Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling

Residential microgrid is widely considered as a new paradigm of the home energy management system. The complexity of Microgrid Energy Scheduling (MES) is increasing with the integration of Electric Vehicles (EVs) and Renewable Generations (RGs). Moreover, it is challenging to determine optimal scheduling strategies to guarantee the efficiency of the microgrid market and to balance all market participants’ benefits. In this paper, a Multi-Agent Reinforcement Learning (MARL) approach for residential MES is proposed to promote the autonomy and fairness of microgrid market operation. First, a multi-agent based residential microgrid model including Vehicle-to-Grid (V2G) and RGs is constructed and an auction-based microgrid market is built. Then, distinguish from Single-Agent Reinforcement Learning (SARL), MARL can achieve distributed autonomous learning for each agent and realize the equilibrium of all agents’ benefits, therefore, we formulate an equilibrium-based MARL framework according to each participant’ market orientation. Finally, to guarantee the fairness and privacy of the MARL process, we proposed an improved optimal Equilibrium Selection-MARL (ES-MARL) algorithm based on two mechanisms, private negotiation and maximum average reward. Simulation results demonstrate the overall performance and efficiency of proposed MARL are superior to that of SARL. Besides, it is verified that the improved ES-MARL can get higher average profit to balance all agents

A Glycolipid α-GalCer Derivative, 7DW8-5 as a Novel Mucosal Adjuvant for the Split Inactivated Influenza Vaccine

Influenza virus infects the host and transmits through the respiratory tract (i.e., the mouth and nose); therefore, the development of intranasal influenza vaccines that mimic the natural infection, coupled with an efficient mucosal adjuvant, is an attractive alternative to current parenteral vaccines. However, with the withdrawal of cholera toxin and Escherichia coli heat-labile endotoxin from clinical use due to side effects, there are no approved adjuvants for intranasal vaccines. Therefore, safe and effective mucosal adjuvants are urgently needed. Previously, we reported that one derivative of α-Galactosylceramide (α-GalCer), 7DW8-5, could enhance the protective efficacy of split influenza vaccine by injection administration. However, the mucosal adjuvanticity of 7DW8-5 is still unclear. In this study, we found that 7DW8-5 promotes the production of secret IgA antibodies and IgG antibodies and enhances the protective efficacy of the split influenza vaccine by intranasal administration. Furthermore, co-administration of 7DW8-5 with the split influenza vaccine significantly reduces the virus shedding in the upper and lower respiratory tract after lethal challenge. Our results demonstrate that 7DW8-5 is a novel mucosal adjuvant for the split influenza vaccine