Eric Sortomme

Coordinating Vehicle-to-Grid Services With Energy Trading

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 LSEs 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.

Coordinated bidding of ancillary services for vehicle-to-grid using fuzzy optimization

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.

An Accurate Synchrophasor Based Fault Location Method for Emerging Distribution Systems

The accurate knowledge of fault location in distribution systems can aid in the repair process, expedite system restoration, and, thus, reduce outage duration. This letter presents a new method for locating a fault in distribution systems using synchrophasor measurements, which are often provided by phasor measurement units (PMUs) with high accuracy and a timestamp. Using voltage and current phasor measurements at substations and/or feeder heads obtained through suitable communication schemes, candidate fault locations are identified by iterating every possible line segment. The measurements from remote devices are used to eliminate all nonfaulted cases. This method works effectively for active and passive networks, radial and looped topologies, high impedance faults, and is not constrained to a certain pattern of PMU locations. The proposed method has been tested in a real distribution system. Results show that it can identify faults within 1% accuracy of the line length.

Electric vehicle charging modulation using voltage feedback control

Electric vehicle integration into the distribution system has been a topic of great interest lately due the potential challenges it poses. Previous works have focused on either centralized charge control or distributed charge control to solve these issues. In this paper a voltage feedback controller for EV charging is proposed that, unlike all other proposed methods, does not require any communication between the EV and the utility. This controller compares the system voltage at the point of charging against a reference. The EV charging is reduced as the system voltage approaches this reference. Simulations show that this method can successfully reduce EV charging to eliminate system voltage violations that would otherwise be caused from EV charging.

Combined bidding of regulation and spinning reserves for unidirectional Vehicle-to-Grid

Vehicle-to-Grid (V2G) has the potential to reduce the cost of owning and operating Electric Vehicles (EVs) while increasing utility system flexibility. Unidirectional V2G is a logical first step because it requires less infrastructure and does not degrade EV batteries from cycling. This work develops an optimal combined bidding formulation for regulation and spinning reserves to be used by an aggregator. Two different aggregator revenue structures are explored as objective functions. These algorithms maximize profits to the aggregator while increasing the benefits to the customers and utility. Simulations over a one month period on the ERCOT system verify the effectiveness of the proposed algorithms and show differences in performance of the two algorithms.

A differential zone protection scheme for microgrids

Microgrids are small networks of loads and sources that can operate either connected to the grid or islanded. In order to operate in islanded mode, the microgrid must have a new protection scheme as classical distribution protection is inadequate. This work develops a differential zone protection scheme that works by comparing current measurements from several devices on the boarders of the zones. This method is as effective at detecting microgrid faults as digital relay differential protection schemes at significantly lower costs. The optimal placement of zones and devices is formulated. A case study shows that the optimal placement solution can reduce costs by more than 50% when compared to having a relay at each line segment.

Optimal charging strategies for unidirectional vehicle-to-grid using fuzzy uncertainties

Vehicle-to-grid (V2G) is a way to effectively integrate electric vehicles (EVs) with the power system network. Unidirectional V2G is especially attractive because it is easily implementable and requires little additional hardware. An EV can be effectively charged during its parking time, and can potentially generate revenues for the EV owner. It can also provide services to the electrical grid through an aggregated scheme. The aggregator combines the capacity of many EVs and bids them into electricity markets. In this paper, a novel charging scheme is proposed that optimizes the charging of EVs and the bidding of ancillary service in the electricity market, considering the different market uncertainties using fuzzy set theory. The electricity market parameters are forecasted using ARIMA models. Simulations performed on a hypothetical group of EVs in the ERCOT area show the benefits of the proposed scheme.

Voltage dependent load models of charging electric vehicles

Plug-in Electric Vehicle (PEV) integration on distribution grids is an important topic of research due to the potential problem involved. In order to accurately assess the impact of EVs on system voltages and power flows, accurate voltage dependent models must be used. In this work, the voltage response of four different EV types is measured and ZIP models are developed for each which can be used in EV integration studies. The measurements show that the behavior varies significantly between the different EVs and thus an accurate generic model cannot be made.

Advanced integration of distributed energy resources

This panel describes the collaboration on a distributed energy resource management system between a utility and a software vendor focused on integrating new technologies through advanced monitoring and control. Key focus technologies include: residential demand response, energy storage systems, distributed generation, and plug-in electric vehicles. In addition to these distributed energy resources, advanced line sensors and smart meters work together operate the distribution grid at a higher efficiency than ever before.

Stochastic-programming-based bidding strategy for V2G services

Vehicle-to-grid (V2G) is an effective technique for the integration of electric vehicles (EVs) into the grid. Aggregation of large numbers of EVs under a single entity to participate in electricity markets is necessary for its implementation. However, trading in the day-ahead energy or regulation market is risky due to uncertain prices, load demand, and deployment signals. In this work, an optimal bidding algorithm for unidirectional V2G is developed while considering the uncertainties associated with regulation up/down prices and deployment signals. Stochastic programming is used to formulate this optimization with the objective to maximize total profit of an EV aggregator participating in the regulation market. Simulation results demonstrate the benefits of the proposed stochastic approach in comparison with a deterministic one.