When it comes to electric power distribution, the fundamental goals of today’s electric utilities have not changed much in fifty years. They comprise a safer distribution system that poses minimal risks to the public and to utility workers, the reliable and stable delivery of power to critical, commercial, and residential customers, and economically sound business operations that meet both shareholder and ratepayer expectations. The challenges to these goals, however, have grown significantly over the past decade. Increasingly intense and frequent weather events raise the threat of outages and environmental risks; the process of shifting from a centralized to decentralized grid to meet decarbonization mandates creates complexities; and the integration of renewable energy sources and the proliferation of electric vehicles are escalating the demands on an aging distribution grid. This presentation gives an overview of how big these underground power grid challenges are and how machine learning and deep learning are tools to tackle some of these challenges. Co-sponsored by: Eversource Energy Center Speaker(s): Steffen Ziegler , Tim Morello Virtual: https://events.vtools.ieee.org/m/324708

When it comes to electric power distribution, the fundamental goals of today’s electric utilities have not changed much in fifty years. They comprise a safer distribution system that poses minimal risks to the public and to utility workers, the reliable and stable delivery of power to critical, commercial, and residential customers, and economically sound business operations that meet both shareholder and ratepayer expectations. The challenges to these goals, however, have grown significantly over the past decade. Increasingly intense and frequent weather events raise the threat of outages and environmental risks; the process of shifting from a centralized to decentralized grid to meet decarbonization mandates creates complexities; and the integration of renewable energy sources and the proliferation of electric vehicles are escalating the demands on an aging distribution grid. This presentation gives an overview of how big these underground power grid challenges are and how machine learning and deep learning are tools to tackle some of these challenges. Co-sponsored by: Eversource Energy Center Speaker(s): Steffen Ziegler , Tim Morello Virtual: https://events.vtools.ieee.org/m/324708

When it comes to electric power distribution, the fundamental goals of today’s electric utilities have not changed much in fifty years. They comprise a safer distribution system that poses minimal risks to the public and to utility workers, the reliable and stable delivery of power to critical, commercial, and residential customers, and economically sound business operations that meet both shareholder and ratepayer expectations. The challenges to these goals, however, have grown significantly over the past decade. Increasingly intense and frequent weather events raise the threat of outages and environmental risks; the process of shifting from a centralized to decentralized grid to meet decarbonization mandates creates complexities; and the integration of renewable energy sources and the proliferation of electric vehicles are escalating the demands on an aging distribution grid. This presentation gives an overview of how big these underground power grid challenges are and how machine learning and deep learning are tools to tackle some of these challenges. Co-sponsored by: Eversource Energy Center Speaker(s): Steffen Ziegler , Tim Morello Virtual: https://events.vtools.ieee.org/m/324708

When it comes to electric power distribution, the fundamental goals of today’s electric utilities have not changed much in fifty years. They comprise a safer distribution system that poses minimal risks to the public and to utility workers, the reliable and stable delivery of power to critical, commercial, and residential customers, and economically sound business operations that meet both shareholder and ratepayer expectations. The challenges to these goals, however, have grown significantly over the past decade. Increasingly intense and frequent weather events raise the threat of outages and environmental risks; the process of shifting from a centralized to decentralized grid to meet decarbonization mandates creates complexities; and the integration of renewable energy sources and the proliferation of electric vehicles are escalating the demands on an aging distribution grid. This presentation gives an overview of how big these underground power grid challenges are and how machine learning and deep learning are tools to tackle some of these challenges. Co-sponsored by: Eversource Energy Center Speaker(s): Steffen Ziegler , Tim Morello Virtual: https://events.vtools.ieee.org/m/324708

When it comes to electric power distribution, the fundamental goals of today’s electric utilities have not changed much in fifty years. They comprise a safer distribution system that poses minimal risks to the public and to utility workers, the reliable and stable delivery of power to critical, commercial, and residential customers, and economically sound business operations that meet both shareholder and ratepayer expectations. The challenges to these goals, however, have grown significantly over the past decade. Increasingly intense and frequent weather events raise the threat of outages and environmental risks; the process of shifting from a centralized to decentralized grid to meet decarbonization mandates creates complexities; and the integration of renewable energy sources and the proliferation of electric vehicles are escalating the demands on an aging distribution grid. This presentation gives an overview of how big these underground power grid challenges are and how machine learning and deep learning are tools to tackle some of these challenges. Co-sponsored by: Eversource Energy Center Speaker(s): Steffen Ziegler , Tim Morello Virtual: https://events.vtools.ieee.org/m/324708

When it comes to electric power distribution, the fundamental goals of today’s electric utilities have not changed much in fifty years. They comprise a safer distribution system that poses minimal risks to the public and to utility workers, the reliable and stable delivery of power to critical, commercial, and residential customers, and economically sound business operations that meet both shareholder and ratepayer expectations. The challenges to these goals, however, have grown significantly over the past decade. Increasingly intense and frequent weather events raise the threat of outages and environmental risks; the process of shifting from a centralized to decentralized grid to meet decarbonization mandates creates complexities; and the integration of renewable energy sources and the proliferation of electric vehicles are escalating the demands on an aging distribution grid. This presentation gives an overview of how big these underground power grid challenges are and how machine learning and deep learning are tools to tackle some of these challenges. Co-sponsored by: Eversource Energy Center Speaker(s): Steffen Ziegler , Tim Morello Virtual: https://events.vtools.ieee.org/m/324708

When it comes to electric power distribution, the fundamental goals of today’s electric utilities have not changed much in fifty years. They comprise a safer distribution system that poses minimal risks to the public and to utility workers, the reliable and stable delivery of power to critical, commercial, and residential customers, and economically sound business operations that meet both shareholder and ratepayer expectations. The challenges to these goals, however, have grown significantly over the past decade. Increasingly intense and frequent weather events raise the threat of outages and environmental risks; the process of shifting from a centralized to decentralized grid to meet decarbonization mandates creates complexities; and the integration of renewable energy sources and the proliferation of electric vehicles are escalating the demands on an aging distribution grid. This presentation gives an overview of how big these underground power grid challenges are and how machine learning and deep learning are tools to tackle some of these challenges. Co-sponsored by: Eversource Energy Center Speaker(s): Steffen Ziegler , Tim Morello Virtual: https://events.vtools.ieee.org/m/324708