Optimal Sizing/Planning Models for Three Offshore Energy Transmission Systems in Python

Python Code, by Jin Lu, Mar 08, 2023.
GitHub
rpglab/OffshoreEgyTransm_Cable_H2Pipeline
Paper
Jesus-JinLu-OWP-Transm
Authors in the RPG Lab
Jin Lu

Optimal Sizing Models for three different Offshore Energy Transmission Systems

The Python codes are for optimal sizing and planning models for three different offshore wind transmission systems.

The model formulation and cost-benefit analysis are presented in the paper below:

  • Jesus Silva-Rodriguez, Jin Lu and Xingpeng Li, “Cost-Benefit Analysis and Comparisons for Different Offshore Wind Energy Transmission Systems”, Offshore Technology Conference, Houston, TX, USA, May 2023.

If you use any of the codes/data in your work, please cite the paper above.

Introduction:

The three types of offshore wind transmission systems are summarized as follows:

  • HVDC case: direct point-to-point HVDC transmission from each wind farm location to an onshore substation.

  • Hybrid case: point-to-point HVDC transmission from each wind farm location to a HSC offshore, then power transmission via high pressure hydrogen pipelines (HPHP) from the HSC to the onshore substation.

  • Hydrogen pipelines (HP) case: hydrogen generation at each wind farm location and transmission via low pressure hydrogen pipelines (LPHP) to the offshore HSC, then all hydrogen collectively transmitted via HPHP from the HSC to the onshore substation. Diagrams for these three configurations are given in Fig. 1. Besides, the onshore substation in Hybrid and HP cases have hydrogen storage and can generate power using the fuel cells.

Python Environment Setup:

  • Recommand Python Version: Python 3.11
  • Required packages: Numpy, pyomo, pypower
  • Require a solver which can be called by the pyomo to solve the optimization problem.
  • Steps to run the simulations:
    • Set up the python environment.
    • Set the solver location: ‘Run_simulation.py’ => ‘solve_model’ function => solver=SolverFactory(‘solver_name’,executable=’solver_location’)

Files:

  • ‘HVDC_Case_Model.py’: The pyomo model & results saving function for HVDC case
  • ‘Hybrid_Case_Model.py’: The pyomo model & results saving function for HVDC case
  • ‘HP_Case_Model.py’: The pyomo model & results saving function for HVDC case
  • ‘Run_Simulation.py’: Run this file to conduct the simulations for the three cases.
  • ‘results_HVDC’ folder: The simulation results for HVDC case are saved in this folder.
  • ‘results_Hybrid’ folder: The simulation results for Hybrid case are saved in this folder.
  • ‘results_HP’ folder: The simulation results for HP case are saved in this folder.
  • ‘Offshore Hydrogen Supergrid Parameters_Jesus_Silva_2022.pdf’: list of parameter values used in the simulations.

Simulation Results:

The files under the ‘results_HVDC’ folders:

  • ‘objvalue.txt’: total revenue ($) of the objective function.
  • ‘p_del.txt’: hourly total energy (KWh) delivered to the onshore substation.
  • ‘pl_num.txt’: total number of electric power transmission lines.
  • ‘wf_pout’: hourly wind generation on each wind farms. Rows represent wind farm, columns represent hours in a day.

The files under the ‘results_Hybrid’ folders:

  • ‘objvalue.txt’: total revenue ($) of the objective function.
  • ‘p_del.txt’: hourly total energy (KWh) delivered to the onshore substation.
  • ‘p_elct’: hourly total electric energy (KWh) consumed by the elctrolyzers.
  • ‘p_fc’: hourly total electic energy (KWh) generated by the fuel cells.
  • ‘wf_pout’: hourly wind generation on each wind farms. Rows represent wind farm, columns represent hours in a day.
  • ‘pl_num.txt’: total number of electric power transmission lines.
  • ‘elct_num.txt’: total number of electrolyzers.
  • ‘hl_num.txt’: total number of hydrogen pipelines.
  • ‘fc_num.txt’: total number of fuel cells.
  • ‘h_elct’: hourly total hydrogen energy (KWh) generated by the elctrolyzers.
  • ‘h_fc’: hourly total hydrogen energy (KWh) consumed by the fuel cells.
  • ‘h_hstrg’: hourly total hydrogen energy stored in the hydrogen storage.

The files under the ‘results_HP’ folders:

  • ‘objvalue.txt’: total revenue ($) of the objective function.
  • ‘p_c.txt’: hourly total electic energy (KWh) consumed by the compressor.
  • ‘p_fc.txt’: hourly total electic energy (KWh) generated by the fuel cells.
  • ‘p_del.txt’: hourly total energy (KWh) delivered to the onshore substation.
  • ‘elct_num.txt’: total number of electrolyzers.
  • ‘fc_num.txt’: total number of fuel cells.
  • ‘hphl_num.txt’: total number of high pressure hydrogen pipelines.
  • ‘lphl_num.txt’: total number of low pressure pipelines.
  • ‘h_elct.txt’: hourly total hydrogen energy (KWh) generated by the elctrolyzers.
  • ‘h_fc.txt’: hourly total hydrogen energy (KWh) consumed by the fuel cells.
  • ‘h_hstrg.txt’: hourly total hydrogen energy stored in the hydrogen storage.
  • ‘h_hstrg_in.txt’: hourly total hydrogen energy input to the hydrogen storage.

Citation:

If you use any of the codes/data published here in your work, please cite the following paper.

  • Jesus Silva-Rodriguez, Jin Lu and Xingpeng Li, “Cost-Benefit Analysis and Comparisons for Different Offshore Wind Energy Transmission Systems”, Offshore Technology Conference, Houston, TX, USA, May 2023.

Paper website: https://rpglab.github.io/papers/Jesus-JinLu-OWP-Transm/

Contributions:

Jin Lu wrote the python codes. Jesus Silva-Rodriguez proposed the optimization models. Xingpeng Li supervised this work.

Contact:

If you need any techinical support, please feel free to reach out to Jin Lu at jlu27@CougarNet.UH.EDU.

For collaboration, please contact Dr. Xingpeng Li at xli83@central.uh.edu.

Website: https://rpglab.github.io/

License:

This work is licensed under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0) license.

Disclaimer:

The author doesn’t make any warranty for the accuracy, completeness, or usefulness of any information disclosed and the author assumes no liability or responsibility for any errors or omissions for the information (data/code/results etc) disclosed.