With increasing penetration of Electric Vehicles (EVs) into the transportation system and smart electricity grid, there is a growing need for integrating them into Home Energy Management Systems (HEMS). This integration within HEMS introduces dynamic user behaviors and time-varying charging demand, thus posing challenges for the HEMS. To mitigate these challenges, this paper proposes a charging model for heterogeneous EVs that covers the range of Plug-in Hybrid EVs (PHEVs), Range-Extender EVs (REEVs) and Battery EVs (BEVs) with/without heat pumps. The proposed heterogeneous EV charging model considers weather conditions, estimated mileage and driver’s experience to describe the dynamic charging demand and the anxiety level influencing their behavior. To optimize the HEMS operation, minimizing the energy cost and ensuring comfort, this paper introduces an offline Deep Reinforcement Learning (DRL) algorithm which learns directly from pre-collected datasets, avoiding the cost and safety issues associated with continuous real-world interactions. The algorithm incorporates the Huber loss and a Q-quantile estimator to mitigate performance degradation from dataset anomalies such as data noise, sensor failure and human error, resulting in more robust HEMS optimization strategies. Experimental results demonstrate the method’s effectiveness in reducing total costs and analyze the performance of household devices with two different electricity rates.

Networked microgrids have been instrumental in facilitating seamless integration of distributed energy resources (DERs) into the power grid. Conversely, managing such a large-scale cyber-physical system of DERs in a distributed environment presents significant challenges in terms of scalability and convergence time of algorithm. This highlights the need to have a scalable and computationally efficient distributed energy management framework. This article presents a hierarchical distributed consensus-based framework to address energy management problem in networked microgrids. The proposed approach utilizes a hierarchical structure to implement phasewise consensus, enabling parallel consensus that accelerates the convergence of algorithm. The effectiveness of the proposed method is affirmed by benchmarking against centralized solution, demonstrating its ability to converge to the optimal solution. Furthermore, simulations across different network topologies validate the approach, showcasing faster convergence and reduced communication overhead compared to the conventional distributed consensus method. Finally, Monte Carlo simulations demonstrate the scalability of the proposed approach, highlighting the feasibility in real-time applications.

The growing integration of distributed energy resources (DERs) is transforming energy management systems (EMS) framework, driving a shift from centralized to distributed and further to hierarchical distributed systems. To ensure the effective implementation of hierarchical EMS, DERs must be clustered to form an optimal partition of the network that ensures fast convergence. This article presents a centrality-based clustering framework to organize the DERs to form a hierarchical distributed structure in networked microgrids. The proposed framework incorporates centrality measures to identify the network leaders, coupled with a distance-based partitioning algorithm to form the optimal partition. Furthermore, an ensemble-based method is used to determine the optimal number of clusters for the most efficient partitioning. Simulation results demonstrate that the proposed clustering framework significantly improves convergence speed in a hierarchical distributed system. The resulting optimal partition reduces the number of iterations required to converge by 52% compared to distributed framework and by 29.6% compared to suboptimal partition. Furthermore, Monte Carlo simulations showcase the logarithmic improvement in convergence performance achieved through optimal partition.

Deep-learning methods have been widely used as an end-to-end modeling strategy of electrical energy systems because of their convenience and powerful pattern recognition capability. However, due to the “closed-box” nature, deep-learning methods have long been blamed for their poor interpretability when modeling a physical system. In this article, we introduce a novel neural network structure, Kolmogorov–Arnold network (KAN), to achieve “glass-box” modeling for electrical energy systems to enhance the interpretability. The most distinct feature of KAN lies in the learnable activation function together with the sparse training and symbolification process. Consequently, KAN can express the physical process with concise and explicit mathematical formulae while retaining the nonlinear-fitting capability of deep neural networks. Simulation results based on three electrical energy systems demonstrate the effectiveness of KAN in the aspects of interpretability, accuracy, robustness, and generalization ability.

Freshwater scarcity continues to pose a significant threat to communities worldwide. With the growing integration of distributed energy resources (DERs), there is an increasing opportunity to couple energy generation with desalination technologies to meet water and energy demands in a unified system. This paper investigates the convergence behavior of distributed consensus algorithms in such coupled water-energy systems, where the interconnection between energy and water processes introduces additional complexity. Specifically, the study examines the impact of different weight-matrix-construction schemes on the convergence speed of the distributed consensus algorithm and its robustness to packet loss. The simulation results show that using the optimal-constant-weight scheme achieves the fastest and most robust convergence but requires global knowledge of the network. However, the maximum-constant-weight scheme, despite requiring global information, has slower convergence speed and is more sensitive to packet loss. In contrast, the local-degree-weight scheme provides comparable performance to the optimal-constant-weight scheme without requiring the system’s global information.

Real-time and accurate health estimation of lithium-ion batteries is necessary to ensure the safe and continuous operation of critical systems such as microgrid energy storage systems and modern electric and hybrid vehicles. Estimation of a battery’s state-of-health (SOH) requires online identification of battery model parameters such as internal resistance or battery capacity. Although many papers discuss either the estimation of these parameters in real-time or the identification of these parameter changes at different operating conditions, a unified framework for SOH estimation considering battery states and environmental conditions has not been widely accepted in the field. This paper focuses on the development of a novel methodology for online SOH estimation for lithium-ion batteries utilizing a newly proposed nonlinear model for electric circuit model (ECM) resistances with dependencies on temperature and state-of-charge (SOC).

To accommodate the exponential integration of distributed energy resources (DERs) into the power grid, there is a pressing need for a scalable and computationally efficient networked microgrid energy management framework. In this context, a Hierarchical Distributed Consensus (HDC) based approach has emerged as a promising solution. A key prerequisite to ensure an effective implementation of the HDC framework is to obtain an optimal hierarchical partition from the network. This paper presents a framework for determining the optimal number of clusters using the ensemble method to form a hierarchical structured networked microgrid. The proposed algorithm incorporates the consensus matrix and the elbow method to analyze and pinpoint the best possible network configuration. The simulation results highlight the importance of obtaining the optimal number of clusters and interdependence of cluster configuration to speed of consensus convergence in the network.

Power restoration is a vital task in post-disaster scenarios. Microgrid-based strategies offers a promising approach that can ensure fast and timely restoration of resources. However, in a distributed microgrid environment, the overall performance is highly dependent on the efficiency and reliability of the communication layer. Identifying critical links can better coordinate the limited communication resources available post disaster. Connectivity Rank Index (CRI) has been proven to be an effective edge centrality metric for evaluating communication links in distributed energy management systems. In this paper, a variant of CRI called Local-CRI, is proposed specifically tailored for hierarchical distributed network structures. Numerical simulations shows that the CRI effectively reflects the importance of communication links in relation to consensus performance, outperforming existing methods. Additionally, the Local-CRI proves effective in quantifying the impact of communication link failures on consensus convergence, particularly within hierarchical distributed network.

Multi-energy systems with energy storage can coordinate various energy carriers to facilitate the integration of large amounts of distributed energy sources and promote the overall efficiency of energy use, which needs distributed dispatch with the requirement of security and privacy. This paper studies the distributed economic dispatch based on information from neighboring agents only. In order to handle the non-convexity due to the complementarity constraint of energy storage, it is proved that simultaneous charging and discharging is suboptimal for the multi-energy systems. Based on this, an equivalent convex problem is reformulated. A scalable distributed algorithm based on parallel ADMM and dynamic consensus mechanism, termed DEED-ADMM, is then proposed. It is shown that DEED-ADMM is scalable in terms of per-agent energy consumption and computational complexity with centralized methods. Moreover, under general convex cost functions, convergence properties of DEED-ADMM are theoretically analyzed by adopting the Lyapunov-based approach. It is proved that the primal problem and the dual problem can be simultaneously solved. Finally, case studies demonstrate the effectiveness of the proposed algorithm. Note to Practitioners—This paper is motivated by the problem of coordinating various energy carriers as well as energy storage in multi-energy systems to promote the overall efficiency of energy use. The coupling among different energy carriers and the complementarity constraint of non-simultaneous charging and discharging of battery storage make the problem non-convex. Existing distributed approaches require stringent assumptions on the cost functions, or suffer from a heavy computational burden. To address the above challenges, a fully distributed algorithm is developed, which is scalable and suitable for large-scale systems. Moreover, it is the first distributed algorithm that solves the economic dispatch problem and the dual problem simultaneously in multi-energy systems with general convex cost functions. Practitioners can easily adjust the coefficients of the proposed algorithm to guarantee convergence for the IEEE 30-bus or even 116-bus systems, as long as the economic dispatch problem is feasible. Our future work will focus on designing resilient mechanisms under potential attacks and considering more practical situations such as power loss.

A freshwater shortage can threaten the well-being of people and communities. With the growing global population and limited access to freshwater resources around the world, desalination has become an important technology for meeting the freshwater demand of communities. Desalination, the process of extracting freshwater from saline water, can be done in a centralized water processing plant or distributed at smaller desalination units. This paper investigates the integration of distributed desalination units within a microgrid for the dual optimization of water and energy generation by developing a distributed consensus algorithm to solve the economic dispatch problem for power and water co-generation. The simulation results validate the algorithm by finding the optimal generation capacity of each element in the microgrid while meeting the dynamic demands of power and water.

The increasing demand for energy has necessitated the expansion of Distributed Energy Resources (DERs), highlighting the critical need for an effective and reliable Energy Management System (EMS). This paper introduces a novel approach to solving the economic dispatch problem in asynchronous networked microgrid systems through a hierarchical distributed gossip consensus-based method. The proposed approach combines the strengths of gossip algorithms with hierarchical consensus strategies, specifically designed to enhance efficiency in asynchronous settings. The effectiveness of this method is validated through extensive simulations, which demonstrate faster convergence times and improved system performance compared to standard distributed gossip consensus configurations. Furthermore, the Monte-Carlo simulations showcases the scalability of the proposed approach, illustrating its suitability in real-time applications.

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from the perspective of the power system and ML model, respectively. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) smart grid applications. Overall, our purpose is to encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs.

Data centers (DCs) have witnessed rapid growth due to the proliferation of cloud computing and internet services. The huge electricity demand and the associated carbon emissions of DCs have great impacts on power system reliability and environmental sustainability. This paper proposes a bilevel model for low-carbon operation of DCs via carbon-integrated locational marginal prices (CLMPs). In the upper level, the power system operator sequentially solves the optimal power flow and the carbon emission flow problems to determine the CLMPs. In the lower level, a joint workload sharing and carbon trading model for DCs is developed to minimize their overall operation cost while keeping each DC's carbon footprint within its carbon allowance. To solve the bilevel model and preserve the privacy of DCs, we propose a bisection-embedded iterative method. It can tackle the issue of oscillation, thereby ensuring convergence. In addition, a filtering mechanism-based distributed algorithm is proposed to solve the lower-level DC problem in a distributed manner with much reduced communication overhead. Case studies on both small-scale and large-scale systems demonstrate the effectiveness and benefits of the proposed method.

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is critical for energy supply systems. In conventional data-driven RUL prediction approaches, the battery's degradation mechanism is difficult into incorporate in the RUL prediction. Furthermore, there are notable limitations in reflecting the significance of different time instances, and the uncertainty in the degradation process. Consequently, a novel data-model interactive RUL prediction approach based on particle filter-temporal attention mechanism-bidirectional gated recurrent unit (PF-BiGRU-TSAM) is proposed. Specifically, BiGRU-TSAM is trained offline through historical data, which assigns corresponding significance to battery capacities at different time instances. Moreover, regarding the interactive data-model for the online prediction phase based on PF-BiGRU-TSAM, the advantages of data-driven and model-based approaches are integrated, which accomplishes the purpose of modifying each other. The proposed PF-BiGRU-TSAM approach is validated with a real-world battery dataset. Experimental results demonstrate the proposed approach is better than some published approaches. Taking the 50th operational cycle of the four batteries B0005, B0006, B0007, and B0018 in the dataset as an instance, the absolute errors of the proposed PF-BiGRU-TSAM are 0, 1, 3, 3, respectively, which represents the proposed approach has an excellent performance.

Average consensus is a key component of multi-agent systems coordination, while data privacy becomes a serious concern. Through the information exchange process, the initial state of an agent may be disclosed to its neighbors. The existing privacy-preserving research mainly addressed the situation of single-neighbor eavesdropping and infinite-time consensus, and they cannot deal with the cases of multi-neighbors eavesdropping and collusion inference attack or ensuring finite-time consensus. In this paper, we prove that it is impossible to preserve a node’s data privacy if all of its neighbors collusively infer the data. Otherwise, we propose a privacy-preserving framework to support conventional average consensus, push-sum consensus, and finite-time average consensus, which integrates multiplying random variables, finite-time error compensation, and updating rule jump. In this paper, each agent exchanges data with its neighbors by multiplying a random variable to its real-time state at each iteration. To eliminate errors caused by the random multiplier, a finite-time error compensation term and updating rule jump are designed, which ensure the accuracy of consensus. We prove that the proposed framework can converge and preserve privacy facing collusion inference attacks in both finite-time and infinite-time consensus, while traditional adding-noise-based methods cannot solve the finite-time case. We also derive the analytical expressions of the maximum privacy disclosure probability for the initial state of each agent, and present the impact of multiplying random variables. Extensive case studies demonstrate the effectiveness of the proposed framework.

The fast adoption of distributed energy resources (DERs) to meet the ever-growing energy demand has created a need for an efficient and reliable energy management system (EMS). This paper proposes a weighted consensus based method for solving the economic dispatch problem in hierarchical distributed networked microgrid system. The combination of cluster size estimation and weighted consensus enable us to obtain an optimally operating energy management framework for our system. The proposed approach is validated through extensive simulations, demonstrating its effectiveness over existing methods in terms of convergence time and system performance optimization in networked microgrids.

The wide application of phasor measurement units (PMUs) increases the interdependence between the smart grid and wide-area measurement systems (WAMS). However, the end-to-end (ETE) delay that the data experienced from PMUs to the phasor data concentrator (PDC) has become non-negligible. A significant delay may affect the data completeness received by the PDC, thereby affecting the performance of the WAMS application, e.g., state estimation (SE). The previous research focuses on centralized path planning to mitigate the physical distance between PMUs and the PDC. Few studies have considered addressing the data completeness problem using the distributed algorithm from the aspect of the ETE delay. This article proposes a distributed bias min-consensus-based approach in path construction to minimize the ETE delay. Simulation results demonstrate the advantages of the proposed approach in addressing the data completeness and enhancing the SE performance. Also, it is computationally efficient in path reconstruction when the links suffer cyber-attacks.

Moving target defense (MTD) is a new defensive mechanism developed in power systems to thwart false data injection attacks (FDIAs). However, since the MTD works by perturbing the branch parameters with the distributed flexible ac transmission system (D-FACTS), it might cause additional infrastructure and operation costs and affect the system dynamics. This is a complicated problem because it is closely related to which branches should be perturbed and how much they are changed. In this article, we analyze the essentials of MTD and construct an effective and low-cost MTD. To begin with, we provide a sufficient and necessary condition for MTD to protect a bus from being affected by the intended FDIA. Based on this result, we propose a new metric to quantify the protection level of MTD and an efficient algorithm to minimize the number of required D-FACTS devices for protecting a specific set of buses. To reduce the operation cost, we develop two strategies to make the increasing operation cost zero for activating the MTD. Furthermore, we analyze the impact of MTD on the system dynamics with a special emphasize on small signal stability. Finally, we conduct extensive simulations to validate our findings with the test cases of power systems in MATPOWER.

Privacy is of great importance for microgrids and has gained much attention recently. By eavesdropping on the communications among the devices, attackers may infer sensitive system operation information and user behavior due the intimate interplay between communication and control in mircogrids. In this paper, in order to facilitate simulational evaluations of privacy preservation techniques for microgrids, we develop a versatile cyber-physical co-simulator which integrates both networked communication and power system control subsystems as a whole. The co-simulator is built upon MATLAB/Simulink and OMNeT++ along with a module that coordinates the two tools in real-time simulations. Based on the co-simulator, we evaluates three privacy-preserving algorithms proposed in the literature, and find that SFPA performs better than PEMA and REP-CoDEMS in aspect of protecting the privacy of controllable inputs, but REP-CoDEMS and PEMA has a better performance considering both controllable inputs and uncontrollable inputs.

Recently, collaborative distributed energy management systems (CoDEMS) have emerged as an effective solution to manage distributed energy resources in microgrid. In CoDEMS, devices collaborate in a distributive manner over communication networks to meet electrical loads and supply balance at minimum cost. However, mutual information exchanges among the devices in CoDEMS may leak important information about the devices states. In this paper, we investigate the challenging problem of how to achieve optimality while preserving the privacy of CoDEMS at relatively low cost. Unlike many previous works that preserve the privacy by using additive noises, we propose a novel random-weight privacy-preserving algorithm with error compensation, termed as REP-CoDEMS, for CoDEMS. In the proposal, each distributed device generates two random weights each time and it communicates with its neighbor conveying values based on the weights, incremental cost estimation and power imbalance estimation information along with a novel error compensation term to eliminate the error induced by the random weights. We theoretically prove that the proposed REP-CoDEMS algorithm converges and preserves the privacy of all devices. We also derive analytical expressions of the maximum privacy disclosure probability for initial and final states of the CoDEMS. In addition, we conduct extensive simulations and the results demonstrate the effectiveness of the proposed algorithm.

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.

Lithium-ion batteries are widely used in many fields of modern life, e.g. wearable devices, electric vehicles and electric grids, etc. The safety and reliability of the lithium-ion battery are critical issues during the battery operation, where the battery management system (BMS) plays a key role. An accurate estimation of the state-of-charge (SOC) of the battery is essential for the BMS. However, due to the intrinsic nonlinearity of the lithium-ion battery, the accurate estimation of the SOC is technically challenging and has drawn lots of attention both from academic and industrial fields. In order to tackle this difficulty, many SOC estimation approaches have been proposed, in which an identification method for the parameters of the battery is normally implemented. However, the additional parameter identification approach greatly reduces the efficiency of SOC estimation and the bias from identification may significantly affect the accuracy of the SOC estimation. This paper proposes a novel data-driven SOC estimation approach based on the adaptive residual generator, which realizes integrating the parameter identification and the SOC estimation into a simultaneous procedure, where the convergences for both the parameter identification and SOC estimation are guaranteed. The proposed adaptive residual generator can estimate the SOC of the battery accurately due to real-time parameter identification that proactively minimizes the modeling error. The effectiveness and the performance of the proposed method are demonstrated through the case studies on a battery simulator. Also, owing to accurately identified parameters, the SOC of the battery is estimated accurately with almost 0% SOC estimation error.

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.

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.

The uncertainties caused by sources [such as wind power and photovoltaic (PV)], load switchings, and the equivalent negative impedance of constant power loads (CPLs) commonly exist in microgrids and often undermine the system stability and damping. In this article, a distributed secondary H∞consensus approach with an eventtriggered communication scheme is proposed for dc microgrids to achieve accurate current sharing and satisfactory performance in the presence of CPLs and uncertainties. Different from many existing works, the proposed eventtriggered communication scheme only requires the information at every fixed sampled interval without the Zenobehavior and continuous-time information. Then, global large-signal stability of the dc microgrid with CPLs and uncertainties under the proposed distributed control is analyzed, where a primary plug-and-play (PnP) voltage controller is considered for each distributed generator (DG). Furthermore, effects of key controller parameters and CPLs on the dynamic performance is analyzed, and a PnP design method is presented for the primary-secondary controllers. With the proposed method, full PnP operation of the dc microgrid can be realized and communication burden can be considerably reduced. Finally, simulation results are presented to validate the proposed method.

The State of Health (SOH) of the battery is often represented either using the decrease in the capacity of the battery or the increase in the internal resistance of the battery. While these indices are commonly used, they do not provide any insight on the reasons for the degradation of the health of the battery. Understanding battery aging and the impact it has on the working/performance of the battery is required to determine the State of Function (SOF) of the battery for that particular application. The SOF of the battery can provide information on the current applicability of the battery to the application. The Remaining Useful Life (RUL) of the battery is also highly dependent on the current and past operating conditions. Determining the reason behind the degradation and the impact on the health can also help determine the RUL or provide feedback to the user on alternate usage patterns to prolong the RUL. This paper uses a first principle based degradation model to determine the sensitivity of the terminal voltage and capacity of the battery to the degradation of the concentration of lithium ions in the anode/negative electrode.

A bargaining game is investigated for cooperative energy management in microgrids. This game incorporates a fully distributed and realistic cooperative power scheduling algorithm [cooperative and distributed energy scheduling (CoDES)] as well as a distributed Nash bargaining solution based method of allocating the overall power bill resulting from CoDES. A novel weather-based stochastic renewable generation (RG) prediction method is incorporated in the power scheduling. We demonstrate the proposed game using a four-user grid-connected microgrid model with diverse user demands, storage, and RG profiles and examine the effect of weather prediction on day-ahead power scheduling and cost/profit allocation. Finally, the impact of users' ambivalence about cooperation and /or dishonesty on the bargaining outcome is investigated, and it is shown that the proposed game is resilient to malicious users' attempts to avoid payment of their fair share of the overall bill.

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.

This paper presents an improved secondary voltage control (SVC) methodology incorporating compressive sensing (CS) for a multiarea 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 the 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.

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.

The microgrid is envisioned to be the building block of the future smart grid, for its abilities to host distributed energy resources, to improve grid reliability, and to enhance system resiliency. One of the most studied research topics of the microgrid is the distributed microgrid energy management system. However, the algorithm prototyping and hardware validation still remain great challenges at the current stage. This paper proposes a highly scalable, customizable, and low-cost DC microgrid testbed framework that enables fast distributed MG-EMS prototyping and provides proof-of-concept validation. Moreover, an application example is presented to demonstrate the functionality and validity of the proposed microgrid testbed.

With the rapid exhaustion of fossil energy, electric vehicles (EVs) become one of the key candidates for the next generation of transportation. Increasingly perfect technology developed makes EVs grow significantly. Therefore, Charging Stations (CSs), as accessories and necessities of EVs, should form a network with optimal planning. Inappropriate CS network design could cause series of negative effects to the popularization of EVs, the layout of the city traffic network and the financial cost of CS network construction, etc. Besides, the charging infrastructure planning for cities with large population and high EV density becomes even difficult. In this paper, an Effective Planning of CSs Network (CSN) is proposed. Comprehensive environmental elements (e.g. cities' and CSs' information, EV charging status, etc.) are considered in CSN. The CSN deals with complicated city planning in giant cities (i.e. large population, high EV density, etc.). Hong Kong is selected as the case study because it can be regarded as a typical giant city. Results show that the proposed CSN can ensure the EVs can find a CS before it is out of power. Besides, the CSN saves ~18% financial cost for the charging infrastructure planning in giant cities.

Lithium-ion batteries are becoming the main energy storage in electric vehicles and electric grids. To elevate the battery capacity and the voltage supply, the battery cells are stacked to form a battery pack. The state-of charge (SOC) of the battery pack requires continuous monitoring for the operation safety. The current developed SOC estimation algorithms shows decent estimation accuracy but they are designed for individual cells. These algorithms stay in the battery cell level because they cannot capture the cell-to-cell difference which exists after manufactured. This paper proposed a battery pack SOC Co-Estimation algorithm based on the estimated battery cell SOC. The proposed battery pack SOC Co-Estimation algorithm can accurately estimates the SOC of a battery pack with three serial connected battery cells but without cell balancing. This algorithm also has the potential to reduce the computation effort on the battery management system (BMS) because it does not need to monitor every single cell in the battery pack.

The paper presents a novel data integrity attack on consensus-based load shedding algorithm. In particular, we show that by sending out elaborately falsified information during the consensus iterations, attackers could manipulate the system operating point to achieve selfish goals, e.g. loads remain being served under contingencies while shedding other loads. More importantly, still maintain the system stability. This data integrity attack has three major features: 1) no additional information about system topology or other devices are required to launch the attack; 2) the attacking effect is accumulative which enables attackers to complete the attacks in either single or multiple iterations; 3) attackers could fulfill their goal using the local agent alone, no other agents need to be compromised during the attack. By revealing such potential risks, this paper conveys the message that besides the efforts of designing novel consensus-based applications, it is equally important to protect distributed smart grid applications from possible malicious cyber-attacks. The potential impact of the data integrity attacks is illustrated on simulation examples.

This paper discusses the impact of calendar ageing on the anode and the concentration of lithium ions in the anode structure. It discusses the modeling of the calendar ageing, its implementation in the 3D First Principle Based Degradation Model (3DM) of the battery and the results that were observed as a result of calendar ageing over a period of 4-6 years. The paper also relates these physical degradation phenomena with the impact that they have on the parameters of the Equivalent Circuit Model of the battery. The paper uses the capacity degradation and the increase in the internal resistance of the battery to showcase the impact of calendar ageing on the State of Health of the battery.

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.

This paper proposes a high resolution anode degradation model of the lithium ion battery based on its physics of operation in 3D and in layers. This model is developed in a multiphysics software called COMSOL and in Matlab. This paper describes the procedure followed to develop the model in 3D and the features of this model. The performance of the model is validated with experimental data obtained from a battery of the same chemistry and capacity. The paper will then relate the effect of the degradation of the anode conductivity on the Thevenin Circuit Model parameters. The established relationship can help identify the parameters that are important for battery degradation and will be invaluable for real-time and online estimation of SOH and SOF of the battery.

Distributed energy management algorithms eliminate the control center from the conventional energy management systems and calculate the optimal schedule for all devices through iterative coordination among neighbors. Most of the existing distributed approaches are developed under the assumption that all devices are secure and willing to achieve an optimal system performance together in a “collaborative” environment. However, unexpected faults and adversaries may emerge in the network and disrupt the convergence of those distributed approaches. In this paper, we extend the cooperative distributed energy scheduling (CoDES) algorithm to improve its resilience against data integrity attacks. Two types of data integrity attacks are considered in this paper - faulty attacks and random attacks. A distributed attack detection algorithm is developed to verify the state of neighboring devices without infringing their private information. A reputation-based mitigation algorithm is introduced to identify the compromised device and act accordingly to maintain the optimal energy scheduling result. The effectiveness of the proposed resilient distributed energy scheduling algorithm is evaluated in the Future Renewable Electric Energy Delivery and Management (FREEDM) microgrid system.

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 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.

The papers in this special section focus on the toptic of networked energy systems, inclusing their architectures, management, and communications services.

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.

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.

This paper discusses the commonly used techniques to estimate the state of health (SOH) and state of function (SOF) of lithium ion batteries and their limitations. Factors affecting the health and SOF of the battery are discussed in this paper. The SOH of the battery is mainly represented by the capacity degradation and the increase in the internal resistance. The other indices that could represent the battery's health are also briefly discussed. The different techniques that are used to estimate the capacity and internal resistance of the battery are discussed along with their limitations. The concept of SOF and its relationship with SOC, SOH and temperature are discussed along with the commonly used techniques to estimate the SOF of the battery. This paper also discusses the limitations in the definition and estimation of the SOF.

Incorporation of data from multiple resources and various structures is necessary for accurate estimation of the driving range for electric vehicles. In addition to the parameters of the vehicle model, states of the battery, weather information, and road grade, the driving behavior of the driver in different regions is a critical factor in predicting the speed/acceleration profile of the vehicle. Following our previously proposed big data analysis framework for range estimation, in this paper we implement and compare different techniques for speed profile generation. Moreover we add the big data analysis classification results to especially improve the performance of the Markov Chain approach. The quantitative results show the significant influence of considering the big data analysis results on range estimation.

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.

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.

Distributed algorithms for economically managing the energy in smart grids are getting more attention because they can remove the need for central controllers and are computationally scalable, as well as robust to single point of failure. The Incremental Cost Consensus (ICC) algorithm has been developed as a cooperative distributed algorithm for solving the Economic Dispatch Problem (EDP). This paper models and analyzes the nominal stability of the ICC algorithm implemented in a smart grid environment. The grid has continuous time dynamics in the physical layer, while the ICC algorithm in the cyber layer has discrete time dynamics. To study the interconnected Cyber-Physical System (CPS), the grid dynamics are linearized and discretized at their operating points. Then, the equivalent discrete time dynamics of the grid are integrated with the ICC dynamics in a joint cyber-physical model. The stability of the interconnected system is then analyzed by studying the eigenvalues of the joint dynamics. The IEEE 9-bus system is used to demonstrate the proposed analysis approach.

Range anxiety is a major contributor in low penetration of electric vehicles into the transportation market. Although several methods have been developed to estimate the remaining charge of the battery, the remaining driving range is a parameter that is related to different standard, historical, and real-time data. Most of the existing range estimation approaches are established on an overly simplified model that relies on a limited collection of data. However, the sensitivity and reliability of the range estimation algorithm changes under different environmental and operating conditions; and it is necessary to have a structure that is able to consider all data related to the range estimation. In this paper, we propose a big-data based range estimation framework that is able to collect different data with various structures from numerous resources; organize and analyze the data, and incorporate them in the range estimation algorithm. MATLAB/SIMULINK code is demonstrated to read real-time and historical data from different web databases and calculate the remaining driving range.

With the development of smart grid technology, the future power system will become a complex cyber-physical system. The control and management of this system will impose new challenges to the current Supervisory Control and Data Acquisition system such as scalability in its computational and communications effort, and robustness to single point of failure. Decentralized cooperative control is promising to control and manage such a system in a more efficient and robust way. Consensus based algorithms have been proposed by researchers to deal with various power system applications in a distributed manner. Because of the sensitivity to communications imperfections, the performance of the consensus based algorithms degrade when the communication packet loss happens in practical applications. In this paper, a Hybrid Incremental Cost Consensus (Hybrid ICC) algorithm is proposed. Different from other consensus based algorithms, the Hybrid ICC algorithm is robust against communications imperfections by integrating gossip algorithm and consensus algorithm together. Several case studies are presented to illustrate the performance of the proposed Hybrid ICC algorithm and demonstrate the scalability and robustness under packet loss scenarios.

In this paper, we study the problem of reaching a consensus among all the agents in the networked control systems (NCS) in the presence of misbehaving agents. A reputation-based resilient distributed control algorithm is first proposed for the leader-follower consensus network. The proposed algorithm embeds a resilience mechanism that includes four phases (detection, mitigation, identification, and update), into the control process in a distributed manner. At each phase, every agent only uses local and one-hop neighbors' information to identify and isolate the misbehaving agents, and even compensate their effect on the system. We then extend the proposed algorithm to the leaderless consensus network by introducing and adding two recovery schemes (rollback and excitation recovery) into the current framework to guarantee the accurate convergence of the well-behaving agents in NCS. The effectiveness of the proposed method is demonstrated through case studies in multirobot formation control and wireless sensor networks.

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 seven papers in this special section explore the deployment of electrification in transportation systems.

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.

Efficient demand management policies at the grid side are required for large scale charging of Plug-in Hybrid Electric Vehicles and Plug-in Electric vehicles (PHEVs/PEVs). The SoC level and Charging Cost should be optimized while the aggregate load is kept under a safety limit to avoid overloads. Conventionally, optimal managing of the charging rates requires gathering and processing data in a center. However, as the scale of the problem increases to consider thousands of charging stations distributed over a vast geographical area, the central approach suffers from vulnerability to single node/link failures as well as scalability. This paper introduces a novel decentralized network cooperative approach for controlling the PHEV/PEV charging rates. In this approach, each charging station acts as a local retailer of energy, selling the power to the plugged in vehicle while coordinating the price with its neighbors. In response to the offered price, the Smart-Charger of the vehicle adjusts the charging current to maximize the utility of the PHEV/PEV user. By iteratively repeating this process, the convergence to the global optimum is attained without the requirement for any central unit. Robustness to single link/node failures is another advantage of our method.

Different models have been proposed so far to represent the dynamic characteristics of batteries. These models contain a number of parameters and each of them represents an internal characteristic of the battery. Since the battery is an entity that works based on many electrochemical reactions, the battery parameters are subject to change due to different conditions of state of charge (SOC), C-rate, temperature and ageing. Referring to our previous work on online identification of the battery parameters, the change in the parameters even during one charging cycle is an experimental fact at least for many lithium-ion batteries. In this paper, the terminal voltage is used as the output to investigate the effect of changes in the parameters on the battery model. Therefore, we analyze the sensitivity of the model to the parameters and validate the analysis by comparing it with the simulation results. Since the output of the model is one of the main components in estimation of the state of charge (SOC), the sensitivity analysis determines the need to update each of the battery parameters in the SOC estimation structure.

The agent based solutions are successfully applied in research and have impressive results. However, at the present, the agent technology is the realm of theory and laboratory simulation. The paper presents an attempt to bridge the gap. To facilitate migration of agent technology to the field, we propose to apply industrial standards such as IEC 61499 to design and implement the agent based algorithms. IEC 61499 is reference architecture for development of distributed systems. As an example of agent based solution to power system problem we consider Incremental Cost Consensus (ICC) algorithm for solving economic dispatch problem. The original system was simulated in Matlab. The agent based 5 node system was developed using IEC 61499 and deployed to 5 industrial controllers, then tested. With the proposed approach the agent algorithms can be designed, tested and deployed within one framework without the overhead of re-coding the solution for different stages. The proposed approach enables directly executable agent solutions and paves the way to the industrial adoption of the agent technology in power system domain.

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.

Estimation of the State of Charge (SOC) is a fundamental need for the battery, which is the most important energy storage in Electric Vehicles (EVs) and the Smart Grid. Regarding those applications, the SOC estimation algorithm is expected to be accurate and easy to implement. In this paper, after considering a resistor-capacitor (RC) circuit-equivalent model for the battery, the nonlinear relationship between the Open Circuit Voltage (VOC) and the SOC is described in a lookup table obtained from experimental tests. Assuming piecewise linearity for the VOC-SOC curve in small time steps, a parameter identification technique is applied to the real current and voltage data to estimate and update the parameters of the battery at each step. Subsequently, a reduced-order linear observer is designed for this continuously updating model to estimate the SOC as one of the states of the battery system. In designing the observer, a mixture of Coulomb counting and VOC algorithm is combined with the adaptive parameter-updating approach and increases the accuracy to less than 5% error. This paper also investigates the correlation between the SOC estimation error and the observability criterion for the battery model, which is directly related to the slope of the VOC- SOC curve.

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have received increasing attention because of their low pollution emissions, petroleum independence, and high fuel economy. The large market penetration of these vehicles is dramatically changing the view of the power distribution system. Unlike other power loads, these vehicles can be connected to power grids anywhere and anytime, which brings more spatial and temporal diversity and uncertainty. There is an urgent need to investigate the impact of PHEV/PEV charging on the power distribution system considering multidisciplinary complexities (e.g., driving behavior, route and departure time choice, charging station location, engineering, policy, economic, environment, technology, and social impact). This paper consolidates the modeling and simulation of power distribution system and transportation network in order to assess the emerging electric vehicle technologies. Moreover, this paper proposes a comprehensive co-modeling/simulation framework for investigating the impact of the electrification of transportation in the real world.

In a smart grid, distributed control algorithms can be embedded in the distributed controllers of generators to solve energy management problems without the presence of a central controller. Incremental Cost Consensus (ICC) algorithm is such a distributed algorithm that solves the economic dispatch problem (EDP) in a distributed manner. This paper considers the sampling rate selection of a system that consists of the ICC algorithm and generator dynamics. The mathematical formulation of the hybrid system is presented and the selection of a proper sampling rate is discussed. In addition, the results of the case studies are presented to show the convergence performance of a hybrid system under different sampling rate choices.

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.

This paper presents a predictive gain scheduler for path tracking control in a networked control system with variable delay. The controller uses the plant model to predict future position and find the amount of travel possible with the global path as a constraint. Based on variable network conditions and vehicle trajectory's curvature the vehicle is allowed to travel farther on the current control signal while the vehicle trajectory matches the path constraint. This method uses path specific characteristics to evaluate the effectiveness of each generated control signal. By scheduling the gain on the control signal the vehicle tracking performance is maintained with an increase in network delay. The tracking time is decreased compared to other methods since the proposed control method allows the controller to look ahead and thus evaluate predicted effect of each control signal before scaling it. The proposed method is compared with existing delay compensation methods through simulation.

Imbalanced data are often encountered in real-world real-world real-world applications, they may incline the performance of classification to be biased. The immune-based algorithm Artificial Immune Recognition System (AIRS) is applied to Duke Energy distribution systems outage data and we investigate its capability to classify imbalanced data. The performance of AIRS is compared with an Artificial Neural Network (ANN). Two major distribution fault causes, tree and lightning strike, are used as prototypes and a tailor-made measure for imbalanced data, g-mean, is used as the major performance measure. The results indicate that AIRS is able to achieve a more balanced performance on imbalanced data than ANN.

The elegant fuzzy classification algorithm proposed by Ishibuchi et al. (I-algorithm) has achieved satisfactory performance on many well-known test data sets that have usually been carefully preprocessed. However, the algorithm does not provide satisfactory performance for the problems with imbalanced data that are often encountered in real-world applications. This paper presents an extension of the I-algorithm to E-algorithm to alleviate the effect of data imbalance. Both the I-algorithm and the E-algorithm are applied to Duke Energy outage data for power distribution systems fault cause identification. Their performance on this real-world imbalanced data set is presented, compared, and analyzed to demonstrate the improvement of the extended algorithm.

This paper presents a method of realizing artificial neural networks (ANNs) hardware implementation using field programmable analog arrays (FPAAs). A simplified realization for neurons with piecewise linear activation functions is used to reduce the complexity of the neural network architecture. A feedforward neural network is implemented using multi-chip FPAAs. Anadigm's commercially available AN221E04 FPAA chips are adopted as the platform for simulation and experiments. The FPAA based ANN classifies two groups of data with zero error at a speed of 6.0 Million Connections Per Second (MCPS). The result is more than 1400 times faster than software implementation. The ANN architecture is also expandable to perform more complicated tasks by incorporating more FPAA chips into the implementation. The programmability of the FPAA makes rapid prototyping possible.

Intelligent space (iSpace) is a large scale mechatronics system. It is a multidisciplinary effort whose aim is to produce a network structure and components that are capable of integrating sensors, actuators, DSP, communication, and control algorithms in a manner that suits time-sensitive applications including real-time navigation and/or obstacle avoidance. There are many challenges that must be overcome in order to put such a distributed, heterogeneous system together. This paper deals with one of these issues, i.e. the adverse effect of network and processing delays on the system. Here a delay-resistant sensory motor module for navigating a differential drive unmanned ground vehicle (UGV) in a cluttered environment is suggested. The module consists of an early vision edge detection stage, a harmonic potential field (HPF) planner, a network based quadratic curve fitting controller and gain schedule middleware, (GSM). Though the different techniques used to implement the navigation system have been well-studied as independent modules, the contribution in this paper is the way all these different modules are integrated together for the first time to create an efficient structure for a network based integrated navigation system. The structure of this module and its components are described. Thorough experimental results along with performance assessment comparing the suggested structure to a previous implementation of iSpace are also provided

In this paper, we employ the clonal optimization method to optimize the detectors in the negative selection algorithm (NSA). Taking advantage of the clonal optimization strategy, the NSA detectors can be optimized for anomaly detection. A new motor fault detection scheme using our NSA is also discussed. We demonstrate the efficiency of the proposed approach with an example of bearings fault detection.

Intelligent Space (iSpace) is a space (room, corridor, or street), which has distributed sensory and mobile agents, that is capable to provide intelligent services. In this paper, experimental setup between Hashimoto Lab, University of Tokyo, Japan and Advanced Diagnosis Automation and Control (ADAC) Lab, North Carolina State University, USA is prepared to form an Inter-Continental Intelligent Space. A mobile robot at Hashimoto Lab is controlled by a path-tracking controller at ADAC Lab to track a predefined path with the tracking performance affected by the network time delay. Gain Scheduler Middleware is employed in the control loop to alleviate the effect of time delay. The experimental results of the mobile robot path-tracking has demonstrated the effectiveness of using Gain Scheduler Middleware to compensate the time-delay effect on teleoperation over IP network.

For part I, see ibid., p.143-50. This paper is the second of two companion papers. The foundations of the proposed method of heuristic constraint enforcement on membership functions for knowledge extraction from a fuzzy/neural architecture was given in Part I. Part II develops methods for forming constraint sets using the constraints and techniques for finding acceptable solutions that conform to all available a priori information Moreover, methods of integration of enforcement methods into the training of the fuzzy-neural architecture are discussed. The proposed technique is illustrated on a fuzzy-AND classification problem and a motor fault detection problem. The results indicate that heuristic constraint enforcement on membership functions leads to extraction of heuristically acceptable membership functions in the input and output spaces. Although the method is described on a specific fuzzy/neural architecture, it is applicable to any realization of a fuzzy inference system, including adaptive and/or static fuzzy inference systems.

Using fuzzy/neural architectures to extract heuristic information from systems has received increasing attention. A number of fuzzy/neural architectures and knowledge extraction methods have been proposed. Knowledge extraction from systems where the existing knowledge limited is a difficult task. One of the reasons is that there is no ideal rulebase, which can be used to validate the extracted rules. In most of the cases, using output error measures to validate extracted rules is not sufficient as extracted knowledge may not make heuristic sense, even if the output error may meet the specified criteria. The paper proposes a novel method for enforcing heuristic constraints on membership functions for rule extraction from a fuzzy/neural architecture. The proposed method not only ensures that the final membership functions conform to a priori heuristic knowledge, but also reduces the domain of search of the training and improves convergence speed. Although the method is described on a specific fuzzy/neural architecture, it is applicable to other realizations, including adaptive or static fuzzy inference systems. The foundations of the proposed method are given in Part I. The techniques for implementation and integration into the training are given in Part II, together with applications.

The conventional two-stage training algorithm of the fuzzy/neural architecture called FALCON may not provide accurate results for certain type of problems, due to the implicit assumption of independence that this training makes about parameters of the underlying fuzzy inference system. In this correspondence, a training scheme is proposed for this fuzzy/neural architecture, which is based on line search methods that have long been used in iterative optimization problems. This scheme involves synchronous update of the parameters of the architecture corresponding to input and output space partitions and rules defining the underlying mapping; the magnitude and direction of the update at each iteration is determined using the Armijo rule. In our motor fault detection study case, the mutual update algorithm arrived at the steady-state error of the conventional FALCON training algorithm as twice as fast and produced a lower steady-state error by an order of magnitude.

The neural network designer must take into consideration many factors when selecting an appropriate network configuration. The performance of a given network configuration is influenced by many different factors such as: accuracy, training time, sensitivity, and the number of neurons used in the implementation. Using a cost function based on the four criteria mentioned previously, the various network paradigms can be evaluated relative to one another. If the mathematical models of the evaluation criteria as functions of the network configuration are known, then traditional techniques (such as the steepest descent method) could be used to determine the optimal network configuration. The difficulty in selecting an appropriate network configuration is due to the difficulty involved in determining the mathematical models of the evaluation criteria. This difficulty can be avoided by using fuzzy logic techniques to perform the network optimization as opposed to the traditional techniques. Fuzzy logic avoids the need of a detailed mathematical description of the relationship between the network performance and the network configuration, by using heuristic reasoning and linguistic variables. A comparison will be made between the fuzzy logic approach and the steepest descent method for the optimization of the cost function. The fuzzy optimization procedure could be applied to other systems where there is a priori information about their characteristics.

After a problem has been formulated for solution by using artificial neural network technology, the next step is to determine the appropriate network configuration to be used in achieving a desired level of performance. Due to the real world environment and implementation constraints, different problems require different evaluation criteria such as: accuracy, training time, sensitivity, and the number of neurons used. Tradeoffs exist between these measures, and compromises are needed in order to achieve an acceptable network design. This paper presents a method using fuzzy logic techniques to adapt the current network configuration to one which is close to (if not at) the optimal configuration. The fuzzy logic provides a method of systematically changing the network configuration while simultaneously considering all of the evaluation criteria. The optimal configuration is determined by a cost function based on the evaluation criteria. The proposed methodology is applied to an elementary classifier network as an illustration. The procedure is then used to automatically configure a network used to detect incipient faults in an induction motor as a real world application.>

The relative robustness of artificial neural networks subject to small input perturbations (e.g. measurement noises) is an important issue in real world applications. This paper uses the concept of input-output sensitivity analysis to derive a relative network robustness measure for different feedforward neural network configurations. For illustration purposes, this measure is used to compare different neural network configurations designed for detecting incipient faults in induction motors. Analytical and simulation results are presented to show that the relative network robustness measure derived in this paper is an effective indicator of the relative performance of different feedforward neural network configurations in noisy environments and that this measure should be considered in the design of neural networks for real time applications. The concept of input-output sensitivity analysis and relative network robustness measure presented can be extended to analyze other neural networks designed for on-line applications.

The generalization ability of a neural network in a specific application is of interest to many neural network designers. In this paper, learning theory is applied to a neural network used for incipient fault detection in single-phase induction motors. This paper will show that learning theory can help determine the proper number of training examples needed to reach a specific performance level, so that excessive and unnecessary training examples can be avoided. Comparisons of the results of learning theory and Monte Carlo estimate are presented, showing that learning theory is a useful and reliable tool to obtain information about the training process of a given neural network.