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

TL;DR

This paper introduces a joint learning and optimization framework using RNNs to design equitable, time-varying electricity tariffs that balance consumer affordability, demand response incentives, and utility revenue recovery.

Contribution

It presents a novel method integrating RNN-based consumer response modeling with non-linear optimization for equitable tariff design, addressing response uncertainties and ensuring utility revenue.

Findings

Protects low-income consumers from price surges

Effectively motivates peak demand reduction

Robust against demand response uncertainties

Abstract

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.