Data association load uncertainty and risk aversion in electricity markets with data center participation in the demand response

作者：Zhuo Wang, Jun Zhang, Jiantao Liu, Jingjing Huang, Guorui Zhu, Chaofan Yu, Hongtao Zhou · 发表于：Energy Reports · 年份：2023 · DOI：10.1016/j.egyr.2023.12.012 · 被引用次数：6 · 研究领域：Smart Grid Energy Management、Electric Power System Optimization、Smart Grid Security and Resilience

Recently, demand response (DR) programs are being promoted in the power market to strengthen the role played by the load side in the power market and to increase the flexibility of loaders to participate in the power market. Data centers are uniquely positioned to participate as large-scale power users in the power market operation process. However, the issue of load uncertainty risk is not adequately considered in recent DR programs for data center participation in the power market. Regarding this issue, we propose a framework for facilitating bids for data center participation in DR programs in the power market. In the proposed framework, the data association Dirichlet process mixture model (DA-DPMM) is introduced to characterize the load uncertainty, and the association of load data is investigated to obtain higher prediction accuracy. To further reduce the uncertainty risk, we propose a two-stage market cleaning model based on the conditional value at risk (CVaR). We also analyze the CVaR, the social welfare derived from the Gaussian process model (GM), the Dirichlet process mixture model (DPMM), and the DA-DPMM. The results demonstrate the superiority of the DA-DPMM approach in a two-stage market cleaning model based on CVaR.