Microgrid Cyberphysical Systems: Renewable Energy and Plug-in Vehicle Integration

Microgrid Cyberphysical Systems: Renewable Energy and Plug-in Vehicle Integration outlines the fundamental concepts on microgrid system design and control in a cyberphysical framework, focusing on the integration of renewables and EVs into microgrids. Including operational, control and management perspectives, the volume aims to optimize the reliability and economic performance of microgrids, focusing on power quality, storage and voltage and frequency control. The work encompasses generation, transmission, protection and load management under uncertainty and discusses critical drivers in robustness, uncertainty and sustainability management. Focusing on applied implementations, chapters are supported by detailed methods, heavy figurative explication, and comparative and integrative analysis.

Case studies range across chapters. In addition, chapters are supported by representative experimental or test bed validations of proposed algorithms or methods which can be directly applied to reader problems.

• Provides advanced controller methodologies to efficiently optimize the operation of microgrids with high levels of connected renewable generators and electric vehicles
• Explores powerful approaches for the prevention of cyberattacks in microgrid systems
• Addresses design issues for power quality filters suitable for microgrid robustness, uncertainty and sustainability handling
• Includes field-tested methods, heavy case studies and an implementation focus with supporting experimental or test bed validations of proposed algorithms or methods in MATLAB

• Language: English
• Publisher: Elsevier Science
• Release date: Jun 3, 2022
• ISBN: 9780323983990

Book preview

Preface

Bidyadhar Subudhi

Pravat Kumar Ray

The contents and structure of this book are organized considering the recent trends in the adoption of electric vehicles (EVs) and integration of renewable energy sources to the electric grid or a microgrid—a small power network. A number of uncertainties in generation and loads are encountered in the operation and control of a microgrid. Hence, suitable power electronic control and protection schemes are required for successful operation of microgrids. Further, supply and demand side management play a key role in the stability of a microgrid. Emphasis needs to be given for improving reliability and economic performance of microgrids, focusing on power quality, storage, and voltage and frequency control.

The book comprises 10 chapters. Each chapter provides an overview of the topics covered in the chapter and simulation/experimental results that allow readers to enhance their knowledge of real-time implementation of smart grids. Case studies on different issues are included to provide the readers with clear suggestions. Major emphasis is also given to experimental or test bed validations of the proposed algorithms or methods for the interest of the readers.

Chapter 1 discusses the prerequisite of cyber resiliency in a microgrid for smooth operation under cyber threats. Denial of service (DOS) in a microgrid-based network system has a high probability of cyber threat, where system stability is challenged by the interruption in the communication on control signal to the actuator or on the measurement of sensor data to the controller. Input to state stability (ISS) of the closed loop microgrid system is preserved by characterizing the DOS duration and frequency. ISS of the microgrid system is achieved under DOS through the determination of adequate arrangement in transmission triggering. The event trigger analysis with suitable inequality is presented both in the absence and in the presence of DOS attack to make the controller design practically possible for the secondary voltage-frequency regulation in an autonomous AC microgrid.

Microgrids facilitate the integration of various distributed energy resources (DERs) such as photovoltaics (PV), battery, wind, and fuel cells. For maintaining the harmony of generation between the grid and the DER, the control of voltage source inverters (VSIs) is important. The DER-led VSIs are operated in three modes, namely (i) grid following (GFL) mode, in which the VSI follows the voltage and frequency of the grid at the local point of common coupling (PCC); (ii) grid supporting mode, in which the VSI supports the grid; and (iii) grid forming (GFM) mode, in which VSI operates in the islanded mode. All the modes of VSI control have been discussed in Chapter 2.

Forecasting solar irradiance based on microscale information, i.e., sky images captured by Total Sky Imager (TSI), is described in Chapter 3. The sky images are processed to determine the cloud cover and compared with the 1-minute-ago sky image to analyze cloud movement using an optical flow algorithm. The cloud condition in the next minute is predicted and, consequently, the solar irradiance values are calculated based on parts of clouds covering the sun. For more accuracy in forecasting, the clearness index is updated using Variable Leaky Least Mean Square (VLLMS) algorithms. Depending on different locations and applications, the same algorithm can be applied accordingly by calibrating the threshold value for the cloud determination algorithm and the clearness index value for solar irradiance calculation.

Chapter 4 describes the need for maintaining constant DC output voltage across the load terminals of a DC microgrid; in particular, the problem of controlling the load voltage of nonminimum phase DC-DC boost converter with the measured load voltage under voltage mode control is addressed. Quantitative feedback theory is used to synthesize a robust PID controller systematically with external disturbances and uncertainties.

Chapter 5 describes the implementation of controllers for EV charging and the use of the Vehicle to Grid concept, where EVs are used to supply power to the grid at times of peak demand. Local microgrids are used for incorporating distributed generating sources to actively support the main power grid by utilizing locally generated renewable energy. Smart control of EV battery charging can significantly help in reducing power exchange between the microgrid and the main grid, resulting in overall cost reduction for the grid as well as the microgrid (or a house) by effectively managing the local energy use and the power exchange with the utility grid.

Chapter 6 provides an overview of the design of a resilient adaptive secondary controller for a stand-alone AC microgrid system. Lyapunov functions are used to construct the proposed controller. Regardless of unforeseen disturbances, the proposed technique has the additional advantages of accurate active and reactive power sharing, along with the restoration of voltage and frequency reference values before the predetermined timeframe. The controller design involves a simple tuning procedure and has a straightforward mathematical formulation that makes it easy to apply. The proposed controller exhibits improved performance in terms of voltage and frequency tracking in the face of uncertainties and load variations.

Chapter 7 investigates the challenges posed by wide area propagation of disturbance instigated by renewable, dominant, uncertain microgrids in evolving deregulated market-oriented grids. Besides, a proactive defense system that utilizes the characteristics of this disturbance propagation to mitigate the spread of the disturbance is presented and discussed. This defense strategy caters to the ancillary service capabilities of the microgrid itself to mitigate the disturbance spread caused by other microgrids. A case study conducted on NY-NE 16 machine 68 bus system substantiates the wide area propagation characteristics of disturbance induced by uncertain, renewable, dominant microgrids and efficacy of the presented proactive defense strategy in alleviating the disturbance spread.

Chapter 8 highlights the implementation of a shunt active power filter (SAPF) for power quality improvement through harmonics compensation. In this chapter, an improved indirect current controller with model reference adaptive controller (MRAC) is proposed to enhance the performance of PV-integrated SAPF. The advantages of this proposed controller are evident by studying the harmonics compensation ratio for various load and filter parameter configurations. Performance enhancement of the proposed MRAC controller is evident by comparing it with conventional indirect current control with proportional integral controller and is also verified with a laboratory developed experimental setup.

New protection schemes for the fast detection of faults and isolation of faulty sections to minimize disruption in power supply to the consumers are presented in Chapter 9. The authors provide a systematic description of protection schemes for microgrids. The chapter mentions the challenges faced by the existing relaying schemes in microgrids and also discusses the development of new protection schemes for mitigation of the challenges.

Chapter 10 presents an insight on the importance and need of the microgrid in the distribution network. The various advantages offered by the microgrid are also discussed in detail and the classification of the microgrid based on size and capacity, electrical power supply, and geographical locations is discussed. Further, the challenges and issues faced by the microgrid are discussed in detail for AC, DC, and hybrid AC/DC microgrids.

It is expected the book will immensely benefit undergraduates, postgraduates, research scholars, faculty members, engineers, and scientists in electrical and computer engineering from different industries and organizations. In particular, the target audience is a broad domain of engineers in the power industry and computer scientists focusing on cyber security design and machine learning.

The book will help readers in enriching their technical knowledge on several aspects including power quality, techniques for smooth operation of microgrids, coordination control of distributed active power filters and STATCOMs in smart power networks, EV integration issues, and stability in situations of high penetration of renewables.

Chapter 1: Denial-of-service attack resilient control for cyber physical microgrid system

Vivek Kumar; Soumya R. Mohanty    Department of Electrical Engineering, Indian Institute of Technology (BHU), Varanasi, India

Abstract

In a microgrid network system, cyber resiliency is a prerequisite for the smooth operation under cyber threats. Denial-of-service (DOS) in microgrid-based network system has high probable cyber threat where system stability is challenged by the interruption in the communication on control signal to actuator or on the measurement of sensor data to the controller. In this chapter, input to state stability (ISS) of closed loop microgrid system is preserved by characterizing the DOS duration and frequency. ISS of the microgrid system is achieved under DOS through the determination of adequate arrangement in transmission triggering. The event trigger analysis with suitable inequality is presented both in absence and presence of DOS attack to make the controller design practically possible for the secondary voltage-frequency regulation in an autonomous AC microgrid. The law for updated control propagation is determined by the sliding mode control analysis. Lyapunov function is used to analyze the stability of microgrid system under sequence of DOS attack. The DOS-based cyber resiliency of proposed scheme is verified in MATLAB/Simulink environment.

Keywords

Cyber-physical systems; Denial-of-service attack; Event trigger; Lyapunov theory; Networked control systems; Sliding mode control

1: Introduction

Microgrid cyber-physical structure can be represented by incorporating communication network, power electronic devices, and software-intensive close loop control. The software in closed loop controls as well as communication networks enhances susceptibility for cyber conciliations in the microgrid [1,2]. Because of the weak distribution grid, absence of generational inertia, and dynamic source-load profiles, the cyber threat becomes more noticeable in the inverter-based microgrid configurations. Microgrids are progressively initiated to renovate into cyber-physical microgrids including distributed and decentralized multi-agent features using advanced power electronics [3]. A cyber-physical microgrid structure comprises both cyber and physical layer as illustrated in Fig. 1. The physical layer signifies an interlink electric power system, whereas cyber layer deals with the communication medium for exchange of data between microgrid agents. These agents are interconnected by means of physical layer, that is, power lines, and ahead of physical nodes, cyber communication and information network connects the cyber components. Cyber safety in microgrids have a dominant importance due to the occurrences of adverse, destructive, and undiminished cyber-attacks, particularly at the distribution grid.

Fig. 1

Fig. 1 Multi-DG-based microgridcyber-physical structure.

The AC microgrid distributed cooperative control [4–6] has now arisen as an alternative to centralize control as it provides improved robustness and smooth control capabilities. In these control strategies, inverters are considered as nodes in a sparse communication digraph. Through the communication digraph, it is ensured that all nodes will reach a common consensus as per measures delivered by the reference node. In AC microgrids, the consensus control is primarily adopted to attain voltage and frequency regulation by information exchange between local neighboring agents. In a multi-agent distributed AC microgrid, different attacks may deteriorate the system performance as represented in Fig. 2. In these categories, attackers can influence the signals from the leader node through the communication channel between nodes. The attacker may interrupt the neighboring node information and propagate false information to local nodes. Also, the attacker can inject an on-off signal to disrupt the local state-feedback signal. These cyber threat intrusions may be unbounded which can disrupt the synchronization of all agent’s mechanisms.

Fig. 2

Fig. 2 Different cyber threats in a microgrid: (A) In the communication channel, (B) at the neighboring agent information for corresponding node, and (C) DGs state feedback signal.

Microgrid control strategies involve three layers of hierarchical structures of control that are primary, secondary, and tertiary in order to achieve smooth and stable operation [7]. In primary control procedure, distributed generations (DGs) have local control loops that are based on the droop control operation. Integrated DGs are the main building component of microgrid that efficiently facilitate the small-scale power system [8,9]. One of the primary control objectives is to take care of frequency and voltage regulation and maintain stability when the microgrid goes into islanded mode. In general, microgrid has two modes of operation which are grid connected and islanded mode of operation [7,10]. Secondary control is followed by the primary operation in hierarchical control structure of microgrid to further restore voltage and frequency that may be deviated due to disturbances and system uncertainties [9–11]. It provides reference to all DGs primary control connected through a common bus. Hence, cyber threat in secondary control layer in practical circuits may be introduced due to involvement of communication network in between the bus interconnections. Cyber-physical networks (CPNs) in the previous decade have the center of attention to research community that is evolved from the integration of networking, computation, and physical procedures [12]. Due to the interconnection of cyber components of microgrid with its physical properties under CPNs environment may introduce various aspect to analysis such as fault [13], observer design [14], security issues [15,16], and stability bounds [17,18].

In most of the existing control, concern is focused on the uncertainty and fault compensation which causes failure of communication in the network control tactics [19,20]. These approaches do not effectively cover the malicious cyber adversary, especially in CPNs cases, where physical parameters are interacted with cyber component and hence vulnerable to cyber intimidations. The main classifications of attacks in microgrid communication link are deception and DOS attacks. Deception attack is concerned with the data reliability by influencing transmitted packets over the communication network [21–25], whereas DOS attacks are primarily planned to loss packets in a way to disrupt the exchange of information timeline [26,27]. Investigations on DOS attacks that interrupt the communication availability in microgrid networks are limited. The effect of DOS attack in the study by Srikantha and Kundur [28] is investigated on microgrid communication network by game theory approach using phasor measurement units. Similarly, Liu et al. [29] describe the impact of DOS attack on frequency in microgrid secondary control operation. In both these studies [28,29], it is shown that the DOS attack upsets microgrid stability by avoiding restoration process of secondary controller. An auxiliary communication interface of power line is utilized in network reconfiguration method in Danzi et al. [30] to overcome the impact of DOS attack. To extent the resiliency of microgrid in islanded mode of operation against DOS attack, a fallback control in Chlela et al. [31] is applied for energy storage. Although these algorithms against DOS attack in microgrid are much impactful and promising, it also lacks to present solid microgrid stability assessment during DOS attack which is a key role for an algorithm against DOS [32,33]. In this chapter, a control scheme based on sample data is considered for DOS attack in microgrid communication network. The DOS attacker intended to introduce instability in the controller by preventing communication on control signal to actuator or on the measurement of sensor data to the controller channels. In open loop, the process develops the control as per the last transmitted sample during DOS attacks, whereas in close loop microgrid configuration, the point of interest is to determine close loop bounds to preserve stability in some predefined sense. Pertaining to this DOS attack, modeling is an issue of concern. In previously reported work, it is difficult to explain the problematic packet drop stimulation for a DOS attacker. This chapter follows a simple model of DOS attack where attacker can target the time constraints only in the form of DOS attack duration and its frequency. This configuration of DOS attack can cover various DOS attacks like periodic, trivial, random, and jamming attacks [34–36].

In this chapter, microgrid stability under DOS attack is preserved by characterizing the duration and frequency of DOS attack in the sense of relating stability with the jamming on-off periods [37]. To avoid DOS impact, transmission times are designated to achieve closed loop stability by satisfying suitable condition at the time of communication under the DOS attack. The method is advantageous in a manner to achieve ISS subjected to disturbances under DOS attack through the general Lyapunov-based stability analysis during on-off timings of DOS