Anurag K. Srivastava

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

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

Impact of Distributed Generations With Energy Storage Devices on the Electric Grid

/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.

Restructuring Choices for the Indian Power Sector

The commonly used distributed generations (DG) technologies include wind generators, photovoltaics, and biomass generators with their sizes varying between several kW to a few MW. Energy storage devices are generally used to smooth variations in DGs MW output due to inherent unpredictability and to minimize exchange of power from grid. Connecting the storage and DGs to the grid have both technical and economic impacts. This paper aims at analyzing the technical and economic impacts of distributed generators along with energy storage devices on the distribution system. The technical analysis includes analyzing the transient stability of a system with DGs and energy storage devices, such as a battery and ultracapacitor. The DGs are represented by small synchronous and induction generators. Different types and locations of faults and different penetration levels of the DGs are considered in the analysis. Energy storage devices are found to have a positive impact on transient stability. For economic analysis, the costs of the system with different DG technologies and energy storage devices are compared using the software tool “hybrid optimization model for electric renewables (HOMER).” Finally, the analysis for cost versus benefits of DGs and energy storage devices is compared briefly.

A novel hybrid approach using wavelet, firefly algorithm, and fuzzy ARTMAP for day-ahead electricity price forecasting

This letter demonstrates the use of line stability index termed as fast voltage stability index (FVSI) in order to determine the maximum loadability in a power system. The bus that is ranked highest is identified as the weakest bus since it can withstand a small amount of load before causing voltage collapse. It involves the experimental process of voltage stability analysis and evaluation of line index based on the load variation. The point at which FVSI close to unity indicates the maximum possible connected load termed as maximum loadability at the point of bifurcation. This technique is tested on the IEEE system and results proved that the proposed technique is able to estimate the maximum loadability in a system.

Modeling Cyber-Physical Vulnerability of the Smart Grid With Incomplete Information

This paper presents a novel hybrid intelligent algorithm utilizing a data filtering technique based on wavelet transform (WT), an optimization technique based on firefly (FF) algorithm, and a soft computing model based on fuzzy ARTMAP (FA) network in order to forecast day-ahead electricity prices in the Ontario market. A comprehensive comparative analysis with other soft computing and hybrid models shows a significant improvement in forecast error by more than 40% for daily and weekly price forecasts, through the application of a proposed hybrid WT+FF+FA model. Furthermore, low values obtained for the forecast mean square error (FMSE) and mean absolute error (MAE) indicate high degree of accuracy of the proposed model. Robustness of the proposed hybrid intelligent model is measured by using the statistical index (error variance). In addition, the good forecast performance and the rapid adaptability of the proposed hybrid WT+FF+FA model are also evaluated using the PJM market data.

A control system testbed to validate critical infrastructure protection concepts

This paper addresses the attack modeling using vulnerability of information, communication and electric grid network. Vulnerability of electric grid with incomplete information has been analyzed using graph theory based approach. Vulnerability of information and communication (cyber) network has been modeled utilizing concepts of discovery, access, feasibility, communication speed and detection threat. Common attack vector based on vulnerability of cyber and physical system have been utilized to operate breakers associated with generating resources to model aurora-like event. Real time simulations for modified IEEE 14 bus test case system and graph theory analysis for IEEE 118 bus system have been presented. Test case results show the possible impact on smart grid caused by integrated cyber-physical attack.

Engineering future cyber-physical energy systems: Challenges, research needs, and roadmap

Abstract This paper describes the Mississippi State University SCADA Security Laboratory and Power and Energy Research laboratory. This laboratory combines model control systems from multiple critical infrastructure industries to create a testbed with functional physical processes controlled by commercial hardware and software over common industrial control system routable and non-routable networks. Laboratory exercises, functional demonstrations, and lecture material from the testbed have been integrated into a newly developed industrial control system cybersecurity course, into multiple other engineering and computer science courses, and into a series of short courses targeted to industry. Integration into the classroom allows the testbed to provide a workforce development function, prepares graduate students for research activities, and raises the profile of this research area with students. The testbed enables a research process in which cybersecurity vulnerabilities are discovered, exploits are used to understand the implications of the vulnerability on controlled physical processes, identified problems are classified by criticality and similarities in type and effect, and finally cybersecurity mitigations are developed and validated against within the testbed. Overviews of research enabled by the testbed are provided, including descriptions of software and network vulnerability research, a description of forensic data logger capability developed using the testbed to retrofit existing serial port MODBUS and DNP3 devices, and a description of intrusion detection research which leverages unique characteristics of industrial control systems.

Application of Genetic Algorithm for Reconfiguration of Shipboard Power System

Cyber-physical energy systems require the integration of a heterogeneous physical layers and decision control networks, mediated by decentralized and distributed local sensing/actuation structures backed by an information layer. With the North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) [1] requirements and presidents visions of more secure, reliable and controllable cyber-physical system, a new paradigm for modeling and research investigation is needed. In this paper, we present common challenges and our vision of solutions to design advanced Cyber-physical energy systems with embedded security and distributed control. Finally, we present a survey of our research results in this domain.

An overview of forecasting problems and techniques in power systems

Reconfiguration of the electrical network in a shipboard power system is a critical activity that is required to either restore service to a lost load or to meet some operational requirements of the ship. Reconfiguration refers to changing the topology of the power system in order to isolate system damage and/or optimize certain characteristics of the system related to power efficiency. When finding the optimal state, it is important to have a method that finds the desired state within a short amount of time, in order to allow fast response for the system. Since the reconfiguration problem is highly nonlinear over a domain of discrete variables, the genetic algorithm method is a good candidate. In this paper, a reconfiguration methodology, using a genetic algorithm, is presented that will reconfigure a given network, satisfying the operational requirements and priorities of loads. It also considers islanding to restore supply to critical loads after a fault is encountered. As a preliminary work, the proposed method has been applied to a simple 8-bus shipboard power system model and the concept will be extended to larger power systems.

Real-Time Implementation of Intelligent Reconfiguration Algorithm for Microgrid

This paper provides an overview of forecasting problems and techniques in power system. Available forecasting techniques have been reviewed with the focus on data mining for wind power prediction. This paper also discusses forecasting issues associated with electricity price and load forecasting.