The blast wave of machine learning and artificial intelligence has also reached the power systems community, amid frenzy methods black-box tools that have been left in its wake, it is sometimes difficult to perceive a glimmer Occam’s razor principle. In this letter, we use unit commitment problem (UCP), an NP-hard mathematical program fundamental system operations, show simplicity must guide an...

Recent advances in artificial intelligence have demonstrated the capability of reinforcement learning (RL) methods to outperform state art decision-making problems under uncertainty. Day-ahead unit commitment (UC), scheduling power generation based on forecasts, is a complex systems task that becoming more challenging light increasing While RL promising framework for solving UC problem, space p...

The issue of unit commitment is one of the most important economic plans in power system. In modern and traditional power systems, in addition to being economical of the planning, the issue of security in unit operation is also of great importance. Hence power system operation confronts units’ participation and input considering network security constrains. The issue of units’ participation is ...

The solution of the unit commitment problem (UCP) is a complex optimization problem. The exact solution of the UCP can he obtained by a complete enumeration of all feasible combinations of generating units, which could be a huge number. The objective is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also me...

As renewable energy penetration rates continue to increase in power systems worldwide, newchallenges arise for system operators in both regulated and deregulated electricity markets tosolve the security constrained unit commitment problem with intermittent generation (due torenewables) and uncertain load, in order to ensure system reliability and maintain cost effec-tiveness. In...

Online learning is machine learning, in real time from successive data samples. Meta online learning consists in combining several online learning algorithms from a given set (termed portfolio) of algorithms. The goal can be (i) mitigating the effect of a bad choice of online learning algorithms (ii) parallelization (iii) combining the strengths of different algorithms. Basically, meta online l...