Optimal Resource Procurement and the Price of Causality

• 成果类型: Article
• 署名作者: Li, Sen; Shetty, Akhil; Poolla, Kameshwar; Varaiya, Pravin
• 署名单位: Hong Kong University of Science & Technology; University of California System; University of California Berkeley
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3019374
• 发表日期: 2021
• 页码: 3489-3501
• 关键词: Causality; resource procurement; robust adjustable optimization

摘要

In this article, we study the problem of procuring diverse resources in a forward market to cover a set E of uncertain demand signals e. We consider two scenarios: 1) e is revealed all at once by an oracle and 2) e reveals itself causally. Each scenario induces an optimal procurement cost. The ratio between these two costs is defined as the price of causality. It captures the additional cost of not knowing the future values of the uncertain demand signal. We consider two application contexts: Procuring energy reserves from a forward capacity market, and purchasing virtual machine instances from a cloud service. An upper bound on the price of causality is obtained, and the exact price of causality is computed for some special cases. The algorithmic basis for all these computations is set containment linear programming. A mechanism is proposed to allocate the procurement cost to consumers who in aggregate produce the demand signal. We show that the proposed cost allocation is fair, budget-balanced, and respects the cost causation principle. The results are validated through numerical simulations.