This week, we learn about an offshore renewable energy platform that can turn sunlight, wind and waves into electricity. Speaking of offshore wind power, a team of researchers developed a passive flow control system that improves the efficiency of wind turbines; they also showed that it’s technically and economically feasible to store the resulting energy as hydrogen. Engineers found a way to stabilize perovskite solar cells, and artificial intelligence is making better batteries and battery management systems.
Hybrid Offshore Renewable Energy Platform
German renewable energy developer SINN Power is introducing its SINN Power Wave Energy Structure, a hybrid floating offshore energy platform that’s modular, scalable and low maintenance. The platform, which the company claims is the first of its kind, includes SINN Power’s own Wave Energy Converters (WECs) and provides ample real estate to attach wind turbines and solar modules in several configurations.
Each individual WEC can deliver up to 24 kW (standard model) or 36 kW (for strong wave climates)—enough to power more than a dozen houses continuously. A small array of WECs can produce half a megawatt, while a large array can generate up to three megawatts. SINN Power is currently looking for solar panel manufacturers to demonstrate their products on a floating platform at the company’s R&D facility in Iraklio, Greece.
Offshore Wind Power
Engineers at the University of the Basque Country, in conjunction with the U.S. National Renewable Energy Laboratory (NREL), have demonstrated the technical and economic feasibility of converting offshore wind power into hydrogen via electrolysis. The resulting hydrogen can then be shipped to the mainland and used in fuel cells to generate electricity on demand, as well as in hydrogen-powered vehicles. Converting the electricity into hydrogen on the spot eliminates the need to run heavy cables to shore, and it offers an inherent energy storage system. The same researchers also examined passive flow control devices that improve the electricity production of wind turbines. The results, which were published in the International Journal of Hydrogen Energy, showed that a 5 MW turbine outfitted with vortex generators and gurney flaps increased a turbine’s output by around 2.5 percent. Assuming a 35 percent capacity factor, that’s an annual increase of 383 MWh—enough to power an extra 36 homes—for each turbine.
Stabilizing Perovskite Solar Cells
Silicon isn’t the most efficient substance for converting sunlight into electricity, but it’s currently the most cost-effective material and is therefore the most widely used in solar cells. Silicon is abundant, durable and inexpensive, and since it’s the same material used in many electronic components, silicon processing is a very mature technology. Perovskite is a promising replacement, offering better efficiency and lower production costs, but it lacks the durability of silicon—specifically, it degrades quickly when exposed to light and heat. Researchers from the Gwangju Institute of Science and Technology (GIST) in South Korea developed a method to remove the defects in the perovskite material and decrease light-induced degradation.
Brookhaven National Laboratory, where the scientists tested their cells, reported on the process, in which the perovskite “sandwich” is vacuum cured in order to bring loose ions—the cause of the material’s instability—to rise to the top layer. A chemical solvent then removes that layer before adding the top electrode. The results show promise, as the cells exhibited little degradation after extensive testing. While perovskite solar cell technology is still “not ready for prime time,” this research brings it another step closer to fruition.
Machine Learning Predicts Battery Health
Rechargeable batteries, including the Li-ion batteries found in electric vehicles and in many grid-level and behind-the-meter energy storage devices, gradually lose their storage capacity with repeated charge-discharge cycles. One responsibility of a battery management system (BMS) is to monitor a battery’s state of health (SoH) in order to optimize charging and predict its remaining life span. Currently, a BMS will determine SoH by monitoring voltage and current levels while charging and discharging, but that doesn’t give the complete picture. Researchers at Cambridge and Newcastle Universities developed an artificial intelligence (AI) system to more accurately assess SoH. By sending brief electrical pulses to a battery and measuring its response, a machine learning algorithm detects subtleties in the battery’s output. This information can help the BMS control the charging process to maximize the battery’s life. In addition, the data helps create better battery models that can be used in a digital twin situation. Finally, the information will provide insights into the causes of battery degradation, helping engineers to design better batteries and charging systems. The researchers trained the AI with more than 20,000 experiments and claim that it’s easy to incorporate the system into an existing BMS. No word yet on how or when they plan to commercialize the technology.
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