PV self-consumption optimization
Evaluate and optimize trade-offs in the design of battery storage for PV systems
With the AEW data of a 60 kWp PV installation, we optimized the solar energy self-consumption by a battery system. For this, we calculated different parameters such as battery load, new self-consumption and new grid supply and estimated the optimal battery capacity with further values such as energy and battery prices, life span, contract duration, minimal charge, maximum load power in Google sheet and as well with Python.
Finally, our customer can select, whether he wants the most economical battery solution or maximise his autarky. Our tools calculate the maximized economic benefit over lifetime.
Google Sheet Prototyp
https://github.com/ehackdays/PV-Optimisation Contains initial Python implementation and some exploratory analysis, see also README below.
Analysis and Design of Battery Storage for PV Systems
Challenge No. 2 @ Energy Data Hackdays 2020
This repo contain some additional analysis scripts that use the original data files (CSV) provided for the challenge.
Data on power generation, grid feed, grid supply and overall consumption of three power plants by AEW Energie AG. First time published for the Challenge 3 of Energy Data Hackdays 2020 in Brugg. A.csv B.csv C.csv
For questions on the data, reach out at https://github.com/zuzfil/PV-optimisation/issues.
The data were created during the operation of AEW-owned PV plants in 2019. The plants are all located in Aargau, Switzerland. The power values in kW refer to the average over the 15min period.