Event-trigger rolling horizon optimization for congestion management considering peer-to-peer energy trading among microgrids

Abstract

This paper designs an event-trigger rolling horizon optimization framework to alleviate electricity congestion caused by peer-to-peer (P2P) energy transactions among microgrids (MGs) under uncertainty. This framework incorporating abounded time dimensions includes three processes: first, a discrete-time linear programming model for the P2P energy trading among MGs with real-time data updating for reaction to underling uncertainties, are formulated to derive initial schemes in prediction time windows; second, a modified optimal power flow model is used to verify congestion in control time windows, with congestion occurrence defined as an event; third, the power rescheduling of MGs is triggered by this event for reducing periodic calculation burden and executed to generate new trading schemes for congestion alleviation in control time windows. We develop an online distributed optimization algorithm to realize independent decisions of MGs and guarantee rolling updates of system parameters through embedding alternating direction method of multipliers into every time window. Finally, simulations in different cases are implemented to illustrate the reasonability and effectiveness of the proposed optimization framework. Results show that congestion is reasonably managed while ensuring the diversity of P2P energy transactions. Compared with the period-driven rolling horizon optimization framework, the computational time is significantly reduced in the proposed framework.