Modernized electrical grid automated to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity.
The use of robots in society could be expanded by using reinforcement learning (RL) to allow robots to learn and adapt to new situations on-line. RL is a paradigm for learning sequential decision making tasks, usually formulated as a Markov Decision Process (MDP). For an RL algorithm to be practical for robotic control tasks, it must learn in very few samples, while continually taking actions in real-time. In addition, the algorithm must learn efficiently in the face of noise, sensor/actuator delays, and continuous state features.
Evaluation of smart grid in the presence of dynamic market-based pricing and complex network of small and large producers, consumers, and distributers is very difficult task. Not only it involves multiple, interacting, heterogeneous cyber-physical domains, it also requires tight integration of power markets, dynamic pricing and transactions, price-sensitive consumer behavior, who may also be producers of power.
