I am trying to solve a multi-objective optimization problem using the Pyomo library in Python. The objective function includes operational optimization of an electrolyzer for hydrogen production based on the grid electricity prices, and sizing of the electrolyzer and hydrogen storage unit based on historical prices in the day-ahead electricity market. The problem is that the capital cost of the electrolyzer and the storage unit would be based on the maximum size of the design variable and has to be minimized to get an optimal solution. I am new to optimization and your advice and comments would be helpful.
Check the Simulated Annealing algorithm:
https://en.wikipedia.org/wiki/Simulated_annealing#:~:text=Simulated%20annealing%20(SA)%20is%20a,space%20for%20an%20optimization%20problem.
or the Particle Swarm optimization:
https://en.wikipedia.org/wiki/Particle_swarm_optimization#:~:text=In%20computational%20science%2C%20particle%20swarm,a%20given%20measure%20of%20quality.
You can also find python implementations for them.
Hope this will be helpful.
Dear Abhinav Bhaskar
At the first stage, the multi-objective optimization problem can be changed into a single-objective one. You can employ the method of Changing variables.
For example:
Total Goal: Maximization
Error_Function = (1/Min) + (Max)
or
Total Goal: Minimization
Error_Function = (Min) + (1/Max)
Best regards.
First of all you need to find the equations for your problem and then look at the method to solve them and then what software you are going to use.
Have look at this
https://www.youtube.com/results?search_query=multi+objective+optimization+in+excel
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