Radial Distribution Networks Reconfiguration with Allocation of DG Using Quasi-Oppositional Moth Flame Optimization
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4626-6_34
Reference19 articles.
1. Tabares A, Puerta GF, Franco JF, Romero RA (2021) Planning of reserve branches to increase reconfiguration capability in distribution systems: a scenario-based convex programming approach. IEEE Access 9:104707–104721
2. Gao Yuanqi, Wang Wei, Shi Jie, Nanpeng Yu (2020) Batch-constrained reinforcement learning for dynamic distribution network reconfiguration. IEEE Trans Smart Grid 11(6):5357–5369
3. Akbari MA, Aghaei J, Barani M, Niknam T, Ghavidel S, Farahmand H, Korpas M, Li L (2018) Convex models for optimal utility-based distributed generation allocation in radial distribution systems. IEEE Syst J 12(4):3497–3508
4. Shaheen Abdullah, Elsayed Abdallah, Ginidi Ahmed, El-Sehiemy Ragab, Elattar Ehab (2022) Reconfiguration of electrical distribution network-based dg and capacitors allocations using artificial ecosystem optimizer: practical case study. Alexandria Eng J 61(8):6105–6118
5. Tan S, Xu J-X, Panda SK (2013) Optimization of distribution network incorporating distributed generators: an integrated approach. IEEE Trans Power Syst 28(3):2421–2432
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