Anurag K. Srivastava

Bio: Anurag K. Srivastava is an academic researcher from Washington State University. The author has contributed to research in topics: Electric power system & Smart grid. The author has an hindex of 36, co-authored 268 publications receiving 5377 citations. Previous affiliations of Anurag K. Srivastava include Indian Institute of Technology Kanpur & Texas A&M University.

Controls for microgrids with storage: Review, challenges, and research needs

Abstract: The interest on microgrid has increased significantly triggered by the increasing demand of reliable, secure, efficient, clean, and sustainable electricity. More research and implementation of microgrid will be conducted in order to improve the maturity of microgrid technology. Among different aspects of microgrid, this paper focuses on controls of microgrid with energy storage. A comprehensive review on current control technology is given with a discussion on challenges of microgrid controls. Basic simulation results are also presented to enhance and support the analysis. Finally, research needs and roadmap for microgrid control are also described.

Demand response for sustainable energy systems: A review, application and implementation strategy

Abstract: The growth of advanced metering infrastructure, enhanced communication infrastructure in power grids, and the ability of end-user consumer to actively participate helps in realizing vision of sustainable energy systems. Demand response (DR) programs are developed in order to deploy this ability and make power grids more efficient, environmental friendly, and reliable. This paper presents a review of DR, existing application and a possible implementation strategy in a smart grid environment. Furthermore, classification and status of DR programs in different U.S. electricity markets have been also discussed.

A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method

Abstract: Price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk, and it also plays a key role in the economic optimization of the electric energy industry. This paper explores a technique of artificial neural network (ANN) model based on similar days (SD) method in order to forecast day-ahead electricity price in the PJM market. To demonstrate the superiority of the proposed model, publicly available data acquired from the PJM Interconnection were used for training and testing the ANN. The factors impacting the electricity price forecasting, including time factors, load factors, and historical price factors, are discussed. Comparison of forecasting performance of the proposed ANN model with that of forecasts obtained from similar days method is presented. Daily and weekly mean absolute percentage error (MAPE) of reasonably small value and forecast mean square error (FMSE) of less than 7$/MWh were obtained for the PJM data, which has correlation coefficient of determination of 0.6744 between load and electricity price. Simulation results show that the proposed ANN model based on similar days method is capable of forecasting locational marginal price (LMP) in the PJM market efficiently and accurately.

Defining and Enabling Resiliency of Electric Distribution Systems With Multiple Microgrids

Abstract: This paper presents a method for quantifying and enabling the resiliency of a power distribution system using analytical hierarchical process and percolation theory. Using this metric, quantitative analysis can be done to analyze the impact of possible control decisions to pro-actively enable the resilient operation of distribution system with multiple microgrids and other resources. Developed resiliency metric can also be used in short term distribution system planning. The benefits of being able to quantify resiliency can help distribution system planning engineers and operators to justify control actions, compare different reconfiguration algorithms, and develop proactive control actions to avert power system outage due to impending catastrophic weather situations or other adverse events. Validation of the proposed method is done using modified CERTS microgrids and a modified industrial distribution system. Simulation results show topological and composite metric considering power system characteristics to quantify the resiliency of a distribution system with the proposed methodology, and improvements in resiliency using two-stage reconfiguration algorithm and multiple microgrids.

Analyzing the Cyber-Physical Impact of Cyber Events on the Power Grid

Abstract: With ongoing smart grid activities, advancements in information and communication technology coupled with development of sensors are utilized for better situational awareness, decision support, and control of the power grid However, it is critical to understand the complex interdependencies between cyber and power domains, and also the potential impacts of cyber events on the power grid In this paper, the impact of three different possible cyber events on physical power grid have been analyzed using an integrated cyber-power modeling and simulation testbed Real-time modeling of end-to-end cyber-power systems have been developed with hardware-in-the-loop capabilities Real-time digital simulator, synchrophasor devices, DeterLab, and network simulator-3 are utilized in this developed testbed with a wide-area control algorithm and associated closed-loop control DeterLab can be used to model real-life cyber events in the developed cyber-physical testbed Man-in-the-middle and denial-of-service attacks have been modeled as specific cases for the IEEE standard test cases Additionally, communication failure impact on the power grid has been analyzed using the testbed

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 …

Ultracapacitors: Why, How, and Where is the Technology

Abstract: The science and technology of ultracapacitors are reviewed for a number of electrode materials, including carbon, mixed metal oxides, and conducting polymers. More work has been done using microporous carbons than with the other materials and most of the commercially available devices use carbon electrodes and an organic electrolytes. The energy density of these devices is 3¯5 Wh/kg with a power density of 300¯500 W/kg for high efficiency (90¯95%) charge/discharges. Projections of future developments using carbon indicate that energy densities of 10 Wh/kg or higher are likely with power densities of 1¯2 kW/kg. A key problem in the fabrication of these advanced devices is the bonding of the thin electrodes to a current collector such the contact resistance is less than 0.1 cm2. Special attention is given in the paper to comparing the power density characteristics of ultracapacitors and batteries. The comparisons should be made at the same charge/discharge efficiency.

Trends in Microgrid Control

Abstract: The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Multiple criteria decision making

Abstract: In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.