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.

Good morning all, Please I need any materials, suggestions and if possible some past Energy Audit reports for buildings or data centers or hospital or commercial facilities etc. That will give an idea on how the manual/traditional way works so that I can use AI Thank you

Please if someone could help with self explanatory code on the attached PDF I would highly appreciate. of course Dr Spyro has covered almost all aspect in his class but I have not reach there yet. This is an Assignment and also exams question in my Msc Python classes. Thank you as I await your inputs please

Good day everyone, I’ve been invited for an initial virtual screening for the above job role at EirGrid in Ireland. Anyone has an idea what they are likely to ask please? Thank you

In the Classroom section, a new online course has been uploaded called "UK Electricity Market in Python: Day-Ahead & Balancing Mechanism". The course focuses on the electricity market in the United Kingdom (UK), but it also applies to the European Union, Australia, parts of the USA, and other areas that have a 'liberalized' electricity market structure. The course explains what the wholesale electricity market is, what the day-ahead market is and what the balancing mechanism is. How all these are part of the overall electricity market. Also what is the difference between the wholesale and retail electricity markets. Then the course shows how an energy company makes money by participating in the wholesale electricity market and specifically in the day-ahead and balancing mechanism. All this is modelled in Python. If you have a monthly subscription, send me a DM to get you access to this course (you can have access to 1 new course every week). If you are an annual subscriber you have access to all courses , including this one.