Use case
Electric vehicles (EVs) can provide services to electric power grids, thanks to their sizable power ratings and the flexibility of their charging patterns. If carried out in an uncoordinated way, charging a high number of EVs may have adverse impacts such as increased energy losses, voltage deviation, transformer overloading, etc.
The EV scheduling problem including reactive power support from EVs is a challenging problem which can be modelled using nonlinear programming.
Artelys Knitro allows to solve this complex problem efficiently, demonstrating that a coordinated dispatch of EVs considering active and reactive power constraints allows high EV penetration levels without impacting the grid.
Start with a tutorial!
You’re not familiar with nonlinear optimization? This tutorial will present some examples of nonlinear problems for various applications. You will discover nonlinear programming methods using the Artelys Knitro solver in a Python notebook, through different examples.
Free trial
Get your trial license to test Artelys Knitro’s performances on your own mathematical optimization problem. The trial package includes free support and maintenance. You can have access to Artelys Knitro for free with a 1-month unlimited version or a 6-month limited version.
Artelys Knitro has unmatched performance
Best Nonlinear Solver
Artelys Knitro has been ranked every year by public benchmarks consistently showing Artelys Knitro finds both feasible and proven optimal solutions faster than competing solvers.
Technical support
The Artelys technical support team comprises Artelys’consultants (PhD-level) who are used to solving the most difficult problems and deploying enterprise-wide optimization solutions. They can advise on algorithmic or software features that may result in enhanced performance in your usage of Artelys Knitro.
Updates and new features
The development team works continuously to provide two releases of Artelys Knitro every year. Based on feedback, we always improve our solver to meet users’ requirements and need to solve larger models faster.
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