A renewable energy optimisation approach with production planning for a real industrial process: An application of genetic algorithms

This article presents the formulation of the optimisation of a manufacturing process, through genetic algorithms, managing the generation and demand of energy in a factory at periodic moments of time. The strategy manages to minimise the daily energy cost and maximise the use of installed renewable energy, also taking advantage of potential battery banks. A time series with a 24-hour horizon of energy production from renewable sources and the electricity supply prices provided by the electricity market operator has been considered. Furthermore, in the simulations, scenarios with different battery capacities have been tested, which has allowed a preliminary study to be carried out for the installation of the electrical storage bank. The results presented in this work show that 6% of energy costs can be saved per day, compared to the current management decided by the manufacturing plant operators.

Automated summary

This article presents a formulation for optimizing a manufacturing process using genetic algorithms to manage energy generation and demand in a factory. The strategy aims to minimize daily energy costs while maximizing the utilization of renewable energy sources and potential battery banks. The study considers a 24-hour time series of renewable energy production and electricity prices from the market operator. Simulation results show that the proposed strategy can achieve a 6% reduction in daily energy costs compared to the current management approach.