I share a recent interview question asked by Baringa (this is a consulting company focused strongly on energy, utilities, and the energy transition). From a student forum / database (for internship). The question was: 'Why do some renewable power plants continue generating electricity even when electricity prices turn negative? and when do prices become negative ? do you know any example? '

I see many students who want to stay home and so they ask for remote work all the time. Also I see many who go to work and do not dress well. Here is the truth for the job: Truth1: Go 3-4 times every week to the office so others see you. Your presence is very important. Smile and be professional. This plays a role for your promotion. If you stay home most of the time, you will not be promoted and people will forget you. Truth2: Do not use AI a lot at workplace because every computer has trackers and gives your managers a distribution of time (with plots) of how much you used AI and for how long. There is a software (hidden) that summarises your activity on the computer at work. So be careful : if they see that you cannot write code, and all you do is copy-paste from Chat GPT the code, they will fire you sooner or later. Silently one day they will fire you and they will not tell you why most likely. Be careful. Also you can use it on your phone, but it is time consuming.. Ofcourse they have AI tools but you must write code yourself. Truth3: If you go to non-code positions, it is more stressful and more competitive than code positions. Often, you get lower salary also. Because people in energy are scared of coding, and do not like it. So you get an advantage if you can code , understand code etc. Not super . Just basic things. Eg understand Python . Understand ML. etc Truth4: Do not share personal stories with co-workers. They are competing with you for promotion so they want you to fail ... Be careful. Do not share sensitive things. Truth5: Becareful of your social media presence. Managers spy on you e.g. they have fake profiles and are your friends. They will find your second, third etc profiles. They can find your anonymous X profile where you troll people . They have the ability to find you because they have software tools that you do not know . Truth6: yes the online courses here are all you need. But please fix your CV. Align it with energy companies. Do not just design your CV yourself. Get feedback here. Most CVs look rubbish. Try work on your CV carefully.

Online Course 5.20 is Now Live in the Classroom! The course includes a PowerPoint presentation, full Python implementation and many hours of video lectures. It is a beginner-friendly course on the valuation of Commodity Options (like options on crude oil), including full Python implementation. No stochastic calculus, and no fancy "martingale" mathematics. Just simple, practical language, explaining exactly how it is used in practice, in finance. Banks like JPMorgan use these exact models to help oil companies guarantee their income, even if oil prices crash. On the other side, oil refineries use this same math to lock in cheap fuel prices. What we need to remember (also great for interviews): When a company exercises a Commodity Option, it doesn't get the commodity (e.g. oil) immediately. Rather, it receives a Futures Contract. Once that contract expires, it then receives the commodity ( crude oil). So: Option --> Futures --> Commodity. The attached cheat sheet compares the two main pricing models in energy finance: 1. Black-Scholes: Used for Energy Equities (Stocks). 2. Black-76: Used for Commodities (Futures). What’s Inside Course 5.20: - Foundations: Spot Price vs. Futures Price vs. Strike Price. - The Math: Calculating "Premium" using the Black-76 Model. - The Code: Full Python implementation for pricing Call and Put options. - Valuation: Understanding Intrinsic vs. Extrinsic (Time) Value. - Mechanics: American vs. European options and Physical Settlement. - Real World: How an Option becomes a Future, and how a Future becomes Oil.

Attached is a new report, just published, in PDF. Click to read and/or download. This report is about how countries can build energy systems that stay reliable during crises like wars, cyber attacks, extreme weather, and energy supply disruptions. Just to understand how many jobs will be needed in Energy just note this from the report: The world needs to add or refurbish 80 million kilometers of electricity grid by 2040 (equivalent to the entire existing global grid) for achieving energy security. This just underlines the massive employment needs in Energy. Another key point: While renewable energy makes a country independent for its electricity generation, it creates new dependencies in manufacturing. More than 75% of solar panels are made in China. Wind turbines? Also dominated by China. The rare earth minerals needed for batteries and electric vehicles? China controls most of the supply chain. Bottom line: Renewable energy gives a country independence in generating electricity, but the country becomes dependent on whoever makes the equipment. Sources used for this report: [1]: Financial Times: https://www.ft.com/content/45d4962d-b4d7-49ac-959a-82a1dbee059f [2]: The Economist: https://www.economist.com/science-and-technology/2025/04/30/the-great-iberian-power-cut-need-not-spell-disaster-for-renewables? [3]: Bloomberg: https://www.bloomberg.com/news/articles/2025-06-12/there-aren-t-enough-cables-to-meet-growing-electricity-demand? [4]: Investors Chronicle: https://www.investorschronicle.co.uk/content/d0ecf236-45f5-4ca2-a897-c6533fc8d3ba? [5]: European Commission: https://commission.europa.eu/news-and-media/news/eu-plans-upgrade-energy-infrastructure-lower-bills-and-boost-independence-2025-12-10_en

Hi everyone, glad to join this community. I’m here to deepen my skills at the intersection of energy economics and data science, and to learn from people already applying ML/AI to real energy-sector problems. My goal is to build a strong foundation in energy data science—both the analytical side and the industry context—so I can contribute meaningfully to projects in optimisation, finance, and innovation-driven energy solutions. Looking forward to connecting, learning from the program, and contributing wherever I can.