The following paper presents a framework for the optimal control of an electric warship using a load profile derived from an operational vignette. This framework consists of three key components: a reduced order model of an electric ship, a discretization of the resulting constitutive equations using an orthogonal spline collocation method, and an optimization engine to solve the resulting formulation. Once assembled, this control framework is validated through its application to a four zone model of a medium voltage DC (MVDC) electric ship using a load profile from an operational vignette,
The Energy Surety Design Methodology (ESDM) provides a systematic approach for engineers and researchers to create a preliminary electric grid design, thus establishing a means to preserve and quickly restore customer-specified critical loads. Over a decade ago, Sandia National Laboratories (Sandia) defined Energy Surety for applications with energy systems to include elements of reliability, security, safety, cost, and environmental impact. Since then, Sandia has employed design concepts of energy surety for over 20 military installations and their interaction with utility systems, including the Smart Power Infrastructure Demonstration for Energy Reliability and Security (SPIDERS) Joint Capability Technology Demonstration (JCTD) project. In recent years, resilience has also been added as a key element of energy surety. This methodology document includes both process recommendations and technical guidance, with references to useful tools and analytic approaches at each step of the process.
Wilson, David G.; Glover, Steven F.; Cook, Marvin A.; Weaver, Wayne W.; Robinett, Rush D.; Young, Joseph Y.; Borraccinia, Joe B.; Ferrese, Frank F.; Amy, John A.; Markel, Stephen M.; McCoy, Tim J.
This report summarizes collaborative efforts between Secure Scalable Microgrid and Korean Institute of Energy Research team members . The efforts aim to advance microgrid research and development towards the efficient utilization of networked microgrids . The collaboration resulted in the identification of experimental and real time simulation capabilities that may be leveraged for networked microgrids research, development, and demonstration . Additional research was performed to support the demonstration of control techniques within real time simulation and with hardware in the loop for DC microgrids .
This research presents a predictive engine that integrates into an on-line optimal control planner for electrical microgrids. This controller models the behavior of the underlying system over a specified time horizon and then solves for a control over this period. In an electrical microgrid, such predictions are challenging to obtain in the presence of errors in the sensor information. The likelihood of instrumentation errors increases as microgrids become more complex and cyber threats more common. In order to overcome these difficulties, details are provided about a predictive engine robust to errors.