A new report has been uploaded in the Classroom section (6.2 - Industry Reports). The report focuses on Chinese companies that manufacture solar Photovoltaics. These companies are now expanding into the battery storage business. So far, they were only selling/manufacturing solar Photovoltaics. However, battery storage is exponentially growing in China and globally. The opportunities are massive for profitability. Chinese solar panel makers like Longi, Trinasolar, and JinkoSolar have made billions selling solar Panels. Solar power has grown so fast that electricity grids can't handle all the electricity being produced during sunny hours. The demand for battery storage is growing exponentially. So, these companies are now moving into combined solar-and-battery systems. So, they keep the solar business, but they also add battery development. This leads to cost reductions and increases their profitability. For the full analysis, visit Classroom → 6.2 Industry Reports. This report has been written based on the following sources below, which are available only if you have subscribed to them (e.g., Financial Times, Wall Street Journal, etc.). However, since you are a subscriber to this Skool community, I bring you the key points from these developments, without you having to subscribe to them. And: you get additional important details not included in the original articles. Sources: [1] Financial Times: https://www.ft.com/content/fb8d1b58-96fd-40af-803c-41fe87cab4f9 [2] The Economist: https://www.economist.com/special-report/2025/11/03/how-china-sparked-a-rooftop-solar-revolution-in-pakistan [3] Wall Street Journal: https://www.wsj.com/world/china-renewable-energy-paris-climate-accord-ff123bfc

The video below describes how the companies 'are thinking' before signing a forward contract. This video has also been added to the online course 5.19 in the Classroom. This process is very simple. This is also an interview question for energy + economics + finance roles of various levels. From commodities trading, to investment banking, and from energy consultants to energy quants and energy data scientists. It is a very popular question. Interview Question and an interesting case study to know: The Saudi Aramco - Sinopec crude oil forward contract. Beginner-friendly explanation (no 'scary' jargon used . No prerequisites needed). The video focuses on the forward contract that Saudi Aramco signed with Sinopec. The details of the contract are private so we are using example dates. On the 1st of December 2025 , the two companies signed a forward contract on crude oil , where Saudi Aramco agreed to produce and sell 250 000 barrels of crude oil and ship it from Saudi Arabia to China, where Sinopec is. The delivery date will be months into the future i.e. 1st of September 2026. Sinopec needs crude oil because it has refineries. These are facilities that use crude oil to produce diesel, jet fuel and other 'refined' products. Siniopec needs crude oil therefore. So they decided on the price of $77.5 / barrel. Here is how they decided on this price: a) They used machine learning to find a reasonable upper bound (maximum value) and lower bound on the spot crude oil price on the delivery date i.e. 1st of September 2026. b) In between these two bounds, they assume possible values for the spot price of crude oil. For each value they calculate the PnL index (profit and loss). PnL = (spot price - forward price)* quantity of crude oil.So if spot price on delivery date is $60/barrel and forward price is $77.5 , then we find the spread (difference) and multiply it with the 250,000 barrels of crude oil agreed in the forward contract. This is the PnL for the buyer (Sinopec) ,and it is negative (bad deal).The whole process is described in this video.

Hello all. Recently there was a project I took part, which enabled me access to some resources. Below I have attached some books on commodities that can be downloaded .

At this stage in my career, there are certain skills I need to learn and develop quickly. Since I want to move into the Quant department, I'll need to work primarily on forecasting electricity markets and understand the dynamics of these markets. Our Quant department typically focuses on demand forecasting, generation forecasting, and price forecasting. Your courses in the Classroom section cover a variety of areas, and I appreciate for all of them. However, since I need to progress quickly in these areas, do you have any alternative pathway you can recommend?

Battery Energy Storage Systems (BESS) is an electrochemical storage installation that can store electricity as chemical energy and discharge it back to the grid. BESS types include: • Lithium-ion • Sodium-ion • Flow batteries • Sodium–sulfur • Lead-acid • Sodium–metal-halide • Metal–air • Solid-state lithium Roughly over 90% of grid-scale BESS installations currently are lithium-ion. Also, globally, Li-ion accounts for around 98% of the grid-scale storage market worldwide. The reason for this is because Lithium-Ion exhibits high round-trip efficiency of over 85% (i.e. the fraction of electricity discharged after losses). Also, it can change its power output on sub-second timescales, making it useful for frequency response and other ancillary services . In addition, on a global average, the Li-ion battery pack costs fell about 20% year-on-year (the pack is the largest cost component of BESS).