Nikolaos Gatsis

Bio: Nikolaos Gatsis is an academic researcher from University of Texas at San Antonio. The author has contributed to research in topics: AC power & Grid. The author has an hindex of 25, co-authored 121 publications receiving 2889 citations. Previous affiliations of Nikolaos Gatsis include University of Texas System & University of Minnesota.

Robust Energy Management for Microgrids With High-Penetration Renewables

Abstract: Due to its reduced communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG) and distributed storage (DS). Distributed economic dispatch for a microgrid with high renewable energy penetration and demand-side management operating in grid-connected mode is considered in this paper. To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced. The approach involves the actual renewable energy as well as the energy traded with the main grid, so that the supply-demand balance is maintained. The optimal scheduling strategy minimizes the microgrid net cost, which includes DG and DS costs, utility of dispatchable loads, and worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion by the local controllers of DG, DS, and dispatchable loads. Numerical results are reported to corroborate the effectiveness of the novel approach.

Residential Load Control: Distributed Scheduling and Convergence With Lost AMI Messages

Abstract: This paper deals with load control in a multiple-residence setup. The utility company adopts a cost function representing the cost of providing energy to end-users. Each residential end-user has a base load, two types of adjustable loads, and possibly a storage device. The first load type must consume a specified amount of energy over the scheduling horizon, but the consumption can be adjusted across different slots. The second type does not entail a total energy requirement, but operation away from a user-specified level results in user dissatisfaction. The research issue amounts to minimizing the electricity provider cost plus the total user dissatisfaction, subject to the individual constraints of the loads. The problem can be solved by a distributed subgradient method. The utility company and the end-users exchange information through the Advanced Metering Infrastructure (AMI)-a two-way communication network-in order to converge to the optimal amount of electricity production and the optimal power consumption schedule. The algorithm finds near-optimal schedules even when AMI messages are lost, which can happen in the presence of malfunctions or noise in the communications network. The algorithm amounts to a subgradient iteration with outdated Lagrange multipliers, for which convergence results of wide scope are established.

Monitoring and Optimization for Power Grids: A Signal Processing Perspective

Abstract: Although the North American power grid has been recognized as the most important engineering achievement of the 20th century, the modern power grid faces major challenges [87]. Increasingly complex interconnections even at the continent size render prevention of the rare yet catastrophic cascade failures a strenuous concern. Environmental incentives require carefully revisiting how electrical power is generated, transmitted, and consumed, with particular emphasis on the integration of renewable energy resources. Pervasive use of digital technology in grid operation demands resiliency against physical and cyberattacks on the power infrastructure. Enhancing grid efficiency without compromising stability and quality in the face of deregulation is imperative. Soliciting consumer participation and exploring new business opportunities facilitated by the intelligent grid infrastructure hold a great economic potential.

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Abstract: Wireless local area networks (WLANs) have become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLANs have been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently garnered great attention due to its promising performance. Notwithstanding, WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and corresponding state-of-the-art solutions. This paper consists of three main parts: 1) conventional localization schemes; 2) state-of-the-art approaches; and 3) practical deployment challenges. Since all proposed methods in the WLAN literature have been conducted and tested in different settings, the reported results are not readily comparable. So, we compare some of the representative localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of the approaches in the literature and guide to future improvement opportunities.

Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks

Abstract: The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis...

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

State of the Art in Research on Microgrids: A Review

Abstract: The significant benefits associated with microgrids have led to vast efforts to expand their penetration in electric power systems. Although their deployment is rapidly growing, there are still many challenges to efficiently design, control, and operate microgrids when connected to the grid, and also when in islanded mode, where extensive research activities are underway to tackle these issues. It is necessary to have an across-the-board view of the microgrid integration in power systems. This paper presents a review of issues concerning microgrids and provides an account of research in areas related to microgrids, including distributed generation, microgrid value propositions, applications of power electronics, economic issues, microgrid operation and control, microgrid clusters, and protection and communications issues.

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

Abstract: The smart grid concept continues to evolve and various methods have been developed to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers' power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system's constraints and the computational complexity of the applied optimization algorithm.

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Abstract: Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems.