Ali T. Al-Awami

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 Trading of Wind and Thermal Energy

Trading wind energy in short-term electricity markets has high associated risks due to the uncertainties in hourly available wind, energy prices, and imbalance penalties. Coordinated trading of wind and thermal energy is proposed to mitigate risks due to those uncertainties. The problem of wind-thermal coordinated trading is formulated as a mixed-integer stochastic linear program. The objective is to obtain the optimal trade-off bidding strategy that maximizes the total expected profits while controlling trading risks. For risk control, a weighted term of the conditional value at risk (CVaR) is included in the objective function. The CVaR aims to maximize the expected profits of the least profitable scenarios, thus improving trading risk control. A case study comparing coordinated with uncoordinated bidding strategies depending on the traders risk attitude is included. Simulation results show that coordinated bidding can improve the expected profits while significantly improving the CVaR.

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.

Optimizing economic/environmental dispatch with wind and thermal units

In this paper, the economic/environmental dispatch for a smart grid with wind and thermal units is formulated. The formulation takes into account the stochastic nature of wind power output and the imbalance charges due to the mismatch between the actual and scheduled wind power outputs. Because minimizing the operating cost of thermal and wind units, and minimizing the emissions of thermal units are two conflicting objectives, multi-objective optimization (MOO) technique is used. With MOO, a set of solutions that are optimal in the Pareto sense is identified. An enhanced multi-objective particle swarm optimization (MO-PSO) is proposed to search for the set of Pareto-optimal solutions. The effect of different system conditions on the Pareto-optimal solutions is investigated. These system conditions include load level and different imbalance cost coefficients. Test results show the effectiveness of the proposed technique in identifying the set of Pareto optimal solutions. This technique is an important tool that system operators require in order to operate the grid with high penetration of wind power more efficiently while maintaining emissions within restricted limits.

A PSO Based Memory Polynomial Predistorter With Embedded Dimension Estimation

Power amplifiers are widely used in RF broadcasting applications. However, they tend to exhibit nonlinear behavior that distorts the input signals both in the time and frequency domains, consequently motivating the development of techniques, such as digital predistortion, which can counteract this behavior. Among the challenges facing the identification of an amplifiers digital predistorter and behavioral model is finding the correct model dimensions, as this requires a priori knowledge of multiple parameters. In this paper, a predistorter based on a cluster-based implementation particle swarm optimization technique with embedded model-size estimation capability is presented. The validation of the proposed technique on a Doherty power amplifier prototype demonstrates its ability to efficiently find the dimensions of a memory polynomial based digital predistorter, while accurately estimating its coefficients.

Statistical characterization of wind power output for a given wind power forecast

The stochastic nature of wind power output makes the integration of high penetration of wind into the power grid a real challenge. In this work, the uncertainty associated with the wind power output for a given wind power forecast is modeled using conditional probability density functions (pdf). Two pdf functions are considered: Beta and extreme value. Simulation results show that, in general, the proposed extreme value distribution outperforms Beta distribution at data bins of high wind power forecast whereas Beta is usually better at low to moderate wind forecast.

A Voltage-Based Controller for an Electric-Vehicle Charger

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 owners 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.

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.

Analysis and Design of UPFC Damping Stabilizers for Power System Stability Enhancement

In this paper, the use of the supplementary controller of a unified power flow controller (UPFC) to damp low frequency oscillations in a weakly connected system is investigated. The potential of the UPFC supplementary controllers to enhance the dynamic stability is evaluated. Two different objective functions are proposed in this work for the controller design problem. The first objective is eigenvalue-based while the second is time domain-based objective function. The UPFC controller design problem is solved using particle swarm optimization (PSO) technique. The effectiveness of the proposed controllers on damping low frequency oscillations is tested and demonstrated through non-linear time simulation. In addition, a comparison between the objectives is carried out. It can be concluded that the time domain-based design improves greatly the system response under fault disturbances

Simultaneous design of damping controllers and internal controllers of a unified power flow controller

In this paper, the use of a supplementary controller of a unified power flow controller (UPFC) to damp low frequency oscillations is investigated. A new technique to design UPFC damping controllers simultaneously with UPFC internal controllers is proposed. An optimization problem to search for the optimal controller settings is formulated so as to optimize a time-domain based objective function that considers all the controllers simultaneously. The effectiveness of the proposed controllers in damping low frequency oscillations is verified through eigenvalue analysis and non-linear time simulation. A comparison with a sequential design of the controllers under study is also .included