A new report on this topic has now been published in Classroom, at the very end under “Energy Industry Support” , a special section featuring reports that explain the current status and key trends in the energy sector. The report is written in simple language, includes illustrative graphs, and draws on official and respected sources such as Financial Times, Bloomberg, Wall Street Journal, The Economist, Forbes, and Investors Chronicle. War in the Middle East can trigger a global economic chain reaction by disrupting energy supplies and pushing oil and gas prices higher. Investors worried that rising energy costs would increase inflation, reduce company profits, weaken consumer demand, and force central banks to keep interest rates high for longer. As a result, both stocks and bonds came under pressure, while markets became increasingly concerned about a prolonged period of slow growth combined with rising prices. This report can be freely used in your projects, work, or studies. It may also be especially useful for interviews, presentations, networking, and broader industry understanding, so it is strongly recommended that you read it and download it for future use. See the attached screenshots for a quick look. Sources: Financial Times: https://www.ft.com/content/3f4a6ad4-9216-4b5c-830f-11854787bb52?syn-25a6b1a6=1 Bloomberg: https://www.bloomberg.com/news/articles/2026-03-18/stock-market-today-dow-s-p-live-updates-? The Economist: https://www.economist.com/finance-and-economics/2026/03/09/the-iran-energy-shock-reverberates-across-financial-markets? Wall Street Journal:https://www.wsj.com/livecoverage/stock-market-today-dow-sp-500-nasdaq-03-19-2026/card/traders-brace-for-longer-natural-gas-crisis-AxkRbptlfWnH2nUO5kzc

🗺️ Where it started To better understand energy networks and their geographic topology, I dove into open source data — specifically OpenStreetMap and GridKit — to map transmission nodes and lines across Europe and the UK. There are already plenty of courses on grids and storage. So instead of passively consuming content... I decided to build something. 🎯 Project Goals Technical side: - Sharpen my Python + mapping skills (Folium / GeoPandas) - Experiment with Vibe Coding (rapid iteration, AI-assisted prototyping) Knowledge side: - Understand the role of energy storage and load balancing in modern grids - Identify the critical materials and minerals behind storage technologies (lithium, cobalt, vanadium, manganese...) - Map out the key players in the sector — utilities, pure-play storage companies, and emerging startups 💡 Open questions — your ideas welcome! Some threads I'm already pulling on: - Where are the bottlenecks in European transmission networks? - How is the storage mix evolving — short-term (batteries) vs. long-term (hydrogen, pumped hydro)? - What business models are emerging around grid flexibility? What would you add to this project? 👇 Series in progress — more in the next post 🔄

I am currently struggling a bit with my future career path. Because job opportunities in the energy sector are limited in my country, I am trying to identify alternative routes. However, I do not want to stop working on energy markets and the sector itself. For me, it seems somewhat easier to enter the finance industry. Which department within finance would best support this long-term goal? I have been thinking that starting in a treasury department might be helpful, since it is directly related to monetary conditions and financial management. What do you think about this? If there is no direct path into energy markets, how should I build an alternative route that would still move me toward that objective?

See the plot. You must explain what is happening. Source: JP Morgan round1 , February 2026. - What is the key conclusion ? Answer: oil prices are very sensitive to major global events. - Oil Prices why would they fall? Answer : When there is a global crisis that slows down the economy (like a recession or a pandemic), people travel less and factories slow down. Because the world needs less oil, the price drops. - Oil Prices why would they Rise? Answer: When there is a war or conflict, especially in areas that produce a lot of oil, markets panic that the global oil supply will be cut off, causing prices to spike. - Explain what we see on 1 Sept. 2008: Answer: When the global financial crisis hit, the global economy crashed, causing the demand for oil to become extremely low. - Explain the 2 March 2020 : When the world went into covid lockdown, travel stopped and industries paused. Because nobody was using fuel, the price of oil plummeted to the lowest ( $20 a barrel). - Explain 3. Feb. 2022 : Russia is one of the world's largest oil producers. When the war started, countries feared Russian oil would be cut off from the market. This panic caused the price to immediately spike back over $100. - Explain 4. Feb. 2026 : Iran sits right next to the Strait of Hormuz, where a massive portion of the world's oil is shipped. Because of the conflict, the market is scared that this vital shipping lane will close, causing a sharp spike in prices.

A new online course has been published in Classroom. It is course 118. Its title is "Kernel Density Estimation". Kernel Density Estimation (KDE) is a highly effective statistical method used in the energy sector. It allows you to take an existing dataset and generate new, realistic values that follow the exact same underlying patterns. This is perfect for when you need to simulate multiple scenarios. Specifically, in this course: - we look at a smart building with uncertain electricity demand, using 8760 hourly values (one year of data). - we want to simulate 1000 unique days where the demand is different but strictly follows the "logic" of our original dataset. - we walk through generating KDE-based data and using it to solve Monte Carlo and two-stage stochastic optimization models. These methods are absolute standards in the energy sector. Best of all, this is a highly applied course. I show you exactly how I used these exact techniques in a real-world energy project, so you can move past academic textbook exercises and start applying this to actual problems. The attached screenshots show the step-by-step process of how KDE is applied in industry. And also the differences between using KDE and non-KDE approaches ; KDE is more realistic. Non-KDE approaches are easier to model but lack realism.