Intelligent energy management of manufacturing systems integrated with renewable sources

Abstract

Abstract. The growing presence of renewable energy sources into manufacturing systems presents massive challenges in their intermittency and uncertainty, causing inefficiency in energy consumption and production planning. The paper introduces a smart energy management network of manufacturing systems that dynamically balances machine activities with the availability of renewable energy. The suggested solution consists of a predictive model of renewable generation and an optimization-based scheduling system to reduce the cost of energy, delays in production, and grid reliance. There is a multi-objective formulation that is created based on energy use, operating limitations and emission parameters. A smart decision-making policy, founded on machine learning-aided prediction and adaptive scheduling, is applied to guarantee real-time responsiveness when operating in different energy conditions. The framework is tested on a realistic load and renewable profile on a representative manufacturing scenario. Findings show that there are considerable increases in the use of renewable energy, decrease in peak grid demand, and general energy cost savings over traditional scheduling methods. The new methodology provides a scalable and viable approach to sustainable and energy-efficient smart manufacturing systems.