A hierarchical predictive control approach for wireless electric vehicle energy network with integrated microgrids incorporating degradation costs

Abstract

Integrating wireless electric vehicle energy network with microgrids benefits vehicle owners and grid operators. Yet, economical and reliable operations for such combinations under renewable energy uncertainties remain examined. To address this, achieving cost-effective and reliable performance, a novel hierarchical predictive control approach is utilized. Its core innovation schedules optimal power dispatches for integrated microgrids using different time frames with upper-level control minimizing energy costs and battery energy storage systems’ degradation costs, whereas lower-level control additionally lowers degradation costs. Moreover, the approach inherently enhances system reliability by minimizing power fluctuations using control references from upper-level control and state variables feedback from lower-level control under renewable energy uncertainties. The unique cross-time-frame integration of this approach enables modeling and incorporating degradation costs to adapt costs associated with longer time frames into optimal power dispatches in shorter time frames, reflecting the unique features of hybrid energy storage systems. Comparative studies reveal that various energy storage systems can be employed at different hierarchical control levels for tailored power distribution objectives. Effectiveness of the utilized approach is confirmed by comparing it with control benchmarks through its average reduction of energy costs by 348$, degradation costs by 10.03$, and expected energy not served down to 0 Wh/quarter.