaborated by Emelyanov, Utkin, and other coresearchers in Soviet Union in the early 1950s [1]. The idea was inspired by the switching actions of electromagnetic relay. The main feature of SMC is that its control function does not remain the same; instead, it switches from one to the other. The system states are driven onto a particular surface (sliding surface) and maintained there. Once the sliding surface is reached, the system is invariant, meaning that the system is completely insensitive to parametric uncertainty and external disturbances. To overcome the undesirable chattering phenomenon of the traditional SMC (first-order sliding mode control [FOSMC]), a type of second-order sliding mode control (SOSMC) algorithm, which changes the discontinuous control function to a continuous one, was proposed. This suppresses the chattering while maintaining the robustness against the uncertainties [2–5].

The design of SMC consists of two steps. The first step is to design a sliding surface such that the sliding motion satisfies certain design specifications, that is, the dynamic behavior of the system can be tailored by the special choice of the sliding surface. The second step is to select a control law that will drive the system states to the sliding surface and maintain their subsequently, thus the required specifications are met, and the closed-loop system becomes totally insensitive to some particular uncertainties, including external disturbance, model parameter uncertainties, and nonlinearity that are bounded.

Consider a nonlinear system

   (1)

   (2)

is the input, s(t, x): Rn+1 →R is the sliding variable, and a(x) and b(x, u) are smooth uncertain functions.

The control focus is to force the sliding variable s to zero. Geometrically, s = 0 is a hyperplane in error space, which is the so-called sliding surface. Hence, the system specifications can be taken into account when defining the sliding surface, consequently, the specifications are met as the system achieves the sliding surface.

Now that the sliding surface has been defined, the next step is to design a control law steering the system trajectories to the sliding hyperplane in finite time. Here we present two algorithms: FOSMC and SOSMC.

1.1 First-order sliding mode control

The FOSMC is the most conventional SMC, which takes the following form:

   (3)

where K is a positive constant sufficiently large. From Eq. (3), it can be seen that the control is switching between two constant values, crossing the hyperplane s = 0. The controlling mechanism is: control u affects the derivative of s, that is, when s > 0, u , vice versa, when s < 0, u always holds, until the hyperplane s = 0 is reached.

The control u will be switching at very high frequency, theoretically infinite in steady state. Since a real physical switch cannot take action that quickly, this would lead to system chattering. To deal with this problem, some other modified SMC algorithms have been proposed, among which the most attractive one is the SOSMC.

1.2 Second-order sliding mode control

One classical form of SOSMC is the super twisting algorithm (STA). This section discusses the STA in a general case for system (1).

The control objective is to force the sliding variable s to zero. By differentiating the sliding variable s(t, x) twice, the following relations are derived:

   (4)

   (5)

If sliding variable s which means it has relative degree one with respect to control input u, then there exist positive constant values Φ, Γm, and ΓM satisfying the following conditions:

   (6)

   (7)

Applying Eqs. (6), (7) to Eq. (5), the following condition is satisfied:

   (8)

Then an STA controller is designed as follows:

   (9)

   (10)

   (11)

where α and λ are design parameters which are decided based on the boundary conditions (6), (7). It can be seen that the STA consists of two terms: one is the integral of its discontinuous sign function while the other is a continuous function of sliding variable s, the following conditions should be satisfied [2, 6]:

   (12)

Remark 1

From Eqs. (3), (9) we can see that, rather than simply using the sign of the sliding surface function, STA adopts the sum of its integral and another continuous function. Thus STA is a continuous function, and the chattering phenomenon appearing in the FOSMC is greatly reduced. In this case, though the total invariance of sliding motion is routinely lost [7], it still presents very good robustness properties.

2 Mathematical models of power converters

2.1 Mathematical model of three-phase two-level AC/DC power converters

Fig. 1 shows the topology of a three-phase two-level power converter under investigation. It operates in rectifier mode and is connected to a grid. van, vbn, and vcn are the balanced three-phase grid voltage, that is, van + vbn + vcn = 0. ia, ib, and ic are the three-phase grid current. L is the filtering inductor with internal resistance of r. RL is the assumed equivalent resistive load connected to the DC-link capacitor C. RL is not known a priori and is considered as an external disturbance. iload is the load current through RL. u = [uaubuc]T are the controlled switching signals for each phase, which are equal to + 1 and − 1, representing ON and OFF status of upper switches. The lower switches just take the opposite values as the upper ones. VDC is the DC-link voltage to be regulated.

Fig. 1 Topology of three-phase two-level power converter.

Through circuit analysis, the mathematical model of the converter under the natural abc coordinate frame is obtained as follows [8, 9]:

   (13)

To facilitate the controller design, the model (13) is transformed into a synchronous (d, q) reference frame through Park’s transformation with the matrix [10, 11]:

   (14)

Applying Eq. (14) to Eq. (13) yields the following system dynamics in the (d, q) frame:

   (15)

where v = [vd, vq]T, i = [id, iq]T, and u = [ud, uq]T are, respectively, grid voltage, grid current, and switching functions in (d, q) frame. ω is the angular speed of grid voltage. In (d, q) frame, the three-phase sinusoidal variables become constant variables, thus they are easier to control [11, 12].

2.2 Mathematical model of DC/DC buck converters

The topology of the buck converter is presented in Fig. 2, which comprises an input DC voltage source vin, a switch device VT, a diode VD, an output capacitor C, a filter inductor L, and the equivalent load RL considered as the unknown load in this work. vo and iL represent the output voltage and inductor current, respectively. Here it should be pointed that we only study the converter operating in continuous conduction mode in this chapter. Denoting the output voltage vo and inductor current iL as the state variables, when the switch is ON, the model of the buck converter can be represented as follows [13, 14]:

   (16)

   (17)

where iL is the inductor current, vo is the output voltage, and u is the control input.

Fig. 2 Topology of DC-DC buck converter.

3 SMC for power converters

3.1 SMC of three-phase AC/DC power converters

3.1.1 Mode uncertainties

The mathematical models (13), (15) are ideal models, which are obtained under the assumption that the electrical and electronic devices and grid voltage are all ideal. However, in real applications, the grid angular speed ω, the smoothing inductor L, and its parasitic resistance r can vary in a small range, which form the model parametric uncertainties. These uncertainties would cause unexpected system behavior under some operating points. Therefore, in order to design a controller which is robust against these uncertainties, the corresponding parameters are defined as follows:

   (18)

where ω0, L0, and r0 are nominal values, and Δω, ΔL, and Δr are the parametric uncertainties which are slowly varying and unknown.

3.1.2 Control objectives

As mentioned in Section 2, the three-phase two-level converter operates in rectifier mode, therefore the control objectives are as follows:

:

(19)

•The grid currents id and iq is manually set to provide a desired power factor.

(20)

3.1.3 Controller design

A grid-connected three-phase two-level power converter system can be disturbed by parameter uncertainties and load variations. Therefore, the controller should be designed to have enough capability to suppress the disturbances. To achieve this, a cascaded control scheme is adopted for the system (15), which consists of a disturbance-observer-based voltage regulation loop and a current tracking loop. For the voltage loop, an extended state observer (ESO) is employed to estimate the external disturbance, which is practically the load power abruptly connected to the DC link. The estimated disturbance is then used to compensate the STA controller in the forward channel, thus the disturbance is actively rejected. Unlike conventional observers, such as high-gain observer [15], unknown input observer [16], and Luenberger observer [17], ESO regards the disturbances as new system states, therefore it is able to estimate both the external disturbances and internal plant states [18–21]. For the current loop, the STA controller is adopted to quickly drive the currents id and iq to their references