An innovative energy management strategy leveraging artificial intelligence is set to optimize fuel consumption and increase solar energy use significantly in photovoltaic/diesel hybrid systems. Researchers have introduced this modified energy management strategy (MEMS) to address the challenges faced by industrial operations relying on hybrid energy systems.
Photovoltaic (PV) and diesel generator (DG) hybrid systems have emerged as reliable solutions to meet energy demands, particularly in remote locations. Combining renewable energy sources with conventional generators ensures continuous power supply, yet the over-reliance on diesel can lead to increased operational costs and environmental impacts.
The MEMS aims to reduce diesel consumption, improve solar energy utilization, and lower greenhouse gas emissions through advanced techniques like the Artificial Protozoa Optimizer (APO). This algorithm, inspired by the foraging behavior of protozoa, adapts intelligently to variations in solar conditions and energy demand, optimizing power distribution between PV panels and DGs.
Simulation results from the study reveal substantial improvements with the implementation of MEMS. Carbon emissions plummeted from 62 kg/day with traditional diesel reliance to just 38 kg/day, marking a noteworthy 38% reduction. Likewise, the fraction of energy sourced from solar power surged from 12% to 35%, emphasizing the strategy's effectiveness.
The necessity for enhanced energy management strategies arises from rising fuel costs and the imperative to transition toward cleaner energy sources across various sectors. The MEMS addresses these concerns by utilizing the FO-PID controller along with the APO algorithm to develop multi-objective optimization strategies—simultaneously maximizing renewable energy usage, minimizing the loss of power supply probability, and reducing net present costs associated with operation.
This comprehensive approach is particularly beneficial for industrial facilities such as the chemical production factory designated for this study. The facility has implemented the hybrid system, which combines solar power generation with diesel backup, recognizing the importance of reliability and cost-efficiency.
The research contributes to the growing literature on renewable energy integration, particularly emphasizing the enhanced sustainability of industrial operations through intelligent systems. Moving forward, the application of similar strategies across diverse industrial contexts may lead to even greater reductions in dependency on fossil fuels, proving beneficial not only for business operations but also for the environment.
Overall, the work successfully demonstrates how adapting advanced AI-driven management strategies can position industries on the forefront of sustainable practices, aligning operational goals with environmental stewardship.