Energy costs are fluctuating over a wide range and global concern to reduce Greenhouse Gas (GHG) emissions grows stronger. Traditional energy sources, such as coal and nuclear, have declined in importance, while supplies from natural gas and renewables are growing consistently. Manufacturers are aware of energy’s role in overall cost structures and emissions, so continuously work to reduce both.
The COVID-19 pandemicimpacted consumption patterns, resulting in drastic price changes for oil and petroleum derived products. While expectations are that overall consumption will recover somewhat in 2021, traditional fuels may never return to their previous price levels. Competing renewables options continue to grow and their costs goes down. Power market prices changing on an hourly basis or even faster are becoming very common around the world.
Even so, for large-scale process plants, energy normally accounts for 50% of operating expenses. Consequently, reducing energy use by 10% can often improve margins by 5%. As companies seek to boost profits and reduce emissions, energy optimization is one of the first places to look.
When companies can accurately measure consumption in real-time, they can reduce total energy use with just a few available actions. A site can make improvements that improve efficiency and reduce consumption and emissions. Large-scale improvement projects, such as building an in-house cogeneration system, must be examined carefully for cost/benefit potential.
Process plants and other manufacturing sites need to consider how best to produce, distribute, and mix available energy options. Whether they use traditional, renewables or both, it is critical to find an effective way to integrate them within existing energy systems.
For example, hydrogen production. The least expensive way for a plant to generate hydrogen is by reforming methane. However, this process produces carbon dioxide as a byproduct that plants vent into the atmosphere. If the plant wants to eliminate this stream, it can follow a more costly approach and use power to electrolyze water into “green hydrogen”. Deciding if this is advantageous depends on the power source and storage options.
Consistency among the decision systems at different time scales, optimizing in real-time but accounting for optimal schedule-imposed constraints is critical. A real-time, Digital Twin can help reduce cost and GHGs emissions.
Knowledge of the sources and uses of energy are necessary. We discuss the importance of coordinating information, forecasts, scheduling, regulations, reporting and control activities, as well as the appropriate set of software tools.