Particle Swarm Optimizations Based DG Allocation in Local PV Distribution Networks for Voltage Profile Improvement

Bhuyan, Swetalina; Das, Mrutyunjay; Bhuyan, Kanhu Charan

doi:10.1109/iccece.2017.8526201

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Step 6: If there are available charging pile(s) at all simulation periods from simulation periods t1 to t2, i.e., mpile, t>0 (∀t∈[t1, t2]), the EVCS can provide charging service for the th ev m EV. Under this condition, update charging power and available number of the charging piles according to (8) and 9, and then go back to Step 5 and continue. Step 7: If the simulation count nsim reaches maximum simulation count of the MCS, denoted here as nmax, stop the simulation.…”

Section: ) Charging Durationmentioning

confidence: 99%

“…In [1], the locations of the DPVSs are optimized based on typical daily production/consummation curves of the DPVSs/loads, aiming to minimize active and reactive power losses. [8] proposes an optimization methodology to identify proper locations and sizes of the DPVSs in the distribution systems, which can be solved by the particle swarm optimization technique. In [9], an optimization is built to optimize locations and sizes of the DPVSs to be connected to the distribution systems.…”

Section: Introductionmentioning

confidence: 99%

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Joint Planning of Distributed PV Stations and EV Charging Stations in the Distribution Systems Based on Chance-Constrained Programming

Zhang

et al. 2021

IEEE Access

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Section: ) Charging Durationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint Planning of Distributed PV Stations and EV Charging Stations in the Distribution Systems Based on Chance-Constrained Programming

Zhang

et al. 2021

IEEE Access

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“…By randomly moving in various directions, each particle searches through the entire space and remembers the previous best solutions of that particle and also the locations of its neighboring particles. [26] Swarm particles dynamically change their location and velocity by communicating the best positions of all particles to each other. Finally, all the particles in the swarm continue to shift to better locations before the swarm finds an appropriate solution.…”

Section: Chapter 4 Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimized Distributed Generation Planning for Radial Distribution System Using Particle Swarm Optimization Algorithm

Faruque

Chowdhury

Nazim

et al. 2021

2021 13th IEEE PES Asia Pacific Power &Amp; Energy Engineering Conference (APPEEC)

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Electrical generation and storage by a range of small grid-connected or distribution systemconnected devices known as distributed energy resources is distributed generation. This paper presents the determination of the optimal position of a Distributed Generation (DG) Unit. There has been analysis of the radial distributed network. The placement of distributed generation and the size of the distribution generation is a prime factor here. The key motive of this paper is to minimize line losses and improve voltage profile. We have followed a robust technique called particle swarm optimization to achieve this objective. The application is illustrated on a 30 bus system of 132KV line Chittagong, Bangladesh power Grid system using Newton Raphson power flow method. Results presents the maximum reduction of real power loss in percentage and optimal location of Distributed Generation.

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“…Oleh karena itu, ketidakpastian dalam sistem tenaga harus menjadi pertimbangan dalam perencanaan sistem tenaga dengan penetrasi DG sumber energi terbarukan. Beberapa penelitian telah membahas alokasi optimal DG [10]- [16]. Misalnya, alokasi DG menggunakan algoritme JAYA yang dibandingkan dengan varian Particle Swarm Optimization (PSO), antara lain RPSO, LPSO, dan PSO-SR [15].…”

Section: Pendahuluanunclassified

Alokasi Optimal DG Sumber Energi Terbarukan Menggunakan Algoritme Multi-Verse Optimizer

Firdaus

Penangsang

Wibowo

et al. 2021

JNTETI

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Dalam situasi saat ini, meminimalkan kehilangan daya pada jaringan distribusi maupun transmisi merupakan salah satu tantangan penelitian. Kehilangan daya yang tinggi terjadi ketika beban yang terhubung pada jaringan distribusi jauh dari pembangkit listrik. Alokasi yang optimal untuk Distributed Generation (DG) sumber energi terbarukan adalah salah satu cara untuk meminimalkan kehilangan daya dan memperbaiki profil tegangan sistem distribusi. Penempatan yang tidak akurat menyebabkan peningkatan kehilangan daya, menurunkan profil tegangan, dan dapat memengaruhi kinerja sistem. Metode Multi-Verse Optimizer (MVO) digunakan untuk menempatkan DG sumber energi terbarukan di lokasi yang tepat. Hasil simulasi pada sistem 33 bus IEEE menunjukkan bahwa dengan penggunaan metode algoritme MVO untuk penambahan dua buah DG energi terbarukan diperoleh lokasi pada bus 18 dan 33 dengan tipe DG PV. Namun, apabila penambahan dua buah DG dilakukan dengan mempertimbangkan sifat intermiten, lokasi DG yang optimal dengan diperoleh kedua DG berada pada bus 18 dengan tipe DG Wind Turbine. Alokasi DG sumber energi terbarukan yang optimal mampu meminimalkan rugi daya aktif 11,8 kV dan daya reaktif 8,2 kVAr serta menaikkan profil tegangan di atas 0,95 pu pada bus under voltage.

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Particle Swarm Optimizations Based DG Allocation in Local PV Distribution Networks for Voltage Profile Improvement

Cited by 9 publications

References 4 publications

Joint Planning of Distributed PV Stations and EV Charging Stations in the Distribution Systems Based on Chance-Constrained Programming

Joint Planning of Distributed PV Stations and EV Charging Stations in the Distribution Systems Based on Chance-Constrained Programming

Optimized Distributed Generation Planning for Radial Distribution System Using Particle Swarm Optimization Algorithm

Alokasi Optimal DG Sumber Energi Terbarukan Menggunakan Algoritme Multi-Verse Optimizer

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