Flexible Design and Operation of a Smart Charging Microgrid

Abstract

The reliability theory of repairable systems is vastly different from that of non-repairable systems. The authors have recently proposed a "decision-based" framework to design and maintain repairable systems for optimal performance and reliability using a set of metrics such as minimum failure free period, number of failures in planning horizon (lifecycle), and cost. The optimal solution includes the initial design, the system maintenance throughout the planning horizon, and the protocol to operate the system. In this work, we extend this idea by incorporating flexibility and demonstrate our approach using a smart charging electric microgrid architecture. The flexibility is realized by allowing the architecture to change with time. Our approach "learns" the working characteristics of the microgrid. We use actual load and supply data over a short time to quantify the load and supply random processes and also establish the correlation between them. The quantified processes are then used to generate load and supply realizations over the long planning horizon. We show how this can reduce the computational effort when simulating microgrids for the entire planning horizon without impeding on their design under various operating scenarios considering uncertainty.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
ADA595081

Entities

People

  • Annette Skowronska
  • David Gorsich
  • Matthew P. Castanier
  • Vijitashwa Pandey
  • Zissimos P. Mourelatos

Organizations

  • Oakland University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Availability
  • Control Systems
  • Equations
  • Extrapolation
  • Genetic Algorithms
  • Maintenance
  • Mathematical Programming
  • Optimization
  • Probability
  • Random Variables
  • Reliability
  • Solar Panels
  • Stochastic Processes
  • Time Intervals
  • Uncertainty
  • White Noise

Fields of Study

  • Computer science
  • Engineering

Readers

  • Energy Conservation and Renewable Energy Engineering.
  • Mathematical Modeling and Probability Theory.
  • Systems Analysis and Design