Microgrid bss scheduling using teaching learning based optimization algorithm
By: Sravani, Yendru
.
Contributor(s): Kumrai, M. Veera.
Publisher: Chennai IASET : International Academy of Science, Engineering and Technology 2023Edition: Vol.12(2), Dec.Description: 1-10p.Subject(s): Electrical EngineeringOnline resources: Click here
In:
International journal of electrical and electronics engineering (IJEEE)Summary: Energy storage serves as a crucial hub for the entire grid, supplementing resources such as wind, solar, and hydropower, as well
as nuclear and fossil fuels, demand side resources, and system efficiency assets. It can function as a generation, transmission, or
distribution asset — all in one unit. Storage is, in the end, an enabling technology. It has the potential to save consumers money
while also improving reliability and resilience, integrating power sources, and reducing environmental impacts.
Battery storage system design is now important for microgrids to prepare a day-ahead schedule for steady
operation. This article discusses the scheduling of BSS, which helps to reduce the average cost imposed on microgrid
consumers in the context of dynamic pricing. For minimizing, a cost function is created and subjected to optimization
based on the restrictions. The search space magnification is 50*(DC– DD + 1), where DC and DD are the maximum charge
and discharge depths in an hour in percentage for a specific BSS, respectively. The programming is done by combining
daily load, generated energy, and grid price forecasts with a microgrid size as specified in the article and implementing
Teaching Learning Based Optimization (TLBO) for achieving an average cost reduction when compared to Net Power
Based Algorithm and Particle Swarm Optimization for a planned BSS.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2024-0449 |
Energy storage serves as a crucial hub for the entire grid, supplementing resources such as wind, solar, and hydropower, as well
as nuclear and fossil fuels, demand side resources, and system efficiency assets. It can function as a generation, transmission, or
distribution asset — all in one unit. Storage is, in the end, an enabling technology. It has the potential to save consumers money
while also improving reliability and resilience, integrating power sources, and reducing environmental impacts.
Battery storage system design is now important for microgrids to prepare a day-ahead schedule for steady
operation. This article discusses the scheduling of BSS, which helps to reduce the average cost imposed on microgrid
consumers in the context of dynamic pricing. For minimizing, a cost function is created and subjected to optimization
based on the restrictions. The search space magnification is 50*(DC– DD + 1), where DC and DD are the maximum charge
and discharge depths in an hour in percentage for a specific BSS, respectively. The programming is done by combining
daily load, generated energy, and grid price forecasts with a microgrid size as specified in the article and implementing
Teaching Learning Based Optimization (TLBO) for achieving an average cost reduction when compared to Net Power
Based Algorithm and Particle Swarm Optimization for a planned BSS.
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