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An agent-based FTR auction simulator

机译：基于代理的FTR拍卖模拟器

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摘要

In this paper, an agent-based simulation of a Financial Transmission Rights (FTR) auction for both types of point-to-point FTRs, obligation and option, is presented. Each auction participant is simulated as an adaptive agent who has the ability of evaluating its environment and acting accordingly, following the decision rules of the naive reinforcement learning algorithm presented in the paper. Initially, a Locational Marginal Pricing (LMP) based ex-ante energy market is assumed. From the solution of the ex-ante energy market the agents calculate their initial FTR bid prices. Subsequently, the ISO solves the FTR auction problem and the agents profits equal the reimbursement they receive in the ex-post energy market for holding the FTR. The agent-based simulator repeats the FTR auction under the same conditions; in each repetition the agents update their bids according to the implemented algorithm and they "learn" their bidding strategies, through exploration by repetition. A five bus test system with five agents is used to illustrate the presented method. The results demonstrate the impact of contingency constraints and the effect of speculation in FTR auctions. Furthermore, the difference between bidding for FTR-obligations and FTR-options is shown.
机译：在本文中，针对两种类型的点对点FTR，义务和期权，提供了基于代理人的金融传输权（FTR）拍卖模拟。每个拍卖参与者都被模拟为一个自适应代理，该代理具有评估其环境并据此采取行动的能力，可以遵循本文提出的朴素强化学习算法的决策规则。最初，假设基于地点边际定价（LMP）的事前能源市场。代理商根据事前能源市场的解决方案计算其初始FTR投标价格。随后，ISO解决了FTR拍卖问题，代理商的利润等于他们在事后能源市场上为持有FTR而获得的报销。基于代理的模拟器在相同条件下重复FTR拍卖；在每次重复中，代理根据实现的算法更新其出价，然后通过重复探索来“学习”其出价策略。具有五个代理的五总线测试系统用于说明所提出的方法。结果证明了意外限制的影响以及FTR拍卖中投机的影响。此外，还显示了FTR义务竞标与FTR期权竞标之间的差异。

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