Abstract
Energy storage technologies are key to increased penetration of renewable energies on the distribution system. Not only do they increase availability of energy, but they contribute to the overall reliability of the system. However, the cost of large-scale storage systems can often be prohibitive, and storage needs to be sized appropriately, both to fill the energy gaps inevitable in renewable energies such as wind and to minimize costs. In this work, a Monte Carlo Simulation is performed to optimally size an energy storage system while minimizing overall system cost. 30 years of historical wind speed data are used to model the probabilistic behaviour of the wind and the seasonal variation of the wind is captured in the model. A generation adequacy assessment shows the system reliability increasing with energy storage. The energy storage is sized for reliable operation of the case study system with 60% wind penetration. The levelized cost of storage is calculated for the optimally sized level of storage and for the level of storage required to make wind power generation reliable.
| Original language | English |
|---|---|
| Title of host publication | UPEC 2020 - 2020 55th International Universities Power Engineering Conference, Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728110783 |
| DOIs | |
| Publication status | Published - Sept 2020 |
| Event | 55th International Universities Power Engineering Conference, UPEC 2020 - Virtual, Torino, Italy Duration: 1 Sept 2020 → 4 Sept 2020 |
Publication series
| Name | UPEC 2020 - 2020 55th International Universities Power Engineering Conference, Proceedings |
|---|
Conference
| Conference | 55th International Universities Power Engineering Conference, UPEC 2020 |
|---|---|
| Country/Territory | Italy |
| City | Virtual, Torino |
| Period | 1/09/20 → 4/09/20 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Embedded Generation
- energy storage
- reliability
- wind power
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