能源社区需求管理的机制设计
Mechanism Design for Demand Management in Energy Communities
摘要：
我们考虑一个能源社区的需求管理问题，其中多个用户从外部组织（如能源公司）获得能源，并根据预先指定的价格支付能源，包括每单位能源的时间依赖性价格，以及一个单独的价格峰值需求。由于用户的效用是他们的私人信息，他们可能不愿意分享，一个中介，被称为规划者，以帮助优化社区的整体满意度（总效用减去总支付）的机制设计。一个机制包括一个消息空间，一个税收/补贴和每个用户的分配功能。每个用户报告从她自己的消息空间中选择的消息，然后接收由分配函数确定的一定量的能量，并支付由税收函数指定的税收。一个理想的机制会引发一场博弈，博弈的纳什均衡（NE）会导致一个与社区最优分配相一致的分配。作为一个出发点，我们设计了一个机制，能源社区与理想的属性，如全面实施，强大的预算平衡和个人的合理性，用户和规划者。然后，我们修改这个基线机制的社区，消息交换只允许在邻里，因此，每个用户的税收/补贴和分配功能只确定从她的邻居的消息。基线机制的所有期望属性都保留在分布式机制中。最后，我们提出了一个学习算法的基线机制，投影梯度下降的基础上，保证收敛到NE的诱导游戏。
英文摘要：
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.
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