13d (edited) • 💻 Technical Skills
New Online Course: Stochastic Optimization
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.
32
10 comments
New Online Course: Stochastic Optimization
powered by
skool.com/software-school-for-energy-7177
A beginner-friendly program for a career in technical roles in the energy sector. Regardless of your location, age, sex, education or experience.
Suggested communities
Powered by