Optimal capacity and placement of distributed generation using metaheuristic optimization algorithm to reduce power losses in Bantul distribution system, Yogyakarta

Shaddiq, Syahrial; Santoso, Dian Budhi; Alfarobi, Fikriyan Fajar; Sarjiya, Sarjiya; Hadi, Sasongko Pramono

doi:10.1109/iciteed.2016.7863279

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…With the same loading conditions as the previous case, DGPV can also reduce the transmission losses of the system. For instance, at loading condition of 20 MVAR the transmission losses reduced to 7 The results of minimization of FVSI and minimization of losses for CMIEP and EP are compared with the pre-installation value and shown in Figure 4. In Figure 4(a), both CMIEP and EP are comparable at load variation of 10 MVAR and 20 MVAR.…”

Section: Fvsi Minimizationmentioning

confidence: 99%

Chaotic Local Search Based Algorithm for Optimal DGPV Allocation

Mustaffa

Musirin

Othman

et al. 2018

IJEECS

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The advent of advanced technology has led to the increase of electricity demand in most countries in the world. This phenomenon has made the power system network operate close to the stability limit. Therefore, the power utilities are looking forward to the solution to increase the loadability of the existing infrastructure. Integration of renewable energy into the grid such as Distributed Generation Photovoltaic (DGPV) can be one of the possible solutions. In this paper, Chaotic Mutation Immune Evolutionary Programming (CMIEP) algorithm is used as the optimization method while the chaotic mapping was employed in the local search for optimal location and sizing of DGPV. The chaotic local search has the capability of finding the best solution by increasing the possibility of exploring the global minima. The proposed technique was applied to the IEEE 30 Bus RTS with variation of load. The simulation results are compared with Evolutionary Programming (EP) and it is found that CMIEP performed better in most of the cases.

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Section: Fvsi Minimizationmentioning

confidence: 99%

Chaotic Local Search Based Algorithm for Optimal DGPV Allocation

Mustaffa

Musirin

Othman

et al. 2018

IJEECS

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“…Previous research related to reducing power losses and enhancing voltage profile through DG placement and capacity optimization has been carried out on these research, viz optimization of reactive power of distributed generation for voltage regulation of distribution systems; optimal capacity and placement of distributed generation using a metaheuristic optimization algorithm to reduce power losses in Bantul 05 feeder, Yogyakarta; and impact of high penetration of photovoltaic generation on voltage fluctuation of transmission and distribution systems have been investigated by [10][11]. Good planning is needed to support the benefits of DG in the power system, including determining the location of placement and a large number of power systems used by the optimization method [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Distributed Generation Placement and Capacity Using Flower Pollination Algorithm Method

Putra

Istiqomah

Shaddiq

et al. 2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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The need for energy, especially electricity, is increasing along with the development of technology. An increase in electrical load and the location of the powerplant far causes voltage drops and causes power line losses. One solution can be chosen by adding a distributed generation (DG) to the distribution network. This study aims to enhance the voltage profile and reduce power losses according to DG-based photovoltaic (PV)'s optimal placement and capacity in the Bantul Feeder 05 distribution network. The flower pollination algorithm (FPA) method is used to determine the optimal DG placement and capacity. The study was conducted using three additional DG scenarios: scenario 1 with single DG and scenario 2 with multi-DG (2 DG and 3 DG). The results showed that the optimal placement and capacity of DG were on buses 9, 19, and 33 with DG sizes of 1.880 kW, 2.550 kW, and 2.300 kW, respectively. This placement can increase the voltage profile and reduce the active power loss from 439.8 kW to 77.5 kW. The research also considers the increase in the reliability of the distribution system in terms of the Energy not Supplied (ENS) and Cost of Energy not Supplied (CENS) index.

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