Extreme disasters may cause the power supply to the distribution system (DS) to be interrupted. The DS is forced to operate in island mode and forms an islanded microgrid (MG). In order to improve the post-disaster resilience of the DS and to provide longer power supply for as many loads as possible with limited generation resources, this paper proposes a multi-agent deep reinforcement learning (DRL) method which realizes a dual control on the source and load sides of the MG. The problem of resilience improvement is converted to a sequential decision making problem, where the objective is to maximize the cumulative MG utility value over the power outage duration. A multi-agent DRL model is proposed to solve the sequential decision making problem. A dual control policy including energy storage management and load shedding strategy is put forward to maximize the utility value of the MG. A reinforcement learning (RL) environment based on OpenAI and OpenDSS for islanded MG is constructed as a simulator, which has a general interface compatible with, and also can be published to, OpenAI Gym. Numerical simulations are performed for an MG equipped with wind turbines, diesel generators, and storage devices to validate the effectiveness of the proposed method. The influences of available generation resources and power outage duration on the control policy are discussed, which validates the strong adaptability of the proposed method in different conditions.
Knowledge management comprises the strategies and methods employed to generate and leverage knowledge within an organization. This report outlines the activities within the Division of Pharmacometrics at the US FDA to effectively manage knowledge with the ultimate goal of improving drug development and advancing public health. The infrastructure required for pharmacometric knowledge management includes provisions for data standards, queryable databases, libraries of modeling tools, archiving of analysis results and reporting templates for effective communication. Two examples of knowledge management systems developed within the Division of Pharmacometrics are used to illustrate these principles. The benefits of sound knowledge management include increased productivity, allowing reviewers to focus on research questions spanning new drug applications, such as improved trial design and biomarker development. The future of knowledge management depends on the collaboration between the FDA and industry to implement data and model standards to enhance sharing and dissemination of knowledge.
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