Search results for: “semiconductors semiconductor silicon SiC”
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Solar: energy payback and embedded energy?
What is the energy payback and embedded energy of solar? We have aggregated the consumption of 10 different materials (in kg/kW) and around 10 other energy-consuming line-items (in kWh/kW). Our base case estimate is 2.5 MWH/kWe of solar and an energy payback of 1.5-years. Numbers and sensitivities can be stress-tested in the data-file.
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TOPCon: maverick?
A new solar cell is vying to re-shape the PV industry, with 2-5% efficiency gains and c25-35% lower silicon use. This 13-page note reviews TOPCon cells, which will take some sting out of solar re-inflation, tighten silver bottlenecks and may further entrench China’s solar giants.
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Chlor-alkali process: the economics?
This data-file captures chlor-alkali process economics, to produce 80MTpa of chlorine and 90MTpa of caustic soda. Our base case requires $600 per ecu for a 10% IRR and a growth project costing $600/Tpa. Electricity is 45% of cash cost. CO2 intensity is 0.5 tons/ton. Interestingly, chlor-alkali plants can demand shift.
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Solar costs: four horsemen?
Solar costs have deflated by an incredible 90% in the past decade to 4-7c/kWh. Some commentators now hope for 2c/kWh by 2050. Further innovations are doubtless. But there are four challenges, which could stifle future deflation or even re-inflate solar. Most debilitating would be a re-doubling of CO2-intensive PV-silicon?
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Energy economics: an overview?
This data-file provides an overview of energy economics, across 175 different economic models constructed by Thunder Said Energy, in order to put numbers in context. This helps to compare marginal costs, capex costs, energy intensity, interest rate sensitivity, and other key parameters that matter in the energy transition.
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Energy intensity of AI: chomping at the bit?
Rising energy demands of AI are now the biggest uncertainty in all of global energy. To understand why, this 17-page note is an overview of AI computing from first principles, across transistors, DRAM, GPUs and deep learning. GPU efficiency will inevitably increase, but compute increases faster. AI most likely uses 300-2,500 TWH in 2030, with…
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Data-centers: the economics?
The capex costs of data-centers are typically $10M/MW, with opex costs dominated by maintenance (c40%), electricity (c15-25%), labor, water, G&A and other. A 30MW data-center must generate $100M of revenues for a 10% IRR, while an AI data-center in 2024 may need to charge $5/EFLOP of compute.
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Energy and AI: the power and the glory?
The power demands of AI will contribute to the largest growth of new generation capacity in history. This 18-page note evaluates the power implications of AI data-centers. Reliability is crucial. Gas demand grows. Annual sales of CCGTs and back-up gensets in the US both rise by 2.5x? This is our most detailed AI report to…
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Data center energy: caps lock?
Everybody knows “AI data centers use round-the-clock power”. Yet one of the biggest power challenges for AI data data centers is precisely that they do not use round-the-clock power. They incur large load transients that cannot be handled by batteries, power grids or most generation. This 15-page report explores data center load profiles, which may…
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Groq: AI inference breakthrough?
Groq has developed LPUs for AI inference, which are up to 10x faster and 80-90% more energy efficient than today’s GPUs. This 8-page Groq technology review assesses its patent moat, LPU costs, implications for our AI energy models, and whether Groq could ever dethrone NVIDIA’s GPUs?
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