Greedy Algorithm for Minimizing the Cost of Routing Power on a Digital Microgrid

Zhengqi Jiang, Vinit Sahasrabudhe, Ahmed Mohamed, Haim Grebel and Roberto Rojas-Cessa
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Zhengqi Jiang: Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Vinit Sahasrabudhe: Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Ahmed Mohamed: Department of Electrical Engineering, City College of City University of New York, New York, NY 10031, USA
Haim Grebel: Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Roberto Rojas-Cessa: Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Authors registered in the RePEc Author Service: Ahmed Elsayed ()

Energies, 2019, vol. 12, issue 16, 1-19

Abstract: In this paper, we propose the greedy smallest-cost-rate path first (GRASP) algorithm to route power from sources to loads in a digital microgrid (DMG). Routing of power from distributed energy resources (DERs) to loads of a DMG comprises matching loads to DERs and the selection of the smallest-cost-rate path from a load to its supplying DERs. In such a microgrid, one DER may supply power to one or many loads, and one or many DERs may supply the power requested by a load. Because the optimal method is NP-hard, GRASP addresses this high complexity by using heuristics to match sources and loads and to select the smallest-cost-rate paths in the DMG. We compare the cost achieved by GRASP and an optimal method based on integer linear programming on different IEEE test feeders and other test networks. The comparison shows the trade-offs between lowering complexity and achieving optimal-cost paths. The results show that the cost incurred by GRASP approaches that of the optimal solution by small margins. In the adopted networks, GRASP trades its lower complexity for up to 18% higher costs than those achieved by the optimal solution.

Keywords: digital microgrid; power grid; integer linear programming; routing energy; distributed energy resources; Dijkstra algorithm; integer linear programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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