Wente Zeng

A Survey on the Electrification of Transportation in a Smart Grid Environment

Economics and environmental incentives, as well as advances in technology, are reshaping the traditional view of industrial systems. The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) into the market brings up many technical problems that are highly related to industrial information technologies within the next ten years. There is a need for an in-depth understanding of the electrification of transportation in the industrial environment. It is important to consolidate the practical and the conceptual knowledge of industrial informatics in order to support the emerging electric vehicle (EV) technologies. This paper presents a comprehensive overview of the electrification of transportation in an industrial environment. In addition, it provides a comprehensive survey of the EVs in the field of industrial informatics systems, namely: 1) charging infrastructure and PHEV/PEV batteries; 2) intelligent energy management; 3) vehicle-to-grid; and 4) communication requirements. Moreover, this paper presents a future perspective of industrial information technologies to accelerate the market introduction and penetration of advanced electric drive vehicles.

Modeling and Optimizing the Performance-Security Tradeoff on D-NCS Using the Coevolutionary Paradigm

Distributed networked control systems (D-NCS) are vulnerable to various network attacks when the network is not secured; thus, D-NCS must be well protected with security mechanisms (e.g., cryptography), which may adversely affect the dynamic performance of the D-NCS because of limited system resources. This paper addresses the tradeoff between D-NCS security and its real-time performance and uses the Intelligent Space (iSpace) for illustration. A tradeoff model for a systems dynamic performance and its security is presented. This model can be used to allocate system resources to provide sufficient protection and to satisfy the D-NCSs real-time dynamic performance requirements simultaneously. Then, the paper proposes a paradigm of the performance-security tradeoff optimization based on the coevolutionary genetic algorithm (CGA) for D-NCS. A Simulink-based test-bed is implemented to illustrate the effectiveness of this paradigm. The results of the simulation show that the CGA can efficiently find the optimal values in a performance-security tradeoff model for D-NCS.

A digital testbed for a PHEV/PEV enabled parking lot in a Smart Grid environment

The anticipation of a large penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) into the market brings up many technical problems that need to be addressed within the next 10 years. In the future, electric-powered vehicles would be plugged into the grid, and their onboard energy storage systems would be recharged using clean, renewable electricity. One of the key missions is to facilitate the smooth interaction between the plug-in vehicle and the power grid. This paper presents a digital testbed for a PHEV/PEV enabled parking lot in a Smart Grid environment. This digital testbed allows us to evaluate a wide range of PHEV/PEV charging scenarios and the corresponding control strategies. In addition, this digital testbed allows us to explore a variety of communication technologies for a PHEV/PEV charging facility such as a parking lot. Moreover, this digital testbed offers more flexibility to prepare for the emergence of new technologies (e.g., Vehicle-to-Grid, Vehicle-to-Building, and Smart Charging), which will become a reality in the near future.

A Reputation-Based Secure Distributed Control Methodology in D-NCS

Distributed networked control systems (D-NCSs) are more vulnerable to malicious attacks with the adoption of distributed control strategies. The misbehaving agents in the D-NCS can disrupt the distributed control algorithms and gradually compromise the entire system. In this paper, a multirobot system using the linear consensus algorithm for formation control is studied where parts of the robots are compromised. A reputation-based secure distributed control methodology with built-in defense is proposed to achieve accurate consensus computation in the presence of misbehaving robots. It embeds four phases (i.e., detection, mitigation, identification, and update) into the distributed control process. The effectiveness of the proposed method is illustrated through simulation analysis and experimental results.

A trade-off model for performance and security in secured Networked Control Systems

Networked Control Systems (NCS) is a fast growing technology that integrates distributed sensors, actuators, and computing processors over a communication network for a vast amount of applications. However, the NCS can be vulnerable to various network attacks when the network used is insecure (e.g., Internet). Thus, secure NCS need to have embedded security mechanism to ensure its security operating requirements, which may sacrifice its performance due to limited system resources. This paper addresses the trade-off between NCS security and its real-time performance and use a secured networked DC motor system for illustration. This paper will present a trade-off model for system dynamic performance and system security. This model can be used to adapt security configurations to provide sufficient protection and satisfy real-time dynamic performance requirements of the NCS simultaneously. The construction of this model includes the development of a set of metrics to quantitatively measure the performance and security levels of NCS and the development of a trade-off objective function incorporating performance and security. A Simulink based test-bed implemented to control the speed of the DC motor is used to illustrate the effectiveness of this model.

Resilient Distributed Energy Management Subject to Unexpected Misbehaving Generation Units

Distributed energy management algorithms are being developed for the smart grid to efficiently and economically allocate electric power among connected distributed generation units and loads. The use of such algorithms provides flexibility, robustness, and scalability, while it also increases the vulnerability of smart grid to unexpected faults and adversaries. The potential consequences of compromising the power system can be devastating to public safety and economy. Thus, it is important to maintain the acceptable performance of distributed energy management algorithms in a smart grid environment under malicious cyber-attacks. In this paper, a neighborhood-watch-based distributed energy management algorithm is proposed to guarantee the accurate control computation in solving the economic dispatch problem in the presence of compromised generation units. The proposed method achieves the system resilience by performing a reliable distributed control without a central coordinator and allowing all the well-behaving generation units to reach the optimal operating point asymptotically. The effectiveness of the proposed method is demonstrated through case studies under several different adversary scenarios.

Economic impact of data integrity attacks on distributed DC optimal power flow algorithm

A variety of distributed energy management algorithms are being developed for DC optimal power flow (DCOPF) application owing to their flexibility and scalability in the presence of high distributed Energy Resources (DERs) penetration. However, these algorithms are vulnerable to malicious cyber attacks due to the absence of control centers. In this paper, we study and analyze the economic impact of the data integrity attack to distributed DC-OPF algorithms. In particular, we demonstrate how a malicious generator could gain more economic profit by compromising the distributed controller of its bus, modifying the information sent to neighboring buses and manipulating the power dispatch commands. To our best knowledge, this is the first paper to show the economic impact of malicious attacks in distributed DC-OPF. By revealing such potential financial risks, this paper conveys the message that besides the efforts of designing novel distributed energy management algorithms to address the DERs integration challenges, it is equally important to protect the distributed energy management algorithms from possible malicious attacks to avoid potential economic loss. The economic impact of the data integrity attack is illustrated in the Future Renewable Electric Energy Delivery and Management (FREEDM) system.

Economic benefits of plug-in electric vehicles using V2G for grid performance-based regulation service

With advancement of the vehicle-to-grid (V2G) technologies, plug-in electric vehicles (PEVs) are able to connect to the electric grid and participate in the grid regulation markets. Thus the large-scale PEV penetration into transportation systems will play an essential role for the grid support in the future. In this paper, a comprehensive daily economic benefit model for the PEV is formulated to analyze its costs and revenues of adopting unidirectional and bidirectional V2G technologies to provide grid performance-based regulation (PBR) services. Case studies considering three different types of PEVs with different charging rates and V2G capabilities are discussed. The simulation results quantitatively demonstrate the economic profit of PEVs to participate in the grid regulation service market. The sensitivities of the profit to battery sizes and charging rates are also analyzed.

An attack-resilient distributed DC optimal power flow algorithm via neighborhood monitoring

Distributed DC optimal power flow (DC-OPF) is vulnerable to malicious cyber attacks due to the absence of a control center. In our previous work, we demonstrated a data integrity attack can manipulate the power dispatch result of distributed DC-OPF by compromising the distributed controller on a bus and modifying the information being sent to the neighboring buses. This vulnerability, in turn, could be exploited by attackers for financial arbitrage in a distributed electricity market. Thus, there is a growing need for attack-resilient control techniques that can fit into the distributed power system framework to ensure the global optimality of the power dispatch result in the presence of unexpected adversaries. In this paper, we proposed a resilient distributed DC-OPF algorithm against data integrity attacks by using a neighborhood monitoring scheme. On one hand, the resilient distributed DC-OPF algorithm is an efficient approach to deal with significant increasing amount of distributed energy resources (DERs) thanks to its flexibility and scalability. On the other hand, its neighborhood monitoring scheme enables its built-in defense to identify the misbehaving distributed controllers relying on each buss local information and recover the optimal power dispatch from the malicious impact of data integrity attacks.

Green city: A low-cost testbed for distributed control algorithms in Smart Grid

As a type of Cyber-Physical Systems (CPSs), Smart Grid has been adding more communication and control capabilities to improve power efficiency and availability. Especially, more and more distributed control algorithms have been developed for Smart Grids because of their flexibility and robustness. In order to deploy them in real electric power systems, distributed control algorithms must be tested, not only in theoretical simulations, but also in testbeds subject to real world constraints that can provide feedback to make the algorithm robust. Implementations of these algorithms in a Smart Grid environment are facing many cyber-physical challenges such as possible communication failures or imperfections, noisy signals, etc. These challenges can lead to increasing economical expenditure or cause failure of the power system. There exist different approaches for testing distributed control algorithms, from using state-of-the-art facilities to software or hardware-in-the-loop simulations. To better emulate real-world electric grid operation scenarios with low capital investment, in this paper the Green City (GC) testbed is proposed as a suitable platform for both control theory researchers in Smart Grid, and for engineering education, allowing students to learn through hands-on experiences. GC has been conceived as a multi-agent networked CPS with the following main features: 1- Smart Grid environment emulation with low-cost physical elements; 2- Fast prototyping capability of distributed control algorithms for Smart Grid.