A Dynamic Game Approach for Demand-Side Management: Scheduling Energy Storage with Forecasting Errors

TL;DR

This paper introduces a dynamic game-based demand-side management scheme for residential energy storage scheduling, accounting for forecasting errors, and demonstrates its robustness and advantages over static approaches.

Contribution

It develops a novel dynamic game model with a closed-form solution for energy storage scheduling, incorporating forecasting errors and proving exponential convergence to equilibrium.

Findings

The dynamic game converges exponentially to Nash equilibrium.

The approach outperforms static game schemes in efficiency.

Robustness is maintained even with high forecasting errors.

Abstract

Smart metering infrastructure allows for two-way communication and power transfer. Based on this promising technology, we propose a demand-side management (DSM) scheme for a residential neighbourhood of prosumers. Its core is a discrete time dynamic game to schedule individually owned home energy storage. The system model includes an advanced battery model, local generation of renewable energy, and forecasting errors for demand and generation. We derive a closed-form solution for the best-response problem of a player and construct an iterative algorithm to solve the game. Empirical analysis shows exponential convergence towards the Nash equilibrium. A comparison to a DSM scheme with a static game, reveals the advantages of the dynamic game approach. We provide an extensive analysis on the influence of the forecasting error on the outcome of the game. A key result demonstrates that our approach is robust even in the worst-case scenario. This grants considerable gains for the utility company organising the DSM scheme and its participants.