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

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-...

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Bibliographic Details
Published in:Energies (Basel) 2019-08, Vol.12 (16), p.3076
Main Authors: Jiang, Zhengqi, Sahasrabudhe, Vinit, Mohamed, Ahmed, Grebel, Haim, Rojas-Cessa, Roberto
Format: Article
Language:English
Subjects:
Algorithms
Alternative energy sources
Blackouts
Computer engineering
Control algorithms
Costs
digital microgrid
Dijkstra algorithm
distributed energy resources
Distributed generation
Electric power
Electric power distribution
Electric power grids
Electricity distribution
Electricity generation
Energy resources
Energy sources
Feeders
Generators
Greedy algorithms
Greenhouse effect
Greenhouse gases
integer linear programming
Integer programming
International conferences
Internet
Legs
Linear programming
Load
Load distribution
Load matching
Loads (forces)
power grid
Problem solving
Renewable resources
Routers
Routing
routing energy
Smart grid technology
Stress concentration
Sustainable yield
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12163076