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Mechanism Design for Aggregated Demand Prediction in the Smart Grid Demand Prediction in the Smart Grid

机译:智能电网智能电网需求预测中汇总需求预测的机制设计

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This paper presents a novel scoring rule-based mechanism that encourages agents to produce costly estimates of future events and truthfully report them to a centre when the budget for payments to the agents is itself determined by their reports. This is applied to a model of aggregated demand prediction within a microgrid where, given estimates of future consumptions, an aggregator must optimally purchase electricity for a set of homes, each represented by self-interested, rational home agents. This in turn reduces the need for costly standby generation within the grid. The aggregator has prior information about the amount each home will consume, and determines the amount to pay each agent based on savings resulting from using the agents' reported information, over its own prior information. Agents use sensory information regarding their property and its occupants to generate these estimates, which they transmit to the aggregator using smart grid technology. The proposed mechanism is dominant strategy incentive compatible and empirical evaluation shows that it encourages agents to exert effort in producing precise estimates. We show that the mechanism is ex ante individually rational for the aggregator, and that it outperforms a simpler mechanism whereby savings are distributed evenly.
机译:本文提出了一种新的评分规则基于规则的机制,鼓励代理商生产昂贵的事件估计,并在向代理人付款的预算本身由其报告确定时,请真实地向中心报告。这适用于微电网内的聚合需求预测模型,其中,给定未来消费的估计,聚合器必须最佳地购买一组房屋,每个家庭由自私合理的家庭代理商代表。这反过来又减少了在网格内昂贵的待机时生成的需求。聚合器具有关于每个家庭将消耗的金额的现有信息,并根据使用代理报告的信息,根据其自身的先前信息确定基于所备份的报告的节省费用的金额。代理使用有关其财产及其居住者的感官信息来生成这些估计,它们使用智能电网技术传输到聚合器。拟议的机制是占主导地位战略的激励与实证评价表明,它鼓励代理人努力制定精确估计。我们表明该机制是针对聚合器的单独合理的前蚂蚁,并且它优于一种更简单的机制,从而节省的均匀分布。

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