An Optimization Framework for Dynamic Hybrid Energy Systems

A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid energy system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes intrinsic properties of the base generation, power cycle, and electrical storage components to minimize variability in the HES. The second problem takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. Variability in electrical power applied to high temperature steam electrolysis is shown to be reduced by 18% in the unconstrained case and 11% in the constrained case. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.

[1]  W. Shyy,et al.  Effective Transport Properties of LiMn2O4 Electrode via Particle-Scale Modeling , 2011 .

[2]  A. Ebenezer Jeyakumar,et al.  Hybrid PSO–SQP for economic dispatch with valve-point effect , 2004 .

[3]  Andreas Junghanns,et al.  The Functional Mockup Interface for Tool independent Exchange of Simulation Models , 2011 .

[4]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[5]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[6]  Xiaosong Hu,et al.  A comparative study of equivalent circuit models for Li-ion batteries , 2012 .

[7]  Michael Wetter,et al.  Co-simulation of building energy and control systems with the Building Controls Virtual Test Bed , 2011 .

[8]  W. Shyy,et al.  Effect of cycling rate, particle size and transport properties on lithium-ion cathode performance , 2010 .

[9]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[10]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[11]  Christiaan J. J. Paredis,et al.  A Rational Design Approach to Gaussian Process Modeling for Variable Fidelity Models , 2011, DAC 2011.

[12]  Joaquim R. R. A. Martins,et al.  Design of a lithium-ion battery pack for PHEV using a hybrid optimization method , 2014 .

[13]  W. Shyy,et al.  Optimization of a Single Lithium-Ion Battery Cell with a Gradient-Based Algorithm , 2013 .

[14]  Joaquim R. R. A. Martins,et al.  Energy Density Comparison of Li-ion Cathode Materials Using Dimensional Analysis , 2013 .

[15]  J. O’Brien,et al.  High-temperature electrolysis for large-scale hydrogen and syngas production from nuclear energy: summary of system simulation and economic analyses , 2010 .

[16]  W. Shyy,et al.  Optimization of LiMn2O4 electrode properties in a gradient- and surrogate-based framework , 2013 .

[17]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[18]  Humberto E. Garcia,et al.  Dynamic analysis of hybrid energy systems under flexible operation and variable renewable generation – Part I: Dynamic performance analysis , 2013 .