EUROPEAN STEEL TECHNOLOGY AND APPLICATION DAYS 2017 - ESTAD 2017

Steel industry is an energy-intensive sector, in particular for the electrical energy consumption. The utilized energy composes a significant part of production costs and has a significant impact on the electricity network. The increasing utilization of variable renewable energy sources, such as solar or wind, lead to key role for the energy-intensive industries demand side flexibility. Thus, there is a growing need of closer cooperation between grid operators and steel industry by improving the power consumption prediction & management. In order to optimally exploit the benefits and challenges of dynamic energy markets a flexible management of the steel production processes is required. Reliable prediction of the plant energy demand, according to the work load, is required as a fundamental basis to be able to react dynamically on market conditions taking advantage from the energy price fluctuations. The production systems must react to the current situation by enabling or disabling process uncritical systems allowing increasing or decreasing the energy consumption on demand. In this case the production systems must adapt their strategy according to the situation focusing on minimization of global costs while considering the process stability. A flexible solution is then required in order to achieve the multi-objective optimisation. A concept based on the multi agent systems in combination with auctions is proposed allowing calculating an optimal combination of enabled or disabled peripheral systems and or processes according to the electrical load demand, energy costs, production needs and network requests. The different plants and machines are represented here by autonomous software agents which known their current relevance for the production process and their energy consumption function. Based on these two information’s each agent calculates its own impact on the global system. This impact is submitted as a bid to the auction system which then decides which agents are chosen in order to realise the foreseen actions.