Based on asset-level insight the new data led product optimises and controls manufacturing processes in-line with asset parameters and energy markets and could generate up to £3,694,827 savings while supporting the grid integrate further renewables into the energy mix by opening up new sources of previously untapped flexibility.
In today’s fast-paced industrial landscape, optimising production isn’t just about meeting deadlines; it’s also about navigating volatile energy prices. The increasing share of renewable energy sources into the energy mix has introduced more variability into the supply side of the electricity market. Fluctuations in energy costs can significantly impact operational expenses, making it imperative for businesses to devise strategies that mitigate against these costs.
In this scenario technology can play a pivotal role in supporting businesses make decisions to minimise the impact of energy price peaks, and ultimately reducing their cost of production by scheduling operations and processes against energy prices.
Traditionally, production schedules were often planned based on historical data and static parameters. But, in today’s dynamic market conditions with energy prices constantly fluctuating, businesses need to adopt a more agile and data-driven approach to production scheduling. Process Optimizer minimises the total energy consumption costs for operations and single-machine scheduling. It makes decisions that identify the launch time for job processing, when the machine must be idle or shut down, and when to start “turning on” and “turning off” times.
By creating a virtual replica of the entire site, Process Optimizer captures every nuance of energy consumption. This proactive approach not only helps minimise the impact of price fluctuations but also allows companies to capitalise on opportunities for cost savings.
In one of our case studies, a site in the cement manufacturing sector was using power to operate throughout the day, despite the cost of power fluctuating across settlement periods. While the site had a series of fixed asset parameters, flexibility was found in the silo assets without impacting overall product quality or operations – providing an opportunity to make cost savings on electricity and generate revenue for the business.
By reducing production at the three silos on site when at or near maximum capacity and using Robotic Trading, energy flexibility was traded in the highest revenue markets. This meant the site earned revenue from flexibility offered in grid programmes in addition to avoiding the peak price power periods and reducing overall production costs by up to £3,694,827 annually while reducing carbon emissions.
“The launch of Process Optimizer shows once again our commitment towards continues innovation and wills to deliver on the market the best product for our customers.” said Mark Davis, CCO at GridBeyond – “This product will empower businesses to better manage production schedules in the face of energy price volatility. By harnessing the power of data analytics, artificial intelligence (AI), and machine learning, companies can gain valuable insights into energy consumption patterns, identify cost-saving opportunities, and optimise production schedules in real-time”.
