Interesting approach. Maybe more countries could do this. This is interesting because in the UK market , gas often sets the electricity price for the whole system, even when wind, solar, and nuclear are providing most of the generation. Reuters says that the government wants to move more older low-carbon generators onto long-term fixed contracts, covering about one-third of Britain’s power supply. The goal is to reduce consumer exposure to gas-price shocks, and make the economy less vulnerable to events like the recent Middle East disruption and earlier post-Ukraine price spikes. https://www.reuters.com/business/energy/uk-accelerates-clean-energy-protect-against-fossil-fuel-price-shocks-2026-04-20/

Dear colleagues, I would like to share the second part of the report I wrote some time ago. This time I have focused on a more niche topic. Also, as of June 20th, I will be participating in a short-term program in Poland. Therefore, I will be in Europe. If you are interested in working together or have any opportunities you can direct me to, I would be happy to be in touch. You can send a message through this application or contact me at [email protected] so we can discuss this in more detail.

Just sharing an article on job security for software engineering and this also applies to energy (which is becoming a major demand for software engineers/ data scientists). Many people say that with AI there will be no need for software engineers in the future, including software engineering in energy, finance, etc. According to official sources, companies are actually increasing demand for software engineering, including energy. "Companies are expanding their software budgets and increasing engineer headcounts, a Bank of America survey found. The long-term outlook for the job appears strong, too. Software developer employment will grow 15% by 2034, the Bureau of Labor Statistics projects." "IBM, for example, is tripling entry-level hiring in the United States, including software developers." Source: https://edition.cnn.com/2026/04/08/tech/ai-software-developer-jobs

Was chatting with a portfolio manager on the Exxon stock price. See plot which is here attached. It shows Exxon Mobil’s stock price (XOM) over last year until now. XOM is the ticker symbol for Exxon Mobil on the New York Stock Exchange. On 28 February the US and Israel struck Iran, and because roughly a fifth of global petroleum consumption passes through the Strait of Hormuz, markets instantly priced in a supply disruption and so Brent jumped about 43% in March. XOM tracked it almost tick-for-tick. XOM tracks Brent very closely. See that the stock was relatively flat for much of 2025, then rose sharply in early 2026, and later pulled back a bit. So at some point, the market became much more positive about Exxon, likely because of stronger oil and gas expectations. See for example the second attached plot . The blue line is Exxon Mobil’s stock price, and the orange dashed line is Brent crude oil. Before late February, both move around, but nothing dramatic happens. Then, around Feb 28, both jump sharply: Brent rises from about $70 to about $104 per barrel, and Exxon jumps to a March peak of $176.41. After that, both come down. So the message is that a geopolitical shock pushed oil prices up quickly, and Exxon’s stock moved up with it. When oil goes up fast, investors expect a company like Exxon to earn more cash, so the stock price also rises.

"Network planners" are companies like - National Grid, UK Power Networks, SSEN, Northern Powergrid in the UK. - Enedis and RTE in France. - e-distribución and Red Eléctrica in Spain. - e-distribuzione and Terna in Italy; - E.ON, Netze BW, and 50Hertz in Germany; - Stedin and TenneT in the Netherlands; - PG&E, Con Edison, Duke Energy, and ERCOT in the US etc. These companies have to think about reinforcing the electricity grid years in advance. E.g. when should we upgrade a line? Which part of the network needs reinforcement first? etc Network Planners use a suite of optimisation models to make these decisions, including: - Deterministic optimisation (solving across one scenario ) - Stochastic Planning (SP) ( many scenarios , weighted by their probabilities) - Least-Worst Regret (LWR) ( ignores probabilities and protects against the worst-case regret). Each framework can point to a different "optimal" investment strategy for the same grid. Knowing why they disagree is essential. This 13-minute video comes from a consultancy project I delivered, training an energy company on the differences between LWR and SP. The company wanted an intuitive understanding of how two key inputs (probabilities and social costs) drive the differences in the optimal solutions these two frameworks produce. In the video, I walk through one worked example: a simple case focused on EV-driven demand growth, where we compare what SP and LWR recommend under different probability assumptions and different social cost values. The results show that in some cases the two frameworks agree completely, in others they point in opposite directions. This is just one example of the kind of analysis that helps planners choose the right framework for the right decision. The video is in course 120. And the new video is "5.1. Effect of Social Cost & Probabilities". The attached slide offers a summary of key results.