Ali Tajer

Capacity Planning Frameworks for Electric Vehicle Charging Stations With Multiclass Customers

In order to foster electric vehicle (EV) adoption, there is a strong need for designing and developing charging stations that can accommodate different customer classes distinguished by their charging preferences, needs, and technologies. By growing such charging station networks, the power grid becomes more congested and, therefore, controlling charging requests should be carefully aligned with the available resources. This paper focuses on an EV charging network equipped with different charging technologies and proposes two frameworks. In the first framework, appropriate for large networks, the EV population is expected to constitute a sizable portion of the light duty fleets. This necessitates controlling the EV charging operations to prevent potential grid failures and distribute the resources efficiently. This framework leverages pricing dynamics in order to control the EV customer request rates and to provide a charging service with the best level of quality of service (QoS). The second framework, on the other hand, is more appropriate for smaller networks, in which the objective is to compute the minimum amount of resources required to provide certain levels of QoS to each class. The results show that the proposed frameworks ensure grid reliability and lead to significant savings in capacity planning.

A Stochastic Sizing Approach for Sharing-Based Energy Storage Applications

In order to foster renewable energy integration, improve power quality and reliability, and reduce hydrocarbon emissions, there is a strong need to deploy energy storage systems (ESSs), which can provide a control medium for peak hour utility operations. ESSs are especially desirable at the residential level, as this sector has the most untapped demand response potential. However, considering their high acquisition, operation, and maintenance costs, isolated deployment of ESSs is not economically viable. Hence, this paper proposes a sharing-based ESS architecture, in which the demand of each customer is modeled stochastically and the aggregate demand is accommodated by a combination of power drawn from the grid and the storage unit when the demand exceeds grid capacity. The optimal size of ESSs is analyzed and an analytical method is developed for a group of customers with a single type of appliances. This framework is also extended to any network size with an arbitrary number of customers and appliance types, where the analytical method provides a tractable solution to the ESS sizing problem. Finally, a detailed cost-benefit analysis is provided, where the results indicate that sharing-based ESSs are practical and yield significant savings in terms of ESS size.

Quickest detection of Gauss-Markov random fields

The problem of quickest data-adaptive and sequential search for clusters in a Gauss-Markov random field is considered. In the existing literature, such search for clusters is often performed using fixed sample size and non-adaptive strategies. In order to accommodate large networks, in which data adaptivity leads to significant gains in detection quality and agility, in this paper sequential and data-adaptive detection strategies are proposed and are shown to enjoy asymptotic optimality. The quickest detection problem is abstracted by adopting an acyclic dependency graph to model the mutual effects of different random variables in the field and decision making rules are derived for general random fields and specialized for Gauss-Markov random fields. Performance evaluations demonstrate the gains of the data-adaptive schemes over existing techniques in terms of sampling complexity and error exponents.

Energy grid state estimation under random and structured bad data

The problems of state recovery and bad data detection in energy grids, while being strongly interconnected, have been treated independently. Furthermore, while state recovery has been studied intensively, it has been less well studied when the measurements are deemed to be contaminated by random bad data (due to sensor failures) or structured bad data (due cyber attacks). This paper provides a unifying framework that takes into account the inherent connection between state recovery and bad data detection in order to accomplish the combined tasks of detecting the presence of random and structured bad data, and providing reliable estimates for the state of the grid and injected bad data. Optimal detectors and estimators are characterized.

A Receiver-centric Approach to Interference Management: Fairness and Outage Optimization

Effective interference management in the multiuser interference channel necessitates that the users form their transmission and interference management decisions in coordination, and adapt them to the state of the channel. Establishing such coordination, often facilitated through information exchange, is prohibitive in fast-varying channels, especially when the network size grows. This paper focuses on the multiuser Gaussian interference channel and offers a receiver-centric approach to interference management. In this approach, the transmitters deploy rate-splitting and superposition coding to generate their messages according to independent Gaussian codebooks. The receivers can freely decode any arbitrary set of interfering messages along with their designated messages in any desired joint or ordered fashion, and treat the rest of the interferers as Gaussian noise. The proposed receiver-centric interference management approach is applied to two class of problems (outage optimization and fairness-constrained rate allocation), and constructive proofs are provided to establish the following properties for the proposed approach: 1) the optimal set of codebooks to be decoded by each receiver is a local decision made by each receiver based on its local channel state information (CSI); 2) the globally optimal transmission rates are related to locally optimal rates computed by the receivers based on their local information, which implies that the transmitters do not require explicit knowledge of the CSI and can determine their rates via limited feedback from the receivers; and 3) obtaining the optimal interference management strategy at each receiver has controlled complexity.

Quickest linear search over correlated sequences

Linear search arises in many application domains. The problem of linear search over multiple sequences in order to identify one sequence with a desired statistical feature is considered. The quickest linear search optimizes a balance between two opposing performance measures, one being the delay in detecting a desirable sequence, and the other one being the quality of the decision. The existing approaches in the quickest search literature rely on the assumption that the sequences are statistically independent. In many applications, however, due to the underlying physical couplings, generations of available sequences are not necessarily independent. Driven by such underlying couplings, this paper considers searching over correlated sequences, in which the distribution of each sequence depends on the distribution of its preceding one. The closed-form characterization of the sampling process for the optimal search is delineated. The analysis reveals that depending on the correlation structure, the optimal search strategy can be similar to (in spirit) or dramatically different from the optimal search strategy over independent sequences.

Energy storage sizing for peak hour utility applications

In future smart grids, energy storage systems (ESSs) are expected to play a key role in reducing peak hour electricity generation cost and the associated level of carbon emissions. Considering their high acquisition, operation, and maintenance costs, ESSs are likely to serve a large number of users. Hence, optimal sizing of energy ESSs plays a critical role as over-provisioning ESS size leads to under-utilizing costly assets and under-provisioning it taxes operation lifetime. This paper proposes a stochastic framework for analyzing the optimal size of energy storage systems. In this framework the demand of each customer is modeled stochastically and the aggregate demand is accommodated by a combination of power drawn from the grid and the storage unit when the demand exceeds grid capacity. In this framework an analytical method is developed, which provides tractable solution to the ESS sizing problem of interest. The results indicate that significant savings in terms of ESS size can be achieved.

Robust false data injection attacks in electricity markets by limited adversaries

Deregulated electricity markets consist of look-ahead and real-time markets, across which energy price is generally volatile. Moreover, dispatch and pricing decisions in the real-time market strongly hinge on the quality of the real-time state estimation routines, which are designed to provide real-time information about operation state of the grid. The adversaries can leverage price volatility in conjunction with the dependence of the real-time markets on the state estimates in order to carry out profitable financial misconduct, e.g., via virtual bidding. When the adversaries can access to complete network information, the attack strategies are studied extensively in the existing literature. This paper focuses on limited adversaries who have only partial network information, in which the uncertainties are modeled as bounded values, and offers realistic attack strategy approach to guarantee the worst-case performance for attackers. Designing such attacks is investigated analytically, and examined in the IEEE 14-bus system.

Relay X channels without channel state information at the transmit sides: Degrees of freedom

This paper focuses on the two-user relay-assisted X channel with no channel state information (CSI) available at the transmitter side. Two relaying modes, namely half-duplex decode-and-forward (DF) and cognitive relays, are considered and the degrees of freedom (DoF) are characterized. It is shown that assisted by a half-duplex DF relay that is equipped with 2M antennas, the X channel with two M-antenna users has 4M/3 DoF, which is achievable through interference alignment (IA). Furthermore, it is shown that in this channel, an M-antenna cognitive relay (with non-causal access to information streams) provides 2M DoF using interference cancellation (IC) technique. In this setting, IC outperforms interference alignment in the cognitive relay mode, since the latter achieves 4M/3 DoF.

Quickest Localization of Anomalies in Power Grids: A Stochastic Graphical Framework

Agile localization of anomalous events plays a pivotal role in enhancing the overall reliability of the grid and avoiding cascading failures. This is especially of paramount significance in the large-scale grids due to their geographical expansions and the large volume of data generated. This paper proposes a stochastic graphical framework, by leveraging which it aims to localize the anomalies with the minimum amount of data. This framework capitalizes on the strong correlation structures observed among the measurements collected from different buses. The proposed approach, at its core, collects the measurements sequentially and progressively updates its decision about the location of the anomaly. The process resumes until the location of the anomaly can be identified with desired reliability. We provide a general theory for the quickest anomaly localization and also investigate its application for quickest line outage localization. Simulations in the IEEE 118-bus model are provided to establish the gains of the proposed approach.