With the increasing integration of renewable energy into power networks, scheduling and operation systems become more challenging, in particular because intermittency wind speed. Thus, appropriate optimization tools are needed to efficiently manage modern systems. This paper considers unit commitment problem under high penetration. To mitigate potential negative effects inaccurate forecast, a c...

Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL relative to existing methods is their ability optimize uncertain systems independently explicit assumption process uncertainty. Recent focus on engineering applications has been directed towards development safe controllers. Previous works proposed approaches account for c...

Scheduling under uncertainty is essential to many autonomous systems and logistics tasks. Probabilistic methods for solving temporal problems exist which quantify and attempt to minimize the probability of schedule failure. These methods are overly conservative, resulting in a loss in schedule utility. Chance constrained formalism address over-conservatism by imposing bounds on risk, while maxi...

Chemical process optimization and control are affected by (1) plant-model mismatch, (2) disturbances, (3) constraints for safe operation. Reinforcement learning policy would be a natural way to solve this due its ability address stochasticity, directly account the effect of future uncertainty feedback in proper closed-loop manner; all without need an inner loop. One main reasons why reinforceme...

Existing electricity market designs assume risk neutrality and lack risk-hedging instruments, which leads to suboptimal outcomes reduces the overall efficiency. This letter enables risk-trading in chance-constrained stochastic by introducing Arrow-Debreu Securities (ADS) derives a risk-averse market-clearing model with trading. To enable trading, probability space of underlying uncertainty is d...