On this interview, we function Stefan Niessen, Head of Expertise Area Sustainable Vitality & Infrastructure at Siemens Expertise, as he sheds mild on the intersection between tutorial analysis and industrial innovation within the realm of sustainable power and infrastructure. With intensive expertise in main technological developments, Mr. Niessen shares insights into the challenges and alternatives in integrating rising applied sciences into present power grids. This interview is a part of AI Time Journal’s AI for Vitality Initiative.
In your twin function, how do you steadiness the calls for of main technological developments in sustainable power with the theoretical and academic elements of your professorship?
We’ve got three levels of maturity wherein we handle these numerous matters. One is pure college analysis as a professor the place I can work on matters the place changing right into a product is 5 years or longer away. Then now we have publicly funded initiatives wherein I’ll take part both as a professor or as a Siemens worker with my crew, permitting us to co-create with different universities, clients, and opponents on pre-development matters. This co-creation mode is nice as a result of any new know-how can solely work if deployed by an ecosystem of companions. The third diploma is inside Siemens initiatives the place my crew does precise product growth work for enterprise models, guiding a promising know-how from the preliminary college levels to an precise product stage the place it may be successfully deployed in actual life.
Are you able to focus on a selected challenge or innovation below your management at Siemens that exemplifies the combination of AI into sustainable power methods?
One instance is the multimodal power storage and suppleness thought I discussed, which requires sensible interplay between grid operators, grid customers, and renewable electrical energy injectors to synchronize consumption with era. The grid operator must estimate how loaded the grid is, which requires AI for forecasting future demand for warmth, cooling, traction, and electrical energy costs and renewable era. Information cleansing and high quality assurance is one other AI component, as everytime you measure information, points come up that want detecting and correcting. Grid state estimation, processing information from completely different sources for an summary of the grid’s working state, is the final component.
What rising applied sciences or strategies in power buying and selling do you consider maintain essentially the most promise for enhancing sustainability globally?
I’ve sympathy for power buying and selling, being a part of the crew that established the European Vitality Trade. Nonetheless, we aren’t but utilizing the complete potential additional down the voltage ranges the place sensible, internet-connected units like charging stations, warmth pumps, batteries, and PV inverters exist however usually are not but actively buying and selling, although they may. To reply your query, I consider automated native power buying and selling inside grid boundaries is a key rising know-how. We can’t rely that grid capability will all the time be absolutely out there, so sensible injection is required. We’ve got field-tested how this could work in initiatives like pebbles in southern Germany and with the Viennese metropolis utility.
Contemplating your work in mobility, what are the most important challenges and alternatives for integrating electrical automobiles into present power grids?
The most important problem is low voltage grid capability, as these grids weren’t designed to attach many charging stations. Sometimes buried underground in Europe, quickly rising capability is tough in comparison with the US the place you’ll be able to add one other line on poles. This implies we should purchase time by making the grid interplay more and more sensible to synchronize charging with native PV era and reduce grid load. Additional variables like warmth pumps, stationary batteries, industrial processes, and buildings offering flexibility as thermal power storage can contribute.
Along with your multimodal focus, how do you method optimizing these methods for effectivity and sustainability with out compromising reliability and accessibility?
I’d argue it truly helps, as decarbonized methods routinely imply extra decentralized buildings with inherent redundancy benefiting reliability. In Germany, there are over 2 million decentralized electrical energy turbines, principally rooftop PV. These multimodal couplings convey flexibility by means of inherent storage performance, as any client in a position to delay consumption gives flexibility, bettering reliability.
Might you elaborate on any current conversion element developments considerably impacting power infrastructure sustainability?
No single know-how solves the power downside, however warmth pumps can now present greater temperatures as much as 150°C or extra at industrial scale, permitting use of waste warmth sources. When fuel costs spiked, many industrial websites invested in warmth pumps to make the most of beforehand wasted decrease temperatures. Developments in battery supplies like sodium-ion as an alternative of lithium, utilizing considerable sodium, is also a breakthrough even when power density is decrease. This mature know-how already has vehicles manufactured in sequence in China. I nonetheless count on extra battery surprises within the subsequent two years because it stays a extremely lively analysis discipline.
How have lifecycle assessments influenced sustainable challenge design and growth at Siemens?
We do intensive lifecycle assessments, aiming for all our merchandise to have environmental product declarations by 2026, going past regulatory necessities. My crew develops the methodology to more and more routinely generate lifecycle assessments for {hardware} merchandise, for software program and repair choices.
In transitioning cities and areas to sustainable power, how important is AI’s function in forecasting, planning and managing these complicated methods?
Presently, it’s not important, however sooner or later AI will help quickly discover believable assumptions when creating decarbonization roadmaps for cities or areas. You want assumptions on future consumption profiles for infrastructure that doesn’t exist but, and AI can contribute believable assumptions.
Lastly, reflecting in your profession, what recommendation would you give younger engineers and researchers aspiring to considerably affect sustainable power and infrastructure?
I’d advise younger engineers to try to actually perceive fundamentals, because the legal guidelines of physics don’t change. Sooner or later, resilience may grow to be as essential as sustainability. The multimodal, sector-coupled power methods benefiting decarbonization are additionally advantageous for safety of provide, so these matters are converging. Engineers additionally are inclined to overlook understanding prices and creating a real curiosity in enterprise fashions is extraordinarily useful.
