面向购买和销售需求响应的在线学习方法
An Online Learning Approach to Buying and Selling Demand Response
AI总结：
本文提出一种在线学习方法，用于协调聚合商在固定居民客户群中购买需求削减，并在双结算批发市场中销售总体需求削减。研究通过动态定价和合同策略，在未知需求模型下最大化预期利润。
摘要：
我们采用聚合商的视角，旨在协调其从固定居民电力客户群中购买需求削减与在双结算批发市场中销售总体需求削减。聚合商通过向客户提供统一价格来获取需求削减，该价格相对于其预定基准。在实现总体需求削减之前，聚合商还必须确定向双结算能源市场出售多少能源。在日间市场中，聚合商承诺一份远期合同，要求在实时市场交付能源。基础需求曲线，将总体需求削减与聚合商提供的价格相关联，被假设为线性且受不可观测的随机冲击影响。假设聚合商最初不知道需求曲线的参数和随机冲击的分布，我们研究聚合商在T天时间窗口内动态调整报价和远期合同以最大化预期利润的程度。具体而言，我们设计了一种动态定价和合同提供策略，解决聚合商学习未知需求模型与最大化时间累积预期利润之间的需求。特别地，所提出的定价策略被证明在T天内产生的遗憾不超过O(log(T)√T)。
英文摘要：
We adopt the perspective of an aggregator, which seeks to coordinate its purchase of demand reductions from a fixed group of residential electricity customers, with its sale of the aggregate demand reduction in a two-settlement wholesale energy market. The aggregator procures reductions in demand by offering its customers a uniform price for reductions in consumption relative to their predetermined baselines. Prior to its realization of the aggregate demand reduction, the aggregator must also determine how much energy to sell into the two-settlement energy market. In the day-ahead market, the aggregator commits to a forward contract, which calls for the delivery of energy in the real-time market. The underlying aggregate demand curve, which relates the aggregate demand reduction to the aggregator's offered price, is assumed to be affine and subject to unobservable, random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the aggregator, we investigate the extent to which the aggregator might dynamically adapt its offered prices and forward contracts to maximize its expected profit over a time window of $T$ days. Specifically, we design a dynamic pricing and contract offering policy that resolves the aggregator's need to learn the unknown demand model with its desire to maximize its cumulative expected profit over time. In particular, the proposed pricing policy is proven to incur a regret over $T$ days that is no greater than $O(\log(T)\sqrt{T})$.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
