From a JP Morgan analysis , sharing here (plots are still in preliminary/draft form) - Looking at the normalized oil chart (attached ) , oil reacted fast and strongly, while natural gas moved much less. The reason is that the gas chart (attached) is Henry Hub, which is the main U.S. natural gas benchmark, and that market is driven much more by U.S. weather, storage, production, and pipeline conditions than by Middle East geopolitics. So the spike shown was not caused by the Iran war. JPM attributes that January jump to severe winter weather. - Key market risk is the Strait of Hormuz, because a very large share of global oil moves through that route, so when traders fear disruption there, oil prices jump first.

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.

Hi, im new here, and recently taking the course to understand energy analytics. Ive been having a look around on linkedin at some jobs as a junior can get into. theres a role for "Power Market Modelling Consultant" - Here is a snippet of their requirements.. What You’ll Be Doing - Building and running market models to explore price evolution, dispatch patterns, capacity dynamics, and policy impacts. - Applying tools such as PLEXOS, Python, R, and advanced spreadsheet modelling to deliver evidence driven insights. - Supporting economic and regulatory assessments, including evaluating future energy scenarios and system wide implications. - Communicating complex findings clearly to internal teams and external stakeholders. - Working with multidisciplinary colleagues on projects for government, industry, and energy ecosystem organisations. About You - A good understanding of UK or European electricity markets, system operation, and market drivers. - Experience using PLEXOS or similar modelling environments, plus familiarity with scripting or statistical tools. - A background in energy modelling, forecasting, simulation, economics, or related analytical fields. - Excellent communication skills, collaborative mindset, and the ability to translate complex modelling outputs into clear messages. can someone just explain what type of knowledge i would require for a job like this? i have general python, sql, excel knowledge so trying to transfer into energy analytics. or can someone recommend some junior analyst roles i can do instead or what the job spec will look like? im just trying to understand, what type of things i should be focusing on. Would really love everyones input.

I was at a conference about future energy technologies, which will be commercialised by 2100: a) Fusion: Nuclear fusion will have matured into a commercially viable, near-limitless source of clean baseload power. Commercial nuclear fusion could provide virtually limitless, clean baseline power by replicating the physical processes that power the sun. This breakthrough would effectively eliminate our reliance on fossil fuels and permanently stabilize the global energy grid. b) Batteries lasting years: Advanced, ultra-dense energy storage, potentially utilizing next-generation solid-state or quantum battery architectures, could allow personal electronics to run for years on a single charge. This leap will eliminate daily charging anxiety entirely and drastically reduce global battery waste. c) Multi-foldable phones: Thanks to hyper-flexible materials like graphene, communication devices will become as pliable as paper, allowing us to roll, wrap, or fold them into any shape. This will effectively blur the lines between smartphones, smartwatches, and smart clothing, making screens an ambient part of our wardrobe. d) Renewables everywhere: Massive leaps in grid-scale energy storage will eliminate the intermittency of solar and wind, allowing us to capture and store vast amounts of surplus energy. This will enable a fully decentralized, globally connected grid powered almost exclusively by renewable sources, regardless of weather conditions. e) High-endurance drones: Powered by those same next-generation batteries, autonomous drone fleets could stay airborne for weeks at a time over cities. These persistent aerial networks will handle everything from instant logistics and heavy human transport (air taxis) to continuous emergency response and environmental monitoring. f) Autonomous, Self-Charging EVs: Electric vehicles will become fully autonomous mobile power banks that wirelessly charge themselves while driving over smart, electrified roads. When parked, they will automatically sync with the local grid via advanced vehicle-to-grid (V2G) technology to power homes and balance neighborhood energy loads.

Inside the 'Classroom' , there is a new course (116) , which shows how to develop, in Python, a 2-stage and a 3-stage stochastic optimization model. The code is available for download, and it is explained through a video of about 1 hour in total. The prerequisites are courses 115 (deterministic optimization) and 116 (Monte Carlo). Stochastic Optimization is used a lot in energy, economics and finance. Anytime we have something uncertain, we use scenarios to describe how the future may play out. For example, the electricity demand tomorrow can be 100 kW, 50kW, 20kW. So we have 3 scenarios. We can have as many scenarios as we think is reasonable. For example in the code we build a 'scenario tree' consisting of 1000 scenarios. And we then assign a probability to each of these scenarios. Then, we have an objective function, which includes probabilities. We have constraints. And this is like any other optimization model. We call it 'stochastic' because it has probabilities in the objective function and because it has scenarios. It is like any other optimization model. So , the jargon may sound a bit intimidating , but it is very simple actually. We apply stochastic optimization to a smart building, which has a solar Photovoltaics unit and also it has residents who consume electricity (electricity demand). And we want to minimize the daily cost of operating this smart building in the future. Since we want to 'minimize' something' we speak about 'optimization'. And also we have uncertainties: the electricity demand is uncertain. Also, the output of the solar PV unit is uncertain. See the two screenshots attached for some extra context.