Pierluigi Siano – författare

Decentralized Frameworks for Future Power Systems: Operation, Planning and Control Perspectives�is the first book to consider the principles and applications of decentralized decision-making in future power networks. The work opens by defining the emerging power system network as a system-of-systems (SoS), exploring the guiding principles behind optimal solutions for operation and planning problems. Chapters emphasize the role of regulations, prosumption behaviors, and the implementation of transactive energy processes as key components in decentralizing power systems. Contributors explore local markets, distribution system operation and proactive load management. The role of cryptocurrencies in smoothing transactive distributional challenges are presented.

Final sections cover energy system planning, particularly in terms of consumer smart meter technologies and distributed optimization methods, including artificial intelligence, meta-heuristic, heuristic, mathematical and hybrid approaches. The work closes by considering decentralization across the cybersecurity, distributed control, market design and power quality optimization vertices.

Nonlinear Optimal Control and Differential Flatness for Renewable Energy Systems and EVs in the Smart Grid covers nonlinear control and estimation for electric power systems, electric vehicles, and the smart grid. The control and estimation methods presented are characterized by global stability and convergence proofs, which means that the function of renewable electric power systems and electric vehicles presented will be of assured performance and it can be ascertained that it will remain reliable under variable operating conditions.

In particular, this new monograph deals with the open research issues and challenges of nonlinear control based on approximate linearization and on global linearization of the associated state-space models. It also successfully addresses the quest for specialized knowledge in renewable electric power systems and in electric vehicles traction systems, bringing new findings in two areas: Firstly, nonlinear control for electric machines (synchronous and asynchronous) with application in the exploitation of renewable energy sources and in the traction system of electric vehicles, and secondly, nonlinear control of power electronics which are used for the connection of renewable energy sources to the electricity grid and for the powertrains of electric vehicles (control of AC to DC converters, control of inverters).

This book is of interest to researchers, graduate students, faculty, engineers, and other technical personnel working on control problems and estimation methods, renewable energy systems, electric vehicles, and smart grid technologies.

High penetration of renewable energy sources (RESs) imposes several techno-economic challenges to distribution system operators (DSOs) due to their variability in power generation and, hence, increases the need for additional operational flexibility. Operational flexibility aims at securely covering the possible variations at the minimum cost using emerging flexible alternatives or designing novel local market mechanisms to incentivize flexibility providers. In such a situation, the DSOs can use the potential of flexible options such as energy storages (ESs), demand response (DR), plug-in electric vehicles (PEVs), or on-site fast run generators. However, each of the mentioned flexible resources has its own specific characteristics and requirements that should be taken into account, and this raises the complexity. Optimal network reconfiguration schemes are the other solution for increasing power system flexibility at the distribution level.

There is a great research gap related to renewable-based distribution network planning from a flexibility point of view. Therefore, this book aims to discuss the additional flexibility needs introduced by RESs and describe general approaches to analyze the need for and provision of additional flexibility in future distribution networks at both the planning and operational time frames.

This book successfully suggests new solutions and techniques to increase the flexibility in distribution systems. It also highlights the needs for moving towards smart distribution grids in order to enhance the flexibility in modern and future power systems.

High penetration of renewable energy sources (RESs) imposes several techno-economic challenges to distribution system operators (DSOs) due to their variability in power generation and, hence, increases the need for additional operational flexibility. Operational flexibility aims at securely covering the possible variations at the minimum cost using emerging flexible alternatives or designing novel local market mechanisms to incentivize flexibility providers. In such a situation, the DSOs can use the potential of flexible options such as energy storages (ESs), demand response (DR), plug-in electric vehicles (PEVs), or on-site fast run generators. However, each of the mentioned flexible resources has its own specific characteristics and requirements that should be taken into account, and this raises the complexity. Optimal network reconfiguration schemes are the other solution for increasing power system flexibility at the distribution level.

There is a great research gap related to renewable-based distribution network planning from a flexibility point of view. Therefore, this book aims to discuss the additional flexibility needs introduced by RESs and describe general approaches to analyze the need for and provision of additional flexibility in future distribution networks at both the planning and operational time frames.

This book successfully suggests new solutions and techniques to increase the flexibility in distribution systems. It also highlights the needs for moving towards smart distribution grids in order to enhance the flexibility in modern and future power systems.

In the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, domestic loads, and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution, automation, energy regulation & control, and energy trading. This book covers the applications of various big data analytics,artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies for modern power systems such as the Internet of Things, Blockchain for smart home and smart city solutions in depth.

Technical topics discussed in the book include:

• Hybrid smart energy system technologies

• Smart meters

• Energy demand forecasting

• Use of different protocols and communication in smart energy

systems

• Power quality and allied issues and mitigation using AI

• Intelligent transportation

• Virtual power plants

• AI based smart energy business models

• Smart home solutions

• Blockchain solutions for smart grids.

In the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, domestic loads, and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution, automation, energy regulation & control, and energy trading. This book covers the applications of various big data analytics,artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies for modern power systems such as the Internet of Things, Blockchain for smart home and smart city solutions in depth.

Technical topics discussed in the book include:

• Hybrid smart energy system technologies

• Smart meters

• Energy demand forecasting

• Use of different protocols and communication in smart energy

systems

• Power quality and allied issues and mitigation using AI

• Intelligent transportation

• Virtual power plants

• AI based smart energy business models

• Smart home solutions

• Blockchain solutions for smart grids.

In the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, and domestic & industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution automation, energy regulation and control, and energy trading.

This book covers the applications of various big data analytics, artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies such as the Internet of Things, blockchain, etc. for smart home solutions, and smart city solutions in depth in the context of the modern power systems.

Technical topics discussed in the book include:

• Hybrid smart energy system technologies

• Energy demand forecasting

• Use of different protocols and communication in smart energy systems

• Power quality and allied issues and mitigation using AI

• Intelligent transportation

• Virtual power plants

• AI business models.

In the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, and domestic & industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution automation, energy regulation and control, and energy trading.

This book covers the applications of various big data analytics, artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies such as the Internet of Things, blockchain, etc. for smart home solutions, and smart city solutions in depth in the context of the modern power systems.

Technical topics discussed in the book include:

• Hybrid smart energy system technologies

• Energy demand forecasting

• Use of different protocols and communication in smart energy systems

• Power quality and allied issues and mitigation using AI

• Intelligent transportation

• Virtual power plants

• AI business models.

Presents a comprehensive guide to transforming power systems through data

Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. As the electricity sector grapples with increasing complexity, this timely volume responds to a growing demand for adaptive strategies that enable accurate forecasting, intelligent tariff design, and optimized resource allocation, underpinned by advanced analytics and machine learning.

Drawing on global expertise and real-world case studies, the book bridges the theoretical and practical dimensions of energy systems management, providing deep insight into how data collected from smart meters, SCADA systems, and IoT devices can be mined for predictive modeling, demand response, and peak load management. The book’s accessible structure and didactic approach make it suitable for a wide readership, while its breadth of topics ensures relevance across the spectrum of energy challenges.

Integrating rigorous analysis with application-oriented strategies, this book:

Designed for a broad audience, Data-Driven Energy Management and Tariff Optimization in Power Systems is ideal for upper-level undergraduate and graduate courses in energy management, power systems analytics, and smart grids as part of electrical engineering or energy policy programs. It is also an essential reference for power system engineers, energy analysts, researchers, and policymakers involved in grid planning and optimization.

Presents a comprehensive guide to transforming power systems through data

Data-Driven Energy Management and Tariff Optimization in Power Systems offers an authoritative examination of how data science is reshaping the energy landscape. As the electricity sector grapples with increasing complexity, this timely volume responds to a growing demand for adaptive strategies that enable accurate forecasting, intelligent tariff design, and optimized resource allocation, underpinned by advanced analytics and machine learning.

Drawing on global expertise and real-world case studies, the book bridges the theoretical and practical dimensions of energy systems management, providing deep insight into how data collected from smart meters, SCADA systems, and IoT devices can be mined for predictive modeling, demand response, and peak load management. The book’s accessible structure and didactic approach make it suitable for a wide readership, while its breadth of topics ensures relevance across the spectrum of energy challenges.

Integrating rigorous analysis with application-oriented strategies, this book:

Designed for a broad audience, Data-Driven Energy Management and Tariff Optimization in Power Systems is ideal for upper-level undergraduate and graduate courses in energy management, power systems analytics, and smart grids as part of electrical engineering or energy policy programs. It is also an essential reference for power system engineers, energy analysts, researchers, and policymakers involved in grid planning and optimization.