Executable flexibility coordination of electric vehicle aggregators via temporal reinforcement learning in coupled electricity markets

Yang Lv

doi:10.1016/j.egyai.2026.100864

Executable flexibility coordination of electric vehicle aggregators via temporal reinforcement learning in coupled electricity markets

2026-08-05

0

OA

AI

Y

Yang Lv

X

Xiangyu Kong *

G

Gaohua Liu

Y

Yuying Ma

Y

Yi Qi

P

Panlong Jin

DOI：10.1016/j.egyai.2026.100864

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Abstract

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• State-of-charge dynamics and power limits define executable flexibility. • Revenue-risk bidding coordinates energy, reserve, and frequency regulation markets. • Temporal reinforcement learning adapts bids to evolving feasible action ranges. • Distribution-system costs fall by 4.8% as executability rises by 6.2%. • Revenue gains reach 14.75% in simulations and 15%−20% in real-world data.

Keywords:

Electric vehicle

Electricity market

Frequency regulation ancillary service

Reserve ancillary service

Deep reinforcement learning

Executable flexibility coordination of electric vehicle aggregators via temporal reinforcement learning in coupled electricity markets

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