Abstract: The conventional approach to day-ahead and intraday coordination aims to minimize the disparities between the day-ahead reference planning and the actual intraday operation for devices like energy storage systems (ESS). However, devices tied to the time dynamics, such as the ESSs, have limited intra-day adjustability, leading to poor economy and flexibility. This paper proposes a novel method for day-ahead and intraday coordination using the optimal economic operation region (OEOR) in active distribution networks (ADN). A linearized ADN scheduling model is established in the day-ahead stage. Numerous stochastic scenarios, generated by the Latin hypercube sampling algorithm, yield the optimal sequential curves. To maximize the coverage of optimal outcomes considering the constraints like charging and discharging of the ESS and the climbing and landslide rates of the micro gas turbine generators, a OEOR model is created. In the intraday stage, the outputs of relevant devices are adjusted within the OEOR using the receding-horizon optimization. If the results approach the OEOR bounds and the adjacent period constraints, the region is expanded to the extreme OEOR (E-OEOR). Case studies confirm the method's efficacy, enhancing the ADN economic efficiency compared to the traditional methods.