Coalitional Game Theory in Power Systems: Applications, Challenges, and Future Directions

Game theory-based approaches have recently gained traction in a wide range of applications, importantly in power and energy systems. With the onset of cooperation as a new perspective for solving power system problems, as well as the nature of power system problems, it is now necessary to seek appropriate game theory-based tools that permit the investigation and analysis of the behavior and relationships of various players in power system problems. In this context, this paper performs a literature review on coalitional game theory's most recent advancements and applications in power and energy systems. First, we provide a brief overview of the coalitional game theory's fundamental ideas, current theoretical advancements, and various solution concepts. Second, we examine the recent applications in power and energy systems. Finally, we explore the challenges, limitations, and future research possibilities with applications in power and energy systems in the hopes of furthering the literature by strengthening the applications of coalitional game theory in power and energy systems.Comment: Presented in Texas Power and Energy Conference (TPEC) 202

Zero Textbook Cost Syllabus for ENV 1003 (Fundamentals of Ecology-lecture)

Fundamentals of Ecology explores ecological characteristics and ecosystem processes through an evolutionary context. The course will demonstrate the interdisciplinary nature of the field of ecology by highlighting its significance to current environmental issues and the interconnectedness of the environment around us

Zero Textbook Cost Syllabus for ENV 1004 (Fundamentals of Ecology-lab)

Fundamentals of Ecological Research is a hands-on course designed to teach students the basic concepts and methodologies used in the field of ecology. The significance of ecological research to current environmental issues, both local and global, will be stressed throughout the course

Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement

Distribution network reconfiguration (DNR) has proved to be an economical and effective way to improve the reliability of distribution systems. As optimal network configuration depends on system operating states (e.g., loads at each node), existing analytical and population-based approaches need to repeat the entire analysis and computation to find the optimal network configuration with a change in system operating states. Contrary to this, if properly trained, deep reinforcement learning (DRL)-based DNR can determine optimal or near-optimal configuration quickly even with changes in system states. In this paper, a Deep Q Learning-based framework is proposed for the optimal DNR to improve reliability of the system. An optimization problem is formulated with an objective function that minimizes the average curtailed power. Constraints of the optimization problem are radial topology constraint and all nodes traversing constraint. The distribution network is modeled as a graph and the optimal network configuration is determined by searching for an optimal spanning tree. The optimal spanning tree is the spanning tree with the minimum value of the average curtailed power. The effectiveness of the proposed framework is demonstrated through several case studies on 33-node and 69-node distribution test systems

Prediction of Subgrade Strength Parameters from Dynamic Cone Penetrometer Index, Modified Liquid Limit and Moisture Content

AbstractSubgrade soil is an essential component for design of both flexible and rigid pavement structures. Laboratory investigation of subgrade strength parameter as California Bearing Ratio beneficial for design of flexible pavement, Coefficient of subgrade reaction K-Value required for design of rigid pavement, raft footing and unconfined compressive strength (UCS) is useful for determination of shear strength parameter of subgrade. These tests are time consuming and demand significant effort but mandatory. This study considers the use of multiple variable regression analysis (MLR) to predict the California Bearing Ratio (CBR), Coefficient of subgrade reaction K-Value, unconfined compressive strength (UCS), Field dry density from Dynamic Cone Penetrometer (DCP), modified liquid limit and moisture content of subgrade. This paper presents the empirical correlations developed from multiple variable regression analysis from test results obtained from experimental investigation of soil sample taken from different locations of Gujarat region in India. The formulations are validated using other sets of tests data. The developed empirical correlations may be useful in quick determination of strength parameters of subgrade from physical properties of subgrade and Dynamic Cone penetrometer. Results obtained from validation of these developed empirical correlation proves their reliably and accuracy to perform subgrade strength evaluation for both rigid and flexible pavement

BATTLE AGAINST PHISHING

Phishing is a model problem for illustrating usability concerns of privacy and security because both system designers and attackers battle using user interfaces to guide (or misguide) users. There are two novel interaction techniques to prevent spoofing. First, our browser extension provides a trusted window in the browser dedicated to username and password entry. We use a photographic image to create a trusted path between the user and this window to prevent spoofing of the window and of the text entry fields. Second, our scheme allows the remote server to generate a unique abstract image for each user and each transaction. This imag e creates a "skin" that automatica lly customizes the browser window or the user interface elements in the content of a remote web page. Our extension allows the users browser to independently compute the image that it expects to receive from the server. To authenticate cont ent from the se rver, the user can visually verify that the images match. We contrast our work with existing anti - phishing proposals. In contrast to other proposals, our scheme places a very low burden on the user in terms of effort, memory and time. To authenticate himse lf the user has to recognize only one image and remember one low entropy password, no matter how many servers he wishes to interact with. To authenticate content from an authenticated server, the us er only needs to perform one visual matching operation to compare two images. Furthermore, it places a high burden of effort on an attacker to spoof customized security indicators