L2RPN is a series of competitions, with the aim of training an Artificial Intelligence agent to operate a power network. The Delft-AI-Energy Lab is hosting a remake of the L2RPN challenge in the Netherlands. The L2RPN Delft 2023 competition offers an exciting opportunity for students and researchers in the Netherlands to test their AI solutions against others in a realistic and challenging environment.
I specialize in combining power system simulation libraries with web technologies. I have experience with power flow simulation, optimization and transient simulation. I also work on solvers for sparse systems of linear equations and sparse matrix ordering algorithms. For web development I typically use Vue with Web Components and have experience with WebAssembly.
Armadillo is particularly notable in that has a Python interface (based on pybind11). Although, sparse matrix support still needs to be added. Also the PyArmadillo API resembles that of MATLAB, which can be convenient when translating.
I am surprised not to see any mention of the OSQP (Operator Splitting Quadratic Program) solver. It is the most impressive open source solver of this type that I have seen published in recent years. It appears to have been developed as a collaboration between Princeton, ETH Zurich, Oxford, Stanford and some other prestigious names. The benchmarks show that it compares favorably with leading proprietary solvers:
The problem described seems to be an ideal use-case for Machine Learning. The MATPOWER Optimal Scheduling Toolkit (MOST) can already solve:
"a stochastic, security-constrained, combined unit-commitment and multiperiod optimal power flow problem with locational contingency and load-following reserves, ramping costs and constraints, deferrable demands, lossy storage resources and uncertain renewable generation."
Much more and it becomes a global optimization problem where you can never really be sure you are not just stuck in a local optimum. The L2RPN (Learning to Run a Power Network) challenge, from RTE-France, is the most interesting effort I have seen applying Machine Learning to energy system management.
https://github.com/casecsv
https://github.com/cktcsv