Operation Of Plug-in Electric Vehicles Research Articles

Risk-based operation of plug-in electric vehicles in a microgrid using downside risk constraints method

Risk-based operation of plug-in electric vehicles in a microgrid using downside risk constraints method

Risk-based operation of plug-in electric vehicles in a microgrid using downside risk constraints method

Risk-based operation of plug-in electric vehicles in a microgrid using downside risk constraints method

Multi-Objective-Based Charging and Discharging Coordination of Plug-in Electric Vehicle Integrating Capacitor and OLTC

The integration of plug-in electric vehicles (PEVs) in residential distribution networks demands a significant amount of electrical load where random and uncoordinated charging affects the quality and performance of the distribution network. Random and uncoordinated charging may increase the peak demand and can increase stress on critical network assets such as line, transformer, and switching devices. Moreover, the charging of PEVs in a low network reduces the voltage of the system below the lower limit. On the other hand, using PEVs as storage in the V2G mode can improve the network condition. Therefore, it is critical to properly manage the charging and discharging operation of PEVs. This paper proposes a multi-objective-based charging and discharging coordination of PEVs with the operation of the capacitor and on-load tap changer (OLTC). With the proposed strategy, the distribution network is operated safely, and charging is ensured for all PEVs connected to the network. The main consideration of this research is to reduce the daily power loss, operational cost, and voltage deviation of the system. The metaheuristic optimization binary firefly algorithm (BFA) has been applied to coordinate PEV charging and discharging as well as capacitor and OLTC operation in the system. A modified IEEE 31 bus 23 kV distribution system is used to implement the proposed strategy. From the obtained results, it is found that the combined PEV charging and discharging coordination with capacitor and OLTC operation reduces the power loss and cost by 34.16% and 12.68%, respectively, with respect to uncoordinated charging and enhances the voltage condition of the network.

15. ZHX2 IS A CANDIDATE GENE UNDERLYING BRAIN OXYMORPHONE CONCENTRATION AND OXYCODONE STATE-DEPENDENT LEARNING OF OPIOID REWARD IN A BALB/C REDUCED COMPLEXITY CROSS

Appearance of PEVs (Plug-in Electric Vehicles) in future transportation sector brings forward opportunities and challenges from grid perspective. Increased utilization of PEVs will result in problems such as greater total loss, unbalanced load factor, feeder congestion and voltage drop. PEVs are mobile energy storages dispersed all over the network with benefits to both owners and utilities in case of V2G (Vehicle-to-Grid) possibility. The intelligent bidirectional power flow between grid and large number of vehicles adds complexity to the system and requires operative tools to schedule V2G energy and subdue PEV impacts. In this paper, DFR (Distribution Feeder Reconfiguration) is utilized to optimally coordinate PEV operation in a stochastic framework. Uncertainty in PEVs characteristics can be due to several sources from location and time of grid connection to driving pattern and battery SoC (State-of-Charge). The proposed stochastic problem is solved with a self-adaptive evolutionary swarm algorithm based on SSO (Social Spider Optimization) algorithm. Numerical studies verify the efficacy of the proposed DFR to improve the system performance and optimal dispatch of V2G.

Optimal coordination of plug-in hybrid electric vehicles: A stochastic market-based approach

The existing models implemented to coordinate the plug-in electric vehicles (PEV) based on the price of electricity almost exclusively use the uniform price methods. These methods are based on price-taker mechanisms where prices are exogenous and unaffected by the PEVs activity. This standpoint, however, suffers from two major issues. First, the important indexes of network operation are neglected, e.g., power losses, network reliability, cost/benefit of PEVs operation. Second, coordination with distributed generations (DG) is not possible, efficiently. In this paper, a new viewpoint based on locational marginal price (LMP) is formulated for coordination of PEVs and DGs in electricity day-ahead markets. The LMPs at fleet and DG connected busses are used as a signal to motivate PEV and DG owners to participate in coordination problem. Also, coordination problem is inserted in the optimal operation problem of the network and an efficient algorithm based on Chaotic Crow Search algorithm (CCSA) is used to meet the optimal operation of the network. To make a bidirectional power flow, the vehicle-to-grid (V2G) is used as the PEVs; and, for modeling random behavior of loads, market price, and PEVs, a stochastic modeling framework based on PEM is implemented. The feasibility and satisfying performance of the proposed method has been tested on an 84-bus test system, and simulation results reveal promising results.

An inclusive methodology for Plug-in electrical vehicle operation with G2V and V2G in smart microgrid environments

Smart initiative concepts as smart grids, cities, and microgrids are redefining the electrical power system. This new scenario enables a completely new perspective of applications that lead to a path of general improvement of grid performance, in which the participation of plug-in electric vehicles has great importance. In this view, this work proposes a comprehensive operation strategy of plug-in electric vehicles for unbalanced smart microgrid environments. The first contribution is the grid-to-vehicle strategy based on buses availability of power whose primary objective is the maintenance of satisfactory operating conditions for the grid while controlling the plug-in electric vehicles charging. The second contribution relates to a holistic vehicle-to-grid coordinated approach, which is triggered in the event of microgrid islanding. The goal, in this case, is to assist the network by using the plug-in electric vehicles available stored energy. At last, the main contribution is obtained by the combination of both methodologies providing a comprehensive operation of plug-in electric vehicles for smart microgrids environments. The proposed method is designed for general three-phase unbalanced distribution systems with a wide range of resources and present versatility in the communication requirements. For validation, simulations are performed in a comprehensive unbalanced three-phase distribution system considering different disruptive scenarios faced by microgrids. The results indicate that the proposed methodology is satisfactory and ensure the operation of the microgrid within acceptable limits in both connected and islanded situations.

Managing Demand for Plug-in Electric Vehicles in Unbalanced LV Systems With Photovoltaics

Although the future impact of plug-in electric vehicles (PEVs) on distribution grids is disputed, all parties agree that mass operation of PEVs will greatly affect load profiles and grid assets. The large-scale penetration of domestic energy storage, such as with photovoltaics (PVs), into the edges of low-voltage grids is increasing the amount of customer-generated electricity. Distribution grids, which are inherently unbalanced, tend to become even more so with the uneven spread of PVs and PEVs. In combination, PEVs and local generation could provide voltage support for distribution networks, and support increased penetration. This paper develops an interactive energy management system for incorporating PEVs in demand response (DR). Using this system, owners can immediately choose whether they want to discharge their PEV battery back into the grid. The system not only provides owners with a flexible scheme for contributing to DR but also ensures that, through real-time collaboration of PEVs and PVs, the three-phase grid operates within acceptable voltage unbalance. An extensive performance evaluation using MATLAB/GAMS simulation of the 123-bus test system verifies the effectiveness of the proposed approach.

Optimal distribution feeder reconfiguration for increasing the penetration of plug-in electric vehicles and minimizing network costs

Appearance of PEVs (Plug-in Electric Vehicles) in future transportation sector brings forward opportunities and challenges from grid perspective. Increased utilization of PEVs will result in problems such as greater total loss, unbalanced load factor, feeder congestion and voltage drop. PEVs are mobile energy storages dispersed all over the network with benefits to both owners and utilities in case of V2G (Vehicle-to-Grid) possibility. The intelligent bidirectional power flow between grid and large number of vehicles adds complexity to the system and requires operative tools to schedule V2G energy and subdue PEV impacts. In this paper, DFR (Distribution Feeder Reconfiguration) is utilized to optimally coordinate PEV operation in a stochastic framework. Uncertainty in PEVs characteristics can be due to several sources from location and time of grid connection to driving pattern and battery SoC (State-of-Charge). The proposed stochastic problem is solved with a self-adaptive evolutionary swarm algorithm based on SSO (Social Spider Optimization) algorithm. Numerical studies verify the efficacy of the proposed DFR to improve the system performance and optimal dispatch of V2G.

Probabilistic management of charge/discharge of EVs: An approximation procedure

This article proposes a novel stochastic framework based on point estimate method (PEM) and firefly algorithm (FA) for the optimal operation of plug-in electric vehicles (PVs) in the distribution systems. The stochastic problem will consider the uncertainties of active and reactive loads, the number of PVs in the fleets and the departure and arrival time at charging locations. According to the high complexity and severity of the problem, a new version of FA called θ-FA is used to search the solutions in the polar space instead of the traditional Cartesian space. The objective function is to minimize the total cost of the system considering the operation and security constraints. The feasibility and high performance of the propose problem are examined on a standard IEEE test system.

Four-Axis Vector-Controlled Dual-Rotor PMSM for Plug-in Electric Vehicles

Dual-rotor permanent-magnet synchronous motors (PMSMs) offer the merits of higher efficiency, higher torque density, and inherent electric differential (ED). This paper proposes a novel four-axis vector-controlled dual-rotor PMSM drive for driving plug-in electric vehicles (PEVs). A four-axis d-q model of the dual-rotor PMSM is developed with the proposed three-phase to four-phase transformation. This transformation separately resolves the stator variables to the d-q-axes of the inner and outer rotor. Instead of applying back electromotive forces and rotor power angles, the d-axis fluxes are used for computing the displacement between the two d-q-axes, which is essential for the transformation. The proposed four-axis vector control of dual-rotor PMSM allows for the independent control of stator quantities corresponding to the inner and outer rotors. This facilitates ED and better control under parametric differences, such as rotor inertia and permanent magnet flux for the inner and outer rotors. The comparison of the dual-rotor PMSM-based PEV operation with the proposed control and two earlier reported schemes reveals that the proposed scheme offers effective ED without the loss of vehicle control. Furthermore, the proposed configuration is validated with different drive cycles, including the loss modeling, sensitivity analysis, and efficiency mapping.

Optimal scheduling for vehicle-to-grid operation with stochastic connection of plug-in electric vehicles to smart grid

Abstract Vehicle-to-grid (V2G) operation of plug-in electric vehicles (PEVs) is attracting increasing attention since it can assist to improve the efficiency and reliability of power grid, as well as reduce the operating cost and greenhouse gas emission of electric vehicles. Within the scheme of V2G operation, PEVs are expected to serve as a novel distributed energy storage system (ESS) to help achieve the balance between supply and demand of power grid. One of the key difficulties concerning its practical implementation lies in that the availability of PEVs as ESS for grid remains highly uncertain due to their mobility as transportation tools. To address this issue, a novel event-triggered scheduling scheme for V2G operation based on the scenario of stochastic PEV connection to smart grid is proposed in this paper. Firstly, the mathematical model is formulated. Secondly, the preparation of input data for systematic evaluation is introduced and the case study is conducted. Finally, statistic analysis results demonstrate that our proposed V2G scheduling scheme can dramatically smooth out the fluctuation in power load profiles.

Coordinating plug-in electric vehicle charging with electric grid: Valley filling and target load following

Plug-in electric vehicles (PEVs) shift energy consumption from petroleum to electricity for the personal transportation sector. This work proposes a decentralized charging protocol for PEVs with grid operators updating the cost signal. Each PEV calculates its own optimal charging profile only once based on the cost signal, after it is plugged in, and sends the result back to the grid operators. Grid operators only need to aggregate charging profiles and update the load and cost. The existing PEV characteristics, national household travel survey (NHTS), California Independent System Operator (CAISO) demand, and estimates for future renewable generation in California are used to simulate PEV operation, PEV charging profiles, grid demand, and grid net load (demand minus renewable). Results show the proposed protocol has good performance for overnight net load valley filling if the costs to be minimized are proportional to the net load. Annual results are shown in terms of overnight load variation and comparisons are made with grid level valley filling results. Further, a target load can be approached in the same manner by using the gap between current load and the target load as the cost. The communication effort involved is quite modest.

Optimal Operation of Plug-In Electric Vehicles in Power Systems With High Wind Power Penetrations

The Danish power system has a large penetration of wind power. The wind fluctuation causes a high variation in the power generation, which must be balanced by other sources. The battery storage-based Plug-In Electric Vehicles (PEVs) may be a possible solution to balance the wind power variations in the power systems with high wind power penetrations. In this paper, the integration of plug-in electric vehicles in the power systems with high wind power penetrations is proposed and discussed. Optimal operation strategies of PEV in the spot market are proposed in order to decrease the energy cost for PEV owners. Furthermore, the application of battery storage based aggregated PEV is analyzed as a regulation services provider in the power system with high wind power penetrations. The western Danish power system where the total share of annual wind power production is more than 27% of the electrical energy demand is chosen as the studied case. The results show that an optimal operation of PEV in both spot market and regulation market can not only decrease the energy costs for PEV owners, but also significantly decrease the power deviations between West Denmark and Union for the Coordination of Electricity Transmission (UCTE) control areas.