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The Role of Prediction And Real-time Learning in Online Optimization and Control

Presented by: 
Na Li
Thursday 2nd May 2019 - 10:00 to 11:00
INI Seminar Room 1
Uncertainties place a key challenge in transforming the grid into one with a large renewable energy integration and active users' participation. The volatile renewable generation constantly introduces disturbance to the grid operation; while the stochastic user behavior makes it difficult to use demand as a reliable resource for balancing real-time supply-demand. This talk will present two lines of work to address these issues. Firstly, we will study how to use a short term prediction, e.g., renewable energy, to improve the performance for online optimization and control, e.g, economic dispatch with ramping cost. Then, we will study how to use real-time learning to design residential demand response programs for achieving grid reliability while balancing the exploitation and exploration of the uncertain user behavior.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons