Distributed Energy Resource Exploitation through Co-Optimization of Power System and Data Centers with Uncertainties during Demand Response

Yu Weng, Yang Liu, Rachel Li Ting Lim and Hung D. Nguyen ()
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Yu Weng: School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore
Yang Liu: School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore
Rachel Li Ting Lim: School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore
Hung D. Nguyen: School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore

Sustainability, 2023, vol. 15, issue 14, 1-18

Abstract: This paper presents a robust bi-level co-optimization model that promotes the active participation of Internet Data Centers (IDCs) in demand response (DR) programs, thereby enhancing the flexibility of power systems. Our approach involves leveraging virtual power lines to migrate workloads among IDCs, optimizing resource allocations, and benefiting both domains. The model incorporates a Gaussian Process Regression (GPR)-constructed DR price–amount curve, which largely contributes to the simplification of the optimization problem with high accuracy and computational efficiency. It also respects the information barriers between the two domains of power systems and IDCs, and thus safeguards the privacy and flexibility of IDCs. The uncertainty in IDC operations is considered by incorporating the variance in GPR into the demand response curve. By integrating IDCs as DR resources, the framework of this research enhances the flexibility of power systems and the efficiency of cross-domain co-optimization. The model and algorithm are validated using modified IEEE test systems.

Keywords: bilevel optimization; demand response; Gaussian process regression; data centers; information barriers; price–amount curves; uncertainties (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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