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basopra

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.

elharrab.github.io

microgrid

Optimizing microgrid performance using reinforcement Learning

pymgrid

pymgrid is a python library to generate and simulate a large number of microgrids.

pyqtgraph

Graphics and widgets library for science/engineering applications based on numpy and PyQt

self-consumption

A toolbox for the simulation and economic evaluation of self-consumption and solar home battery systems

timeserieseventdetection_datascience

This script analyzes a timestamped power consumption signal for an electrical device to detect active usage start/stop times.