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.

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.