A Distributed Dual-Optimization Framework for Ancillary-Service Coordination Between MV Microgrids and LV Distribution Networks

Quan, Hao; Utkarsh, Kumar; Srinivasan, Dipti

doi:10.1109/jsyst.2022.3159387

Cited by 4 publications

(2 citation statements)

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“…To address issues of charging efficiency and stability, a voltage feedback controller for EV supply equipment was proposed 17 . Additionally, an optimal bidding strategy for an EV aggregation was presented to coordinate its participation across ancillary service markets such as frequency regulation and reserve provision 18 .Previous works have explored optimization and coordination of EV charging at regional and station-specific levels. Regional energy management and operating strategies for EVs and battery swapping stations were proposed to mitigate uncoordinated impacts on utilities 19 .…”

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Carbon Management in Large-Scale Electric Vehicle Temporal and Spatial Scheduling with Automotive Electronic Forensics

Cao,

Zeng,

Zhao

2024

Preprint

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Electric vehicles (EVs) offer a promising and environmentally friendly solution to reduce reliance on fossil fuels. As the adoption of electric vehicles and solar power grows, both opportunities and challenges are presented for power systems. An increasing EV fleet, paired with more distributed solar generation, brings both promise and complexity to grid operations. High penetration levels of EVs and rooftop photovoltaics will complicate load forecasting and add variability that power systems were not traditionally designed to accommodate. This study focuses on the collaborative optimization scheduling of generators, EVs, and solar power to address this issue. A novel approach is proposed in this paper, involving a two-layer optimization problem for the distribution systems. This strategy aims to optimize the charging and discharging schedules of EVs in both time and space domains while considering solar power and digital evidence collection. In the upper layer problem, the coordination between EVs, thermal generators, and base load is optimized in the transmission grid, taking into account the availability of solar power. This optimization problem aims to determine the optimal periods for EV charging with digital evidence collection in the time domain. We focus on the spatial scheduling of EV load in the distribution grid in the lower layer problem. To evaluate the proposed two-layer optimization strategy, simulations were performed using a benchmark power system model. This model incorporates an 8-unit transmission network interconnected with an IEEE 33-bus distribution feeder. Through the simulations, the impacts of various factors are assessed. The analysis considers different electricity pricing scenarios and EV adoption levels. It also examines how the placement of EV charging load across the distribution grid affects results. The simulation results demonstrate that the proposed strategy effectively integrates solar power and improves the economic efficiency of grid operation, while also benefiting EV users by optimizing the charging and discharging of electric vehicles. Furthermore, the performance highlights the significance of considering the location of EV load in distribution network planning. The strategy also considers forensic data collection from electric vehicles, which can provide valuable insights for distribution network operators. The location-specific forensic data collection allows network constraints and opportunities to be assessed based on real-world EV usage at the distribution level.

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