Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy
A micro-grid with the capacity for sustainable energy is expected to be a distributed energy system that exhibits quite a small environmental impact. In an independent micro-grid, “green energy,” which is typically thought of as unstable, can be utilized effectively by introducin...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Japan Society of Mechanical Engineers
2008-09-01
|
| Series: | Journal of Thermal Science and Technology |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/jtst/3/3/3_3_474/_pdf/-char/en |
| _version_ | 1819205335850680320 |
|---|---|
| author | Shin'ya OBARA |
| author_facet | Shin'ya OBARA |
| author_sort | Shin'ya OBARA |
| collection | DOAJ |
| description | A micro-grid with the capacity for sustainable energy is expected to be a distributed energy system that exhibits quite a small environmental impact. In an independent micro-grid, “green energy,” which is typically thought of as unstable, can be utilized effectively by introducing a battery. In the past study, the production-of-electricity prediction algorithm (PAS) of the solar cell was developed. In PAS, a layered neural network is made to learn based on past weather data and the operation plan of the compound system of a solar cell and other energy systems was examined using this prediction algorithm. In this paper, a dynamic operational scheduling algorithm is developed using a neural network (PAS) and a genetic algorithm (GA) to provide predictions for solar cell power output. We also do a case study analysis in which we use this algorithm to plan the operation of a system that connects nine houses in Sapporo to a micro-grid composed of power equipment and a polycrystalline silicon solar cell. In this work, the relationship between the accuracy of output prediction of the solar cell and the operation plan of the micro-grid was clarified. Moreover, we found that operating the micro-grid according to the plan derived with PAS was far superior, in terms of equipment hours of operation, to that using past average weather data. |
| first_indexed | 2024-12-23T04:50:05Z |
| format | Article |
| id | doaj.art-d0502b7b97bc4e97a5484bc866f54019 |
| institution | Directory Open Access Journal |
| issn | 1880-5566 |
| language | English |
| last_indexed | 2024-12-23T04:50:05Z |
| publishDate | 2008-09-01 |
| publisher | The Japan Society of Mechanical Engineers |
| record_format | Article |
| series | Journal of Thermal Science and Technology |
| spelling | doaj.art-d0502b7b97bc4e97a5484bc866f540192022-12-21T17:59:29ZengThe Japan Society of Mechanical EngineersJournal of Thermal Science and Technology1880-55662008-09-013347448510.1299/jtst.3.474jtstDevelopment of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable EnergyShin'ya OBARA0Tomakomai National College of TechnologyA micro-grid with the capacity for sustainable energy is expected to be a distributed energy system that exhibits quite a small environmental impact. In an independent micro-grid, “green energy,” which is typically thought of as unstable, can be utilized effectively by introducing a battery. In the past study, the production-of-electricity prediction algorithm (PAS) of the solar cell was developed. In PAS, a layered neural network is made to learn based on past weather data and the operation plan of the compound system of a solar cell and other energy systems was examined using this prediction algorithm. In this paper, a dynamic operational scheduling algorithm is developed using a neural network (PAS) and a genetic algorithm (GA) to provide predictions for solar cell power output. We also do a case study analysis in which we use this algorithm to plan the operation of a system that connects nine houses in Sapporo to a micro-grid composed of power equipment and a polycrystalline silicon solar cell. In this work, the relationship between the accuracy of output prediction of the solar cell and the operation plan of the micro-grid was clarified. Moreover, we found that operating the micro-grid according to the plan derived with PAS was far superior, in terms of equipment hours of operation, to that using past average weather data.https://www.jstage.jst.go.jp/article/jtst/3/3/3_3_474/_pdf/-char/enmicro-gridoperation planningenergy storagerenewable energyneural networkweather prediction |
| spellingShingle | Shin'ya OBARA Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy Journal of Thermal Science and Technology micro-grid operation planning energy storage renewable energy neural network weather prediction |
| title | Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy |
| title_full | Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy |
| title_fullStr | Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy |
| title_full_unstemmed | Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy |
| title_short | Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy |
| title_sort | development of a dynamic operational scheduling algorithm for an independent micro grid with renewable energy |
| topic | micro-grid operation planning energy storage renewable energy neural network weather prediction |
| url | https://www.jstage.jst.go.jp/article/jtst/3/3/3_3_474/_pdf/-char/en |
| work_keys_str_mv | AT shinaposyaobara developmentofadynamicoperationalschedulingalgorithmforanindependentmicrogridwithrenewableenergy |