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Electric vehicle (EV) integration into the distribution system has been a topic of great interest lately due to the potential challenges that it poses. Previous works have focused on either centralized charge control or distributed charge control to solve these issues. In this paper, an adaptive voltage-feedback controller for an onboard EV charger is proposed, which, unlike other proposed methods, requires no real-time communication between the EV and the utility. This controller compares the system voltage at the point of charging with a preset reference voltage. EV charging is reduced as the system voltage approaches this reference. The reduced charging rate takes into account the EV battery state of charge (SOC) and the owner's end-of-charge time (ECT) preference. To validate the proposed control structure, extensive simulations are carried out on a distribution system with and without other voltage control mechanisms. Simulation results show that this method can eliminate system voltage violations that would otherwise be caused by EV charging while ensuring fairness among the various EVs, even with different system configurations and EV penetration levels. The proposed controller shows good performance in the presence of other voltage control devices and distributed generation units. Moreover, it can integrate with vehicle-to-grid services as the lowest level of hierarchical control. [ABSTRACT FROM PUBLISHER]

Electric vehicles (EVs) can be effectively integrated with the power grid through vehicle-to-grid (V2G). V2G has been proven to reduce the EV owner cost, support the power grid, and generate revenues for the EV owner. Due to regulatory and physical considerations, aggregators are necessary for EVs to participate in electricity markets. The aggregator combines the capacities of many EVs and bids their aggregated capacity into electricity markets. In this paper, an optimal bidding of ancillary services coordinated across different markets, namely regulation and spinning reserves, is proposed. This coordinated bidding considers electricity market uncertainties using fuzzy optimization. The electricity market parameters are forecasted using autoregressive integrated moving average (ARIMA) models. The fuzzy set theory is used to model the uncertainties in the forecasted data of the electricity market, such as ancillary service prices and their deployment signals. Simulations are performed on a hypothetical group of 10000 EVs in the electric reliability council of Texas electricity markets. The results show the benefit of the proposed fuzzy algorithm compared with previously proposed deterministic algorithms that do not consider market uncertainties. [ABSTRACT FROM PUBLISHER]

Vehicle-to-grid (V2G) has the potential of reducing the cost of owning and operating electric vehicles (EVs) while increasing utility system flexibility. Unidirectional V2G is a logical first step because it can be implemented on standard J1772 chargers and it does not degrade EV batteries from cycling. In this work an optimal combined bidding formulation for regulation and spinning reserves is developed to be used by aggregators. This formulation takes into account unplanned departures by EV owners during contract periods and compensates accordingly. Optional load level and price constraints are also developed. These algorithms maximize profits to the aggregator while increasing the benefits the customers and utility. Simulations over a three month period on the ERCOT system show that implementation of these algorithms can provide significant benefits to customers, utilities, and aggregators. Comparisons with bidirectional V2G show that while the benefits of unidirectional V2G are significantly lower, so are the risks. [ABSTRACT FROM PUBLISHER]

Energy trading in day-ahead electricity markets can be highly risky, especially for entities with considerable stochastic energy penetration. This is because of the uncertain energy prices, balancing prices, stochastic energy availability, and demand. In this work, coordinating unidirectional vehicle-to-grid (V2G) services with energy trading is proposed to mitigate these trading risks. This coordination is possible due to the recent advances in smart grid technologies. The case of a load-serving entity (LSE) that owns thermal and wind plants, and is obligated to serve a load with a significant number of electric vehicles (EVs) is investigated. The problem of energy trading in coordination with V2G services is formulated as a mixed-integer stochastic linear program. The objective is to maximize the LSE's profits while maintaining its risks within acceptable levels. The conditional value at risk is used as a risk control measure. A case study that compares coordinated vs. uncoordinated EV charging is presented. The simulation results demonstrate the benefits of coordinating V2G services with other generation assets for the LSE and the EV owners as well as the impact of this coordination on the environment. [ABSTRACT FROM PUBLISHER]

Vehicle-to-grid (V2G), the provision of energy and ancillary services from an electric vehicle (EV) to the grid, has the potential to offer financial benefits to EV owners and system benefits to utilities. In this work a V2G algorithm is developed to optimize energy and ancillary services scheduling. The ancillary services considered are load regulation and spinning reserves. The algorithm is developed to be used by an aggregator, which may be a utility or a third party. This algorithm maximizes profits to the aggregator while providing additional system flexibility and peak load shaving to the utility and low costs of EV charging to the customer. The formulation also takes into account unplanned EV departures during the contract periods and compensates accordingly. Simulations using a hypothetical group of 10 000 commuter EVs in the ERCOT system using different battery replacement costs demonstrate these significant benefits. [ABSTRACT FROM PUBLISHER]

As the number of plug-in hybrid vehicles (PHEVs) increases, so might the impacts on the power system performance, such as overloading, reduced efficiency, power quality, and voltage regulation particularly at the distribution level. Coordinated charging of PHEVs is a possible solution to these problems. In this work, the relationship between feeder losses, load factor, and load variance is explored in the context of coordinated PHEV charging. From these relationships, three optimal charging algorithms are developed which minimize the impacts of PHEV charging on the connected distribution system. The application of the algorithms to two test systems verifies these relationships approximately hold independent of system topology. They also show the additional benefits of reduced computation time and problem convexity when using load factor or load variance as the objective function rather than system losses. This is important for real-time dispatching of PHEVs. [ABSTRACT FROM PUBLISHER]

Vehicle-to-grid (V2G) has been proposed as a way to increase the adoption rate of electric vehicles (EVs). Unidirectional V2G is especially attractive because it requires little if any additional infrastructure other than communication between the EV and an aggregator. The aggregator in turn combines the capacity of many EVs to bid into energy markets. In this work an algorithm for unidirectional regulation is developed for use by an aggregator. Several smart charging algorithms are used to set the point about which the rate of charge varies while performing regulation. An aggregator profit maximization algorithm is formulated with optional system load and price constraints analogous to the smart charging algorithms. Simulations on a hypothetical group of 10 000 commuter EVs in the Pacific Northwest verify that the optimal algorithms increase aggregator profits while reducing system load impacts and customer costs. [ABSTRACT FROM PUBLISHER]

Microgrids have been proposed as a way of integrating large numbers of distributed renewable energy sources with distribution systems. One problem with microgrid implementation is designing a proper protection scheme. It has been shown that traditional protection schemes will not work successfully. In this paper a protection scheme using digital relays with a communication network is proposed for the protection of the microgrid system. The increased reliability of adding an additional line to form a loop structure is explored. Also a novel method for modeling high impedance faults is demonstrated to show how the protection scheme can protect against them. This protection scheme is simulated on a realistic distribution system containing a high penetration of inverter connected Distributed Generation (DG) sources operating as a microgrid. In all possible cases of operation the primary and secondary relays performed their intended functions including the detection of high impedance faults. This system is simulated using Matlab Simulink's SimPowerSystems toolbox to establish the claims made in this paper. [ABSTRACT FROM PUBLISHER]