Asynchronous consensus for optimal power flow control in smart grid with zero power mismatch

Abstract

The heterogeneous nature of smart grid components and the desire for smart grids to be scalable, stable and respect customer privacy have led to the need for more distributed control paradigms. In this paper we provide a distributed optimal power flow solution for a smart distribution network with separable global costs, separable non-convex constraints, and inseparable linear constraints, while considering important aspects of network operation such as distributed generation and load mismatch, and nodal voltage constraints. An asynchronous averaging consensus protocol is developed to estimate the values of inseparable global information. The consensus protocol is then combined with a fully distributed primal dual optimization utilizing an augmented Lagrange function to ensure convergence to a feasible solution with respect to power flow and power mismatch constraints. The presented algorithm uses only local and neighbourhood communication to simultaneously find the mismatch between power generation, line loss and loads, to calculate nodal voltages, and to minimize distributed costs, leading to a completely distributed solution of the global problem. An IEEE test feeder system with a reasonable number of nodes is used to illustrate the proposed method and efficiency.

Decentralized Management System for Solid-State Voltage Regulators in Nodes of Distribution Power Networks

“…An asynchronous averaging consensus protocol [14] is proposed to estimate the values of inseparable global information. The consensus protocol is then combined with a fully distributed primal dual optimization utilizing an augmented Lagrange function to ensure convergence to a feasible solution with respect to power flow and power mismatch constraints.…”

Asynchronous Distributed Power Control of Multimicrogrid Systems

“…Moreover, in this way, constant stepsizes can be used under convergence guarantee, which is much easier to implement. This is different from the literature [20]- [22], [25]. In [20], [25], diminishing stepsizes are required, while an extra line search needs to be performed at every iteration in [21], [22] to determine the stepsize.…”

“…In [18], [19], the energy trading game is investigated using Bayesian game theory, where the communication package loss is considered. In [21], [22], primal-dual gradient and consensus-based methods are utilized to solve the optimal power flow problem, where asynchronous iterations are considered. In [20], a distributed algorithm is proposed to solve the optimal DER coordination problem over lossy communication networks with packet-dropping communication links, where diminishing stepsizes are required to guarantee the convergence.…”

“…The convergence proof is not introduced in [18], [19], while [20], [25] can only guarantee the convergence with diminishing stepsizes. In [21], [22], a stricter requirement on the stepsize is required, which needs to perform an extra line search at every iteration. In addition, the load demand in these papers is usually assumed to be known and not varying.…”