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

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 power setpoint reference at substation in response to transmission-level requests while concurrently minimizing social utility loss ensuring security of voltages. Conventional optimization methods are rarely feasible optimal solutions fast variable environments using fine-grained sampling interval where underlying problem evolves iterations algorithms. contrast, based on convex (OCO), developed approach uses algebraic update compute next round decisions relying current feedback measurements. Notably, time-coupling constraints IACs decoupled implementation technique. An incentive scheme is tailored coordinate customer-owned assets lieu direct control from network operators. Optimality convergency characterized analytically. Finally, we corroborate method modified version 33-node test feeder. Benchmark tests show that computationally economically efficient, outperforming existing