Considering the multi-time scale rolling optimization scheduling method of micro-energy network connected to electric vehicles

TL;DR

This paper introduces a multi-time scale rolling optimization method for micro-energy networks with electric vehicles, improving energy efficiency, reducing curtailment, and enhancing grid safety amid uncertainty.

Contribution

It proposes a novel aggregation model for electric vehicle resources and integrates demand response mechanisms into a two-stage scheduling framework.

Findings

Reduces preventive curtailment and enhances energy utilization.

Decreases economic penalties and operating costs.

Improves system economy under uncertain conditions.

Abstract

The large-scale access of electric vehicles to the power grid not only provides flexible adjustment resources for the power system, but the temporal uncertainty and distribution complexity of their energy interaction pose significant challenges to the economy and robustness of the micro-energy network. In this paper, we propose a multi-time scale rolling optimization scheduling method for micro-energy networks considering the access of electric vehicles. In order to solve the problem of evaluating the dispatchable potential of electric vehicle clusters, a charging station aggregation model was constructed based on Minkowski summation theory, and the scattered electric vehicle resources were aggregated into virtual energy storage units to participate in system scheduling. Integrate price-based and incentive-based demand response mechanisms to synergistically tap the potential of source-load two-side regulation; On this basis, a two-stage optimal scheduling model of day-ahead and intra-day is constructed. The simulation results show that the proposed method reduces the scale of "preventive curtailment" due to more accurate scheduling, avoids the threat of power shortage to the safety of the power grid, and has more advantages in the efficiency of new energy consumption. At the same time, intra-day scheduling significantly reduces economic penalties and operating costs by avoiding output shortages, and improves the economy of the system in an uncertain forecasting environment.