This book introduces readers to the fundamentals of the IEC 62559 Use Case Methodology, explains how it is related to the Smart Grid Architecture Model (SGAM), and details how a holistic view for both architecture and requirements engineering can be achieved. It describes a standardized and holistic approach to requirements engineering for smart grid projects based on work conducted in the context of the EU M/490 standardization mandate. Over the last years, this method has been established in Europe as the basic building block of requirements engineering in the utilities sector. The authors present a canonical, structured approach that users can apply to the Use Case Methodology and the SGAM, as well as open tools for this purpose. The application in various domains outside the smart grid is also discussed, as it can be used for critical infrastructures or system-of-systems domains like Industrie 4.0 and Ambient Assisted Living. Accordingly, the book also presents various architecture models for different fields of application, like EMAM, SCIAM, RAMI 4.0, and MAF.

The scope of the research presented includes semantic-based integration of data services in smart grids achieved through following the proposed (S2)In-approach developed corresponding to design science guidelines. This approach identifies standards and specifications, which are integrated in order to build the basis for the (S2)In-architecture. A process model is introduced in the beginning, which serves as framework for developing the target architecture. The first step of the process stipulates to define requirements for smart grid ICT-architectures being derived from established studies and divided into two classes: architecture and non-functional requirements (NFR). Based on the architecture requirements, the following specifications have been basically selected: The IEC CIM representing a domain-specific data model, the OPC UA being a communication standard with special respects to information modeling, and WSMO as an approach to realize the concept of Semantic Web Services. The next step specifies to develop both, a semantic information model (integration of CIM and OPC UA) and semantic services (integration of CIM and WSMO). These two components are then combined to obtain the target architecture, which allows precise descriptions of services as well as their combination and semi-automatic execution. Finally, the NFR are considered in order to evaluate the architecture based on simulated, representative use cases.

Energy efficiency and low-carbon technologies are key contributors to curtailing the emission of greenhouse gases that continue to cause global warming. The efforts to reduce greenhouse gas emissions also strongly affect electrical power systems. Renewable sources, storage systems, and flexible loads provide new system controls, but power system operators and utilities have to deal with their fluctuating nature, limited storage capabilities, and typically higher infrastructure complexity with a growing number of heterogeneous components. In addition to the technological change of new components, the liberalization of energy markets and new regulatory rules bring contextual change that necessitates the restructuring of the design and operation of future energy systems. Sophisticated component design methods, intelligent information and communication architectures, automation and control concepts, new and advanced markets, as well as proper standards are necessary in order to manage the higher complexity of such intelligent power systems that form smart grids. Due to the considerably higher complexity of such cyber-physical energy systems, constituting the power system, automation, protection, information and communication technology (ICT), and system services, it is expected that the design and validation of smart-grid configurations will play a major role in future technology and system developments. However, an integrated approach for the design and evaluation of smart-grid configurations incorporating these diverse constituent parts remains evasive. The currently available validation approaches focus mainly on component-oriented methods. In order to guarantee a sustainable, affordable, and secure supply of electricity through the transition to a future smart grid with considerably higher complexity and innovation, new design, validation, and testing methods appropriate for cyber-physical systems are required. Therefore, this book summarizes recent research results and developments related to the design and validation of smart grid systems.

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book.

The text comprehensively discusses smart grid operations and the use of big data analytics in overcoming the existing challenges. It covers smart power generation, transmission, and distribution, explains energy management systems, artificial intelligence, and machine learning–based computing. Presents a detailed state-of-the-art analysis of big data analytics and its uses in power grids Describes how the big data analytics framework has been used to display energy in two scenarios including a single house and a smart grid with thousands of smart meters Explores the role of the internet of things, artificial intelligence, and machine learning in smart grids Discusses edge analytics for integration of generation technologies, and decision-making approaches in detail Examines research limitations and presents recommendations for further research to incorporate big data analytics into power system design and operational frameworks The text presents a comprehensive study and assessment of the state-of-the-art research and development related to the unique needs of electrical utility grids, including operational technology, storage, processing, and communication systems. It further discusses important topics such as complex adaptive power system, self-healing power system, smart transmission, and distribution networks, and smart metering infrastructure. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology.

This book constitutes the refereed proceedings of the 9th International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2019, held in Linz, Austria, in August 2019. The 14 full papers presented were carefully reviewed and selected from 15 submissions, and 2 invited papers were also included. The papers are organized in the following topical sections: invited talks; methodologies and framework; agent-based production scheduling and control; data and knowledge; and MAS in various areas.

This book documents recent advances in the field of modeling, simulation, control, security and reliability of Cyber- Physical Systems (CPS) in power grids. The aim of this book is to help the reader gain insights into working of CPSs and understand their potential in transforming the power grids of tomorrow. This book will be useful for all those who are interested in design of cyber-physical systems, be they students or researchers in power systems, CPS modeling software developers, technical marketing professionals and business policy-makers.

Smart grid (SG) is considered a form of intelligent system that allows the electric grid to perform its functions efficiently. The SG is a network that allows for the flow of electrical energy and data, where the data is used to make intelligent decisions in the operation of the electric grid. Artificial intelligence (AI) techniques, such as expert system (ES), Machine Learning (ML), and deep Learning (DL) have brought an advancing frontier in power electronics and power engineering with their powerful data processing capabilities. The SG relies on the flow of data to make its intelligent control; therefore, AI technology is a perfect fit for the SG. The application of AI technology in the SG has the potential to improve the intelligence of the SG. This research topic is focused on ways of improving the data analysis and control of SG by leveraging technologies. Manuscripts with the progress made in solving a range of miscellaneous and critical problems in SG by leveraging AI methods such as ES, ML, and DL methods are welcome. Reviews and original research that describe the latest developments in this field are considered for publication in this research topic. The scope of this Research Topic will include the following themes, but are not limited to: 1. Data-driven and artificial intelligence approaches to enhancing flexibility and resilience of SG. 2. Expert system, Machine Learning and Deep Learning, reinforcement learning and transfer learning for applications in SG. 3. AI for development in ensuring high reliability and stability of electric power system with high penetration of renewable energy. 4. AI for studies in operation protection, integrated planning, and control of SG systems. 5. AI for development in diagnostics and diagnostics for SG. 6. Health monitoring of a modern wind generation system using an adaptive neuro-fuzzy system. 7. Space vector fault pattern identification of a smart grid subsystem by neural mapping. 8. Control techniques, mathematical programming methods, optimization techniques and metaheuristics applied in SG. 9. AI and optimization techniques for green energy and carbon footprint. 10. Novel applications of AI-based smart grids in smart cities, smart transportation, smart healthcare, and smart manufacturing.