• 

  黄建伟

  优德官网 副院长兼群体智能中心主任、港中大（深圳）校长讲座教授、理工学院副院长

  执行主席

  Bio

  Abstract
• 

  鞠培中

  俄亥俄州立大学博士后研究员

  Distribution-Level Markets under High Renewable Energy Penetration

  Bio

  鞠培中，，，，，，，美国俄亥俄州立大学博士后研究员。。。。。他于2021年在普渡大学获得博士学位，，，，，，，2016年在北京大学获得学士学位。。。。。他最近获得了 ACM e-Energy 2022的最佳论文奖。。。。。他现在的研究兴趣包括机械学习、优化和网络。。。。。

  Abstract

  We study the market structure for emerging distribution-level energy markets with high renewable energy penetration. Renewable generation is known to be uncertain and has a close-to-zero marginal cost. In this work, we use solar energy as an example of such zero-marginal-cost resources for our focused study. We first show that, under high penetration of solar generation, the classical real-time market mechanism can either exhibit significant price-volatility (when each firm is not allowed to vary the supply quantity), or induce price-fixing (when each firm is allowed to vary the supply quantity), the latter of which leads to extreme unfairness of surplus division. To overcome these issues, we propose a new rental-market mechanism that trades the usage-right of solar panels instead of real-time solar energy. We show that the rental market produces a stable and unique price (therefore eliminating price-volatility), maintains positive surplus for both consumers and firms (therefore eliminating price-fixing), and achieves the same social welfare as the traditional real-time market. A key insight is that rental markets turn uncertainty of renewable generation from a detrimental factor (that leads to price-volatility in real-time markets) to a beneficial factor (that increases demand elasticity and contributes to the desirable rental-market outcomes).

• 

  吴辰晔

  香港中文大学（深圳）助理教授、优德官网 副研究员

  Learning for the Future Power Grid

  Bio

  吴辰晔，，，，，，，香港中文大学（深圳）理工学院助理教授，，，，，，，校长青年学者。。。。。吴教授划分于2009年，，，，，，，2013年在清华大学电子工程系、清华大学交织信息研究院获得学士学位与博士学位（师从图灵奖得主姚期智院士）。。。。。吴教授主要从事电力市场设计、电网清静及危害评估、电力系统控制等研究。。。。。现在，，，，，，，吴教授已揭晓高水平期刊/国际顶级聚会论文（如 IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, IEEE Transactions on Sustainable Energy, ACM e-Energy 等）80余篇，，，，，，，是中国工业与应用数学学会金融科技与算法专委会委员，，，，，，，中国能源学会综合专家组专委会委员，，，，，，，自2022年2月起担当 IEEE 系统科学汇刊（IEEE Systems Journal）编委（Editorial Board Member, Associate Editor），，，，，，，2022年 IEEE 智能电网通讯聚会（IEEE SmartGridComm）数据与盘算分会配合主席，，，，，，，2022年 ACM 未来能源大会（ACM e-Energy）数字聚会配合主席，，，，，，，先后三次获得能源领域旗舰聚会的最佳论文奖（包括2012年 IEEE SmartGridComm 最佳论文奖，，，，，，，2013年和2020年 IEEE PES General Meeting 最佳论文奖）。。。。。

  Abstract

  Advanced learning frameworks are reshaping the landscape of power grid operation and the electricity market design. This talk shares two stories, both of which seek to use learning frameworks to enhance the future power grid. The first one investigates the storage control problem for consumers. Specifically, we consider that consumers face dynamic electricity prices and seek to use storage to reduce their electricity bills. The challenges come from the uncertainty in the electricity price and consumers' demand. We propose a practical learning-based online storage control policy. The second story studies a classical procedure in the electricity market, the economic dispatch problem, i.e., matching the electricity supply and demand at the minimal generation cost. The critical challenge is again from the uncertainty in the system demand. Hence, the conventional approach is to conduct the dispatch based on predicted demand. However, we submit that this conventional approach can be suboptimal, and we propose a model-free algorithm for economic dispatch based on the end-to-end learning framework.

视频回首