This PDF document is the Energy Profile for Saudi Arabia, published by IRENA (the energy company in the UAE ) . Saudi Arabia has one of the world's most fossil-fuel-dependent energy systems. Oil and gas supplied around 99% of Saudi Arabia's total energy in 2022, with renewables under 1%.... And this makes Saudi Arabia a major net energy exporter, with energy self-sufficiency of 275%. Renewables still generated only about 1% of electricity in 2023. But Saudi makes progress in renewables also. Such as 1755 MW of new solar capacity was added in 2024 alone. Saudi Arabia has world-class solar potential, moderate wind potential, and very limited biomass potential. Renewable energy supply grew more than tenfold between 2017 and 2022, though from a very low base. Renewable electricity capacity reached around 5% of the total power mix in 2024, up from effectively zero in 2017. For 2030 its goal is to generate 50% of electricity from renewables . And to have renewable capacity equal to 130 GW, with 58.7 GW from solar and 40 GW from wind. Download below

I found many Morgan Stanley recent interview questions. For the role of " Sustainable Finance or Utilities Equity Research Associate" and similar. It is related to the S&P Global Clean Energy Transition Index . Here is the link : https://www.spglobal.com/spdji/en/indices/sustainability/sp-global-clean-energy-transition-index/?#overview Study the index for trading jobs also. "In 2023, clean energy stocks dropped about 20% while the global stock market went up about 22%. Why did clean energy do so badly, and what would you buy or avoid in clean energy today?" One answer is that Interest rates went up almost everywhere in the world . And clean energy projects (solar farms, wind farms) cost a lot upfront and pay back slowly over 20+ years. When borrowing gets expensive, those projects become much less profitable. Meanwhile, the broader market was lifted by AI/tech stocks that don't have this problem.

I've uploaded slides and a written explanation on Benders Decomposition, with the full code available inside online course 90 , found by clicking the 'Classroom' menu. The course presents a mixed-integer linear optimisation problem of the kind used by electricity network planners , which are the companies that decide which parts of the grid need to be reinforced (for example, upgrading the capacity of transmission lines). The objective is to minimise the total cost of expanding and operating the power system , i.e. deciding what to build and how to run it . The problem also accounts for sources of uncertainty. Problems like this become very large (many decision variables, many constraints), which makes them slow and sometimes practically impossible to solve . To get around this, we decompose them into smaller pieces and solve those instead. This course presents the Benders Decomposition approach, named after Benders, the researcher who introduced it and this approach is very widely used in power system planning (deciding when, where and how to reinforce the grid). This approach splits the original optimization problem into two subproblems . The full code and slides are available to download. Such a model and similar ones are used in electricity transmission / distribution companies : - UK & Ireland: National Grid, NESO, SSEN, SP Energy Networks, UK Power Networks, Northern Powergrid, EirGrid - Europe: TenneT (Netherlands/Germany), Elia (Belgium), RTE (France), Terna (Italy), 50Hertz (Germany), Red Eléctrica (Spain), Statnett (Norway), Swissgrid (Switzerland) - North America: PJM, MISO, ERCOT, CAISO, NYISO, ISO-NE, AESO (Alberta), IESO (Ontario) - Asia-Pacific & Middle East: AEMO (Australia), POSOCO/Grid-India, TEPCO (Japan), KEPCO (South Korea) Typical job titles that actually design / use such models at work, include: - Network Planning Engineer, - Transmission Planning Analyst, - System Development Engineer, - Power System Modeller, - Investment Planning Analyst, - Whole-System Strategy Analyst, - Quantitative Analyst at energy consultancies (e.g. AFRY, DNV, Baringa, Aurora Energy Research, Pöyry, E3).

Link with historical oil prices. Massive dataset to train ML models for anyone interested click below to download. By EIA. https://www.eia.gov/dnav/pet/hist/rbrted.htm