市场随机出清下风险规避主体的策略行为

IF 0.9 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2025-09-11 DOI:10.1016/j.orl.2025.107365

Vincent Leclère , Andy Philpott

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引用次数: 0

摘要

我们提出了一个商品拍卖的模型，其中卖方和买方（代理人）使用一致的风险度量来表示风险。这些信息被传达给拍卖师，拍卖师在完全风险交易的情况下计算社会最优交易。将该模型应用于不确定条件下单结算批发电力池的经济调度和系统边际电价问题。如果系统运营商知道代理商的风险度量，则价格形成一个社会最优调度，该调度是收入充足的，并且在风险调整后的预期中恢复代理商的成本。我们构造了一个非合作博弈来证明代理人有动机歪曲他们的风险度量来提高他们的风险调整利润。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Strategic behavior of risk-averse agents under stochastic market clearing

We present a model of a commodity auction in which sellers and buyers (agents) represent risk using coherent risk measures. These are communicated to the auctioneer who computes socially optimal transactions assuming complete risk trading. The model is applied to economic dispatch and system marginal prices in a single-settlement wholesale electricity pool under uncertainty. If agents' risk measures are known by the system operator then prices form a socially optimal dispatch which is revenue adequate and recovers agents' costs in risk-adjusted expectation. We construct a non-cooperative game to show that agents have incentives to misrepresent their risk measures to improve their risk-adjusted profit.

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来源期刊

Operations Research Letters 管理科学-运筹学与管理科学

CiteScore

2.10

自引率

9.10%

发文量

111

审稿时长

83 days

期刊介绍： Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.