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The uncertainties of renewable energy, loads, and electricity prices pose significant challenges to the economical and secure energy management of smart grids. In this article, a hybrid stochastic/robust (HSR) optimization method is developed to minimize the overall cost of all units. The proposed approach takes advantage of stochastic programming, robust optimization, and distributed optimization methods while considering various system constraints. First, stochastic electricity price scenarios are selected by the Latin hypercube sampling method. Second, the uncertainties of renewable energy generation and loads are managed by the proposed robust optimization method under each price scenario. Then, an improved distributed optimization method is proposed to solve the formulated HSR optimization problem, which considerably enhances the convergence with the accelerated gradient method. Numerical case studies of both small-scale and large-scale power systems demonstrate the accuracy, effectiveness, and scalability of the proposed distributed HSR approach. Additionally, the optimality and convergence of this proposed distributed algorithm are mathematically proven and analyzed.

In an integrated energy system, the growing number of distributed heat and electric power generation units will bring new technical challenges to the existing centralized economic dispatch strategies. This paper proposes a distributed optimization approach for the economic system operation in a multienergy system by considering various equality and inequality constraints to accommodate the integration of intermittent renewable generations. The proposed distributed neurodynamic-based approach only requires the information exchange among neighboring units and offers flexibility, adaptivity, scalability, faster convergence, and lower communication burden compared with some traditional centralized methods. The simulation results of two integrated energy systems validate the effectiveness of the proposed distributed approach. Comparisons with other centralized and distributed optimization methods quantify the advantages of the proposed distributed approach in terms of convergence speed and computation complexity.

This paper investigates the issue of power management networked ac–dc microgrids (MGs) interconnected by interlinking converters with the consideration of unbalanced single-/three-phase ac MGs as well as power quality improvement. An integrated hierarchical distributed coordinated control approach is developed, which mainly consists of an up-layer event-triggered method of power sharing among MGs, and an event-triggered dynamic power flow routing approach to navigate the power flow among phases of the single-/three-phase ac MGs to balance the power of the MG. With the proposed control method, balanced output phase powers for the three-phase distributed generation (DGs) and enhanced voltage quality at the point of common coupling and DG terminals can be achieved besides proportional active power sharing among MGs and reduced communication. Simulation results are presented to demonstrate the proposed control method.

Cyber-physical systems (CPS) refers to the integrative system consisting of interconnected computing and control devices interacting with the physical infrastructure via sensors and actuators. Recently, there is a swift growth of CPSs ranging from smart grids to smart buildings, robotics, and other industrial control systems. They have formed the keystone of the sustainable growth of the economy, manufacturing, and smart and connected communities. Due to extensive applications of CPSs, their resilience, reliability, and security are paramount. Many factors, however, pose significant threats to CPSs and lead to high economic losses and social impacts. Software defects also make CPSs vulnerable to security attacks and coordinated cyber and physical attacks. To address this issue, emerging technologies and methods for understanding and improving the resilience, reliability, and security of CPSs are needed. This Special Section aims to provide a platform to help define, understand, and quantify the resilience, reliability, and security of CPSs.

This paper presents an improved secondary voltage control (SVC) methodology incorporating compressive sensing (CS) for a multi-area power system. SVC minimizes the voltage deviation of the load buses while CS deals with the problem of the limited bandwidth capacity of the communication channel by reducing the size of massive data output from phasor measurement unit (PMU) based monitoring system. The proposed strategy further incorporates the application of a Morphological Median Filter (MMF) to reduce noise from the output of the PMUs. To keep the control area secure and protected locally, Mathematical Singular Entropy (MSE) based fault identification approach is utilized for fast discovery of faults in the control area. Simulation results with 27-bus and 486-bus power systems show that CS can reduce the data size up to 1/10th while the MSE based fault identification technique can accurately distinguish between fault and steady state conditions., Comment: 10 pages, 16 figures

This paper introduces a novel data integrity attack on the well-developed consensus-based energy management algorithm. In particular, we show that by sending out elaborately falsified information during the consensus iterations, attackers could manipulate the system operating point and gain extra economic benefits. Meanwhile, the system-level and device-level constraints are still satisfied, e.g., the power generation and demand are balanced, and the operation of individual device respects physical constraints. This data integrity attack has two major features: First, attackers rely only on local information to complete the attack; neither additional information about system topology nor additional colluders are required; second, the attacking effect is accumulative, which enables attackers to choose to finish in either single or multiple iterations. By revealing such vulnerability of consensus-based applications to data integrity attack, this paper conveys the message that besides the efforts of designing novel distributed energy management algorithms to address the renewable energy integration challenges, it is equally important to protect the distributed energy management algorithms from possible malicious attacks to avoid potential economic losses. The proposed attack is illustrated in the Future Renewable Electric Energy Delivery and Management system.

The transient stability control of power systems with growing penetration of renewable energy resources is challenging due to inherent small damping of generators and complicated operating conditions. To address the drawbacks of existing control approaches which need accurate systemwide network parameters, a model-free fuzzy controller is proposed to enhance the transient and frequency stability of power systems. Also, an adaptive parameter estimation scheme is developed to eliminate the fuzzy approximation errors and compensate the external disturbances. The proposed strategy is implemented based on the multiagent framework, which enables the sharing of communication and computation burdens among local controllers for fast and coordinated response. The convergence of the proposed distributed control approach is rigorously proved using the Graph theory and Lyapunov stability theory. Simulation studies validate the effectiveness of the proposed distributed control approach.

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.

The papers in this special section provide a timely discussion on technical trends and challenges of some classical and emerging issues, such as networked control and filtering, fault detection and tolerant control, event-triggered control, security control, distributed control, sensor networks, and real-time network protocol design, over networks with resource constraints for industrial systems.

With the increasing trend in adoption of plug-in hybrid and plug-in electric vehicles, they will play a prominent role in the future electric energy market by acting as responsive loads to increase the grid stability and facilitate the integration of renewables. However, due to the large number of controllable devices in the future grid, central vehicle to grid (V2G) management would be challenging and vulnerable to single points of failure. This paper introduces a novel distributed approach for optimal management of unidirectional V2G considering multiple energy suppliers. Each charging station as well as each energy supplier is equipped with a local price regulator to control the price paid to the energy suppliers and the price paid by the vehicles through coordination with their neighbors. In response to the updated prices, the vehicles adjust their charging rates and energy suppliers adjust their production to maximize their benefit. The main advantages of the proposed approach are that it manages unidirectional V2G in a fully distributed way considering multiple energy suppliers and vehicles, and it converges to the global optimum despite the greedy behavior of the individuals.

The papers in this special section focus on technological and system developments in designing power grids. The power grid is a large interconnected infrastructure for delivering electricity from power plants to end users. Now, traditional grids are facing kinds of challenges, and the world is proposing to modernize legacy and make strides toward smart grid. It is widely recognized that demand response is the core feature of smart grid, which can be formally defined as “changes in electric use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized. With the support of the advanced information and communication technologies, demand response is able to improve the efficiency, reliability, economics, and sustainability of power generation, distribution, and utilization.

The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users’ energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

Efficient and reliable demand side management techniques for community charging of plug-in hybrid electrical vehicles (PHEVs) and plug-in electrical vehicles (PEVs) are needed, as large numbers of these vehicles are being introduced to the power grid. To avoid overloads and maximize customer preferences in terms of time and cost of charging, a constrained nonlinear optimization problem can be formulated. In this paper, we have developed a novel cooperative distributed algorithm for charging control of PHEVs/PEVs that solves the constrained nonlinear optimization problem using Karush-Kuhn-Tucker (KKT) conditions and consensus networks in a distributed fashion. In our design, the global optimal power allocation under all local and global constraints is reached through peer-to-peer coordination of charging stations. Therefore, the need for a central control unit is eliminated. In this way, single-node congestion is avoided when the size of the problem is increased and the system gains robustness against single-link/node failures. Furthermore, via Monte Carlo simulations, we have demonstrated that the proposed distributed method is scalable with the number of charging points and returns solutions, which are comparable to centralized optimization algorithms with a maximum of 2% sub-optimality. Thus, the main advantages of our approach are eliminating the need for a central energy management/coordination unit, gaining robustness against single-link/node failures, and being scalable in terms of single-node computations.

The ability to perform accurate localization is a fundamental requirement of the navigation systems intended to guide unmanned ground vehicles in a given environment. Currently, the use of vision-based systems is a very suitable alternative for some indoor applications. This paper presents a novel distributed FPGA-based embedded image processing system for accurate and fast simultaneous estimation of the position and orientation of remotely controlled vehicles in indoor spaces. It is based on a network of distributed image processing nodes, which minimize the amount of data to be transmitted through communication networks and hence allow dynamic response to be improved, providing a simple, flexible, low-cost, and very efficient solution. The proposed system works properly under variable or nonhomogeneous illumination conditions, which simplifies the deployment. Experimental results on a real scenario are presented and discussed. They demonstrate that the system clearly outperforms the existing solutions of similar complexity. Only much more complex and expensive systems achieve similar performance.

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 system's 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-NCS's 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.

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.