Smart Force Optimization (SFO) and its application for determining optimal component configurations for flexible biogas plants based using a modified exhaustive search method

Smart Force Optimisation (SFO)

This repository contain the SFO building blocks and an examplary applied relating to the paper "Determining optimal component configurations for flexible biogas plants based on power prices of 2020 – 2022 and the legislation framework in Germany"

The paper was published in Renewable Energy and is open accessible.
DOI: https://doi.org/10.1016/j.renene.2024.121252

Abstract of the paper

The aim of this study is to find optimal component configurations for flexible biogas plants considering electricity prices in Germany, for maximum annuities considering costs and revenues. Cases for solely power-price driven operation and cogeneration-led operation were investigated for electricity prices in 2020, 2021, and 2022 in hourly resolution.
Additional revenue from flexible operation increases as price volatility increased from 2020 to 2022. Interestingly, the choice of capacity for the combined heat and power unit (CHPU) is determined by the expected service life of the CHPU. Irrespective of electricity market signals, power quotients (PQ) of at least PQ = 4 avoid reinvestment for the CHPU within 20 years.
Another result is that the optimality region for CHPU capacity flattens for 2022 and shift to capacities above PQ = 4. Thus, high price volatility will allow plant operators to amortise even larger CHPUs, which could stimulate the installation of peak load by flexible biogas plants. Considering the day-ahead market can provide clear recommendations for optimal component configurations. 
The presented Smart-Force-Optimisation (SFO) combines a prefiltering of schedules matching given constraints and a brute force optimisation to the filtered subset. Thus, SFO has an improved performance comparing to bare brute force approaches.

Smart Force Optimization (SFO)

SFO in a nutshell:
The innovative aspect about the sfo approach is the combination of a data base for storing all possible schedules and their corresponding constraints in advance and the subsequently selection of those schedules which are in compliance with individual constraints for a defined biogas plant set up by a SQL-query. This significantly reduced amount of schedules can than be used for a rather simple brute force optimization, which otherwise would not be an appropriate approach  for the basic population.

Requirements
The SFO is an open source optimization approach to generate schedules for price driven operation of flexible biogas plants, considering typical constraints like gas storage, maximum number of strokes per day, daily runtime and also heat supply obligations. It was developed and tested under python 3.7.14 and uses the following external packages (no supplied as part of the python core):  

• pandas
• numpy
• matplotlib

For inititialising the schedules database at least 11GB of free RAM are needed and the ready to use package will need aproximately 3 GB of disc space.

Repository structure

root

├─ csv        - contains raw data for default heat load profiles (HLP) and power price profiles (PPP)

├─ db         - this folder will contain the databases of the schedules

├─ doc       - contains raw the documentation of the results data base

├─ fig         - figures showing the resulst of the aplied reserach aproach

├─ licence  - contain the licence

├─ pub       - contain python scripts for the used workflows and auxiliarx functions for the publication

├─ py          - contain python scripts for the used workflows and auxiliarx functions for the publication

└─ xls          - MS Excel files storing the raw investment cost data set, used for cost function fitting

Copyright and licnece
The smart-force-optimization (SFO) is licencend under a Creative Commons Attribution 4.0 International Public License.
The lincence is part of this package and is referring to creativecommons.org.  

Contact information
The author can be reached via M.Dotzauer@posteo.eu.