  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.
