Combined Economic and Emission Dispatch Incorporating Renewable Energy Sources and Plug-In Hybrid Electric Vehicles

Conventional transportation and electricity industries are considered as two major sources of greenhouse gases (GHGs) emission. Improvement of vehicle’s operational efficiency can be a partial solution but it is necessary to employ Plug-In Hybrid Electric Vehicles (PHEVs) and Renewable Energy Sources (RESs) in the network to slow the increasing rate of the GHGs emission. However, it is crucial to investigate the effectiveness of each solution. In this paper, a combination of generation cost and GHGs emission of the two mentioned industries, as economic and environmental aspects of using PHEVs and RESs will be analyzed. The effectiveness of five different scenarios of utilizing the mentioned elements is studied on a test system. To have a realistic evaluation, an extended cost function model of wind farm is employed in optimal power dispatch calculations. Particle Swarm Optimization (PSO) algorithm is applied to the combined economic and emission dispatch (CEED) non- linear problem

Automated Anomaly Detection in Distribution Grids Using uPMU Measurements

The impact of Phasor Measurement Units (PMUs) for providing situational awareness to transmission system operators \ has been widely documented. Micro-PMUs (uPMUs) \ are an emerging sensing technology that can provide similar \ benefits to Distribution System Operators (DSOs), enabling a \ level of visibility into the distribution grid that was previously \ unattainable. In order to support the deployment of these \ high resolution sensors, the automation of data analysis and \ prioritizing communication to the DSO becomes crucial. In this \ paper, we explore the use of uPMUs to detect anomalies on \ the distribution grid. Our methodology is motivated by growing \ concern about failures and attacks to distribution automation \ equipment. The effectiveness of our approach is demonstrated \ through both real and simulated data

A Real-Time Testbed Environment for Cyber-Physical Security on the Power Grid

The trustworthiness and security of cyber-physical systems (CPSs), such as the power grid, are of paramount importance to ensure their safe operation, performance, and economic efficiency. The aim of many cyber-physical security techniques, such as network intrusion detection systems (NIDSs) for CPSs, is to ensure continuous reliable operation even in exposed network environments. But the validation of such methods goes well beyond standard network analysis, since meaningful tests must also integrate realistic understanding of the physical systems behavior and response to the network activity. Our goal in this paper is to showcase an example of a testbed environment that can support such validation. In it, real network traffic, emulating and industrial control network, interacts with simulated physical models in real-time, extending and leveraging "hardware-in-the-loop" and "cyber-in-the-loop" capabilities. The testbed is a bridge between theory and practice and offers a number of features, including network communications, data management, as well as the virtualization of cyber-physical state analytics performed by the NIDS. The traffic is captured by real network taps and is forwarded to a real data management environment, receiving also the data reports from the simulated industrial control environment. To illustrate the capabilities of our testbed we show how the data are cross-checked by a "physics aware" NIDS, identifying network traffic that does not comply with its cyber-physical security rules

A Real-Time Testbed Environment for Cyber-Physical Security on the Power Grid

The trustworthiness and security of cyber-physical systems (CPSs), such as the power grid, are of paramount importance to ensure their safe operation, performance, and economic efficiency. The aim of many cyber-physical security techniques, such as network intrusion detection systems (NIDSs) for CPSs, is to ensure continuous reliable operation even in exposed network environments. But the validation of such methods goes well beyond standard network analysis, since meaningful tests must also integrate realistic understanding of the physical systems behavior and response to the network activity. Our goal in this paper is to showcase an example of a testbed environment that can support such validation. In it, real network traffic, emulating and industrial control network, interacts with simulated physical models in real-time, extending and leveraging "hardware-in-the-loop" and "cyber-in-the-loop" capabilities. The testbed is a bridge between theory and practice and offers a number of features, including network communications, data management, as well as the virtualization of cyber-physical state analytics performed by the NIDS. The traffic is captured by real network taps and is forwarded to a real data management environment, receiving also the data reports from the simulated industrial control environment. To illustrate the capabilities of our testbed we show how the data are cross-checked by a "physics aware" NIDS, identifying network traffic that does not comply with its cyber-physical security rules

Online Thevenin Parameter Tracking Using Synchrophasor Data

There is significant interest in smart grid analytics based on phasor measurement data. One application is estima- tion of the Thevenin equivalent model of the grid from local measurements. In this paper, we propose methods using phasor measurement data to track Thevenin parameters at substations delivering power to both an unbalanced and balanced feeder. We show that for an unbalanced grid, it is possible to estimate the Thevenin parameters at each instant of time using only instantaneous phasor measurements. For balanced grids, we propose a method that is well-suited for online applications when the data is highly temporally-correlated over a short window of time. The effectiveness of the two methods is tested via simulation for two use-cases, one for monitoring voltage stability and the other for identifying cyber attackers performing "reconnaissance" in a distribution substation

Micro Synchrophasor-Based Intrusion Detection in Automated Distribution Systems: Towards Critical Infrastructure Security

Electric power distribution systems are undergoing many technological changes and concerns are surfacing on possible additional vulnerabilities. Resilient cyber-physical systems (CPSs) in general must leverage state measures and operational models that interlink the physical and the cyber assets that compose them, to assess the global state. In this paper we describe a viable process of abstraction to obtain this holistic system state exploration tool, through the analysis of data from Micro Phasor Measurement Units (μPMUs) combined with the monitoring of Distribution Supervisory Control and Data Acquisition (DSCADA) traffic, and using semantics to interpret these data that expresses the specific system physical and operational constraints in both cyber and physical realms