Smart Force Optimization: Determining optimal component configurations for flexible biogas plants based using a modified exhaustive search method

Abstract

German energy transition is reshaping the power market and price turbulence in 2021 and 2022 may be a flavor of price volatility in the future. Electricity price volatility is the major market signal for flexible biogas plants and the basic prerequisite for additional revenues. To operate in line with power price signals, flexible biogas plants need to configure the main components related to revenues and cost.

This study aims at optimal component configurations for the combined heat and power unit (CHPU), the gas-storage (GS) and if applicable heat storage (HS). Additional revenues for electricity have to exceed the costs for main components at most. Two cases are distinguished: solely power-price driven operation (PPO) and cogeneration-led operation (CLO), and investigated for power prices of 2020, 2021, and 2022.

Additional revenues by flexible operation increase as price volatility rise from 2020 to 2022. Interestingly, the choice for a certain CHPU capacity, determining the annual runtime and thus the expected service life has a vital importance. Independent of power market signals, power quotients (PQ) of at least PQ = 4 avoid a reinvestment for the CHPU within 20 years of investigation

As another main result, the slope of the optimality region for the CHPU capacity almost flattens for 2022 and capacities above PQ = 4. Thus, high price volatilities will allow plant operators to amortise even larger CHPU, which might stimulate higher installations of renewable peak load by flexible biogas plants.

Considering the day-ahead market can give clear recommendations for optimal component configurations for flexible biogas plants, for different assumptions of price volatility. Optimisation was computed with the innovative smart-force-approach (SFO), a modified exhaustive search method.

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.
An comprehensive description of the method an a demonstration for a generic biogas plant is given in the following scientific paper:
"Smart Force Optimisation – a modified exhaustive search method for flexible biogas plants under practical constraints".
The manuscript was recently submitted, the final doi of the publication will be added here after the manuscript will be accepted,a preprint version is available in the sub directory /paper.

Configuration
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

Repository structure

root

├─ concept  - contain figures and the data base structure as well as the SFO-icon

├─ 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

├─ licence  - contain the licence

├─ png      - contain template figures for the illustration of the diffrent plant classes 

├─ pub      - contain the [publication](pub/optimal_config_flexible_biogas.pdf) and the workflows

│    ├─ component costs  - component costs    

│    ├─ figures          - figures   

│    ├─ manuscript    - preprint of the manuscript    

│    ├─ python           - 3d_array_plotter, cost_curves, invest_assesment, main-py, supplemental_functions    

│    └─ results           - results collection with two subdirectories for the SFO_results.db and plots of schedules

│

└─ py       - contain the python modules for the SFO core libraray

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.