We present a hierarchical framework aimed at decentralizing the distribution systems market operations using localized peer-to-peer energy markets. A hierarchically designed decision-making algorithm approaches the power systems market operations from a bottom-up perspective. The three layers of the hierarchical framework operate in orchestration to enable prosumers (the grass-root ac- tors) to maximize their revenues - hence, a prosumer-centric framework. The design of the framework incorporates existing smart grid technologies (Virtual Power Plants, Microgrids, Distributed Energy Resources) and redefines their functional objectives to align them with the decentralization paradigm focused on empowering the bottom-up grid operations approach. On one hand, the framework is enabling prosumers with simultaneous access to the buy-sell choices that help them maximize their cost savings while ensuring their consumption patterns and preferences are not being traded off as a result of top-down operational decisions. On the other hand, it is designed to operate in harmony with the existing top-down grid operations mechanisms - thereby reducing the potential friction in its adaptation. This marriage of the top-down and bottom-up operational approaches is facilitated through meticulous orchestration of operational timescales. The frameworkâ\euro™s novel design also incorporates scalability and interoperability considerations, thereby tackling the challenge of decentralization holistically.

Developing aerial robots that can both safely navigate and execute assigned mission without any human intervention â\euro“ i.e., fully autonomous aerial mobility of passengers and goods â\euro“ is the larger vision that guides the research, design, and development efforts in the aerial autonomy space. However, it is highly challenging to concurrently operationalize all types of aerial vehicles that are operating fully autonomously sharing the airspace. Full autonomy of the aerial transportation sector includes several aspects, such as design of the technology that powers the vehicles, operations of multi-agent fleets, and process of certification that meets stringent safety requirements of aviation sector. Thereby, Autonomous Advanced Aerial Mobility is still a vague term and its consequences for researchers and professionals are ambiguous. To address this gap, we present a comprehensive perspective on the emerging field of autonomous advanced aerial mobility, which involves the use of unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft for various applications, such as urban air mobility, package delivery, and surveillance. The article proposes a scalable and extensible autonomy framework consisting of four main blocks: sensing, perception, planning, and controls. Furthermore, the article discusses the challenges and opportunities in multi-agent fleet operations and management, as well as the testing, validation, and certification aspects of autonomous aerial systems. Finally, the article explores the potential of monolithic models for aerial autonomy and analyzes their advantages and limitations. The perspective aims to provide a holistic picture of the autonomous advanced aerial mobility field and its future directions.

Electricity load forecasting for buildings and campuses is becoming increasingly important as the penetration of distributed energy resources (DERs) grows. Efficient operation and dispatch of DERs require reasonably accurate predictions of future energy consumption in order to conduct near-real-time optimized dispatch of on-site generation and storage assets. Electric utilities have traditionally performed load forecasting for load pockets spanning large geographic areas, and therefore forecasting has not been a common practice by buildings and campus operators. Given the growing trends of research and prototyping in the grid-interactive efficient buildings domain, characteristics beyond simple algorithm forecast accuracy are important in determining the algorithm’s true utility for smart buildings. Other characteristics include the overall design of the deployed architecture and the operational efficiency of the forecasting system. In this work, we present a deep-learning-based load forecasting system that predicts the building load at 1-hour intervals for 18 hours in the future. We also discuss challenges associated with the real-time deployment of such systems as well as the research opportunities presented by a fully functional forecasting system that has been developed within the National Renewable Energy Laboratory’s Intelligent Campus program.

Point-of-view article. Abstract: For more than a century, the grid has operated in a centralized top-down fashion. However, as the penetration of the distributed energy resources (DERs) grows, the grid edge is increasingly infused with intelligent computing and communication capabilities. Thus, the bottom-up approach to grid operations inclined toward decentralization of energy systems will likely gain momentum alongside the existing centralized paradigm. In this point of view article, we aim to highlight the need for and outline a credible path toward restructuring the current operational architecture of the electricity grid in view of the ongoing decentralization trends with an emphasis on peer-to-peer energy trading. We further suggest that blockchain is an effective technology for facilitating the synergistic operations of top-down and bottom-up approaches to grid management.