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As Europe faces a period of energy insecurity — and while ministers gather to debate measures and interventions to soften the blow for residents and businesses alike — companies need to reduce their exposure to a volatile energy market. Digital twins may bring much-needed relief.
The soaring price of natural gas and, consequently, electricity across Europe has forced energy-intensive businesses to make difficult short-term decisions about continuing production with modest or negative profitability — as manufacturers shutter factories, shut down production shifts and lay off workers.
As always, the efficiency adage rings true: the cheapest energy is the energy we do not consume. This is particularly relevant for manufacturers relying on cogeneration (heat and power generation), heat recovery systems, turbines or boilers to meet their large and complex energy needs, and whose operational costs are mounting.
Industrials currently purchasing energy on the spot market urgently need to optimize the performance of their assets, reduce their consumption and expenditures on gas and energy, and potentially earn revenues through ancillary services. Even companies protected by more price-friendly, long-term contracts can benefit from enhanced efficiency, leveraging their existing contracts to their financial advantage, and should keep in mind that their contracts won’t last forever. In any case, large energy consumers should consider using software to create a digital twin of their energy cogeneration assets to enhance their sophisticated operational processes while reducing their CO2 emissions.
Companies’ need for a quick solution to reduce their energy use has shifted from desirable to imperative.
Optimizing the operational performance of their cogeneration energy systems can help achieve peak efficiency, but they might have concerns about the best way to do so. Variable energy vectors (heat, power, steam, etc.) and flexible load requirements can make a system overly complicated to operate for a plant manager.
One efficient solution to this complexity is a digital twin. It can drill down to the granular level to simulate the performance of each individual component of a system, analyze the energy flows and dispatching modes between those components, and enhance the operational performance of the plant.
Some plant operators may be concerned about whether they have sufficient data to use this technology. The more data you have, the better, but historical or even manufacturer data typically suffice to find the optimal operational settings. Smart, digital twin solutions do not necessarily work by analyzing big data, but by modeling the cogeneration energy system and running different scenarios (intra-day, day ahead, week or month ahead) with various parameters (loads, commodity prices, weather) to identify the most efficient setpoints. The modeling process can also reveal anomalies or detect physical leaks that might otherwise go unnoticed and can be very costly.
Another key issue is the cost of implementing energy-reduction measures. Companies know they should be decarbonizing their operations, yet still worry about the expense of getting started. The great advantage of using digital tools to improve operational performance is that it can be done quickly for a small cost, as it requires no expenditure on hardware. Digital twins function by getting the most out of your existing system, and is typically remunerated on a ‘no cure, no pay’ basis.
In the current crisis environment, deploying a digital twin for optimizing cogeneration operations might just make the difference between a profitable year and running out of steam. While governments are urgently seeking to identify the most feasible mitigative measures, there is no telling where the gas prices will go from here and how long Europe’s energy insecurity will last. But companies with highly energy-intensive and complex operations do not need to wait and hope for regulatory relief or an improbably swift decline in gas prices. Smart solutions are available right now, require minimal lead time, are easy to implement, deliver significant savings and bolster avoided emissions.
Our experience shows that by monitoring efficiency deviations and defining the best dispatch for complex energy assets like combined heat and power (CHP) cogeneration, a site can lower its fuel consumption by an estimated 4-5%. For a large energy consumer navigating the current and projected energy market, even a back-of-the-envelope calculation will make clear that the cost reduction on one’s energy bill such a solution is likely to provide could be significant.
The moment has arrived where market pressures, corporate ambition, and technology have converged. Organizations that can effectively leverage existing data sources through advanced analytics, planning, and collaboration will dramatically improve their chances of achieving Net Zero transformation.
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