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.

"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.

I'd like to share a short report I wrote some time ago. It's a brief overview of the Turkish energy market and its key players. I'd be happy to hear your thoughts and comments.

5 new videos are now inside the Classroom; specifically inside online course 120. This course focuses on electricity distribution planning under an uncertain number of electric vehicles (EV) in the grid. The course applies the Stochastic Planning and the Minimax Regret frameworks to this case study. Both frameworks are used in practice for finding the optimal ways to reinforce the electricity grid when there is uncertainty. Each video comes with downloadable Powerpoint slides, Excel files etc. A brief description of each of the 5 new videos follows: - The first video (11 minutes) shows how to calculate the peak electricity demand caused by electric vehicles, in an electricity distribution grid. The calculations, the assumptions, and the structure are identical to what was used in practice. As a reminder, course 120 examines Stochastic Optimization, and Minimax Regret, which are known as "planning frameworks" and are used for finding the optimal investment decisions needed in the grid, when there is uncertainty. - The second video (13 minutes) focuses on the social cost. Specifically , costs are of two types: investment cost and operational cost. The operational cost includes a cost of unserved energy i.e. the welfare loss incurred when electricity demand cannot be served because electricity network lines lack sufficient capacity. Because this cost reflects the welfare loss borne by end-users, we refer to it as a social cost of unserved energy. In this course we focus on the social cost associated with unserved EV charging demand. This cost can be expressed equivalently in £/MWh (per unit of unserved charging electricity) or in £/EV (per vehicle unable to charge). The two units are related through the per-EV charging energy requirement; this video shows how to convert between them. - The third video (11 minutes) shows how to find the number of EVs that cannot be charged with electricity due to network constraints. The social cost of unserved EV charging (in £/MWh) is an input parameter to the optimization. When it is set to a low value, the weight placed on avoiding unserved demand in the objective becomes small relative to the marginal cost of network reinforcement. The optimizer then finds it cost-optimal to defer some line upgrades and accept a positive level of unserved EV charging demand at the optimum; conversely, a high social cost drives earlier and more extensive reinforcement. The video shows for example that in a bus, a total of 0.91 MW of EV peak demand is unserved and it shows how to find that this corresponds to 131 EVs unable to charge.

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.