Safe Reinforcement Learning for Strategic Bidding of Virtual Power Plants in Day-Ahead Markets

For this reason, their applicability in practice is limited. Growing environmental concerns and advancements in communication The above-mentioned scalability issues can be addressed and monitoring technologies have led to the increased by employing deep RL methods like the Deep Deterministic deployment of Distributed Energy Resources (DERs) Policy Gradient (DDPG) algorithm [10], which utilizes neural in power networks [1], comprising renewable energy sources networks to extend the Q-learning capabilities to continuous and prosumers. The market integration of these units is facilitated state and action spaces. The authors in [11]-[13] propose deep by their large-scale aggregation under financial entities, RL methods for the economic dispatch and market participation commonly known as Virtual Power Plants (VPPs), which have of DERs aggregated in a VPP. The main limitation the capacity for trading in wholesale electricity markets [2], of these works is that they fail to account for the complex [3]. As a self-interested market participant, a VPP aims at internal physical constraints of large-scale VPPs, such as maximizing its own profit generated by its market participation power generation limits and power flow constraints, in order to and the fulfillment of contractual obligations towards its ensure a safe operation.