Learning-Based Stackelberg Equilibrium Seeking with Application to Demand-Side Energy Management

TL;DR

This paper introduces a learning-based algorithm for demand-side energy management that efficiently converges to equilibrium tariffs while preserving user privacy and reducing interaction needs.

Contribution

It develops a zeroth-order, data-driven incentive design method for hierarchical energy management modeled as a Stackelberg game, with proven convergence and privacy features.

Findings

Converges to equilibrium tariffs efficiently.

Reduces the number of interactions compared to existing methods.

Demonstrates effectiveness with real-world data simulations.

Abstract

Demand-side management (DSM) enables distribution system operators (DSOs) to steer electricity consumption through dynamic price signals or incentive mechanisms, thereby leveraging end-users' flexibility potential for delivering grid services. The resulting hierarchical interaction between the DSO and the end-users can be formulated as a Stackelberg game, where the operator dynamically sets the prices and the end-users optimally respond to them. Efficiently designing these price signals is challenging, as the users' response models are unknown or difficult to estimate. In this paper, we propose a learning-based zeroth-order algorithm for incentive design, in which the iterative update of the incentive signals is efficiently assisted by a data-driven online estimation of the users' responses. The proposed method is then proven to converge to an equilibrium tariff while allowing the DSO to estimate the decision-making problems at the user level. Moreover, the method preserves users' privacy, as the update rule of the DSO is solely based on observations of communicated end-user actions. Numerical simulations employing real-world data illustrate the efficient convergence of our learning-based proposed method, while significantly reducing the number of required interactions between the DSO and the end-users with respect to the state-of-the-art approach.