Energy markets with retail choice enable customers to switch energy plans among competitive retail suppliers. Despite the promising benefits of more affordable prices and better savings to customers, there appears subsided participation in energy retail markets from residential customers. One major reason is the complex online decision-making process for selecting the best energy plan from a multitude of options that hinders average consumers. In this paper, we shed light on the online energy plan selection problem by providing effective competitive online algorithms. We first formulate the online energy plan selection problem as a metrical task system problem with temporally dependent switching costs. For the case of constant cancellation fee, we present a 3-competitive deterministic online algorithm and a 2-competitive randomized online algorithm for solving the energy plan selection problem. We show that the two competitive ratios are the best possible among deterministic and randomized online algorithms, respectively. We further extend our online algorithms to the case where the cancellation fee is linearly proportional to the residual contract duration. Through empirical evaluations using real-world household and energy plan data, we show that our deterministic online algorithm can produce on average 14.6% cost saving, as compared to 16.2% by the offline optimal algorithm, while our randomized online algorithm can further improve cost saving by up to 0.5%.

翻译：具有零售选择功能的能源市场允许客户在竞争性零售供应商之间切换能源计划。尽管为客户提供了更实惠的价格和更好的节省潜力，但住宅客户对能源零售市场的参与度似乎有所下降。一个主要原因是，从众多选项中选择最佳能源计划的复杂在线决策过程阻碍了普通消费者。在本文中，我们通过提供有效的竞争性在线算法，深入探讨在线能源计划选择问题。我们首先将在线能源计划选择问题建模为具有时间依赖切换成本的度量任务系统问题。针对固定取消费用的情况，我们提出了一种3-竞争性确定性在线算法和一种2-竞争性随机在线算法来解决能源计划选择问题。我们证明这两个竞争比分别在确定性在线算法和随机在线算法中是最优的。我们进一步将在线算法扩展到取消费用与剩余合同期限线性成比例的情况。通过使用真实世界家庭和能源计划数据进行实证评估，我们表明，与离线最优算法相比，我们的确定性在线算法平均可节省14.6%的成本，而我们的随机在线算法可进一步将成本节省提高至多0.5%。