Indrani Kar

Bio: Indrani Kar is an academic researcher from Indian Institute of Technology Guwahati. The author has contributed to research in topics: Adaptive control & Control theory. The author has an hindex of 16, co-authored 67 publications receiving 923 citations. Previous affiliations of Indrani Kar include Indian Institute of Technology Kanpur & Indian Institutes of Technology.

Implementation of Vehicle to Grid Infrastructure Using Fuzzy Logic Controller

Abstract: With high penetration of electric vehicles (EVs), stability of the electric grid becomes a challenging task. A greater penetration level would demand a proper coordination amongst the various EVs as they charge or discharge to the grid. Coordination here refers to controlling the charging and discharging patterns of different EVs depending on their individual battery states and the present grid condition. Therefore, a good coordination between EVs is required for making the grid stable. With high penetration of EVs, the vehicle to grid (V2G) concept can be explored where excess energy of the battery can be supplied back to the grid in controlled fashion. Discharging EVs' battery energy to the grid in coordination can make V2G utilization as distributed energy storage. Charging EVs in coordination can flatten the voltage profile of a distribution node. In this work, a typical distribution system of a city is modeled to demonstrate V2G capabilities such as meeting peak demand and voltage sag reduction. The simulation of the distribution system with V2G capabilities are tested using fuzzy logic controller (FLC). Two controllers have been developed, namely the charging station controller and the V2G controller. Together they decide the proper energy flow between the EVs and the grid. Energy discharge to the grid from EVs or energy required for charging EVs is controlled and tested for the real time scenario.

A Multi Charging Station for Electric Vehicles and Its Utilization for Load Management and the Grid Support

Abstract: This paper discusses the modeling of a multi charging station for Electric Vehicles (EVs) and its utilisation for grid support. Allowing the EVs to charge and discharge without any control may lead to voltage variations in the grid. However, if the charging/discharging of the EVs is done in an intelligent fashion, they can act as distributed energy sources and can smoothen the load profile of the distribution network by providing peak shaving and valley filling. The presented work mainly focuses on the control architecture for using the EVs' batteries as distributed energy storage systems. This enables in maintaining the node voltage within the prescribed limit by valley filling and peak shaving. The control architecture is based on fuzzy control techniques. The proposed architecture is implemented on the distribution network of Guwahati city. Dynamic load profile is used to check the effectiveness of the proposed architecture for flattening the load profile.

Intelligent Systems and Control Principles and Applications

Abstract: Intelligent Systems and Control: Principles and Applications is a textbook for undergraduate level courses on intelligent control, intelligent systems, adaptive control, and non-linear control. The book covers primers in neural networks, fuzzy logic, and non-linear control so that readers can easily follow intelligent control techniques. Design principles for fuzzy and neural control schemes have been enumerated with an easy understanding for readers. Stability analysis of control systems have been provided with rigour. Intelligent control systems have been simulated for benchmark non-linear systems across disciplines such as electrical system, electro-mechanical systems, and process control systems. Details of real-time experiments for cart-pole inverted pendulum system and seven degrees of freedom (DOF) robot manipulator using intelligent control schemes have been included in the book to illustrate efficacy of these advanced control schemes. A chapter on quantum neural networks and its application has been included to illustrate the importance of the emerging research in quantum computational intelligence in control. Many examples with Matlab codes have been provided for readers to comprehend the subject matter provided in this book. Each chapter includes a set of exercise problems for readers to get well-versed with the subject. C-codes for selected exercise problems have been included in the CD accompanying the book. Simulation results and experimental videos are also included in the CD. This book can be used as a reference for courses such as Artificial Neural Networks and Fuzzy Logic, Artificial Intelligence, Instrumentation and Control, and Advanced Control Systems. Also practicing engineers in R&D sectors will be greatly benefitted from this book.

Influence of EV on grid power quality and optimizing the charging schedule to mitigate voltage imbalance and reduce power loss

Abstract: The penetration of Electric Vehicle (EV) on the Indian grid and its positive impact can be seen if the EV's are co-ordinated. The co-ordinate charging and discharging of EV's can improve the voltage profile and reduce the power transmission loss. Primary distribution of Guwahati City is simulated using actual data. Voltage profile and transmission loss have been analyzed considering various levels of EV penetration and charging patterns. It is shown that coordinated charging and discharging of EV's on the grid will flatten the voltage profile of a bus as well as reduce the power loss.

Real-Time Coordination of Electric Vehicles to Support the Grid at the Distribution Substation Level

Abstract: In this paper, the charging stations (CSs) of electric vehicles (EVs) and their coordination at the substation level are presented. It is considered that the EVs of a particular area arrive at the CS in their idle time to charge their batteries. Fuzzy logic controllers (FLCs) have been designed at the substation and the CS level. The FLC at the substation level decides the amount of power to be compensated by the entire CSs, and the FLC at the CS level determines the power to be exchanged by individual CS. The aggregator at the substation level will distribute the power among the CSs connected to different subfeeders. Also, every subfeeder has an aggregator which distributes the power among different CSs connected to the same subfeeder. Batteries of EVs have been modeled which can handle the capacity loss at different charging/discharging rates $(C_{\rm rate})$ . The $C_{\rm rate} $ of the battery is controlled to achieve the desired rate of power flow between the grid and the EV battery. The main focus of this paper is to coordinate the EVs, present at the CSs, and support the grid by peak shaving and valley filling.

Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles

Abstract: This paper reviews the current status and implementation of battery chargers, charging power levels, and infrastructure for plug-in electric vehicles and hybrids. Charger systems are categorized into off-board and on-board types with unidirectional or bidirectional power flow. Unidirectional charging limits hardware requirements and simplifies interconnection issues. Bidirectional charging supports battery energy injection back to the grid. Typical on-board chargers restrict power because of weight, space, and cost constraints. They can be integrated with the electric drive to avoid these problems. The availability of charging infrastructure reduces on-board energy storage requirements and costs. On-board charger systems can be conductive or inductive. An off-board charger can be designed for high charging rates and is less constrained by size and weight. Level 1 (convenience), Level 2 (primary), and Level 3 (fast) power levels are discussed. Future aspects such as roadbed charging are presented. Various power level chargers and infrastructure configurations are presented, compared, and evaluated based on amount of power, charging time and location, cost, equipment, and other factors.

Review of the Impact of Vehicle-to-Grid Technologies on Distribution Systems and Utility Interfaces

Abstract: Plug-in vehicles can behave either as loads or as a distributed energy and power resource in a concept known as vehicle-to-grid (V2G) connection. This paper reviews the current status and implementation impact of V2G/grid-to-vehicle (G2V) technologies on distributed systems, requirements, benefits, challenges, and strategies for V2G interfaces of both individual vehicles and fleets. The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural, and technical obstacles. Although V2G operation can reduce the lifetime of vehicle batteries, it is projected to become economical for vehicle owners and grid operators. Components and unidirectional/bidirectional power flow technologies of V2G systems, individual and aggregated structures, and charging/recharging frequency and strategies (uncoordinated/coordinated smart) are addressed. Three elements are required for successful V2G operation: power connection to the grid, control and communication between vehicles and the grid operator, and on-board/off-board intelligent metering. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure. A charging/discharging infrastructure must be deployed. Economic benefits of V2G technologies depend on vehicle aggregation and charging/recharging frequency and strategies. The benefits will receive increased attention from grid operators and vehicle owners in the future.

Quantum Models of Cognition and Decision

Abstract: Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, 'contextuality', is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, 'quantum entanglement', allows cognitive phenomena to be modeled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.

Optimal Scheduling for Charging and Discharging of Electric Vehicles

Abstract: The vehicle electrification will have a significant impact on the power grid due to the increase in electricity consumption. It is important to perform intelligent scheduling for charging and discharging of electric vehicles (EVs). However, there are two major challenges in the scheduling problem. First, it is challenging to find the globally optimal scheduling solution which can minimize the total cost. Second, it is difficult to find a distributed scheduling scheme which can handle a large population and the random arrivals of the EVs. In this paper, we propose a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging. We first formulate a global scheduling optimization problem, in which the charging powers are optimized to minimize the total cost of all EVs which perform charging and discharging during the day. The globally optimal solution provides the globally minimal total cost. However, the globally optimal scheduling scheme is impractical since it requires the information on the future base loads and the arrival times and the charging periods of the EVs that will arrive in the future time of the day. To develop a practical scheduling scheme, we then formulate a local scheduling optimization problem, which aims to minimize the total cost of the EVs in the current ongoing EV set in the local group. The locally optimal scheduling scheme is not only scalable to a large EV population but also resilient to the dynamic EV arrivals. Through simulations, we demonstrate that the locally optimal scheduling scheme can achieve a close performance compared to the globally optimal scheduling scheme.