ENGIE Impact: Optimizing sustainable electric power systems

Electric power systems are at the heart of the energy trilemma: affordability, sustainability and reliability. Powerful optimization software tools are required to plan electric power systems in order to achieve a twofold result: improving profitability of investments and ensuring reliable operating conditions.

IPSO (Integrated Power System Optimizer) is a planning and analysis tool, developed by ENGIE Impact, for solving advanced Optimal Power Flow (OPF) problems. The tool allows users flexibility to tackle various problems that extend beyond traditional applications of OPF. Potential applications are the computation of the maximum power that can be securely transferred, the definition of an optimal investment plan to compensate the reactive power and keep the voltage within bounds and, lastly, the identification of preventive and/or corrective control actions needed to deal with failing equipment.

These applications consider the full complexity of real power systems, such as the nonlinearities of power flow equations and the discrete nature of numerous control variables. The resulting complex nonlinear and non-convex problems require a powerful nonlinear solver able to handle very large optimization problems. Artelys Knitro is the right answer to tackle these. Indeed, its state-of-the-art interior point method allows IPSO to optimize power systems with thousands of generation and transmission elements (e.g. power system of a country or of a part of a continent).

IPSO is used to plan and operate sustainable electric power systems all over the world, thanks to its efficiency in solving diverse OPF problems and its comprehensive diagnosis on the main constraining factors.

See how large-scale optimization models can be used in the healthcare industry!

Artelys Knitro and AMPL are used by Masoud Zarepisheh, Assistant Professor, at the Memorial Sloan Kettering Cancer Center (MSKCC) to plan radiation for cancer patients! The approach consists in prioritizing clinical objective in order to maximize tumor radiation coverage while minimizing the impact on healthy tissues.

The ECHO project (Expedited Constrained Hierarchical Optimization) is an automated planning process developed at MSKCC based on hierarchical constrained optimization technique to prioritize clinical objective. The automation relies on complex large-scale optimization models solved with Artelys Knitro. One key feature that speeds up the resolution of such complex models is the problem structure exploitation to automatically apply convex specializations.

The automation of radiation therapy cancer treatment results in faster treatment delivery, more accurate tumor irradiation and healthy tissue sparing. The method has been implemented by the hospital since April 2017 and was already successfully used in the treatment process of more than 800 patients! 

Artelys Knitro and AMPL are used by Masoud Zarepisheh, Assistant Professor, at the Memorial Sloan Kettering Cancer Center (MSKCC) to plan radiation for cancer patients! The approach consists in prioritizing clinical objective in order to maximize tumor radiation coverage while minimizing the impact on healthy tissues.

The ECHO project (Expedited Constrained Hierarchical Optimization) is an automated planning process developed at MSKCC based on hierarchical constrained optimization technique to prioritize clinical objective. The automation relies on complex large-scale optimization models solved with Artelys Knitro. One key feature that speeds up the resolution of such complex models is the problem structure exploitation to automatically apply convex specializations.

The automation of radiation therapy cancer treatment results in faster treatment delivery, more accurate tumor irradiation and healthy tissue sparing. The method has been implemented by the hospital since April 2017 and was already successfully used in the treatment process of more than 800 patients! 

See how tax policy models in Julia can be solved efficiently with Artelys Knitro!

Economists work routinely with tax policy models formulated as inequality-constrained nonconvex optimization problems. They have a degeneracy feature: the number of active inequality constraints at a solution is typically several times larger than the number of variables in the problem, which makes them particularly difficult to solve.

The NCL algorithm, designed by Kenneth Judd, Ding Ma, Michael Saunders, and Dominique Orban as part of Ding Ma’s PhD thesis at Stanford, addresses the difficulty in solving such models. The idea is to approximate the ill-posed problem with a sequence of well-posed subproblems without the degeneracy feature. Early subproblems can be solved loosely. Accuracy is only required as we approach a solution of the original model.

The first version of NCL is implemented entirely in AMPL and calls Artelys Knitro’s barrier algorithm with appropriate options to solve the subproblems tackling tax policy models with thousands of variables and half a million constraints.

NCL’s success on the tax models prompted them to investigate a generalization for any optimization model. The Julia language offers the requisite tools: the Julia interface to Artelys KNITRO and the JuliaSmoothOptimizers (JSO) infrastructure for optimization. In particular, JSO’s generic modeling features gives access to large test sets by way of the Julia interface to the CUTEst collection and to AMPL models.

Thanks to the work of Pierre-Élie Personnaz during his internship at GERAD, numerical results on realistic tax problems were presented at the ICCOPT 2019 conference in Berlin with performance that surpass all previous results. The NCL solver showcases how powerful the combination of Julia, JSO, and Artelys Knitro can be for optimization. Ongoing improvements include primal and dual warm starts, progressive tolerances, and parameter tuning.

See how derivative-free optimization
is used in Macro-economics!

Among the domain of macroeconomics are a series of models called DSGE (Dynamic Stochastic General Equilibrium), that try to explain the effects of economic policies on economic growth. Such models are frequently used by Central Banks to predict global growth of a country.

TotalEnergies: optimize oil blending in a refinery

TotalEnergies owns and operates several refineries worldwide, producing millions of barrels per day by implementing complex refinery operations. In order to improve processes within its refineries, TotalEnergies wished to provide its planners with an operations management tool based on an optimization engine.

A refinery has to satisfy product deliveries contracted with its customers over a given time period. Several products such as fuel or diesel can be shipped through various shipment types like trucks or pipelines. As a consequence, a set of operations is planned to meet the product delivery requirements.

The main operation type consists of hydrocarbon blends for which components are blended to generate the final product to be delivered. The calculation engine based on Artelys Knitro optimizes the blend recipes as well as the planning of these mixtures in order to minimize the costs of the operations carried out as well as the delays of the customer deliveries while meeting the product quality requirements.

The model complexity comes from the quality specification constraints of the blends and deliveries which involve complex nonlinear equations. The project leverages the advanced black box capabilities of Artelys Knitro to efficiently tackle instances with dozens of product qualities, specifications and planned operations over several weeks.

See how the MIT Biomimetic Robotics Lab uses Artelys Knitro to achieve High speed landing of quadruped robots

There are various and fascinating projects adopting Artelys Knitro. The MIT Biomimetic Robotics Lab used our nonlinear solver on quadruped robot. Artelys Knitro has been found to be the only non-linear solver capable of performing fast enough to be deployed onboard their robot for a real-time Optimal Landing Control.

Trajectory optimization

The Biomimetic Robotics Lab at the Massachusetts Institute of Technology (MIT) has been using Artelys Knitro for real-time control of their legged robots: the MIT Mini Cheetah and the MIT Humanoid. Trajectory optimization is an essential process for real-time control of the legged robots. Optimal control for legged systems is a challenging problem that must contend with nonlinear, hybrid dynamics and complicated, high degree-of-freedom kinematics. In the case of the Mini Cheetah, this optimization process is used to plan complicated motions such as jumps, aerial spins, back flips, and barrel rolls.

The ability to perform these motions greatly expands the range of environments the robot is capable of traversing, which is crucial as these robots are meant to be deployed operationally. Since incorporating Artelys Knitro into the software, the lab has been able to implement a number of new, real-time controllers on their robots using nonlinear optimization. Even after testing other state-of-the-art nonlinear optimization solvers, Artelys Knitro was the only one capable of performing fast enough to be deployed onboard their robot.

Model-predictive controllers

For the Mini Cheetah, a nonlinear model predictive controller (MPC) was implemented for landing. The optimization was formulated with complementarity constraints, which make the nonlinear program (NLP) challenging for many solvers, but Artelys Knitro was able to find solutions at roughly 5-10 Hz (i.e., between 5 and 10 problems are solved every second). The team used a nonlinear trajectory optimization including contact complementary constraints to find optimal landing postures. Because real-time performance is so important in the short duration of a fall, Artelys Knitro was used over other solvers for its speed and reliable convergence.

For the MIT Humanoid, a nonlinear predictive controller was implemented that leverages the MIT Humanoid’s arms to improve balance and locomotion. The complex nonlinear optimization can solve complex arm motions in response to large disturbances. Artelys Knitro solved these motions at 40 Hz and helped demonstrate successful landings in both simulation and hardware.