In , a single-period co-optimization model of energy and reserve is considered to better illustrate the properties of the co-optimization model and the associated market mechanism.
We consider some crucial problems related to the secure and reliable operation of power systems with high renewable penetrations: how much reserve should we procure, how should reserve resources distribute among different locations, and how should we price reserve and charge uncertainty sources.
As wind power penetration increases, the wind farms are required by newly released grid codes to provide frequency regulation service.
To provide a practical Volt/Var control (VVC) strategy for such networks, a data-driven VVC method is proposed in this paper.
In this paper, we propose an online multi-agent reinforcement learning and decentralized control framework (OLDC) for VVC.
In the sequential online stage, we transfer the offline agent safely as the online agent to perform continuous learning and controlling online with significantly improved safety and efficiency.