TC-LEG: Topology-Constrained Sequential Energy Management in Digital-Twin Smart Grids

INTRODUCTION: The increasing integration of renewable energy, distributed storage, and flexible loads has introduced substantial uncertainty and operational complexity into modern smart grids. Existing energy management methods can optimize multi-period dispatch or encode the physical grid topology, but they often do not explicitly represent the execution order and conditional dependencies among heterogeneous control actions.

Objectives

This paper aims to develop a safe and energy-efficient sequential control generation framework for digital-twin smart grids that reduces operating cost and renewable curtailment while maintaining voltage security and action feasibility.

Methods

A topology-constrained latent execution graph learning framework, termed TC-LEG, is proposed. TC-LEG uses a physical grid graph to encode electrical connectivity and operating states, while a distinct latent execution graph represents state-dependent dependencies among storage dispatch, renewable curtailment, demand response, reactive compensation, and topology switching. A hybrid discrete–continuous control sequence generator produces multi-step actions, and a simulation-oriented digital twin synchronizes the current grid state, verifies each candidate action, projects infeasible actions onto a state-dependent feasible set, and feeds the accepted action and updated state back to subsequent generation steps.

Results

On the IEEE 33-bus system, TC-LEG achieves a normalized operating cost of 0.811, a voltage violation rate of 0.9%, a renewable curtailment rate of 4.9%, and an action feasibility rate of 98.5%. Its average inference time is 18.6 ms on the IEEE 33-bus system and 28.4 ms on the IEEE 69-bus system, both substantially shorter than the 15-minute control interval.

Conclusion

TC-LEG provides a topology-aware and interpretable approach to sequential energy management by combining electrical-topology representation, action-dependency learning, hybrid control generation, and digital-twin verification in a closed-loop inference process.