  Microgrids with distributed energy resources are being favored in various
communities to lower the dependence on utility-supplied energy and cut the CO2
emissions from coal-based power plants. This paper presents a co-optimization
strategy for distributed energy resource planning to minimize total annualized
cost at the maximal fuel savings. Furthermore, the proposed scheme aids the
community microgrids in satisfying the requirements of U.S. Department of
Energy (DOE) and state renewable energy mandates. The method of Lagrange
multipliers is employed to maximize fuel savings by satisfying
Karush-Kuhn-Tucker conditions. With the Fourier transform and particle swarm
optimization, the right mix of distributed energy resources is determined to
decrease the annualized cost. A case study to test the proposed scheme for a
community microgrid is presented. To validate its effectiveness, an economic
justification of the solution and its comparison with HOMER Pro are also
illustrated.
