Application of a genetic algorithm to solve the problem of scaling hydrogen systems

Abstract

The work aims to develop a robust tool for scaling hydrogen systems and their energy consumption using a genetic algorithm.

Relevance. The most common method of hydrogen production is water electrolysis, which requires a sufficient amount of electricity. If electricity sources are insufficient, this can put additional strain on the power grid, especially during peak consumption periods. Since 87% of hydrogen plants currently use hydrogen on-site (instead of generating it and then transporting it for use), there is a need for optimization in this area to improve energy efficiency and sustainability.

Current research analyzes the improvement of hydrogen systems in terms of the cost-effectiveness of systems using renewable energy sources and the reduction of hydrogen logistics costs by applying linear programming and particle swarm optimization methods.

However, these works are mainly focused on hydrogen production systems based on a single electrolyzer and do not aim to assess the feasibility of using multiple units. As a result, the topic of cost optimization and maintenance strategies for multi-electrolyzer systems remains less explored, as well as the related problem of their dispatching.

Research methods. Stochastic methods were used to solve the problem of finding the best startup queue for electrolysis units, and the effectiveness of the genetic algorithm for solving this problem was tested.

Results. A model for optimizing the peak power consumption of an electrolysis system was built, and the configuration evaluation function and objective function for system optimization were determined. The choice of a stochastic optimization method is justified by checking the objective function for the properties necessary for the effectiveness of traditional optimization methods, namely, continuity, differentiability, smoothness, and convexity. The effectiveness of the genetic method was tested in comparison with the gradient descent method on examples with different configurations of electrolyzers (similar and different types).

Conclusions. These calculations have confirmed that the genetic algorithm has stable results and is effective in finding the global optimum, while the gradient descent may stop at local minima and require additional adjustments to achieve the optimal solution.

Using the genetic algorithm method, we obtain results that give an approximate optimal result for a fixed number of steps. This approximate result, as shown in the problem with the placement of 10 electrolyzers, gives significant results — the peak electricity consumption has decreased by almost 40%.

Further research can be aimed at improving the parameters of the algorithm, in particular, adaptive tuning of the mutation and crossover operators to increase the convergence rate.