Time-varying pricing tariffs incentivize consumers to shift their electricity demand and reduce costs, but may increase the energy burden for consumers with limited response capability. The utility must thus balance affordability and response incentives when designing these tariffs by considering consumers' response expectations. This paper proposes a joint learning-based identification and optimization method to design equitable time-varying tariffs. Our proposed method encodes historical prices and demand response data into a recurrent neural network (RNN) to capture high-dimensional and non-linear consumer price response behaviors. We then embed the RNN into the tariff design optimization, formulating a non-linear optimization problem with a quadratic objective. We propose a gradient-based solution method that achieves fast and scalable computation. Simulation using real-world consumer data shows that our equitable tariffs protect low-income consumers from price surges while effectively motivating consumers to reduce peak demand. The method also ensures revenue recovery for the utility company and achieves robust performance against demand response uncertainties and prediction errors.

基于学习的方法设计公平的时变定价电费策略，结合历史价格和需求响应数据，使用循环神经网络（RNN）捕捉高维度和非线性的消费者价格响应行为，并将RNN嵌入到定价优化模型中，提出了基于梯度的快速可扩展的解决方案。仿真使用真实消费者数据表明，公平定价策略能够保护低收入消费者免受价格飙升的冲击，同时有效激励消费者减少峰值需求，该方法还能确保公用事业公司的收益回收，并对需求响应的不确定性和预测误差具有鲁棒的性能。

公平的时变价格定价策略设计：一种联合学习和优化方法