In this paper, a branch of Deep Learning models, known as Standard Neural Networks, are used to predict electricity consumption and photovoltaic generation with the purpose of reduce the energy wasted, by managing the storage system using Reinforcement Learning technique.
To overcome the challenges, such as fixed control parameters and insufficient damping, we propose to use a deep reinforcement learning-based approach for energy storage control.
This study introduces a novel approach that leverages Deep Reinforcement Learning (RL) algorithms to develop optimal bidding strategies for collocated RES with Battery ESS (BESS) configurations, enabling multi-market participation in both energy and ancillary
This paper addresses this problem by using a model-free deep reinforcement learning (DRL) method to optimize the battery energy arbitrage considering an accurate battery degradation model.
This chapter proposes an energy storage solution controlled by Deep Reinforcement Learning (DRL) to address fluctuating electricity costs in the smart grid (SG).
This paper proposes a self-adaptive energy management strategy based on deep reinforcement learning (DRL) to integrate renewable energy sources into a system comprising compressed air energy storage, battery energy
This study introduces a novel approach that leverages Deep Reinforcement Learning (RL) algorithms to develop optimal bidding strategies for collocated RES with Battery ESS (BESS) configurations, enabling multi-market participation in both energy and ancillary services (AS) markets.
Abstract This research focuses on the grid-forming energy storage system (ESS). The deep Q-network (DQN) method is employed to optimize the capacity configuration and operation strategy of the ESS. In this study, an isolated microgrid on a small island is selected as the research subject.
This paper proposes a novel approach to assess the practical benefits of CESS deployment in a residential community by decreasing the daily electricity cost and maximizing the self-consumption of PV energy.
The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and renewable-based energy generation.
