Computational Intelligence Applications in Smart Grids: Enabling Methodologies for Proactive and Self Organizing Power Systems

This book considers the emerging technologies and methodologies of the application of computational intelligence to smart grids. From a conceptual point of view, the smart grid is the convergence of information and operational technologies applied to the electric grid, allowing sustainable options to customers and improved levels of security. Smart grid technologies include advanced sensing systems, two-way high-speed communications, monitoring and enterprise analysis software, and related services used to obtain location-specific and real-time actionable data for the provision of enhanced services for both system operators (i.e. distribution automation, asset management, advanced metering infrastructure) and end-users (i.e. demand side management, demand response). In this context, a crucial issue is how to support the evolution of existing electrical grids from static hierarchal systems to self-organizing, highly scalable and pervasive networks. Modern trends are oriented toward the employment of computational intelligence techniques for deploying advanced control, protection and monitoring architectures that move away from the older centralized paradigm to systems distributed across the field with an increasing pervasion of intelligence devices. The large-scale deployment of computational intelligence technologies in smart grids could lead to a more efficient tasks distribution amongst energy resources and, consequently, to a sensible improvement of the electrical grid flexibility

SECURE AND RELIABLE POWER SUPPLY WITH CHALLENGES- SMART GRID

Smart Grid implementations will certainly increase the quantity, quality and use of information available from advanced sensing, computing and communications hardware as well as software. There is virtually universal agreement that it is necessary to upgrade the electric grid to increase overall system efficiency and reliability. Many technologies currently in use by the grid is outdated and in many cases unreliable. The reliance on old technology leads to inefficient systems, costing unnecessary money to the utilities, consumers and taxpayers. To upgrade the grid and to operate an improved grid will require significant dependence on distributed intelligence and broadband communication capabilities. The access and communications capabilities require the latest in security technology which are reliable for extremely large, wide-area communications networks. This paper discusses features, reasons of development, barriers and their solutions for a smart grid system

Computational approaches for voltage stability monitoring and control in power systems

The electric power grid is a complex, non-linear, non-stationary system comprising of thousands of components such as generators, transformers, transmission lines and advanced power electronics based control devices, and customer loads. The complexity of the grid has been further increased by the introduction of smart grid technologies. Smart grid technology adds to the traditional power grids advanced methods of communication, computation and control as well as increased use of renewable energy sources such as wind and solar farms and a higher penetration of plug-in electric vehicles among others. The smart grid has resulted in much more distributed generation, bi-directional powerflows between customers and the grid, and the semi-autonomous control of subsystems. Due to this added complexity of the grid and the need to maintain reliable, quality, efficient, economical, and environmentally friendly power supply, advanced monitoring and control technologies are needed for real-time operation of various systems that integrate into the transmission and distribution network. In this dissertation, the development of computational intelligence methods for on-line monitoring of voltage stability in a power system is presented. In order to carry out on-line assessment of voltage stability, data from Phasor Measurement Units (PMUs) is utilized. An intelligent algorithm for optimal location of PMUs for voltage stability monitoring is developed. PMU information is used for estimation of voltage stability load index in a power system with plug-in electric vehicle and wind farm included. The estimated voltage stability index is applied in the development of an adaptive dynamic programming based optimal secondary voltage controller to coordinate the reactive power capability of two FACTS devices --Abstract, page iii

An AI-Layered with Multi-Agent Systems Architecture for Prognostics Health Management of Smart Transformers:A Novel Approach for Smart Grid-Ready Energy Management Systems

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning

A New Ranking Approach to Efficiently Detect Anomalies in Cyber Security of Substation

Smart Grid advancements present an undetermined level of risk to electric grid reliability. The coupling of the power infrastructure with complex computer networks substantially expand current cyber-attack surface area and will require significant advances in cyber security capabilities. New capabilities for smart grid system and networks, such as broadband and distributed intelligence capabilities can greatly enhance efficiency and reliability, but they may also create much new vulnerability if not deployed with the appropriate security controls. Providing security for a large system may seem an unfathomable task, and can leave utilities open to cyber-attacks. The problem is to provide an efficient security mechanism to the power grids. Already many mechanisms are proposed for network and host based cyber security these methods does not provide efficient security mechanism. A new mechanism is proposed based on ranking the network and host based anomalies using Gaussian approximation algorithm. This method will monitor the anomalies occurs in the substation and rank the continuous network level security by implementing additional features such as Traffic Analyzing System, Address Blocks System and Packet Filtering System. DOI: 10.17762/ijritcc2321-8169.15030

Large Scale Integration of Electric Vehicles into the Power Grid and Its Potential Effects on Power System Reliability

In this thesis, the potential effects of large scale integration of electric vehicles into the power grid are discussed in both the beneficial and detrimental aspects. The literature review gives a comprehensive introduction about the existing smart charging algorithms. According to the system structure and market mechanism, the smart charging algorithms can be divided into centralized and distributed method. With the knowledge of driving patterns and charging characteristics of electric vehicles, both the centralized and decentralized smart charging algorithms are studied in this research. Based on the smart charging pricing and sequential price update mechanism, a multi-agent based distributed smart charging algorithm is used in this research to flatten the load curve and therefore mitigate the potential detrimental effects caused by uncoordinated charging. Each EV agent has some extent of intelligence to solve its own charging scheduling problem. The optimization method used in this research is the binary hybrid GSA-PSO algorithm, which combines the merits of particle swarm optimization (PSO) and gravitational search algorithm (GSA), and has very good exploration and exploitation abilities. A V2G enabled centralized smart charging algorithm is also introduced in this thesis, each EV can earn revenues by discharging power into the grid. The dominant search matrix is used to resolve the \u27\u27curse of dimensionality\u27\u27 problem existing in the centralized optimization problems. Numerical case studies show both the distributed and V2G enabled smart charging algorithms can effectively transfer the charging load from the peak load period to the load valley hours. Because of the limited integration ratio of electric vehicles, most power system reliability methods do not evaluate the charging load of EVs separately in their analytical procedures. However, with a fast increasing integration level, the potential effects of large scale integration of EVs on the power system reliability should be comprehensively evaluated. The effects of EV charging on power system reliability in the planning phase is analyzed in this research based on the RBTS. The results show the uncontrolled charging will deteriorate the reliability level while the smart charging can effectively decrease the detrimental effect. The potential application of aggregated EV providing operating reserve to the grid as a kind of ancillary service is also discussed, and the related effects on power system reliability in operating phase are calculated using the modified PJM method. The case study shows the unit commitment risk of the system can decrease to a very low level with the additional operating reserve capacity provided by aggregated EVs, which can not only improve the system\u27s reliability level but also save the cost

Novel processes for smart grid information exchange and knowledge representation using the IEC common information model

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The IEC Common Information Model (CIM) is of central importance in enabling smart grid interoperability. Its continual development aims to meet the needs of the smart grid for semantic understanding and knowledge representation for a widening domain of resources and processes. With smart grid evolution the importance of information and data management has become an increasingly pressing issue not only because far more data is being generated using modern sensing, control and measuring devices but also because information is now becoming recognised as the ‘integral component’ that facilitates the optimal flexibility required of the smart grid. This thesis looks at the impacts of CIM implementation upon the landscape of smart grid issues and presents research from within National Grid contributing to three key areas in support of further CIM deployment. Taking the issue of Enterprise Information Management first, an information management framework is presented for CIM deployment at National Grid. Following this the development and demonstration of a novel secure cloud computing platform to handle such information is described. Power system application (PSA) models of the grid are partial knowledge representations of a shared reality. To develop the completeness of our understanding of this reality it is necessary to combine these representations. The second research contribution reports on a novel methodology for a CIM-based model repository to align PSA representations and provide a knowledge resource for building utility business intelligence of the grid. The third contribution addresses the need for greater integration of information relating to energy storage, an essential aspect of smart energy management. It presents the strategic rationale for integrated energy modeling and a novel extension to the existing CIM standards for modeling grid-scale energy storage. Significantly, this work has already contributed to a larger body of work on modeling Distributed Energy Resources currently under development at the Electric Power Research Institute (EPRI) in the USA.Dr. Martin Bradley on behalf of National Grid Plc. and the Engineering and Physical Sciences Research Council (EPSRC

Artificial Intelligence for Resilience in Smart Grid Operations

Today, the electric power grid is transforming into a highly interconnected network of advanced technologies, equipment, and controls to enable a smarter grid. The growing complexity of smart grid requires resilient operation and control. Power system resilience is defined as the ability to harden the system against and quickly recover from high-impact, low-frequency events. The introduction of two-way flows of information and electricity in the smart grid raises concerns of cyber-physical attacks. Proliferated penetration of renewable energy sources such as solar photovoltaic (PV) and wind power introduce challenges due to the high variability and uncertainty in generation. Unintentional disruptions and power system component outages have become a threat to real-time power system operations. Recent extreme weather events and natural disasters such as hurricanes, storms, and wildfires demonstrate the importance of resilience in the power system. It is essential to find solutions to overcome these challenges in maintaining resilience in smart grid. In this dissertation, artificial intelligence (AI) based approaches have been developed to enhance resilience in smart grid. Methods for optimal automatic generation control (AGC) have been developed for multi-area multi-machine power systems. Reliable AI models have been developed for predicting solar irradiance, PV power generation, and power system frequencies. The proposed short-horizon AI prediction models ranging from few seconds to a minute plus, outperform the state-of-art persistence models. The AI prediction models have been applied to provide situational intelligence for power system operations. An enhanced tie-line bias control in a multi-area power system for variable and uncertain environments has been developed with predicted PV power and bus frequencies. A distributed and parallel security-constrained optimal power flow (SCOPF) algorithm has been developed to overcome the challenges in solving SCOPF problem for large power networks. The methods have been developed and tested on an experimental laboratory platform consisting of real-time digital simulators, hardware/software phasor measurement units, and a real-time weather station

Multi-Agents Implementation Frameworks - An Overview

Large scale deployment of Micro Grids besides the advanced metering, demand response, reliable communications infrastructure set up has been incorporated into the technological road map of the future smart power grid. To congregate the operation and control needs of distributed energy resources in Micro-Grids the Multi-Agent System (MAS) seem to have splendid features. MAS is an emerging sub-field of Distributed Artificial Intelligence that has the potential to manage the changing face of electric power grid by inculcating intelligent agents into Micro-Grids. To create agents and implement MAS a framework, a platform is obligatory where in the agents reside and operate from. There is a wide range of Multi-agent platforms available on the web like Aglet, Grasshopper, DESIRE, Jadex, ZEUS, JADE etc. Each agent platform has to be evaluated according to the some criteria that have been mentioned in this endeavor. A brief relative appraisal of an assortment of agent platforms has been provided. According to various noteworthy researches the most used platform in micro-grid applications is JADE. This paper presents an architectural and functional overview of the agent building toolkit JADE framework for Multi-Agent System implementation. DOI: 10.17762/ijritcc2321-8169.150210