Autonomous Operation of Stationary Battery Energy Storage Systems—Optimal Storage Design and Economic Potential
Global warming requires a changeover from fossil fuel based to renewable energy sources on the electrical supply side and electrification of the demand side. Due to the transient nature of renewables and fluctuating demand, buffer capacities are necessary to compensate for supply/demand imbalances....
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/5/1333 |
| _version_ | 1797416561880858624 |
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| author | Bernhard Faessler Aleksander Bogunović Jakobsen |
| author_facet | Bernhard Faessler Aleksander Bogunović Jakobsen |
| author_sort | Bernhard Faessler |
| collection | DOAJ |
| description | Global warming requires a changeover from fossil fuel based to renewable energy sources on the electrical supply side and electrification of the demand side. Due to the transient nature of renewables and fluctuating demand, buffer capacities are necessary to compensate for supply/demand imbalances. Battery energy storage systems are promising. However, the initial costs are high. Repurposing electric vehicle batteries can reduce initial costs. Further, storage design optimization could significantly improve costs. Therefore, a battery control algorithm was developed, and a simulation study was performed to identify the optimal storage design and its economic potential. The algorithm used is based on autonomous (on-site) optimization, which relies on an incentive determining the operation mode (charge, discharge, or idle). The incentive used was the historic day-ahead stock market price for electricity, and the resulting potential economic gains for different European countries were compared for the years 2015–2019. This showed that there is a correlation between economic gain, optimal storage design (capacity-to-power ratio), and the mean standard deviation, as well as the mean relative change of the different day-ahead prices. Low yearly mean standard deviations of about 0.5 Euro Cents per kWh battery capacity lead to yearly earnings of about 1 €/kWh, deviations of 1 Euro Cent to 10 €/kWh, and deviations of 2 Euro Cents to 20 €/kWh. Small yearly mean relative changes, lower than 5%, lead to capacity-to-power ratios greater than 3, relative changes around 10% to an optimal capacity-to-power between 1.5 and 3, and for relative changes greater than 10% to an optimal capacity-to-power ratios of 1. While in countries like the United Kingdom, high potential earnings are expected, the economic prospective in countries like Norway is low due to limited day-ahead price performance. |
| first_indexed | 2024-03-09T06:06:00Z |
| format | Article |
| id | doaj.art-19eed587b17141f4aa41e98049091645 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T06:06:00Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-19eed587b17141f4aa41e980490916452023-12-03T12:03:29ZengMDPI AGEnergies1996-10732021-03-01145133310.3390/en14051333Autonomous Operation of Stationary Battery Energy Storage Systems—Optimal Storage Design and Economic PotentialBernhard Faessler0Aleksander Bogunović Jakobsen1Faculty of Engineering and Science, University of Agder, Jon Lilletuns Vei 9, 4879 Grimstad, NorwayFaculty of Engineering and Science, University of Agder, Jon Lilletuns Vei 9, 4879 Grimstad, NorwayGlobal warming requires a changeover from fossil fuel based to renewable energy sources on the electrical supply side and electrification of the demand side. Due to the transient nature of renewables and fluctuating demand, buffer capacities are necessary to compensate for supply/demand imbalances. Battery energy storage systems are promising. However, the initial costs are high. Repurposing electric vehicle batteries can reduce initial costs. Further, storage design optimization could significantly improve costs. Therefore, a battery control algorithm was developed, and a simulation study was performed to identify the optimal storage design and its economic potential. The algorithm used is based on autonomous (on-site) optimization, which relies on an incentive determining the operation mode (charge, discharge, or idle). The incentive used was the historic day-ahead stock market price for electricity, and the resulting potential economic gains for different European countries were compared for the years 2015–2019. This showed that there is a correlation between economic gain, optimal storage design (capacity-to-power ratio), and the mean standard deviation, as well as the mean relative change of the different day-ahead prices. Low yearly mean standard deviations of about 0.5 Euro Cents per kWh battery capacity lead to yearly earnings of about 1 €/kWh, deviations of 1 Euro Cent to 10 €/kWh, and deviations of 2 Euro Cents to 20 €/kWh. Small yearly mean relative changes, lower than 5%, lead to capacity-to-power ratios greater than 3, relative changes around 10% to an optimal capacity-to-power between 1.5 and 3, and for relative changes greater than 10% to an optimal capacity-to-power ratios of 1. While in countries like the United Kingdom, high potential earnings are expected, the economic prospective in countries like Norway is low due to limited day-ahead price performance.https://www.mdpi.com/1996-1073/14/5/1333fluctuating electric supply and demandbattery energy storage systemsautonomous optimizationday-ahead stock market priceoptimal storage designeconomic potential |
| spellingShingle | Bernhard Faessler Aleksander Bogunović Jakobsen Autonomous Operation of Stationary Battery Energy Storage Systems—Optimal Storage Design and Economic Potential Energies fluctuating electric supply and demand battery energy storage systems autonomous optimization day-ahead stock market price optimal storage design economic potential |
| title | Autonomous Operation of Stationary Battery Energy Storage Systems—Optimal Storage Design and Economic Potential |
| title_full | Autonomous Operation of Stationary Battery Energy Storage Systems—Optimal Storage Design and Economic Potential |
| title_fullStr | Autonomous Operation of Stationary Battery Energy Storage Systems—Optimal Storage Design and Economic Potential |
| title_full_unstemmed | Autonomous Operation of Stationary Battery Energy Storage Systems—Optimal Storage Design and Economic Potential |
| title_short | Autonomous Operation of Stationary Battery Energy Storage Systems—Optimal Storage Design and Economic Potential |
| title_sort | autonomous operation of stationary battery energy storage systems optimal storage design and economic potential |
| topic | fluctuating electric supply and demand battery energy storage systems autonomous optimization day-ahead stock market price optimal storage design economic potential |
| url | https://www.mdpi.com/1996-1073/14/5/1333 |
| work_keys_str_mv | AT bernhardfaessler autonomousoperationofstationarybatteryenergystoragesystemsoptimalstoragedesignandeconomicpotential AT aleksanderbogunovicjakobsen autonomousoperationofstationarybatteryenergystoragesystemsoptimalstoragedesignandeconomicpotential |