Online Optimization for Networked Distributed Energy Resources With Time-Coupling Constraints

Shuai Fan, Guangyu He, Xinyang Zhou, Mingjian Cui

Research output: Contribution to journalArticlepeer-review

57 Scopus Citations


This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks (ADNs) with numerous photovoltaic inverters and inverter air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the social utility loss and ensuring the security of voltages. Conventional distributed optimization methods are rarely feasible to track the optimal solutions in fast variable environments using a fine-grained sampling interval where the underlying optimization problem evolves with the iterations of the algorithms. In contrast, based on the framework of online convex optimization (OCO), the developed approach uses a distributed algebraic update to compute the next round decisions relying on the current feedback of measurements. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. Benchmark tests show that the proposed method is computationally and economically efficient, and outperforming existing algorithms.

Original languageAmerican English
Article number9145691
Pages (from-to)251-267
Number of pages17
JournalIEEE Transactions on Smart Grid
Issue number1
StatePublished - Jan 2021

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-78886


  • Active distribution networks
  • inverter air conditionings
  • online distributed optimization
  • photovoltaic


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