Large-Scale smart grids as system of systems

Smart Grids are advanced power networks that introduce intelligent management, control, and operation systems to address the new challenges generated by the growing energy demand and the appearance of renewal energies. In the literature, Smart Grids are presented as an exemplar SoS: systems composed of large heterogeneous and independent systems that leverage emergent behavior from their interaction. Smart Grids are currently scaling up the electricity service to millions of customers. These Smart Grids are known as Large-Scale Smart Grids. From the experience in several projects about Large-Scale Smart Grids, this paper defines Large-Scale Smart Grids as a SoS that integrate a set of SoS and conceptualizes the properties of this SoS. In addition, the paper defines the architectural framework for deploying the software architectures of Large-Scale Smart Grid SoS

Integrated modeling of the peer-to-peer markets in the energy industry

Over time, the number of smart grids installed worldwide is gradually increasing. However, the major portion of the required electricity is still being produced by traditional large-scale and centralized power systems. The main requirement, then, is to study and develop mathematical methods that attend the integration between the two systems previously announced. In this paper, a novel model that addresses this issue is presented. The model minimizes the total operating cost of the large-scale system considering the participation of the smart grid as a dynamic entity, entailing a close relationship between both systems. This approach distinguishes the novel proposal from others that solve similar situations by taking into account the two systems in isolation. Besides, the models that represent the most common organizational structures of the smart grids are also presented in this paper. They are needed to develop the integrated model. Many similar problems in the literature are solved by implementing decomposition techniques, which might obtain a local optimum different from the global one. By contrast, problems with this proposal are solved by using mixed-integer linear programming models that ensure the reaching of a global optimum. The real test case is the integrated Argentine large-scale system and the Armstrong smart grid. Results indicate that the novel model can reach solutions that are 5% lower in comparison with the traditional techniques of considering in isolation. Efficient CPU times enable the possibility of promptly obtaining solutions if there is any change in the parameters. In addition, other benefits, apart from the economical reductions, are also achieved. Operating information closer to the reality of both systems is obtained because it considers the effects of the smart grid in large-scale system solving.Fil: Alvarez, Gonzalo Exequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Reinforcement Learning and Game Theory for Smart Grid Security

This dissertation focuses on one of the most critical and complicated challenges facing electric power transmission and distribution systems which is their vulnerability against failure and attacks. Large scale power outages in Australia (2016), Ukraine (2015), India (2013), Nigeria (2018), and the United States (2011, 2003) have demonstrated the vulnerability of power grids to cyber and physical attacks and failures. These incidents clearly indicate the necessity of extensive research efforts to protect the power system from external intrusion and to reduce the damages from post-attack effects. We analyze the vulnerability of smart power grids to cyber and physical attacks and failures, design different gametheoretic approaches to identify the critical components vulnerable to attack and propose their associated defense strategy, and utilizes machine learning techniques to solve the game-theoretic problems in adversarial and collaborative adversarial power grid environment. Our contributions can be divided into three major parts:Vulnerability identification: Power grid outages have disastrous impacts on almost every aspect of modern life. Despite their inevitability, the effects of failures on power grids’ performance can be limited if the system operator can predict and identify the vulnerable elements of power grids. To enable these capabilities we study machine learning algorithms to identify critical power system elements adopting a cascaded failure simulator as a threat and attack model. We use generation loss, time to reach a certain percentage of line outage/generation loss, number of line outages, etc. as evaluation metrics to evaluate the consequences of threat and attacks on the smart power grid.Adversarial gaming in power system: With the advancement of the technologies, the smart attackers are deploying different techniques to supersede the existing protection scheme. In order to defend the power grid from these smart attackers, we introduce an adversarial gaming environment using machine learning techniques which is capable of replicating the complex interaction between the attacker and the power system operators. The numerical results show that a learned defender successfully narrows down the attackers’ attack window and reduce damages. The results also show that considering some crucial factors, the players can independently execute actions without detailed information about each other.Deep learning for adversarial gaming: The learning and gaming techniques to identify vulnerable components in the power grid become computationally expensive for large scale power systems. The power system operator needs to have the advanced skills to deal with the large dimensionality of the problem. In order to aid the power system operator in finding and analyzing vulnerability for large scale power systems, we study a deep learning technique for adversary game which is capable of dealing with high dimensional power system state space with less computational time and increased computational efficiency. Overall, the results provided in this dissertation advance power grids’ resilience and security by providing a better understanding of the systems’ vulnerability and by developing efficient algorithms to identify vulnerable components and appropriate defensive strategies to reduce the damages of the attack

In-situ QAM-based power line communication for large-scale intelligent battery management

The use of power line communication (PLC) within a large-scale battery will allow for smart cells to communicate within a decentralised system, with an external battery management system (BMS), and also with an external smart grid network. By using PLC, the smart battery is further enhanced by allowing the BMS real-time access to in-situ cell sensor data, without the need of an additional wire harness within the battery. This paper presents experimental studies of a PLC system on four distinct lithium-ion battery pack configurations, in order to determine its suitability and limitations for large-scale energy storage systems such as for use in smart grids, battery electric vehicles, and robotic systems. Quadrature amplitude modulation (QAM) is tested up to 1024-QAM for its benefits in high bit rate communication. Recommendations on the parameters of this PLC system based upon experimental results are presented

A design methodology for smart LED lighting systems powered by weakly regulated renewable power grids

The increasing use of intermittent renewable energy sources to decarbonize electric power generation is expected to introduce dynamic instability to the mains. This situation is of particular concern for mini-grids or isolated grids in which wind and/or solar power sources are the dominant or the sole power sources. In this paper, we utilize the photo-electro-thermal theory to develop a design methodology for LED lighting systems for weakly regulated voltage sources, with the objectives of minimizing the fluctuation of the human luminous perception and adopting reliable LED driver with long lifetime and robustness against extreme weather conditions. The proposed LED system, practically verified in a 10 kVA small power grid driven by an ac voltage source and a wind energy simulator, can be considered as a smart load with its load demand following the power generation. A typical swing of 40 V in the mains will cause only 15% actual light variation in a 132 W LED system when compared with 40% change in 150 W high-pressure-sodium lamp system. The design methodology enables future large-scale LED systems to be designed as a new generation of smart loads that can adapt to the voltage and power fluctuations arising from the intermittent nature of renewable energy sources. © 2011 IEEE.published_or_final_versio

Distributed Real-time Anomaly Detection in Networked Industrial Sensing Systems

Reliable real-time sensing plays a vital role in ensuring the reliability and safety of industrial cyber-physical systems (CPSs) such as wireless sensor and actuator networks. For many reasons, such as harsh industrial environments, fault-prone sensors, or malicious attacks, sensor readings may be abnormal or faulty. This could lead to serious system performance degradation or even catastrophic failure. Current anomaly detection approaches are either centralized and complicated or restricted due to strict assumptions, which are not suitable for practical large-scale networked industrial sensing systems (NISSs), where sensing devices are connected via digital communications, such as wireless sensor networks or smart grid systems. In this paper, we introduce a fully distributed general anomaly detection (GAD) scheme, which uses graph theory and exploits spatiotemporal correlations of physical processes to carry out real-time anomaly detection for general large-scale NISSs. We formally prove the scalability of our GAD approach and evaluate the performance of GAD for two industrial applications: building structure monitoring and smart grids. Extensive trace-driven simulations validate our theoretical analysis and demonstrate that our approach can significantly outperform state-of-the-art approaches in terms of detection accuracy and efficiency

PMU-Based FOPID Controller of Large-Scale Wind-PV Farms for LFO Damping in Smart Grid

Due to global warming problems and increasing environmental pollution, there is a strong tendency to install and apply renewable energy power plants (REPPs) around the world. On the other hand, with the increasing development of information and communication technology (ICT) infrastructures, power systems are using these infrastructures to act as smart grids. In fact, future modern power systems should be considered as smart grids with many small and large scale REPPs. One of the main problems and challenges of the REPPs is uncertainty and fluctuation of electrical power generation. Accordingly, a suitable solution can be combination of different types of REPPs. So, the penetration rate of large-scale wind-PV farms (LWPF) is expected to increase sharply in the coming years. Given that the LWPFs are added to the grid or will replace fossil fuel power plants, they should be able to play the important roles of synchronous generators such as power low-frequency oscillation (LFO) damping. In this paper, an LFO damping system is suggested for a LWPF, based on a phasor measurement unit (PMU)-based fractional-order proportional–integral–derivative (FOPID) controller with wide range of stability area and proper robustness to many power system uncertainties. Finally, the performance of the proposed method is evaluated under different operating conditions in a benchmark smart system

Fuzzy Numbers Based Algorithm for Interruptions Frequency Estimation on Distribution Smart Grids

The reliability of a distribution system can be considered from the point of view of a particular customer or from the entire supply system, e.g. average interruption frequency (SAIFI) and system average interruption duration (SAIDI). The reliability indices which are referring at the interruption frequency are mainly influenced by the adopted operational configuration and the minimization in operation of SAIFI represents an important aim. A substantial change in the power distribution systems behavior appears because of the large-scale introduction of distributed generation as distributed generators connected directly to the main distribution system or inside of microgrids which are also connected to the main distribution system. In the paper, authors propose an original fuzzy based method for the estimation of the average interruption frequency on distribution smart grids