Researchers have used the quality search algorithm to optimize the placement and benchmark for solar -PV distributed generation. They have tested the algorithm on an IEEE 33-bus system, with one, two or three PV implementation scenarios, and are performance compared to those of a dozen competing optimization techniques.
A team of scientists from India has developed a new method for the optimum placement and measure of Solar PV Distributed Generation (DG) systems.
The new approach is based on a metahuristic optimization technique that is known as the Jellyfish Search Algorithm (JSA), which is inspired by the foraging behavior of jellyfish in the ocean. The marine animal uses two types of search mechanisms, namely diversification and intensification, which mimics the algorithm. The JSA switches between the two search mechanisms through a time control mechanism (TCM) to effectively tackle complex optimization problems.
The objectives of the algorithm have been set by the research group to minimize real asset losses (RPL), minimize voltage deviation (VDI) and to maximize the voltage stability index (VSI).
The researchers explained that, although utilities of the energy system supply electricity through transmission and distribution options, consumers are driven through distribution peepower Networks (DPN). “DG optimization is considered more effective than condenser, SVC (static VAR compensator), Statcom (static synchronous compensator) and UPFC (Unified Power Flow Controller) because it can support the DPN with both real and reactive power injection,” they emphasized.
To test the JSA, the team simulated an IEEE 33-BUS system via Matlab 2022B software. In the case of Baseline, without any DG in the system, the RPL turned out to be 210.98 K, the VDI was 1,8047 PU and the minimum VSI was 0.6671. When asked to optimize an IEEE 33-BUS system with one PV DG, the total RPL was reduced by 51.23% and the total VDI by 1.2716 PU, with a minimum VSI of 0.7559.
“On the other hand, the total RPL and VDI were lowered by 60.66% and 1,1529 PU for two PV -DG -DG -Optimization units. After allocating three units PV DG systems, the RPL and Total VDI was lowered by 67.01% and 1,4754 PU minimum VSVIEM6 PU minimum VSV66 PU PUVIMME VSVE VSV66 and 0.848 and 0.848 and 0.848 and 0.848 and 0.848 and 0.848 and 0.848 and 0.848 and 0.848 and 0.848 and 0.848 and 0.848 and 0.848166616616661666616 6666666666666666666666. Systems, “the group further explained” simulation results indicate that JSA optimized DG placements are considerably improved, achieving a substantial reduction in RPL, while the voltage stability and overall grid weather power are improved. “
After this demonstration, the team tested the JSA on other optimization methods, namely LSF-Sca, SCA, BSOA, GA, ALO, TLBO-GWO, WIPSO-GSA, GWO, BFOA, KHA, BA, WOA and AIS. They were also optimized for the recording of single, two and three PV system allocation.
Their analysis showed that, for a single DG optimization, JSA results perform better than LSF-SCA, SCA, BSOA, GA, ALO and TLBO-GWO methods by giving maximum PL reduction and better VBUS improvement. “Similarly, JSA optimized two DG placements give better PL and VD minimization than LSF-SCA, SCA, BSOA, WIPSO-GSA and GWO methods,” they concluded. “In addition, JSA optimized three units of PV system allocation produced superior results than other methods.”
The new approach was presented in “Optimized placement and size of solar photo photovoltaic distributed generation using the search algorithm for searching for jellyfish for improved wealth system performance“Published in Scientific reports. Researchers from the Vel Tech Rangarajan of India Dr.Sagaghala R&D Institute of Science and Technology, Hindusthan Institute of Technology, Mohan Babu University, Christ (considered University) and Matttu University participated in the study.
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