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This research aims to develop an optimization framework for sizing and placing distributed generation units and electric vehicles in distribution networks.

July 15, 2026EnergiesOpen Access

A Hybrid GA–MCS Framework for Stochastic Optimization of DG and EV Integration in Distribution Networks

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Authors

MSMratyunjay SinghDr. A.P.J. Abdul Kalam Technical University BSBindeshwar SinghMadan Mohan Malaviya University of Technology SSSubhi SinghGovernment Medical College

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Randomized trial shows optimization of power loss reduction in distribution networks, suggesting efficiency improvements.

Key Points

This research aims to develop an optimization framework for sizing and placing distributed generation units and electric vehicles in distribution networks.
Implemented a hybrid Genetic Algorithm–Monte Carlo Simulation framework for optimization.
Analyzed three configurations (single-, double-, and triple-DG) with various EV categories.
Conducted 50 Monte Carlo scenarios and 50 GA iterations for performance assessment.
DG2 minimizes real power loss best among single-DG cases.
The DG1–DG2 configuration reduces real power loss by 6.50–10.07% compared to the best single-DG setup.
The DG1–DG2–DG4 configuration achieves an additional 11.89–11.96% reduction in power loss under EREV penetration.

Cite This Study

Singh et al. (2026) studied this question.

synapsesocial.com/papers/6a57245a88b21df875480c34 https://doi.org/10.3390/en19143284

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A Hybrid GA–MCS Framework for Stochastic Optimization of DG and EV Integration in Distribution Networks

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Key Points

Cite This Study

A Hybrid GA–MCS Framework for Stochastic Optimization of DG and EV Integration in Distribution Networks

Discussion

Member takes

Implication

Key Points

Cite This Study

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