A zero-dynamics attack detection scheme for networked power systems with electric vehicles: Watermark-based auxiliary function viewpoint

The rising concerns over global climate change and depleting fossil fuel reserves are two of the main reasons for the ongoing efforts towards the electrification of the transportation sector. While greenhouse gases (GHGs) emissions from other sectors are generally falling, emissions from the road transport have increased over the past few decades, with both full electric vehicles (FEVs) and plug-in hybrid electric vehicles (PHEVs) being recognized as potential alternatives to combat climate change and reduce GHG emissions. However, wide-spread integration of FEVs and PHEVs will substantially increase the load on the power system which will eventually affect the reliability of existing power systems. In this paper, a probabilistic model for integrating FEVs and PHEVs with existing power grids is proposed that incorporates important FEV and PHEV characteristics, such as battery capacity, charge depleting distance, and charging rates. In addition, user behavior is taken into account through time of recharging, arrival and departure times, and daily miles driven. Furthermore, different charging strategies, i.e., opportunistic charging and controlled charging with and without vehicle-to-grid (V2G) scheme have been considered to evaluate the impact of FEVs and PHEVs on the composite power system. IEEE-RTS-79 system is used to examine the proposed probabilistic technique considering different FEV and PHEV penetration levels as well as charging strategies. Simulation results show that even a relatively low penetration level of FEVs or PHEVs might have a significant impact on the system reliability unless a proper charging and/or discharging schemes are utilized.

Nowadays, environmental pollution and the limitation of fossil fuels are taken into consideration by many countries. Worries over the pollution from fossil fuels in the transportation sector have prompted the inclination to use electric vehicles (EVs) instead of the conventional internal combustion engines. It is predicted that at least 10% of the US transportation fleet will be changed to EVs by 2020, and they could have 50% of vehicle market share by 2050. Furthermore, by increasing the penetration of the renewable energy resources in the power system, we can use these clean energies to charge EVs; in this way, we can solve air pollution and fossil fuels problems. Implementing this procedure needs many infrastructures to handle the charging demand of the EVs properly. It should be mentioned that by increasing the penetration of the EVs in the power system, we are confronted with large load demand by sharp stochastic behavior which can have significant effects on the power system parameters such as voltage variations and power loss. To handle this problem, we need to charge EVs in smart mode by considering the power system constraints. In this way, some internal units between EV owners and power system operator, which are named as aggregators, are considered. These units buy electricity energy from the power system operator and sell it to the customers (EV owners). The main goal of these units is to find the optimal charging procedure of EVs by considering power system limitations and minimizing the EV owners charging cost. Here, the main challenge for aggregators to find the optimal charging solution is to model the EVs travel behavior and estimate the value and time of their charging demand in a precise manner. If they can estimate these parameters accurately, it is highly profitable for them and has a significant effect on their income. In this regard, we need to find the precise approach to forecast the EVs travel behavior with high accuracy which has a potential to handle large dimension data; because in the near future by increasing the penetration of the EVs in the transportation fleet, we will face big data problem. To handle the large dimension data sets, machine learning tasks, which are developed based on artificial intelligence concept, can be regarded as the best solution, and these approaches have acceptable performance on other data engineering tasks such as time series forecasting, image and voice processing, and pattern recognition. In this chapter, we introduce an artificial intelligence-based approach for modeling the EVs travel behavior and describe it in full details.

With the extensive promotion of new energy vehicles, the number of electric vehicles (EVs) in China has increased rapidly. Electric vehicles are densely parked in garages, which means parking garages contain a large amount of idle energy storage resources. How to make this idle energy storage in garages participate in power system dispatch and evaluate the network loss and system carbon emissions considering electric vehicle energy storage has become an important research topic. The uncertainty around parking habits for electric vehicles causes it to be difficult to predict compared with the traditional energy storage system. Therefore, it is necessary to study its influence on the synergistic effect of loss reduction and carbon reduction as energy storage access. The benefits of new energy power generation output growth, energy waste reduction, and carbon emission reduction brought by loss reduction measures can be well reflected in the loss reduction index system of a power system in a low-carbon scenario. In this paper, a large amount of parking information in a certain area is collected, and the approximate parking habits of all vehicles in the simulated garage are obtained by the Monte Carlo method. Then, the load aggregation model is established, which is incorporated into the power system as an energy storage model. The synergy of loss reduction and carbon reduction is considered in this paper and comprehensively optimizes the strategy of integrating electric vehicles into the power system from the perspectives of electricity and carbon. In the scenarios of carbon flow calculation and network loss calculation, the YALMIP and CPLEX of MATLAB are applied, with various constraints input for simulation, so that the benefit evaluation method of carbon reduction and loss reduction under a coordinated transportation–electricity network is obtained.

The contribution of electric vehicles (EV) in a microgrid with high intermittent renewable energy penetration could strengthen power system security and reliability. This paper investigates the contribution of EV in regulating and improving primary frequency response of the power system. EV is designed to halt charging and inject power to the grid during an under-frequency event and to consume surplus energy following an over-frequency event when EV state-of-charge (SOC) is available. The minimum SOC threshold is kept to a higher value to ensure EV is available to be driven at any given time. Simulation results demonstrate that EV enhances primary frequency response of the system and also better frequency regulation is observed as the levels of EV penetration increases. In addition, high EV power output during a short period of time indicates its low impact on EV SOC and the need of high power EV charger to reduce the number of EV participation.

The government policies and cost benefits led to the surge in penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs) into both public and private sectors, challenging the electric grid's potential to maintain its stability and efficiently serve the purpose. The penetration of PHEVs brings technological advancements and environmental benefits to the traditional transportation and electrical power systems. Designing of appropriate charging methods to satisfy PHEV owners helps to improve system performance and avoid transmission and distribution system expansion.;The primary objective of this thesis is to model a coordinated charging algorithm to charge maximum number of PHEVs with the existing infrastructure at a Charging Station (CS) located in residential locality. The stochastic driving patterns of PHEVs are studied and different charging strategies are developed. The Optimized charging scheme is obtained for American Electric Power utility distribution feeder modeled in OpenDSS. Further, PHEV customers are modeled into price responsive consumers capable of participating in Demand Response programs to economically charge their vehicles and render support to the stabilization of power system. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices (PEMs) which are utilized to calculate the level of demand response.;This thesis also proposes a concept of converting the existing Personal Rapid Transit (PRT) system from guideway power rail propelling vehicles to Battery Electric Vehicles (BEVs) by developing a novel routing algorithm based on the data provided by West Virginia University's Department of Transportation and Parking. This turns into a feasibility analysis of implementing new EV technologies in electrification of public transportation. The routing algorithm involves Mixed Integer Linear Programming (MILP) Optimization resulting in hourly schedule of all the vehicles satisfying the passenger demand. The effect of charging load of BEVs on the electrical distribution system is analyzed by performing a time series simulation on AEP distribution feeder.;Additionally, the impact of PHEV charging on power market prices i.e. Locational Marginal Prices (LMP) in a distribution microgid scenario is presented. Locational marginal pricing is a market based wholesale pricing approach adopted by six out of ten national electricity markets. The uncoordinated charging of EVs results in unplanned peak demand causing system changes and fluctuation of LMP. The stabilizing effect of coordinated charging scheme is demonstrated on IEEE 13 Node test system.

With problems of environmental pollution and energy crisis, because of the advantages of zero emissions and low noise level, electrical vehicles (EV) are gaining more and more attention. Large number of EVs connected to power systems for charging will have significant impact on power system, especially massive uncontrolled charging will cause bottlenecks in supplying capacity and expose power system to severe security risks. This paper establishes a charging load model of EVs, and V2G is also included. Based on the EVs' response to the change of power price, a Time-of-use (TOU) price model is proposed to influence the EVs' charging habits in the perspectives both of grid and EV owners.

The high penetration of renewable energy incurs serious power fluctuations in the power system. Large scale electric vehicles (EVs) under the centralized control are able to provide considerable regulation capacity for the power system. In existing modeling methods for centralized EVs, the accurate control results for the regulation services were achieved by modeling each EV individually. However, the computational complexity was a serious problem for modeling large scale EVs. In this paper, a reduced modeling method for centralized EVs is developed by describing a population of EVs with a partial differential equation (PDE). This PDE modeling method uses a finite number of state-of-charge (SOC) intervals to describe the flows of charging EVs, idle EVs and discharging EVs. Considering the three connecting states (CNS) of EVs, this PDE modeling method realizes the power regulation for centralized EVs by changing the CNS of EVs located in different SOC intervals. Comparative simulations validate that the PDE modeling method for large scale centralized EVs achieves high control accuracy and high computational efficiency.

The smart charging techniques of electrical vehicles (EVs) are developed to minimize EVs' adverse effects on the power system. However, the existing techniques have some drawbacks. First, they use direct methods, which take the decision authority away from EVs' owner and reduce the EVs' acceptance and security. Second, they do not consider real-time effects of EVs' charging and discharging demand on the market. Finally, most of the existing techniques do not investigate the power system constraints due to nonlinearity of corresponding equations. In this paper, an indirect real-time charging technique based on Cournot imperfect competition model is developed. We propose a two-stage heuristic iterative method to find the market equilibrium and minimize each customer's cost while considering nonlinear constraints. The effectiveness of the proposed method is demonstrated by conducting simulations in the IEEE 37-bus test system with more than 300 EVs. The results show that the proposed technique can control the EVs charging optimally in real-time.

Electric vehicles (EVs) can be regarded as a kind of demand response (DR) resource. Nevertheless, the EVs travel behavior is flexible and random, in addition, their willingness to participate in the DR event is uncertain, they are expected to be managed and utilized by the EV aggregator (EVA). In this perspective, this paper presents a composite methodology that take into account the dynamic road network (DRN) information and fuzzy user participation (FUP) for obtaining spatio-temporal projections of demand response potential from electric vehicles and the electric vehicle aggregator. A dynamic traffic network model taking over the traffic time-varying information is developed by graph theory. The trip chain based on housing travel survey is set up, where Dijkstra algorithm is employed to plan the optimal route of EVs, in order to find the travel distance and travel time of each trip of EVs. To demonstrate the uncertainties of the EVs travel pattern, simulation analysis is conducted using Monte Carlo method. Subsequently, we suggest a fuzzy logic-based approach to uncertainty analysis that starts with investigating EV users’ subjective ability to participate in DR event, and we develop the FUP response mechanism which is constructed by three factors including the remaining dwell time, remaining SOC, and incentive electricity pricing. The FUP is used to calculate the real-time participation level of a single EV. Finally, we take advantage of a simulation example with a coupled 25-node road network and 54-node power distribution system to demonstrate the effectiveness of the proposed method.

<div class="section abstract"><div class="htmlview paragraph">The adoption of electric vehicles (EVs) has been announced worldwide with the aim of reducing CO<sub>2</sub> emissions. However, a significant increase in electricity demand by EVs might impact the stable operation of the existing power grid. Meanwhile, EV charging is acceptable to most users if it is completed by the time of the next driving event. From the viewpoint of power grid operators, flexibility for shifting the timing of EV charging would be advantageous, including making effective use of renewable energy.</div><div class="htmlview paragraph">In this work, an EV model and simulation tool were developed to make clear how the total charging demand of all EVs in use will be influenced by future EV specifications (e.g., charge power) and installation of charging infrastructure. Among the most influential factors, EV charging behavior according to use cases and regional characteristics were statistically analyzed based on the real-world usage data of over 14, 000 EVs and incorporated in the simulation tool. Using the resultant statistical model, the Monte Carlo method was applied to conduct a parameter simulation study of continuous EV usage. The impact of EV charging demand on the power grid in a future scenario and also the benefit of controlling EV charging were evaluated quantitatively in the study. Important findings indicated that active smart charging will be necessary rather than passive control based on time-of-use tariffs.</div></div>

Electric vehicles (EVs) as distributed storage devices have the potential to provide frequency regulation services due to the fast adjustment of charging/discharging power. In our previous research, decentralized vehicle-to-grid (V2G) control methods for EVs were proposed to participate in primary frequency control. In this paper, our attention is on bringing a large number of EVs into the centralized supplementary frequency regulation (SFR) of interconnected power systems. An aggregator is the coordinator between EVs and the power system control center. The aggregator calculates the total frequency regulation capacity (FRC) and expected V2G (EV2G) power of EVs based on the data communicated between the aggregator and individual EVs or EV charging stations. With FRC and EV2G power, a V2G control strategy is proposed for the aggregator to dispatch regulation requirements to EVs and EV charging stations. In individual EV charging stations, the FRC is calculated on the basis of the V2G power at present time, and EV2G power is presented considering both frequency regulation and charging demands. Besides, V2G control strategies are developed to distribute regulation requirements to each EV. Simulations on an interconnected power grid based on a practical power grid in China have demonstrated the effectiveness of the proposed strategies.

With large-scale integration of intermittent renewable energy sources (RESs), power systems need more ancillary services to guarantee system stability and security. When properly managed, large-scale electric vehicles (EVs), whose markets have been growing rapidly in recently years, can provide frequency regulation service for power systems, benefiting both EVs and the grid. However, interests balance between different parties and regulation signals uncertainty are not well considered when deploying EVs to provide regulation services in previous studies. In this article, a leader–follower game is proposed for individual EVs and their aggregator to determine optimal day-ahead charging and frequency reserve scheduling considering regulation signals uncertainty. Acting as the leader in the game, the EV aggregator prices for its charging service and offers for regulation service to encourage EVs’ participation. EVs act as price-takers and aim at achieving a tradeoff between costs from electricity consumption and revenues from providing regulation service through an aggregative game. The proposed leader–follower game is then converted to a bilevel optimization problem, which can be solved by introducing an aggregate EV model. Furthermore, for the sake of privacy protection, a distributed approach is designed for EVs to optimize their scheduling. Case studies with the proposed scheduling model are carried out to demonstrate its effectiveness.

Plug-in Hybrid Electric Vehicle (PHEV) have been widely used today. Grid to Vehicle (G2V) and Vehicle to Grid (V2G) will be the main challenge of the future smart networks with the increasing population of electric vehicles (EVs). Apart from all the advantages of massive use of electric vehicles, this can cause some problems in the electricity network system, such as the reliability and power quality of the distribution network system. For instance, when the battery charging takes place, the transformer may become overloaded, and another problem such as faster aging of a transformer may occur. Utilization of PV in charging station (SPKLU) or parking lot (PVP) of electric vehicles may become a solution. Another possible solution is to separate the general load or customers from the electric vehicle loads through additional utility transformers, so that the reliability and power quality of general customers will not be impacted by the electric car battery charging activity. In this research, the combined effects of EVs and PVP on the loading and aging of utility transformers are examined based on IEEE Std C57.91-1995. EVs load profile is modeled with monte carlo simulations and tested on a distribution system with 4 different scenarios. It was obtained that the use of PVP and the separation of EVs and general loads may improve the reliability and quality of the distribution network system and maintain transformer lifetime.

Future electric vehicles (EVs) will be linked to the electric power system infrastructure; the vehicles will operate through frequent electric charging, as is the case with electric trains. Conventional batteries require a long recharging time; therefore, supercapacitors, rather than batteries, will play an important role in the future for charging of EVs. Recently, we manufactured small EVs powered only by supercapacitors. Supercapacitors have a long operating life, large current density, and environmentally friendly composition. Further, their energy level can be estimated from their terminal voltage. Because EVs powered by supercapacitors can operate for more than 20 min after being charged for only 30 s, the requirement for constant recharging of EVs is reduced substantially, thereby increasing the efficiency of these EVs. Wireless power transfer based on magnetic resonance is an extremely important technique that needs to be considered for enhancing the efficiency of EVs. In a laboratory experiment, this technique enabled approximately 50 W power transfer with more than 95% efficiency at a distance of more than 50 cm. In order to improve energy efficiency and safety of future EVs, the implementation of novel motion control techniques for EVs is required. Since EVs are powered by electric motors, they have three major advantages: motor torque generation is quick and accurate, a motor can be attached to each wheel, and motor torque can be estimated precisely. These advantages enable the realization of highperformance antilock braking and traction control systems, control of two-dimensional chassis motion, and estimation of road surface condition. In summary, we can achieve a large-scale development of future vehicles that employ three techniques: Electric Motors, Supercapacitors, and Wireless Power Transfer. This eliminates the requirement for engines, high performance Li-ion batteries, and large charging stations.

With the continuous increase of renewable energy, the power system will face the problem of insufficient frequency response capability. Electric vehicles (EVs) combine the characteristics of mobile energy storage and controllable loads and can provide capacity support in frequency control. However, as a distributed frequency regulation resource, there is inevitably a communication delay in the process of electric vehicles participating in frequency regulation, which will affect the stability of the system frequency. Therefore, this paper proposes a frequency regulation delay compensation control strategy based on model predictive control (MPC). Firstly, the model predictive control is utilized to design an electric vehicle frequency response model controller and establish a frequency regulation model of the power system containing electric vehicles; then, by establishing the selection rules of the control signal, the multi-step control signal is dynamically adjusted during the rolling optimization of the predictive model to form a delay compensation mechanism considering delay and packet loss; finally, multi-scenario simulation is conducted, and the results show that the proposed strategy can roll-optimize the auxiliary frequency regulation function of electric vehicles, significantly reduce the influence of communication time delay, and improve the system frequency stability.