Optimal configuration for improved system performance of droop-controlled DC microgrid with distributed energy resources and storage

dc.contributor.authorMathew, D.
dc.contributor.authorPrabhakaran, P.
dc.date.accessioned2026-02-03T13:20:58Z
dc.date.issued2024
dc.description.abstractThe placement of sources and loads in DC microgrids (DCMGs) influences the system's voltage regulation, span, and losses. In order to minimize losses and enhance voltage regulation, a unique algorithm for configuring a radial DCMG under droop control in an optimal way is presented in this paper. The suggested approach solves the optimal design problem by applying the power flow analysis technique. The genetic algorithm (GA), a heuristic method, is used to determine the ideal configuration because of the complexity of the optimization problem. An improved particle swarm optimization (IPSO)-based technique is also proposed for resolving the optimization issue to improve the convergence rate and computing efficiency. Appropriate modifications are proposed to yield an optimal configuration that results in the maximum achievable span for the radial, droop-controlled DCMG. To limit the bus voltage variations within the bounds, the objective functions of the optimization problem are appropriately formulated. In addition, the proposed algorithm is used to find the best position and power rating of a new distributed energy resource (DER) or load in the DCMG, in order to reduce system losses. A 5-bus, 500 W, radial, droop-controlled DCMG system's comprehensive numerical and simulation results are presented to validate the effectiveness of the proposed approaches. The findings are significant and useful for DCMG consumers as well as system designers. © 2024 Elsevier Ltd
dc.identifier.citationComputers and Electrical Engineering, 2024, 120, , pp. -
dc.identifier.issn457906
dc.identifier.urihttps://doi.org/10.1016/j.compeleceng.2024.109809
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/20781
dc.publisherElsevier Ltd
dc.subjectDC distribution systems
dc.subjectGenetic algorithms
dc.subjectLoad flow control
dc.subjectDC microgrid
dc.subjectDistributed Energy Resources
dc.subjectMicrogrid
dc.subjectOptimal configuration
dc.subjectOptimization problems
dc.subjectParticle swarm
dc.subjectParticle swarm optimization
dc.subjectPower flow analyze
dc.subjectSwarm optimization
dc.subjectSystems performance
dc.subjectLoad flow optimization
dc.titleOptimal configuration for improved system performance of droop-controlled DC microgrid with distributed energy resources and storage

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