Co-Optimization Scheme for Distributed Energy Resource Planning in Community Microgrids

TL;DR

This paper introduces a co-optimization strategy for community microgrids that minimizes costs and maximizes fuel savings by optimally planning distributed energy resources using advanced mathematical techniques.

Contribution

It presents a novel co-optimization scheme employing Lagrange multipliers, Fourier transform, and particle swarm optimization for resource planning in community microgrids.

Findings

Reduces total annualized costs of microgrids

Maximizes fuel savings effectively

Outperforms HOMER Pro in case studies

Abstract

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.