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Several major blackouts in the past have shown the need for advanced wide-area monitoring and control (WAMC) techniques. Advanced synchrophasor visualization and monitoring can be realized with time-referenced power system data that are collected by phasor measurement units (PMUs) using synchronized clocks. Presently, PMUs are being deployed around the world at a rapid rate for various power system applications. When a necessary number of PMUs are installed at optimal locations throughout the network, the complete state of the system can be observed. PMUs take real-time measurements to determine the state of the power system and can be used to enhance state estimation efficiency. With PMUs in place, there are many control and protection schemes that can be implemented successfully to take preventive and corrective actions. These remedial action schemes (RASs) are classified by the IEEE Power System Relaying Committee (PSRC) as system integrity protection schemes (SIPSs). This work involved the development of a real-time hardware test bed in order to analyze the transient stability of a simulated power system by using synchrophasors to visualize system stress across a transmission line with and without load-shedding schemes. The real time digital simulator (RTDS®) was used to model the power system in real time. PMUs, a satellite-synchronized clock, and a synchrophasor vector processor (SVP) were used to test the synchrophasor application. Copyright © 2010 John Wiley & Sons, Ltd.

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.

This paper evaluates the usefulness of publicly available electricity market information in predicting the hourly prices in the PJM day-ahead electricity market using recursive neural network (RNN) technique, which is based on similar days (SD) approach. RNN is a multi-step approach based on one output node, which uses the previous prediction as input for the subsequent forecasts. Comparison of forecasting performance of the proposed RNN model is done with respect to SD method and other literatures. To evaluate the accuracy of the proposed RNN approach in forecasting short-term electricity prices, different criteria are used. Mean absolute percentage error, mean absolute error and forecast mean square error (FMSE) of reasonably small values were obtained for the PJM data, which has correlation coefficient of determination (R2) of 0.7758 between load and electricity price. Error variance, one of the important performance criteria, is also calculated in order to measure robustness of the proposed RNN model. The numerical results obtained through the simulation to forecast next 24 and 72 h electricity prices show that the forecasts generated by the proposed RNN model are significantly accurate and efficient, which confirm that the proposed algorithm performs well for short-term price forecasting. Copyright © 2009 John Wiley & Sons, Ltd.

Plug-in hybrid electric vehicle may be prime candidates for the next generation of vehicles, but they offer several technological and economical challenges. This article assesses current progress in PHEV technology, market trends, research needs, challenges ahead and policy options for integrating PHEVs into the electric grid.

This paper presents a new approach for assessing power system voltage stability based on artificial feed forward neural network (FFNN). The approach uses real and reactive power, as well as voltage vectors for generators and load buses to train the neural net (NN). The input properties of the NN are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The performance of the trained NN is investigated on two systems under various voltage stability assessment conditions. Main advantage is that the proposed approach is fast, robust, accurate and can be used online for predicting the L-indices of all the power system buses simultaneously. The method can also be effectively used to determining local and global stability margin for further improvement measures.

This paper presents a sensitivity analysis of neural network (NN) parameters to improve the performance of electricity price forecasting. The presented work is an extended version of previous works done by authors to integrate NN and similar days (SD) method for predicting electricity prices. Focus here is on sensitivity analysis of NN parameters while keeping the parameters same for SD to forecast day-ahead electricity prices in the PJM market. Sensitivity analysis of NN parameters include back-propagation learning set (BP-set), learning rate (c), momentum (a) and NN learning days (dN N). The SD parameters, i.e. time framework of SD (d = 45 days) and number of selected similar price days (N=5) are kept constant for all the simulated cases. Forecasting performance is carried out by choosing two different days from each season of the year 2006 and for which, the NN parameters for the base case are considered as BP-set=500, c=0.8, a=0.1 and dNN = 45 days. Sensitivity analysis has been carried out by changing the value of BP-set (500, 1000, 1500); c (0.6, 0.8, 1.0, 1.2), a (0.1, 0.2, 0.3) and dNN (15, 30, 45 and 60 days). The most favorable value of BP-set is first found out from the sensitivity analysis followed by that of c and a, and based on which the best value of dNN is determined. Sensitivity analysis results demonstrate that the best value of mean absolute percentage error (MAPE) is obtained when BP-set = 500, c= 0.8, a=0.1 and dNN = 60 days for winter season. For spring, summer and autumn, these values are 500, 0.6, 0.1 and 45 days, respectively. MAPE, forecast mean square error and mean absolute error of reasonably small value are obtained for the PJM data, which has correlation coefficient of determination (R2) of 0.7758 between load and electricity price. Numerical results show that forecasts generated by developed NN model based on the most favorable case are accurate and efficient. This paper presents a sensitivity analysis of neural network (NN) parameters to improve the performance of electricity price forecasting. The presented work is an extended version of previous works done by authors to integrate NN and similar days (SD) method for predicting electricity prices. Focus here is on sensitivity analysis of NN parameters while keeping the parameters same for SD to forecast day-ahead electricity prices in the PJM market. Sensitivity analysis of NN parameters include back-propagation learning set (BP-set), learning rate (c), momentum (a) and NN learning days (dN N). The SD parameters, i.e. time framework of SD (d = 45 days) and number of selected similar price days (N=5) are kept constant for all the simulated cases. Forecasting performance is carried out by choosing two different days from each season of the year 2006 and for which, the NN parameters for the base case are considered as BP-set=500, c=0.8, a=0.1 and dNN = 45 days. Sensitivity analysis has been carried out by changing the value of BP-set (500, 1000, 1500); c (0.6, 0.8, 1.0, 1.2), a (0.1, 0.2, 0.3) and dNN (15, 30, 45 and 60 days). The most favorable value of BP-set is first found out from the sensitivity analysis followed by that of c and a, and based on which the best value of dNN is determined. Sensitivity analysis results demonstrate that the best value of mean absolute percentage error (MAPE) is obtained when BP-set = 500, c= 0.8, a=0.1 and dNN = 60 days for winter season. For spring, summer and autumn, these values are 500, 0.6, 0.1 and 45 days, respectively. MAPE, forecast mean square error and mean absolute error of reasonably small value are obtained for the PJM data, which has correlation coefficient of determination (R2) of 0.7758 between load and electricity price. Numerical results show that forecasts generated by developed NN model based on the most favorable case are accurate and efficient.

This paper presents a sensitivity analysis of similar days (SD) parameters to increase the accuracy of artificial neural network (ANN) and SD-based short-term price forecasting. Work presented in this paper is an extended version of previous works done by the authors to integrate ANN and SD method for predicting electricity price. The focus here is on sensitivity analysis of SD parameters while keeping the parameters same for ANN to forecast hourly electricity prices in the Pennsylvania-New Jersey-Maryland (PJM) (regional transmission organization in northeast America) electricity market. Several cases are simulated by choosing (a) two, (b) three, (c) four, and (d) five SD parameters to calculate the norm. In addition, sensitivity analysis has been carried out by changing the time framework of SD (d = 15, 30, 45, 60) and the number of selected similar price days (N = 5, 10). From sensitivity analysis, it is identified that the optimized mean absolute percentage error (MAPE) is obtained using case-c with d = 30 and N = 10. MAPE of reasonably small value, along with forecast mean square error and mean absolute error of around 2$/MWh and 1$/MWh, is obtained for the PJM data, which has correlation coefficient of determination (R2) of 0.7758 between load and electricity price. Numerical results show that forecasts generated by the developed ANN model based on the optimized case are accurate and efficient.

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.

A big challenge for smart grid application scenarios will be handling the massive amount of data that is expected to be collected from data sources such as smart meters and sent through the communication backhaul to the grid operator. One approach to reduce data volume given some application sampling rate is to aggregate the data stream within the network itself. While data aggregation along the communications network can reduce data volume and alleviate congestion issues, it can also reduce the granularity of application data reaching the utility control center, thus being not as useful. This work explores the tradeoff between the quality of application data collected and communications network congestion. It defines the concept of data granularity and presents two case studies on the impact of data granularity on application quality/accuracy. Finally, an algorithm is proposed and evaluated that demonstrates how the data granularity and application quality tradeoff can be managed at intermediate relay nodes such as data concentrators.

With the increasing complexity and advancements in the power system, several algorithms and tools have been developed to assess the voltage stability using synchrophasor measurements or system model or both. Several new algorithms have been proposed by researchers and some of them have been converted into commercial tools. Most of these algorithms have several advantages, but at the same time also have some limitations. Local synchrophasor measurement based algorithms are generally based on window based approach to estimate system parameters to compute proximity to voltage stability margin. Model based approach is suitable for offline and planning studies but may not be suitable for online real-time implementation. There are limited number of hybrid algorithms using measurements and model both, but more work need to be done to meet the accuracy and time-line requirements. This paper discusses about limitations of the existing online voltage stability algorithms in detail and the reasons for a need of fast and robust online voltage stability algorithm. A number of operating conditions have been modeled and simulated for standard IEEE test systems.

The distribution power system in ship is very similar to a microgrid and supplies energy to navigation and operation system as well as sophisticated systems of weapons and communications. After a fault is encountered, reconfiguration refers to changing the topology of the microgrid distribution network in order to isolate system damage and/or optimize certain characteristics of the system. Reconfiguration problem in microgrid is nonlinear with numerous discrete variables and additional constraints. Traditional optimization methods are not the best solution due to tendency of getting stuck to a suboptimal solution. In this work, intelligent methods such as genetic algorithm (GA) and particle swarm optimization (PSO) have been applied for microgrid reconfiguration with shipboard power system (SPS) as an example. Proposed methods are capable to satisfy the operational constraints and consider load priorities. Graph theory is utilized to represent the microgrid network topology. Proposed intelligent reconfiguration algorithms were implemented using MATLAB and tested on 8-BUS and 13-BUS SPS models including distributed generations (DGs) and islands. Test systems were reconfigured in three different possible scenarios by considering load priority, load magnitude, and by combining these two simultaneously. Developed reconfiguration algorithm was also implemented in real time using controller-in-the-loop with real time digital simulator. Simulation results show satisfactory performance for several test case operating scenarios.

Most of the ongoing smart grid projects are focused on power distribution system automation and upgrades. Although there is no electricity market in the distribution system yet, this will be a natural step with increasing penetration of distributed energy resources (DER). This work extends transmission locational marginal price (LMP) concept to distribution level. A new Optimal Power Flow (OPF) methodology based on Three-phase Current Injection Method (TCIM) is proposed in this paper to compute distribution locational margin price (DLMP). The proposed OPF method is used to compute LMP for three-phase unbalanced distribution system. The computed DLMP is used for price stability computation utilizing Generalized Autoregressive Conditional Heteroskedastic (GARCH). The case study is implemented with a modified IEEE 13-bus system. The results demonstrate the satisfactory performance of the developed OPF based DLMP algorithm and allows for the evaluation of the price stability of the system. Indicating the level of price stability can enable electric market participants and operators to make informed decisions.

The purpose of smart grid projects worldwide is to revitalize the aging power system infrastructure, and make it more reliable, more resilient and more sustainable. Technological advances has led to diversity of power sources and lesser dependence on fossil fuels; however, it has also increased the complexity of control of the network, which may have a counter-effect on its resiliency and reliability. Also, weather induced power disruptions or targeted attacks on critical power system infrastructure have increased in numbers. Thus there is a need for formal metrics to quantify resiliency of the different distribution system, or different configurations of same network. This paper presents definitions of resiliency of power distribution system, and approach towards resilient design of future power distribution systems with distributed energy resources. These are eventually used to identify parameters for quantification of resiliency. Simulation results for several test cases have been presented to validate the developed resiliency metrics.

This paper addresses the modeling and control of the More Electric Aircraft (MEA) system used in modern airplanes in real time simulation environment. In particular, this paper examines the effect of replacing the conventional gas turbine based Auxiliary Power Unit (APU) system with a fuel cell based APU system. The voltage dynamics in the aircraft electric microgrid system and impact of the fuel cell based APU is discussed. The modeling and control of the fuel cell and battery combination including the power electronic interface in the APU is discussed. The advantages of this APU unit is examined, and the performance of the system under various loading and operating conditions is studied.

With the development of the smart grid technology, Information and Communication Technology (ICT) plays a sig- nificant role in the smart grid. ICT enables to realize the smart grid, but also brings cyber vulnerabilities. It is important to analyze the impact of possible cyber-attacks on the power grid. In this paper, a real-time, cyber-physical co-simulation testbed with hardware-in-the-loop capability is discussed. Real-time Digital Simulator (RTDS), Synchrophasor devices, DeterLab, and a wide- area monitoring application with closed-loop control are utilized in the developed testbed. Two different real life cyber-attacks, including TCP SYN flood attack, and man-in-the-middle attack, are simulated on an IEEE standard power system test case to analyze the the impact of these cyber-attacks on the power grid.

The power sector in India at present comprises of five separate regional electricity grids having practically no integrated operation in between them. This study analyses the utility planning, environmental and economical effects of integrated power sector development at the national level in which the regional electric grids are developed and operated as one integrated system. It also examines the effects of selected CO2 emission reduction targets in the power sector and the role of renewable power generation technologies in India. The study shows that the integrated development and operation of the power system at the national level would reduce the total cost including fuel cost by 4912 million $, total capacity addition by 2784 MW, while the emission of CO2, SO2 and NOx would be reduced by 231.6 (1.9%), 0.8 (0.9%), 0.4 (1.2%) million tons, respectively, during the planning horizon. Furthermore, the study shows that the expected unserved energy, one of the indices of generation system reliability, would decrease to 26 GWh under integrated national power system from 5158 GWh. As different levels of CO2 emission reduction targets were imposed, there is a switching of generation from conventional coal plants to gas fired plants, clean coal technologies and nuclear based plants. As a result the capacity expansion cost has increased. It was found that wind power plant is most attractive and economical in the Indian perspective among the renewable options considered (Solar, wind and biomass). Copyright © 2003 John Wiley & Sons, Ltd.

Ongoing smart grid activities have resulted in proliferation of intelligent devices and associated Information and Communication Technologies (ICT) to enable enhanced system monitoring and control. Integration of ICT has led to an increase in the number of cyber assets and requires cyber-physical study for system analysis. In order to realize the vision of a smarter grid, it is necessary to understand the complex relationship between cyber and physical domains, and potential impacts on the power grid due to successful cyber-physical attacks. In order to understand this coupling, cyber physical test bed can help to model and simulate the smart grid with sufficient level of detail. In this chapter, an introduction to the smart electric grid and the challenges associated with the development of cyber-power test bed is presented. The integration of Real Time Digital Simulator (RTDS) and Network Simulator 3 (NS3) to realize a real time cyber-power test bed is discussed with the implementation of an example application.

This paper presents a Multi-Agent System (MAS) to model and enable an active power management in a multiple microgrids system consisting of batteries, photovoltaic and diesel micro sources in islanded or grid-connected operation. The multi-agent system was developed in JADE (Java Agent DEvelopment Framework), a Foundation for Intelligent Physical Agents (FIPA) and a compliant open source multi-agent platform. All the six case studies presented in this paper were modeled using JADE. The cases were chosen considering the real life possible scenarios, such as: discharging the battery (boost mode), energy export from the microgrid, charging the battery (buck mode), energy import from diesel generator, energy import from the grid and the system halt. However, considering that the results for the first two cases can demonstrate the performance and features of the proposed MAS, this paper focuses on the results of these cases. The results demonstrate the effectiveness of the proposed control algorithms, necessary for coordinated control and power management, and show the possibility of autonomous built-in operation of a microgrid with a multi-agent system using JADE.

With the increasing loads, changing generation mix, environmental limitations on the expansion of transmission system and increasing system complexity, power systems are being pushed to their stability limits. System instability behavior is often characterized by voltage instability, sometimes even leading to a blackout. With the installation of synchrophasor devices, real time voltage stability problem detection can be possible, which in turn can make fast and efficient online voltage stability control possible. This paper introduces a new software application ‘RT-VSMAC Tool’ for online voltage stability control of power systems. Simulation results using different IEEE test cases have been presented to show and validate the performance of this developed tool.

Fuel cell-based power units have increasingly become an attractive option to provide clean and efficient electricity in certain niche applications. This paper discusses the characteristics of a proton exchange membrane (PEM) fuel cell for a battery extender auxiliary power unit and explains the steps of the design process. A two-leg converter topology is proposed to control the fuel cell output, battery charge and discharge process, and the voltage of the DC link. Different operating modes of the system are analyzed and the functions of energy management system are studied. Sizing for the fuel cell, battery, power electronic converter, and passive components are presented, and the controllers of the power electronic converter are designed. Simulation case studies in both steady state and transient conditions are presented to validate the effectiveness of the presented fuel cell-based battery extender power unit and the proposed design process.

With the ongoing smart electric grid development, several algorithms and tools have been developed that make use of synchrophasor data for online wide area voltage stability monitoring. Most of these algorithms have several advantages, but at the same time also have limitations. For wide area real time monitoring, there is a need for faster and accurate online voltage stability algorithm. This paper discusses briefly about requirements for online voltage stability monitoring and limitations of few existing approaches. A new algorithm has been presented using a hybrid approach that is not dependent on time series PMU data and at the same time not fully relying on system models. This algorithm has been used to develop the Real Time Voltage Stability Monitoring (RT-VSM) tool to enable system operators in a control center to monitor the wide area voltage stability of a power system. The focus of this paper is investigating the computational performance of the developed algorithm in offline and online environment using simulation platform. Results obtained using the RTVSM tool are presented for different IEEE test cases.

Microgrids enable campus-scale facilities, like universities, or large corporations, a greater flexibility to manage their own distributed energy sources and in meeting their energy demands, even with power disturbances or outages in the grid. Simulation of these microgrids, which can disconnect or connect itself from the grid through a point of common coupling (PCC), yields insights into behavior of each load, if modeled in detail. Also, the simulation models are useful to investigate the effects of integrating new renewable generation in the distribution system. In this paper, the Real Time Digital Simulator (RTDS) is used to model the distribution system in real time and study the system dynamics in events of connection/disconnection to/from the main grid and disturbances. This paper reports results from simulation of a campus microgrid and its operation in several scenarios, by considering a representative model of the Washington State University campus electricity distribution system.

This paper discusses energy management and control algorithms of microgrid with energy storage. Specific management framework within microgrid presented here includes: a) help prioritizing the renewable energy sources (RES) to provide power to local loads, b) manage battery to avoid deep-discharging and overcharging, c) maintain dc link voltage at a specified value, and d) regulate ac voltage buses at nominal voltage and frequency. The system analyzed here is adopted from Consortium for Electric Reliability Technology Solutions (CERTS) microgrid test bed. While the generation priority is from RES, the system also has a thermal generation unit to compensate for renewable energy variability and intermittency. Depending on battery state of charge (SOC) and power difference between photovoltaic (PV) and the load, the energy management can work with several modes: charging, discharging, export power, import power, and halt. The system is simulated in PSCAD platform and shows that energy management and control algorithms work as expected.

Microgrids with renewable distributed generation and energy storage offers sustainable energy solutions. To maintain the availability of energy to the connected loads and to interrupt the smallest portion of the microgrid under any abnormal conditions, reconfiguration is critical to restore service to a section or to meet some operational requirements. Reconfiguration is the process of modifying the microgrid's topological structure by changing the status (open/close) of circuit breakers or switches. This paper offers novel real time implementation of intelligent algorithm for microgrid reconfiguration. Intelligent algorithm is based on genetic algorithms (GA) and has been tested on two test systems including shipboard power system (SPS) and modified CERTS microgrid. Real time test bed utilizes real time digital simulator (RTDS) and commercial real time controllers from Schweitzer Engineering Lab. Reconfiguration algorithm has been implemented in real time using real time test bed for an example microgrid system and satisfactory results were obtained.

Norms shift and emerge in response to technological innovation. One such innovation is Smart Meters - components of Smart Grid energy systems capable of minute-to-minute transmission of consumer electricity use information. We integrate theory from sociological research on social norms and privacy to examine how privacy threats affect the demand for and expectations of norms that emerge in response to new technologies, using Smart Meters as a test case. Results from three vignette experiments suggest that increased threats to privacy created by Smart Meters are likely to provoke strong demand for and expectations of norms opposing the technology and that the strength of these normative rules is at least partly conditional on the context. Privacy concerns vary little with actors' demographic characteristics. These findings contribute to theoretical understanding of norm emergence and have practical implications for implementing privacy protections that effectively address concerns of electricity users.

With ongoing smart electric grid development, several algorithms have been developed that make use of synchrophasor technology for online wide area voltage stability monitoring. Most of these algorithms have several advantages, but at the same time also have some limitations. Measurement based algorithms are generally based on iterative approach to estimate system parameters to compute proximity to voltage stability margin. There is a need for fast and robust online voltage stability algorithm utilizing synchrophasor measurements. This paper discusses briefly about limitations of few existing online voltage stability algorithms and presents a new real time algorithm for voltage stability monitoring based on local measurements only, which also considers detailed load models. Developed algorithm has been tested offline and online on different IEEE standard test cases under different scenarios. Simulation results show satisfactory performance.

Trustworthy critical power grid infrastructures require suitable cyber and power security protection efforts. Expert system operators play a crucial role in protecting complex power-grid networks; however, inefficient training of system operators regarding potential cyber attacks and physical security threats can potentially result in malicious compromises and catastrophic consequences. In this paper, we present SECPSIM, a user-friendly framework based on mathematical models of corrective control actions against various intrusions and failure scenarios. Our experimental results show that SECPSIM can help in learning and provide appropriate corrective cyber-physical control actions efficiently, and provide inexperienced individuals with an effective tutorial user interface.

One of the most important goals of smart grid is sustainability. Load management in coordinated manner may help in saving more energy and hence move towards sustainability. Advanced metering infrastructures (AMI) and bidirectional communication between electricity grid and endusers, novel technologies and designs are required to exploit full potential of smart grid investments. New analysis tools and control strategies are needed for distribution systems to augment these investments. Heating, ventilation and air conditioning (HVAC) systems, as thermostatic controllable loads, consume major portion of electric energy. In this work, a detailed load model of thermostatic electric loads is used to develop a novel approach of look-ahead controller over different time frames. Developed controller has been tested for cooling mode operation on a typical summer day and shows satisfactory performance.

This paper presents a multi-agent system for realtime operation of simulated microgrid using the Smart-Grid Test Bed at Washington State University. The multi-agent system (MAS) was developed in JADE (Java Agent DEvelopment Framework) which is a Foundation for Intelligent Physical Agents (FIPA) compliant open source multi-agent platform. The proposed operational strategy is mainly focused on using an appropriate energy management and control strategies to improve the operation of an islanded microgrid, formed by photovoltaic (PV) solar energy, batteries and resistive and rotating machines loads. The focus is on resource management and to avoid impact on loads from abrupt variations or interruption that changes the operating conditions. The management and control of the PV system is performed in JADE, while the microgrid model is simulated in RSCAD/RTDS (Real-Time Digital Simulator). Finally, the outcome of simulation studies demonstrated the feasibility of the proposed multi-agent approach for real-time operation of a microgrid.

With the advent of synchrophasor technology, real time voltage stability analysis using synchrophasor data has been the focus of many researchers resulting in the development of several new algorithms. These developed algorithms have several advantages as well as limitations. This paper discusses briefly about the shortcomings of some existing synchrophasor measurement based voltage stability indicators. Most of the existing model-free algorithms are iterative in approach, thus making them computationally slow as well as making non-convergence a possibility. Some of these algorithms also have accuracy issues when the system conditions change fast by large amounts. Thus a new algorithm is necessary that is devoid of these limitations. A new algorithm for distributed real time voltage stability monitoring based on local phasor measurements has been presented in this paper. The newly developed algorithm to compute the Voltage Stability Assessment Index (VSAI) can be used for normal operation as well as for contingency cases to monitor dynamic security of a system. Results of the new algorithm tested on different IEEE test cases under different system conditions show its satisfactory performance.

Voltage - Var Control and Optimization (VVC&O) mechanism in distribution system assisted by local controllers and Distributed Management System (DMS) helps in meeting operating criterion and achieving energy savings. Conservation Voltage Reduction (CVR) with VVC&O allows reduction in peak load and energy consumption, by having flat voltage profile near lower bound of ANSI Standards (114V), while maintaining power factor within limit. To estimate the energy saving, CVR factor is needed and accurate load modeling is required to estimate CVR factors. In this work, distribution system was modeled and simulated utilizing data from one of the feeder in Pullman, WA. This paper presents the results of the study done on improving the VVC capabilities by integrating Smart Meter Infrastructure. Load Modeling was done by observing the behavior of loads at each house and comparing the smart meter data with the simulation results. Precision of developed load model provides enough information to make a decision on required changes in voltage at a substation at a given time for energy saving without violating operation criterion. Results of VVC&O show that energy saving of 4-5% can be achieved at the typical distribution substation.

With ongoing efforts to upgrade the traditional electric power system into a smart grid, emphasis is on the integration of state-of-the-art computer based online monitoring and control tools along with advanced communication technology. However, testing and validation of these devices and algorithms are required before implementation in a physical grid. New testing and validation method needs to be developed in a simulated environment in a lab. This paper discusses some of the applications of the “smart grid test bed” developed at the “Smart Grid Development and Research Investigation Lab (SGDRIL)” at Washington State University. The specific applications discussed in this paper include synchrophasor device testing, microgrid reconfiguration, voltage stability and vulnerability analysis.

This paper presents a novel hybrid intelligent algorithm based on the wavelet transform (WT) and fuzzy ARTMAP (FA) network for forecasting the power output of a wind farm utilizing meteorological information such as wind speed, wind direction, and temperature. The prediction capability of the proposed hybrid WT+FA model is demonstrated by an extensive comparison with a benchmark persistence method, other soft computing models (SCMs) and hybrid models as well. The test results show a significant improvement in forecasting error through the application of a proposed hybrid WT+FA model. The proposed hybrid wind power forecasting strategy is applied to real life data from Kent Hill wind farm located in New Brunswick, Canada.

With ongoing smart grid activities, more amount of information is available from end-users. Objective is to use this information to make power system more efficient, environmental friendly, reliable, and secure. Major part of smart grid investment is going towards distribution systems. One of the goals within smart grid is development of infrastructure to avail price signal to end users in distribution system. This price signal is currently based on rough estimate of nodal price in transmission system. Transmission nodal price does not reflect fair pricing signal for distribution system without consideration of distribution system constraints. Solving optimal power flow (OPF) with applicable constraints is necessary to obtain locational marginal price (LMP) for each node that can be used in real-time pricing (RTP). Formulation and implementation of distribution LMP (DLMP) is needed for fair pricing. In this work, DLMP formulation is developed consisting of three major parts similar to that of LMP in transmission system but implemented at distribution level. Also, price responsive load brings another interesting prospective of demand response (DR) as shown in results. Effect of DR on DLMP using 12 bus distribution system has been presented.

The electric power system is moving towards the smart grid development for improved reliable, secure and economic operation. Many researchers are now concentrating their research that is aimed at upgrading the power system by using state-of-the-art computer based online monitoring and control tools along with advanced communication facilities. Implementation of such a system requires enhanced testing and validation of smart grid technologies as well as development of new approaches to fully utilize the capabilities of these technologies. This paper is a modest attempt to present the summary of the effort to model a smart power grid in real time by developing a “smart grid test bed”. Additionally, some of the applications of the developed test bed have been briefly described, with special emphasis on testing of synchrophasor devices like PMUs and PDCs.

With ongoing smart grid activities, there are a number of active demonstration projects in different parts of USA. In the northwest, an ongoing demonstration project is conducted by Avista Utilities to improve the reliability and efficiency of the distribution system. This project includes automated management of distribution system and bi-directional communication capabilities between the devices in the field and the control center. With the added communication capacity, the newly installed equipment coupled with the distribution automation system has the ability of enhanced monitoring and control as well as the potential to decrease the overall power demand. This research explores one aspect of the smart grid in distribution automation called the Volt-VAr Control (VVC) scheme. Voltage regulation over a distribution feeder serves the purpose of achieving a flat voltage profile and maintaining the permissible voltage (plus or minus 5% of rated voltage) at the end-use customer. VAr control allows power factor correction in near real time. To accurately predict the benefits and plan Volt/VAr control, an accurate representation of the load models is essential. The benefits of VVC include reduction of losses, maintenance costs, operating costs, voltage variation, and increase in the power delivery capacity of existing equipment. Resulting analysis of the VVC control shows demand savings of 2–3% for every 2.5% voltage drop.

This paper overviews our experience with design and development of a new smart grid course focused on cyber-security. The course is team-taught by power and computer science faculty members and intended for seniors and graduate students from computer science and engineering. Key areas of focus, covered in this paper are the need for a new course and the course description along with our experience in locating and creating course materials.

Simulation-based evaluation of the behavior of the electric grid is complex, as it involves multiple, heterogeneous, interacting cyber-physical system like domains. Each simulation domain has sophisticated tools, but their integration into a coherent framework is a very difficult, time-consuming, laborintensive, and error-prone task. This means that computational studies cannot be done rapidly and the process does not provide timely answers to the planners, operators and policy makers. Furthermore, grid behavior has to be tested against a number of scenarios and situations, meaning that a huge number of simulations must be executed covering the potential space of possibilities. Designing and efficiently deploying such computational experiments by utilizing multi-domain tools for integrated smart grid is a major challenge. This paper addresses these important issues by integrating multiple modeling tools from diverse domains in a single coherent framework for integrated simulation of smart grids.

Price forecasting is a crucial information for market participants in an electricity market. However, the electricity price is a complex signal due to its nonlinearity, stochasticity, and time dependent behavior. This paper presents a novel hybrid intelligent algorithm that uses the combination of a data filtering technique based on the wavelet transform (WT), an optimization technique based on firefly (FF) algorithm, and a soft computing model based on fuzzy ARTMAP (FA) network. The innovative contribution of this paper is an application of the FF algorithm to optimize FA network that utilizes the historical ill-behaved energy price time-series through the WT. Good forecast performance and adaptability of the proposed hybrid WT+FF+FA m o d e l to changes in the data is illustrated using the Ontario market power system data. The test results demonstrates that the proposed hybrid technique is able to improve the day-ahead price forecasting performance significantly when compared with other conventional soft computing models available in literature.

Texas continues to lead as the top wind power producer in the USA. The Electric Reliability Council of Texas (ERCOT) is an Independent System Operator (ISO), transmission operator and reliability coordinator in Texas. In recent years, numerous policies and incentives to promote renewable energies have been enacted. These policies were very appealing to many states including Texas because of its richness in renewable energy potential. It has been almost a year since ERCOT transitioned from the zonal to nodal market operation. This electricity market has seen significant improvements and consumers are reaping the benefits of lower spot electricity price based on a recent ERCOT report for the first 6 months since the transition took effect. This paper discusses the impact of wind power generation, based on favorable renewable energy policies coupled with the transition from zonal to nodal market, on the electricity price. The analysis performed in this paper indicates that it is very difficult to compute fairly the impact of wind energy on electricity price due to the number of uncertain variables involved and the scale of the problems.

Threat assessments play a key role in determining appropriate mitigation strategies to counter credible threats to the power system. In order to further understand the capability of a malicious agent to coordinate an attack on the power grid given limited system information, graph theory based centrality measures are utilized for power systems vulnerability analysis. Results are compared to vulnerability analysis indices utilizing DC power flow based linear sensitivity factors with complete power system information. Correlations of centrality and linear sensitivity factor based vulnerability rankings are performed, and matched pair comparisons of top rankings are presented using the nonparametric Wilcoxon signed rank statistical test. Evidence is presented in support of the edge betweeness centrality measure in determining sensitive line outages based solely on the branch impedance values of a power system. Results obtained for four different test case systems indicate the threat potential of a system attack planned from limited topology information.

The penetration of renewable resources in power system has been increasing in recent years. Many of these resources are uncontrollable and variable in nature, wind in particular, are relatively unpredictable. At high penetration levels, volatility of wind power production could cause problems for power system to maintain system security and reliability. One of the solutions being proposed to improve reliability and performance of the system is to integrate energy storage devices into the network. In this paper, unit commitment and dispatch schedule in power system with and without energy storage is examined for different level of wind penetration. Battery energy storage (BES) is considered as an alternative solution to store energy. The SCUC formulation and solution technique with wind power and BES is presented. The proposed formulation and model is validated with eight-bus system case study. Further, a discussion on the role of BES on locational pricing, economic, peak load shaving, and transmission congestion management had been made.

The real time modeling and simulation of smart grid behavior under the disturbance helps in the analysis and planning of system in order to minimize the possible damage. Real time modeling and simulations with hardware in the loop capabilities allows testing the smart grid control algorithms and new equipments.

Reconfiguration is a control action including topology changes, load/ generation shedding and other measures to redirect power flow to the remaining unaffected loads in a microgrid. In this paper, genetic algorithm (GA) and graph theory based methodologies were used to reconfigure a network, satisfying the operational requirements and priorities of loads. Applications of the GA for reconfiguration were implemented in MATLAB and tested on 8-bus shipboard power system (SPS) and modified CERTS microgrids with consideration of distributed generation and islanding operation. Satisfactory simulations results were obtained for several possible test case scenarios in a real-time and non-real-time case study.

An aging workforce and the need to modernize the electric power system are significant challenges facing many nations, and in particular the United States. This complex problem requires a multi-faceted solution that starts with outreach to pre-university students. The aim of these outreach programs is to introduce students to power engineering concepts and to encourage them to choose power engineering as an area of future study and vocation. Outreach activities may also be targeted at guidance counselors, teachers, parents and the general public to the increase the awareness and image of the power engineering profession. This paper identifies pre-university power engineering outreach resources. Funding sources and organizations involved with engineering outreach are also identified. Specific on-line multimedia and in-person, experiential learning lesson plans and activities are discussed. These resources are intended to be used by power engineering faculty and other interested individuals.

Competitive electricity market has been of interest for more than a decade, with every electricity market presenting its own unique characteristics and features. The Electric Reliability Council of Texas (ERCOT) is one of the eight independent system operators (ISO) in North America which is currently structured as a zonal model. ERCOT is planning to introduce some changes in market design and transitioning into the nodal market operation. Originally scheduled for October 2006, implementation has been deferred to December 2010. This paper discusses some operational challenges in ERCOT, the ongoing discussions of market redesign, and the current status of the evolution of the ERCOT market.

This paper presents an approach for security-constrained unit commitment (SCUC) with phase shifter application to improve transmission congestion, wind curtailment and enhance system reliability. Phase shifter is used to control the flow of real power in transmission line by manipulating the phase angle difference. By controlling the flow, it mitigates transmission congestion, improves transmission system stability, and increases the access to wind and other renewable resources through available transmission lines. The main contribution of this paper is the development of SCUC formulation with an alternative solution technique to optimize phase shifter. The performance is evaluated through simulations based on eight-bus system. The paper also analyzes the impact of phase shifter on locational electricity price, system contingency, wind curtailment, and economic.

This paper summarizes ongoing research in the application of intelligent methods to the design, modeling, simulation and control of microgrids including optimal design of microgrids, and centralized and decentralized control.