Search

Electric vehicle (EV) charging management systems control and schedule EV load according to the measurements of local building load, solar generation, and dynamic electricity price. Within this information network, any data replaced or modified by an attacker will disrupt the EV charging schedule and could cause damage to the electricity grid. Under real circumstances, these measurements are correlated in a way that is not true for false data. This paper examines the relationship of pairwise measures within the system to establish a correlation-invariant network, and a multivariate time-series segmentation method along with a weighted k nearest neighbor (kNN) classifier is proposed to detect the changes in correlations and identify anomalous data within the network.

WOS:000612836800166 In recent years, there has been an increasing interest in the integration of photovoltaic (PV) systems in the power grids. Although PV systems provide the grid with clean and renewable energy, their unsafe and inefficient operation can affect the grid reliability. Early stage fault detection plays a crucial role in reducing the operation and maintenance costs and provides a long lifespan for PV arrays. PV Fault detection, however, is challenging especially when DC short circuit occurs under the low irradiance conditions while the arrays are equipped with an active maximum power point tracking (MPPT) mechanism. In this case, the efficiency and power output of a PV array decrease significantly under hard-to-detect faults such as active MPPT and low irradiance. If the hard-to-detect faults are not detected effectively, they will lead to PV array damage and potential fire hazard. To address this issue, in this paper we propose a new sparse representation classifier (SRC) based on feature extraction to effectively detect DC short circuit faults of PV array. To verify the effectiveness of the proposed SRC fault detection method, we use numerical simulation and compare its performance with the artificial neural network (ANN) based fault detection.

Flexible AC transmission system series compensation, such as series switched capacitors including gate-controlled series capacitor (GCSC) plays an important role to enhance grid system transfer power, stability, power quality and loss reduction. GCSC devices are implemented using fixed or switched capacitor in parallel with a pair of anti-parallel gate-commuted switches. They are connected in series of transmission and distribution lines and are commonly used to control the power flow on congested transmission lines. This study presents a review of GCSC devices and future perspectives.

In this paper, we propose an active fault tolerant control (FTC) to regulate the active and reactive output powers of a voltage source converter (VSC) in the case of actuator failure. The active fault tolerant controller of the VSC which connects a distributed energy resource to the distribution power grid is achieved through the fault diagnostic and controller reconfiguration units. The diagnostic unit reveals the actuator failure by comparing the known inputs and measured outputs of VSC with those of the faultless model of the system and testing their consistency. In the case of actuator failure, the reconfiguration unit adapts the controller to the faulty system which enables the VSC to track the desired active and reactive output powers. The reconfiguration unit is designed using the virtual actuator which does not interfere with the regular controller of the VSC. The effectiveness of the proposed active FTC is evaluated by the numerical simulation of a VSC connected to the AC distribution grid.

Increasing the number of electric vehicles (EVs) in the transportation sector can change the load pattern in power grids and affect its service quality and operational reliability. As EVs are mostly parked during the day, an EV load management (EVLM) system has been shown effective in mitigating those effects on power grids. The uncertainties in EV load and the computational burden of its coordination in the large-scale EV penetration, however, make EVLM challenging. In this paper, we address the former using the non-parametric diffusion kernel density estimation (DKDE) to estimate the expected charging energy demand and EV availability. To deal with the latter, we formulate EVLM considering the power flow constraints as a consensus problem which is solved by the alternating direction methods of multipliers (ADMM) in a distributed manner. Using real data, we evaluate the performance of DKDE and compare its results with Gaussian kernel density (GKDE). Owing to the smoothing properties of linear diffusion process and optimal bandwidth selection, DKDE is more adaptive to the training dataset and results in more accurate load estimation. Applying the prediction results to IEEE-13 bus system, the effectiveness of the proposed EVLM in nodal voltage improvement and feeder congestion mitigation is validated.

The rapid pace of Electrification in the transportation sector, realized through plug-in electric vehicle (EV) integration, necessitates the smart charging infrastructures. These systems built on real-time data collection and decision making coordinate the charging demand to facilitate high penetration of PEV s in the power grids. Accordingly, the inherent cyber-physical characteristic of smart charging networks makes them susceptible to cyber-physical threats. Due to the lack of a consistent cyber-physical attack assessment in EV networks, this paper aims to propose the vulnerability analysis and risk assessment for the smart charging infrastructures. To this end, we define several potential failure scenarios for the WinSmartEV™ charging system on the UCLA campus and study the impacts of potential cyber-physical attacks. Moreover, we outline a codified methodology and taxonomy for assessing vulnerability and risk of cyber-physical attacks on the EV charging networks in order to create a generalizable and comprehensive solution. The outcome is a framework to prioritize the degree of the vulnerabilities and risks in the EV networks and to develop effective countermeasures.

This paper introduces a scalable framework to coordinate the net load scheduling, sharing, and matching in a neighborhood of residential prosumers connected to the grid. As the prosumers are equipped with smart appliances, photovoltaic panels, and battery energy storage systems, they take advantage of their consumption, generation, and storage flexibilities to exchange energy with neighboring prosumers through negotiating on the amount of energy and its price with an aggregator. The proposed framework comprises two separate multi-objective mixed integer nonlinear programming optimization models for prosumers and the aggregator. Prosumers' objective is to maximize the comfort level and minimize the electricity cost at each instant of time, while aggregator intends to maximize its profit and minimize the grid burden by matching prosumers' supply and demand. The evolutionary nondominated sorting genetic algorithm-III (NSGA-III) is employed to generate a set of feasible nondominated solutions to the optimization problem of each individual prosumer and the aggregator. As a bilateral negotiation between each prosumer and the aggregator results in significant computational and communication overhead, a virtual power plant is introduced as an intermediator on behalf of all prosumers to proceed the negotiation with the aggregator in a privacy-preserving noncooperative environment, where no private information is shared. Hence, an automated negotiation approach is embedded in the framework, which enables the negotiators to reactively negotiate on concurrent power and price using private utility functions and preferences. To converge to an acceptable agreement, the negotiation approach follows an alternating-offer production protocol and a reactive utility value concession strategy. The effectiveness of the framework is evaluated by several economic and environmental assessment metrics through a variety of numerical simulations.

In this paper, a hierarchical distributed method consisting of two iterative procedures is proposed for optimal electric vehicle charging scheduling (EVCS) in the distribution grids. In the proposed method, the distribution system operator (DSO) aims at reducing the grid loss while satisfying the power flow constraints. This is achieved by a consensus-based iterative procedure with the EV aggregators (Aggs) located in the grid buses. The goal of aggregators, which are equipped with the battery energy storage (BES), is to reduce their electricity cost by optimal control of BES and EVs. As Aggs' optimization problem increases dimensionally by increasing the number of EVs, they solved their problem through another iterative procedure with their customers. This procedure is implementable by exploiting the mathematical properties of the problem and rewriting Aggs' optimization problem as the \textit{sharing problem}, which is solved efficiently by the alternating direction method of multipliers (ADMM). To validate the performance, the proposed method is applied to IEEE-13 bus system., This paper has been accepted to IEEE PES General Meeting 2019

Renewable energy resources (RESs) and electric vehicles (EVs) have emerged as powerful concepts which can replace the conventional energy and transportation systems with more flexibility and efficiency. Due to low inertia of microgrid and intermittent nature of RESs, the rapid increase in the penetration level of RESs and EVs in smart grids may lead to frequency stability issue. However, EVs, as the mobile energy storage system, can contribute to the improvement of the frequency fluctuation and stability. This paper proposes a method to control the EVs integrated in an islanded microgrid so that they participate in load frequency control (LFC). On the contrary to the approaches proposed in the literature, the proposed control method enables the investigation of time delay effect on LFC and considers the communication latency on the LFC stability. The delay-dependent stability criterion of the proposed method is derived based on Lyapunov theory in the form of linear matrix inequality (LMI). The LMI is solved to find the parameters impacting the maximum allowable delay (MAD) and to obtain the MAD by which the stability of the LFC system is guaranteed. The effectiveness of the proposed method is validated through numerical simulation of a case study.

We propose a distributed electric vehicle (EV) charging scheduling to minimize load variance in the distribution grid and reduce EV charging cost. To predict the availability and load demand of the EVs, we use nonparametric diffusion-based kernel density estimator (DKDE) to model the stochasticity of charging load. DKDE is based on smoothing properties of linear diffusion process which is more adaptive to the training dataset and results in optimal bandwidth selection comparing to Gaussian kernel density estimator (GKDE). Then, we formulate the optimal charging problem as a sharing problem which is solved efficiently by alternating direction method of multipliers (ADMM). Using real data numerical simulation, we evaluate DKDE prediction accuracy and verify the EV charging scheduling performance.

This paper proposes a hybrid kernel density estimator (HKDE) that uses both Gaussian- and Diffusion-based KDE (GKDE and DKDE) to predict the stay duration and charging demand of electric vehicles (EVs), which are essential parameters for optimizing EV charging schedule. While DKDE has higher accuracy in general, GKDE tends to result in better estimation for users who charge the EV irregularly. Therefore, the HKDE evaluates and categorizes the charging pattern regularity of a user, and determines which KDE to use by a novelty detection method based on the user's historical data. The estimations are then applied to an optimal EV charging algorithm to minimize load variance in an EV charging infrastructure and reduce EV charging cost. Real data is used for the numerical simulation to show the effectiveness of the proposed approach for predicting EV user behavior and scheduling EV charging load.

Electric vehicles (EVs) are controllable loads from which distribution grid operator can benefit in order to minimize the load profile variations. In this paper, we proposed a hierarchical distributed optimization framework such that EV management system (EVMS), as a part of distribution grid management system, minimizes the load variance of the grid in communication with the EV aggregators which control EV charging load of the distribution system feeders. The hierarchical distributed framework, based on alternative direction method of multipliers (ADMM), increases the scalability of the EV charging infrastructure while decreases computational burden. In our proposed approach, each EV aggregator schedules the EV charging profiles of its feeder in a distributed fashion which avoids sharing the EV owners' desired charging profile information and enables privacy preserving. To show the performance of our approach, we apply it to a case study with 100% EV penetration, including 4 feeders and 60 EVs, and show how the load variance of the system and charging cost of individual EVs decrease.

The increased greenhouse gas emissions and the global warming from fossil fuels to produce electrical power generation and transportation, they become the most critical concern of governments to find an alternative source to fossil fuels such as renewable energies like wind and solar; Additionally, transportation is one of the main sources of environmental pollution, to this end, PHEV grid is presented, but the widespread use of PHEV will be creating a significant load on the grid. For this reasons in this paper, a model for the optimal application of green house, was studied in 24 hours. Energy management issue is considered in zero energy buildings with solar hybrid power sources, fuel cell, electrolyzer, hydrogen tank, compressor, reformer, anaerobic reactors and converters as well as plug-in hybrid electric vehicle (PHEV) must be provided, designed and implemented. The green house serves bilateral energy exchange with the upstream distribution network and if the surplus energy provided, can be sold to the network. Daily house trashes use to decline the bioenvironmental contaminations and also heat of house and hot water is supplied using fuel cell heat and if necessary we should purchase gas from network. In order to optimize this house, an objective function is extracted and aggregation Swarm algorithm to minimizing costs was carried out using MATLAB program. Finally, optimal operation is presented for the green house and results have been analyzed.

APF can reduce some types of power quality disturbances such as harmonics, reactive power, and unbalanced load currents. The APF uses fast switching solidstate devices and GTO emerging as a viable in smart grid applications, battery charging scheme battery storage system. Smart grid uses distributed-STATCOM (D-STATCOM) for energy efficiency, energy saving, loss reduction, and power factor enhancement. The chapter presents different APF topology with modified control application and case studies for smart grid and renewable energy utilization. New emerging application for APFs in campus intelligent met heuristic optimization and search algorithm for optimal PWM using symmetrical, a symmetrical, inverse sine carrier, and optimum point on wave switching strategy with online search to minimize dominating harmonic and THD at the point of interface with the AC smart grid.

This research investigates electric vehicle (EV) charging behavior and aims to find the best method for its prediction in order to optimize the EV charging schedule. This paper discusses several commonly used machine learning algorithms to predict charging behavior, including stay duration and energy consumption based on historical charging records. It is noted that prediction error increases along with the rise of data entropy or the decrease of data sparsity. Thus, this paper accounts for both indicators by defining the entropy/sparsity ratio (R). When R is low, support vector regression (SVR) and random forest (RF) regression show better accuracy for stay duration and energy consumption predictions, respectively. While R is high, a diffusion-based kernel density estimator (DKDE) performs better for both predictions. The three methods are assembled as the proposed Ensemble Predicting Algorithm (EPA) to improve predicting performance by decreasing 11 % of the duration and 22 % of the energy consumption prediction errors. The prediction results are then applied to an optimal EV charging scheduling algorithm to minimize load variance while reducing the EV charging cost. A numerical simulation using real charging data is conducted to show the effectiveness of improved predictions and EV load management. The results show that the charging scheduling combined with EPA prediction can reduce 27% of peak load, 10% of load variation, and 4% cost reduction, compared to uncoordinated charging.

An optimal power management strategy is the key to benefit from hybridization of a vehicle powertrain. Designing such a strategy requires knowledge of the vehicle energy requirements during its drive cycle. Therefore, information available through Intelligent Transportation Systems (ITS) can play a critical role in designing such an optimal powertrain management. To avoid the implementation and practical issues of global optimal solutions, sub-optimal methods such as Equivalent Consumption Minimization Strategy (ECMS) have been introduced for the power distribution in Hybrid Electric Vehicles (HEV). However, the dependency of ECMS on prior knowledge about the driving cycle is a deterring effect for real-time implementation. Accordingly, on-line decision making about the equivalent factor value used in ECMS, which translates the electrical energy into the equivalent fuel energy, is the challenge captivating researches'' attention. In this paper, an adaptive method for enhancement of the ECMS based on the prediction of driving conditions is proposed. Using the approximated future energy requirement of the vehicle over the prediction time horizon, the sub-optimal value of equivalent factor is updated. Simulation results validate the effectiveness of the proposed method to decrease fuel consumption while charge sustainability is satisfied.

In this paper, a novel Switched/Modulated C type Filter/Capacitive-Compensator developed by the First Author is presented and digitally validated for smart grid applications. The proposed FACTS filter/compensation device comprises a hybrid series and shunt switched capacitor banks installed in series with the transmission line and in shunt at Generator bus. This hybrid filter/compensator scheme is controlled by a novel dynamic time-scaled and multi-regulator/ multi-loop error driven inter-coupled weighted modified PID controller. The effectiveness of the proposed FACTS hybrid series-parallel device for a Single Machine-infinite bus SMIB-study system, with pulse width modulated switching strategy is validated using the MATLAB-Simulink digital simulation software environment. The coordinated, inter-coupled dynamic controller scheme ensures the FACTS filter/compensation device effectiveness in reducing inrush current conditions, transient voltage excursions, improving voltage stabilization, regulating feeder voltage, reducing total harmonic distortion, and improving power factor at both generator and load buses. The unified SMIB-study system with the inserted FACTS device is validated for normal operating conditions, open circuit and short circuit fault conditions, load-excursions, and load rejections.

A novel FACTS-based hybrid switched capacitor-filter compensator developed by the First Author is validated for smart grid-electric vehicle battery mobile/onboard charging schemes. The proposed scheme includes a low cost switched hybrid AC-DC filter connected between AC and DC sides of the rectifier in the DC common bus, and it is controlled by a novel tri-loop dynamic error-driven Weighted - Modified PID controller. A buck-boost DC/DC chopper is utilized on the DC side of the rectifier to achieve the best charging mode for fast impact and minimal inrush battery charging conditions. The buck-boost chopper is regulated using a hybrid Voltage-Current-Power weighted tri-loop dynamic error-driven controller. A pulse width modulated switching scheme is utilized by both the FACTS device and DC-DC buck-boost chopper in order to turn on/off IGBT/MOSFET switches. Using the proposed Modified grid scheme, better dynamic performance with minimum inrush currents and voltage excursions can be achieved. Enhanced DC bus stabilization and improved power factor and power quality are achieved using the modified grid scheme, compared with a basic battery charging scheme without the novel FACTS Device. The effectiveness of the scheme is validated using MATLAB/Simulink/Sim-Power Toolbox.

This paper presents a novel hybrid series-parallel switched/modulated FACTS-based filter/compensation scheme (SCC) developed by the First Author for smart grid applications. The proposed FACTS filter/compensation device comprises a hybrid series and shunt switched capacitor-banks controlled by a dynamic time decoupled multi-regulator multi-loop error driven inter-coupled Weighted-Modified PID (WMPID) controller. The effectiveness of the proposed low cost Pulse Width Modulated (PWM) scheme is validated using MATLAB-Simulink digital simulation results. The coordinated dynamic controller ensures FACTS device effectiveness in limiting inrush current conditions, enhancing bus voltage stabilization, improving feeder voltage regulation, reducing total harmonic distortion, and enhancing power factor at both source and load buses. The unified AC system was validated for both normal operation and load change conditions.

This paper proposes an adaptive variable-structure control scheme for a three-phase PWM converter under unbalanced conditions. The controller regulates the DC output of PWM converter in presence of unknown disturbance and uncertainty in model parameters of the converter. Using differential geometric tools, the system is transformed to a normal form. Then an adaptive control scheme is designed to regulate the output of the transformed system. The stability of the proposed controller is proved using the Lyapunov theory. Simulation results illustrate the effectiveness of the proposed method.

The paper presents a novel FACTS device developed by the First Author to improve the power quality, reduce total harmonic distortion, decrease AC and DC inrush currents, and provide device voltage stabilization. The novel FACTS device is a AC-DC coupling neutral point filter compensation (NP-FC) which is connected between AC and DC sides of the converter and increases the level of power. A DC-DC buck-boost chopper is also utilized in DC side of the converter to control battery charging and decrease the ripples of the DC side voltage and current. A multi-regulator multi-loop error driven Modified-PID controller is used to control on-off pulsing sequence for NP-FC FACTS device and DC-DC buck-boost chopper. The proposed scheme is validated for V2H battery charging scheme.

Underfrequency relays are the most reliable tools for load shedding in active power generation deficiency conditions especially industrial power systems. In this paper, a new algorithm is proposed to set underfrequency load shedding relays based on transient stability studies (frequency dynamic study). The proposed algorithm utilizes a comprehensive transient stability module to find out the unwanted events which are not safe for the operation of the plant and the load shedding actions are unavoidable. Underfrequency load shedding relays are set in such a way that the power system preserves the electricity in service for critical loads and frequency is maintained in permissible limits. The application of the proposed method on a test case is described and the results and related discussions are presented.