Meeting Corporate Renewable Power Targets

灵活性(工程) 文件夹 启发式 采购 可再生能源 计算机科学 经济 马尔可夫决策过程 微观经济学 启发式 运筹学 环境经济学 数学优化 马尔可夫过程 工程类 财务 统计 数学 管理 人工智能 电气工程 操作系统
作者
Alessio Trivella,Danial Mohseni-Taheri,Selvaprabu Nadarajah
出处
期刊:Management Science [Institute for Operations Research and the Management Sciences]
卷期号:69 (1): 491-512 被引量:16
标识
DOI:10.1287/mnsc.2022.4354
摘要

Several corporations have committed to procuring a percentage of their electricity demand from renewable sources by a future date. Long-term financial contracts with renewable generators based on a fixed strike price, known as virtual power purchase agreements (VPPAs), are popular to meet such a target. We formulate rolling power purchases using a portfolio of VPPAs as a Markov decision process, accounting for uncertainty in generator availability and in the prices of electricity, renewable energy certificates, and VPPAs. Obtaining an optimal procurement policy is intractable. We consider forecast-based reoptimization heuristics consistent with practice that limit the sourcing of different VPPA types and the timing of new agreements. We extend these heuristics and introduce an information-relaxation based reoptimization heuristic, both of which allow for full sourcing and timing flexibilities. The latter heuristic also accounts for future uncertainties when making a decision. We assess the value of decision flexibility in rolling power purchases to meet a renewable target by numerically comparing the aforementioned policies and variants thereof on realistic instances involving a novel strike price stochastic process calibrated to data. Policies with full timing flexibility and no sourcing flexibility reduce procurement costs significantly compared with one with neither type of flexibility. Introducing sourcing flexibility in the former policies results in further significant cost reduction, thus providing support for using VPPA portfolios that are both dynamic and heterogeneous. Computing near-optimal portfolios of this nature entails using our information-relaxation based reoptimization heuristic because portfolios constructed via forecast-based reoptimization exhibit higher suboptimality. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4354 .
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