# Equitable Time-Varying Pricing Tariff Design: A Joint Learning and Optimization Approach

arXiv: https://arxiv.org/abs/2307.15088 · HTML: https://mlsanity.com/paper/2307.15088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Gradient Descent Finds Global Minima of Deep Neural Networks](https://mlsanity.com/paper/1811.03804/) (2018)
- [On the Convergence Rate of Training Recurrent Neural Networks](https://mlsanity.com/paper/1810.12065/) (2018)
- [Physics-Informed Neural Networks for AC Optimal Power Flow](https://mlsanity.com/paper/2110.02672/) (2021)
- [Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics](https://mlsanity.com/paper/2011.05987/) (2020)
- [Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks](https://mlsanity.com/paper/1711.02278/) (2017)
- [Risk-Sensitive Learning and Pricing for Demand Response](https://mlsanity.com/paper/1611.07098/) (2016)
- [Achieving Reliable Coordination of Residential Plug-in Electric Vehicle Charging: A Pilot Study](https://mlsanity.com/paper/2112.04559/) (2021)

## Abstract (arXiv)

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.