Basopra

Basopra

alefunxo

BASOPRA - BAttery Schedule OPtimizer for Residential Applications. Daily battery schedule optimizer (i.e. 24 h optimization framework), assuming perfect day-ahead forecast of the electricity demand load and solar PV generation in order to determine the maximum economic potential regardless of the forecast strategy used. Include the use of different applications which residential batteries can perform from a consumer perspective. Applications such as avoidance of PV curtailment, demand load-shifting and demand peak shaving are considered along with the base application, PV self-consumption. Different battery technologies and sizes can be analyzed as well as different tariff structures. Aging is treated as an exogenous parameter, calculated on daily basis and is not subject of optimization. Data with 15-minute temporal resolution are used for simulations. The model objective function have two components, the energy-based and the power-based component, as the tariff structure depends on the applications considered, a boolean parameter activate the power-based factor of the bill when is necessary.

26 Stars
7 Forks
26 Watchers
Python Language
gpl-3.0 License
Cost to Build
$612.1K
Market Value
$738.0K

Growth over time

7 data points  ·  2021-07-01 → 2023-04-01
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What is the alefunxo/Basopra GitHub project? Description: "BASOPRA - BAttery Schedule OPtimizer for Residential Applications. Daily battery schedule optimizer (i.e. 24 h optimization framework), assuming perfect day-ahead forecast of the electricity demand load and solar PV generation in order to determine the maximum economic potential regardless of the forecast strategy used. Include the use of different applications which residential batteries can perform from a consumer perspective. Applications such as avoidance of PV curtailment, demand load-shifting and demand peak shaving are considered along with the base application, PV self-consumption. Different battery technologies and sizes can be analyzed as well as different tariff structures. Aging is treated as an exogenous parameter, calculated on daily basis and is not subject of optimization. Data with 15-minute temporal resolution are used for simulations. The model objective function have two components, the energy-based and the power-based component, as the tariff structure depends on the applications considered, a boolean parameter activate the power-based factor of the bill when is necessary.". Written in Python. Explain what it does, its main use cases, key features, and who would benefit from using it.

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How to clone Basopra

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git clone https://github.com/alefunxo/Basopra.git

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[email protected]:alefunxo/Basopra.git

Download ZIP

Download master.zip

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