Robust energy management through aggregation of flexible resources in multi-home micro energy hub

Home energy management strategy to schedule multiple types of loads and energy storage device with consideration of user comfort: a deep reinforcement learning based approach

“…Then, how to obtain the optimal dispatch strategy of the HEMS is a crucial problem (Xiong et al, 2024). Normally, the optimization-based methods such as stochastic programming method (SP) (Hussain et al, 2023) and robust optimization method (RO) (Wang et al, 2024) are utilized to solve the optimization problem of HEMS. Tostado et al in (Tostado-Véliz et al, 2023a) develops a novel SP-based home energy management model considering negawatt trading.…”

“…Tostado et al in (Tostado-Véliz et al, 2023b) proposes a fully robust home energy management model, which accounts for all the inherent uncertainties that may arise in domestic installations. Wang et al in (Wang et al, 2024) proposes a multi-objective two-stage robust optimization to address the inherent uncertainty of DES, aiming to concurrently realize energy savings, carbon emission reduction, and load smoothing. However, the optimization results calculated by RO method are usually conservative and utilize only one dispatch solution to deal with all uncertainties of whole dispatch period.…”

“…Moreover, compared with the optimization-based methods in Refs. (Hussain et al, 2023;Wang et al, 2024;Xiong et al, 2024), the applied DDPG method can achieve fast decision making since the learned policy can be generalized to other situations without resolving the optimization model after the agent is trained.…”