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/
https://github.com/rte-france/l2rpn-baselines
The competition has been renewed for 2022 and has been accepted for the IEEE World Congress on Computational Intelligence in July.
https://wcci2022.org
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/
https://github.com/rte-france/l2rpn-baselines
The competition has been renewed for 2022 and has been accepted for the IEEE World Congress on Computational Intelligence in July.
https://wcci2022.org