Smart Grid as a Dynamical System of Complex Networks: A Framework for Enhanced Security

This lecture treats the problem of designing local control agents for cooperating components in a network of interacting dynamic systems. Each local control agent must ensure that all local specifications are met, but at the same time must ensure that the different components help each other in achieving good global behavior as well as good local behavior. This problem will be illustrated by using urban traffic control and smart electric power grids as examples. Centralized or hierarchical control approaches are not robust against failures in communication networks, and require unrealistic assumptions on the knowledge of each agent about the overall model. A completely decentralized approach, where each local control agent selfishly tries to achieve its local specifications only, runs a high risk of global interactions that may destabilize the system, making it impossible to achieve the specifications. This talk proposes two paradigms for distributed feedback control that require very little information exchange and very little global model knowledge. The leader/follower control paradigm is illustrated for urban traffic control: heavily loaded leader agents send messages to their follower neighbors requesting that these followers give green only to platoons of vehicles travelling towards the leader intersection at those times when this will be optimal for the performance of the leader. Another coordination paradigm is called the coordinating model predictive control (CMPC). Consider a power transmission network that has been partitioned in interacting regions, where CMPC is used in order to prevent the spread of the disturbances following incidents like line or machine failure. CMPC tries to resolve this by having each local control agent apply a model predictive controller, using as on-line available information not only the local voltage and current measurements, but also information on the planned sequence of future control actions of neighboring agents, communicated to it from time to time. This talk will discuss some of the minimal requirements for modeling, communication and control agent set-up in order to robustly achieve specifications using distributed control.

In this paper, we consider the problem of distributed load shedding optimization for disaster recovery in smart grids. We develop distributed second-order interior-point based load shedding algorithms that enjoy a fast quadratic convergence rate. Our main contributions are two-fold: (i) We propose a rooted spanning tree based reformulation that enables our distributed algorithm design; (ii) Based on the spanning tree reformulation, we design distributed computation schemes for our proposed second-order interior-point based load shedding. Collectively, these results serve as an important first step in load shedding and disaster recovery that uses second-order distributed techniques.

Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.

Availability importance measures of components in smart electric power grid systems

Guest Editorial

Big data applications such as in smart electric grids, transportation, and remote environment monitoring involve geographically dispersed sensors that periodically send back information to central nodes. In many cases, data from sensors is not available at central nodes at a frequency that is required for real-time modeling and decision-making. This may be due to physical limitations of the transmission networks, or due to consumers limiting frequent transmission of data from sensors located at their premises for security and privacy concerns. Such scenarios lead to partial data problem and raise the issue of data veracity in big data applications. We describe a novel solution to the problem of making short term predictions (up to a few hours ahead) in absence of real-time data from sensors in Smart Grid. A key implication of our work is that by using real-time data from only a small subset of influential sensors, we are able to make predictions for all sensors. We thus reduce the communication complexity involved in transmitting sensory data in Smart Grids. We use real-world electricity consumption data from smart meters to empirically demonstrate the usefulness of our method. Our dataset consists of data collected at 15-min intervals from 170 smart meters in the USC Microgrid for 7 years, totaling 41,697,600 data points.

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

Analysis of information flow security in cyber–physical systems

Electrification of the transportation sector can play an essential role in curbing fossil fuel scarcity and oil shortages in the world. Electric vehicles (EVs) can significantly reduce CO2 emissions, improve urban pollution. Apart from these advantages of EVs, they may also pose challenges to the distribution grid. Increasing penetration of EVs puts an extra burden and leads to affect the grid severely. Load congestions, voltage drops/regulation, overloading are some of the issues that might incur in the grid because of improper charging of EVs. The uncertain nature of EV owners makes it very difficult to predict the charging pattern. So the grid operator may face a daunting task to avoid overloading if huge EVs are charged without following any smart charging management strategies. We propose a framework that allows EV users to follow a well-coordinated charging schedule and fulfill various objectives like load variance minimization and social welfare maximization for both electric utility and EV owners. A proof of concept of distributed resource allocation for EV charging is implemented using the output consensus approach. Microgrids are components of a smart power distribution grid that can operate as a decentralized/localized grid when disconnected from the main grid. A microgrid can enhance the grid resiliency and provide adequate power to a community or a region in case of grid failure caused by natural disasters or any other disruptions. We also propose the optimal scheduling model for Distributed Energy Resources (DERs) including an EV charging station and battery energy storage system (BESS) in a community microgrid. This model also minimizes the cost incurred in operating the DERs, charging station, BESS, and the grid load. Due to the two-way power flow in the smart grid because of the EVs and distributed energy resources (DERs) involvement, the distribution system operator (DSO) must provide control & protection to equipment and manage the reverse power flow without violating system constraints. Artificial Intelligence (AI) leads the path for a promising smart grid future and helps the DSO tackle such challenges. Given the large size of data flow in the smart grid due to phasor measurement units (PMUs) and smart sensors, AI-based techniques help the grid operator to manage and analyze such data effectively and improve grid resiliency. Machine learning can help the grid operator forecast the uncertainty in supply-demand because of the integration of renewable energy sources. For system planning and operation, the utility needs to perform the

The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In a Smart Grid platform, the optimal coordination of distributed voltage controllers is one of the main issues to address. In this field, the application of traditional hierarchical control paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of distribution generation systems require more scalable, more flexible control and regulation paradigms. To try and overcome these challenges, this paper proposes the concept of a decentralized non-hierarchal voltage regulation architecture based on intelligent and cooperative smart entities. The distributed voltage controllers employ traditional sensors to acquire local bus variables and mutually coupled oscillators to assess the main variables that characterize the operation of the global Smart Grid. These variables are then amalgamated by a fuzzy inference system in order to identify proper control actions aimed at improving the grid voltage profile and reducing power losses.

Smart grid technologies are expected to increase system efficiency and reliability using advancement in automation, communication, computation, and optimization. Several control algorithms have been developed or enhanced for operation and control of smart distribution grid. In recent years, volt/var control (VVC) algorithms have been proposed for energy saving in distribution systems due to advancement provided by smart grid automation and control. VVC helps to lower distribution feeder voltage to decrease energy consumption. However, quantification of this impact is hard due to several external factors such as weather and end users' behavior. On the other hand, existing evaluation methods of VVC algorithms are either not suitable for long-term study or intrusive. This paper addresses these issues by proposing a new technique to estimate “distribution system demand without smart grid implementation” utilizing “data measured from system with smart grid implementation.” Interdependence of voltage and load is considered based on estimated load characteristic. Our key contribution is developing a method for quantifying the real-time energy saving of smart grid automation and control focused on VVC and implementing in an electric utility in eastern Washington, USA. The algorithm is developed using both commercial and open-source software.

The operation of smart power grids is supported by interconnected sensors and devices that provide real-time monitoring. Smart grids provide more secure and dependable electricity than traditional grids because of constant monitoring. This paper reviews the technology used in smart grids, specifically sensors and communication technologies. It also talks about the challenges that smart grids face, such as providing power to the network's sensors, islanding, cyber-security, and issues that occur when using distributed generators. Current solutions for sensing changes and communication of sensed data in smart grid networks are also discussed.

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.

Considering near-real-time data available on the smart grid, analytics can be used to determine the best-case scenario for optimal and reliable distribution of power. However, the distributed integration of renewable sources and demand response adds complexity to the modeling, control and optimization of smart grid operations. Latest concepts aim for using new model-based computational intelligence; that requires a combination of capabilities for system optimization, stochastic power flow, system state prediction, and solution checking. The statistical model-based optimization for developing dynamic, stochastic, computationally efficient, and scalable platforms is intended in this paper. Furthermore, an analytics-based optimization is proposed to the optimal reactive power dispatch considering load variations. This illustration uses analytics obtained from empirical modeling of recorded load data.

ABSTRACTSmart electric grids add digital technologies to the grid. While some suggest that they offer many environmental and social benefits, others remain critical and call them a neoliberal project. Considering smart grids a boundary object [Star, Susan L., and James R. Griesemer. 1989. “Institutional Ecology, Translations and Boundary Objects: Amateurs and Professionals in Berkeley’s Museum of Vertebrate Zoology, 1907–39.” Social Studies of Science 19 (3): 387–420], I examine how multiple social groups come together in cooperation and conflict in the installation of one smart grid. In what follows, I first argue that participation is Gramscian common sense (1971), a taken-for-granted good in producing a smart grid. Gramsci points out that common sense can be used to reinforce oppressive ideologies of a hegemonic status quo, but that it also contains “good sense” that can be developed into counter-hegemonic narratives and movements. Second, I argue that in the course of cooperation and conflict, the smart grid indeed becomes more neoliberal, and this occurs through participation. While the utility often seeks or accepts public participation, the meaning of participation gradually becomes limited to individualistic and financially motivated “choice.” In the discussed case, many of the (less neoliberal) social and environmental benefits of the grid and more collectivist forms of participation were precluded. This article offers a grounded examination of a smart grid and a sympathetic critique of common-sense participation.