Nonlinear Power Flow Control Design Methodology for Navy Electric Ship Microgrid Energy Storage Requirements
Creators
- 1. R&D Controls Engineer, PI, Sandia Electric Ship Program, Sandia National Labs, Albuquerque, NM, USA
- 2. Professor, Power Electronics/Controls/Power System, Michigan Technological University, Houghton, MI, USA
- 3. Professor, Nonlinear Controls & Optimization, Michigan Technological University, Houghton, MI, USA
- 4. Chief Scientist, Nonlinear Optimization and Predictive Controls, OptimoJoe, LLC, Albuquerque, NM, USA
- 5. R&D Manager, PM, Sandia Electric Ship Program, Sandia National Labs, Albuquerque, NM, USA
- 6. R&D Software Engineer, Lead Agents/Informatics Controls, Sandia National Labs, Albuquerque, NM, USA
- 7. Director Electric Ships Office PMS320 Program, NAVSEA, PMS 320, Washington, D.C., USA
- 8. Former Director of Navy Electric Ship and Power Systems Expert, McCoy Consulting, Box Elder, ND, USA
Description
As part of the U.S. Navy’s continued commitment to protecting U.S. interests at home and abroad, the Navy is investing in the development of new technologies that broaden U.S. warship capabilities and maintain U.S. naval superiority. NAVSEA is developing power systems technologies for the Navy to realize an all-electric warship. New nonlinear power system controls approaches are being developed to improve system performance in light of new electrically powered weaponry that behave as pulsed-loads. Advancements include the identification of pulsed-load profiles that identify Energy Storage System (ESS) requirements. A dynamic optimization engine has been developed and serves as the feedforward receding horizon control portion of the Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) feedback controls for ESS networked microgrid system. A Coalition Warfare Program (CWP) test scenario was selected. The CWP is defined with a Reduced Order Model (ROM) that includes; generation, ESS, and mission pulsed-loads. Several numerical simulation studies were conducted. The CWP scenario is bounded by a baseline mission load local ESS contrasted with no ESS full nonlinear metastable boundaries. The main goal is to minimize ESS size and weight while maintaining power system performance. This paper focuses on the control and optimization of ESS as an integral part of supporting critical mission loads and real-time control algorithm development to improve future energy efficiency for multi-mission activities.
Files
INEC 2018 Paper 077 Wilson FINAL.pdf
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Additional details
References
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