论文标题
能源社区需求管理的机理设计
Mechanism Design for Demand Management in Energy Communities
论文作者
论文摘要
我们考虑了一个能源社区的需求管理问题,其中一些用户从能源公司等外部组织那里获得能源,并根据预先指定的价格支付能源,该价格由每单位能源的时间依赖价格组成,以及高峰需求的单独价格。由于用户的公用事业是他们的私人信息,他们可能不愿意分享,因此引入了一个被称为计划者的调解人,以帮助通过机制设计来优化社区(总公用事业总付款)的整体满意度。一种机制由消息空间,税/补贴和每个用户的分配功能组成。每个用户报告从自己的消息空间中选择的消息,然后收到由分配功能决定的一定数量的能量,并支付由税收功能指定的税款。理想的机制引起了游戏,即NASH均衡(NE),其分配与社区的最佳分配相吻合。 作为起点,我们为能源社区设计了具有理想属性的机制,例如全面实施,强大的预算平衡和个人合理性。然后,我们为仅在社区内允许消息交换的社区修改这种基线机制,因此,每个用户的税收/补贴和分配功能仅由她邻居的消息确定。基线机制的所有理想特性都保留在分布式机理中。最后,我们根据预计的梯度下降提供了一种基线机制的学习算法,该算法可以保证将其融合到诱导游戏的NE。
We consider a demand management problem of an energy community, in which several users obtain energy from an external organization such as an energy company, and pay for the energy according to pre-specified prices that consist of a time-dependent price per unit of energy, as well as a separate price for peak demand. Since users' utilities are their private information, which they may not be willing to share, a mediator, known as the planner, is introduced to help optimize the overall satisfaction of the community (total utility minus total payments) by mechanism design. A mechanism consists of a message space, a tax/subsidy and an allocation function for each user. Each user reports a message chosen from her own message space, and then receives some amount of energy determined by the allocation function and pays the tax specified by the tax function. A desirable mechanism induces a game, the Nash equilibria (NE) of which result in an allocation that coincides with the optimal allocation for the community. As a starting point, we design a mechanism for the energy community with desirable properties such as full implementation, strong budget balance and individual rationality for both users and the planner. We then modify this baseline mechanism for communities where message exchanges are allowed only within neighborhoods, and consequently, the tax/subsidy and allocation functions of each user are only determined by the messages from her neighbors. All the desirable properties of the baseline mechanism are preserved in the distributed mechanism. Finally, we present a learning algorithm for the baseline mechanism, based on projected gradient descent, that is guaranteed to converge to the NE of the induced game.