Coordination of operational planning and real-time optimization in microgrids

TL;DR

This paper presents a value function-based method for coordinating hierarchical control levels in microgrids, improving decision-making from operational planning to real-time optimization under forecast uncertainties.

Contribution

It introduces a novel approach that propagates information between planning and real-time control, enhancing microgrid management with forecast-based decision support.

Findings

The proposed method outperforms rule-based controllers in simulations.

It effectively manages forecast errors in microgrid operations.

The approach reduces energy costs and improves reserve planning.

Abstract

Hierarchical microgrid control levels range from distributed device level controllers that run at a high frequency to centralized controllers optimizing market integration that run much less frequently. Centralized controllers are often subdivided into operational planning controllers that optimize decisions over a time horizon of one or several days, and real-time optimization controllers that deal with actions in the current market period. The coordination of these levels is of paramount importance. In this paper, we propose a value function-based approach as a way to propagate information from operational planning to real-time optimization. We apply this method to an environment where operational planning, using day-ahead forecasts, optimizes at a market period resolution the decisions to minimize the total energy cost and revenues, the peak consumption and injection-related costs, and plans for reserve requirements. While real-time optimization copes with the forecast errors and yields implementable actions based on real-time measurements. The approach is compared to a rule-based controller on three use cases, and its sensitivity to forecast error is assessed.