Abstract
Remote microgrids are a viable option for electrification where the main grid expansion is either impossible or not economical. Typically, remote microgrid consists of diesel generator as a primary source of energy which has a high fuel cost. Renewable energy sources can be used to reduce the fuel consumption with proper coordination and scheduling methods. Storage devices, usually battery, used in remote microgrid are expensive and toxic in nature; therefore battery lifetime is another important parameter to be considered during microgrid scheduling. In this paper, four test cases were developed for the study of fuel consumption. Problems were formulated as mixed integer programming (MIP) and solved using GAMS/CPLEX 12.6 solver. Furthermore, the battery lifetime model was included in the optimization model and fuel consumption was compared with other cases. Results show the slight increment in fuel consumption when the battery lifetime model was included.
| Original language | English |
|---|---|
| Title of host publication | 2014 11th IEEE/IAS International Conference on Industry Applications, IEEE INDUSCON 2014 - Electronic Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781479955510 |
| DOIs | |
| State | Published - 12 Mar 2014 |
| Externally published | Yes |
| Event | 11th IEEE/IAS International Conference on Industry Applications, IEEE INDUSCON 2014 - Juiz de Fora, Brazil Duration: 7 Dec 2014 → 10 Dec 2014 |
Publication series
| Name | 2014 11th IEEE/IAS International Conference on Industry Applications, IEEE INDUSCON 2014 - Electronic Proceedings |
|---|
Conference
| Conference | 11th IEEE/IAS International Conference on Industry Applications, IEEE INDUSCON 2014 |
|---|---|
| Country/Territory | Brazil |
| City | Juiz de Fora |
| Period | 7/12/14 → 10/12/14 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Battery life
- microgrid
- mixed integer programming
- multiobjective optimization
- photovoltaic
- renewable energy
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