Optimization solvers: missing link for fully open-source energy system modeling

• osqp_benchmarks

  2 90 0.0 Python

  QP Benchmarks for the OSQP Solver against GUROBI, MOSEK, ECOS and qpOASES

  

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:

https://github.com/osqp/osqp_benchmarks

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://l2rpn.chalearn.org/

• l2rpn-baselines

  1 74 5.2 Python

  L2RPN Baselines a repository to host baselines for l2rpn competitions.

  

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