H3P PROJECT - Modular Peak Power Plant
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      • Electrochemistry
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    • Approach
    • Parameters Definition
    • MATLAB Model
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    • Other Considerations
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    • Bibliography
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  • Home
  • Context
  • Project
    • Project Introduction >
      • Background
      • Concept & Definition
      • Individual components
    • Theory >
      • Electrochemistry
      • System Losses
      • Assumptions & Symbols
    • Fuel Cell Measurements
  • Model
    • Approach
    • Parameters Definition
    • MATLAB Model
  • Results & Conclusions
    • H3P - Results
    • Discussion
    • Conclusions
  • Additional Information
    • Further Developments
    • Other Considerations
    • Alternative Applications
    • Acknowledgments
    • Bibliography
  • Team

MODEL

Approach
Parameters Definition
MATLAB Model

MATLAB Model

        In order to easily run our model for a wide range of scenario, we decided to develop a tool. The first calculations were done using Excel and Matcad. Then, in order to gain time and flexibility, we develop a MATLAB program (using Matlab R2015b), able to run 9 scenarios at the same time - for 3 different demand profiles and 3 different required refilling time.
        We named this tool the H3P Tool (for Hydrogen Peak Power Plant Tool).
Picture
Click here to download the H3P tool source codes !


Programme Structure

        The program is made up with four different scripts: 1 main script and 3 modules. Here is a brief description for each of them.

  • H3P.m
This is the main script of the program ; the one to run in order to get results. After the execution, it will ask for the required output of the module, and some other parameters. Then it will call the other modules and do the calculations.

  • define_param.m
This script can be seen as the catalogue of the tool. Important parameters, like demand profiles and required time to refill can be amend there. For our study we used the Agressive, Moderate and Light profiles, and we chose 3 refilling times (1h, 10h and 20h). Some financial parameters can also be changed here.

  • size_module.m
This script is called to calculate physical characteristics of the model. The kind of question this script answers is: given the chosen efficiencies, how much hydrogen do we have to store to supply enough electricity, and how much electricity do we have to buy in order to get this amount of hydrogen ?

  • price_assess.m
This script focuses on the financial aspect on this study. Based on the output of size_module.m, on the pricing information and on financial parameters, it will assess the cost-effectiveness of the module, calculating particularly the annual surplus, the possible capital investment and the required capital cost.


Validation

        Several approaches to validation of the model were used.
  • Sizing part of the model
        The sizing part of the model, which calculated the necessary volume of hydrogen in the store to supply 6MWh of electricity, was initially constructed using theoretical energy available from oxidation of hydrogen (using the enthalpy of oxidation - higher heating value of hydrogen) and representative efficiencies for the fuel cell and power electronics. 

        This part was validated using data gained in the laboratory. Data for the hydrogen consumption rate, fuel cell power output, and other measures of efficiency, were gathered simultaneously, so it was possible to independently calculate the volume of hydrogen required.  These separate calculations for hydrogen volume (based on measured hydrogen consumption rate, and also predicted from the higher heating value of hydrogen, and efficiencies) were used to check the calculated volume of hydrogen.

  • Annual financial performance
       A separate manual calculation (in Mathcad) was performed for a few selected scenarios, to calculate the annual financial surplus / loss.

  • Loan payback calculation
        This part of the model was checked by inputting the mortgage repayments for the flat of one of the team members.

  • Overall
        The various calculations were compared, and the model adjusted as necessary.  Following these validation steps, the team had confidence in further use of the model to run different scenarios.
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