Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Iowa Disaster Recovery Framework

This Iowa Disaster Recovery Framework (IDRF) is meant to detail a lasting, flexible structure and system to coordinate and manage disaster recovery in the long-term. The IDRF provides a structure to engage stakeholders such as individual Iowans, local and tribal governments, businesses, voluntary, faith-based and community organizations as well as state and federal agencies to identify and resolve recovery challenges both before and after disaster events. It applies to all disasters, recovery partners, and recovery activities

Oceans and the Sustainable Development Goals: Co-Benefits, Climate Change & Social Equity

Achieving ocean sustainability is paramount for coastal communities and marine industries, yet is also inextricably linked to much broader global sustainable development—including increased resilience to climate change and improved social equity—as envisioned by the UN 2030 Agenda for Sustainable Development. This report highlights the co-benefits from achieving each SDG 14 target: progress towards each of the other 161 SDG targets when ocean targets are met, given ten-year lag times between ocean targets and other SDG targets. The identification of co-benefits is based on input from more than 30 scientific experts in the Nereus Program. Below we highlight notable co-benefits of achieving each target within SDG 14

Resilient Load Restoration in Microgrids Considering Mobile Energy Storage Fleets: A Deep Reinforcement Learning Approach

Mobile energy storage systems (MESSs) provide mobility and flexibility to enhance distribution system resilience. The paper proposes a Markov decision process (MDP) formulation for an integrated service restoration strategy that coordinates the scheduling of MESSs and resource dispatching of microgrids. The uncertainties in load consumption are taken into account. The deep reinforcement learning (DRL) algorithm is utilized to solve the MDP for optimal scheduling. Specifically, the twin delayed deep deterministic policy gradient (TD3) is applied to train the deep Q-network and policy network, then the well trained policy can be deployed in on-line manner to perform multiple actions simultaneously. The proposed model is demonstrated on an integrated test system with three microgrids connected by Sioux Falls transportation network. The simulation results indicate that mobile and stationary energy resources can be well coordinated to improve system resilience.Comment: Submitted to 2020 IEEE Power and Energy Society General Meetin

Resilience of modern power distribution networks with active coordination of EVs and smart restoration

Abstract In this modern era of cyber–physical–social systems, there is a need of dynamic coordination strategies for electric vehicles (EVs) to enhance the resilience of modern power distribution networks (MPDNs). This paper proposes a two‐stage EV coordination framework for MPDN smart restoration. The first stage is to introduce a novel proactive EV prepositioning model to optimize planning prior to a rare event, and thereby enhance the MPDN survivability in its immediate aftermath. The second stage involves creating an advanced spatial–temporal EV dispatch model to maximize the number of available EVs for discharging, thereby improving the MPDN recovery after a rare event. The proposed framework also includes an information system to further enhance MPDN resilience by effectively organizing data exchange among intelligent transportation system and smart charging system, and EV users. In addition, a novel bidirectional geographic graph is proposed to optimize travel plans, covering a large penetration of EVs and considering variations in traffic conditions. The effectiveness is assessed on a modified IEEE 123‐node test feeder with real‐world transportation and charging infrastructure. The results demonstrate a significant improvement in MPDN resilience with smart restoration strategies. The validation and sensitivity analyses evidence a significant superiority of the proposed framework