Based on realistic data, a hypothetical case study has been carried out to investigate how strongly three different energy tariff scenarios might influence the energy bill for a typical 24-hour scheduling problem. The scenarios are each assumed to buy a fixed amount of electricity at a known rate using a base load contract. The total energy bill can be reduced by reselling any surplus electricity. The committed load aspect is also taken into consideration.
The first scenario represents a day with “normal” electricity prices in the volatile day-ahead market. When the scheduling driven by energy price is employed, the net electricity cost is around $ 110,000.
The second scenario uses weather-driven prices, which result in an additional cost of $ 27,000. The third scenario ignores energy price considerations, ie, only production throughput is optimized – resulting in a cost double that of the second scenario. This demonstrates how much the plant could potentially save by collaborative scheduling and energy optimization on a day with extreme prices.
In this case study, energy-driven scheduling contributes to significant reductions of the electricity bill. However, comparison of the schedules of scenarios two and three clearly shows that the energy-driven schedule tries to avoid extreme prices of the peak hours (marked in red and orange in the graph below at the expense of extending the overall make-span (the total time needed for production).
Some of the production operations are delayed – perhaps incurring reheating costs. In the study, the cost of thermal losses has not been included in the savings calculation. However, with realistic cooling models it is certainly possible to account for potential costs that can be associated with production delays.
A new ABB production scheduling system based on MILP technology was deployed in a very complex melt shop belonging to Acciai Speciali Terni SpA, a member of ThyssenKrupp and one of the world’s leading producers of stainless steel flat products. With the new system in place, the production scheduler is able to automatically and optimally create a new schedule, or manually update an existing
one, for up to seven days of production within just a few minutes. The system is flexible enough to support different melt shop configurations, as well as to include all other information necessary – such as processing, transportation, setup and cleanup times – to generate a feasible production schedule. It also takes into account maintenance plans, the current status of the melt shop and availability of different equipment, due dates, penalties for lateness and violation of holdup times between stages in the process, etc. In addition, the steel plant created a Web-based GUI that allows the user to flexibly select what to optimize and schedule.