On The Detection of Minimum Forecast Horizon For Real-Time Scheduling of Energy Storage Systems in Smart Grid

TL;DR

This paper introduces a trajectory-alignment-based method to determine the minimum forecast horizon needed for optimal real-time energy storage control in smart grids, validated with real market data and a realistic ESS model.

Contribution

It proposes a novel algorithm for identifying the minimum forecast horizon ensuring control decisions match full-horizon optimization, addressing limitations of existing methods.

Findings

60-hour forecast horizon achieves exact control simulation.

Existence of a forecast horizon is sensitive to system parameters.

Full convergence may not be guaranteed under certain configurations.

Abstract

The increasing integration of energy storage systems (ESSs) into power grids has necessitated effective real-time control strategies under uncertain and volatile electricity prices. An important problem of model predictive control of ESSs is identifying the minimum forecast horizon needed to exactly simulate the globally optimal control trajectory. Existing methods in the literature provide only sufficient conditions and might ignore real-world inconsistencies in control actions. In this paper, we introduce a trajectory-alignment-based definition of the minimum forecast horizon and propose an algorithm that identifies the minimum planning horizon for which all rolling-horizon control decisions match those of the full-horizon global optimization. Using real price data from the bidding zone DK1 in Denmark of the Nord Pool day-ahead market and a realistic ESS model, we illustrate that $60$ hours of forecast horizon allows us to exactly simulate the global control sequence and economic outcomes. In addition, we illustrate that under other parameter configurations, no forecast horizon ensures full convergence, demonstrating the sensitivity of the existence of a forecast horizon to various parameters. Our findings provide an operationally significant framework for minimum forecast horizon detection in storage scheduling and pave the way for the analytical description of this important planning measure.