Dynamic Optimal Power Flow in Microgrids using the Alternating Direction Method of Multipliers

TL;DR

This paper extends prior work on the alternating direction method of multipliers (ADMM) for solving the dynamic optimal power flow (D-OPF) problem, and introduces a two-stage approach to managing discrete decisions and uncertainty.

Abstract

Smart devices, storage and other distributed technologies have the potential to greatly improve the utilisation of network infrastructure and renewable generation. Decentralised control of these technologies overcomes many scalability and privacy concerns, but in general still requires the underlying problem to be convex in order to guarantee convergence to a global optimum. Considering that AC power flows are non-convex in nature, and the operation of household devices often requires discrete decisions, there has been uncertainty surrounding the use of distributed methods in a realistic setting. This paper extends prior work on the alternating direction method of multipliers (ADMM) for solving the dynamic optimal power flow (D-OPF) problem. We utilise more realistic line and load models, and introduce a two-stage approach to managing discrete decisions and uncertainty. Our experiments on a suburb-sized microgrid show that this approach provides near optimal results, in a time that is fast enough for receding horizon control. This work brings distributed control of smart-grid technologies closer to reality.

Figures (4)

• Figure 1: Conversion to component orientated representation.
• Figure 2: Convergence of primal residuals.
• Figure 3: Cost and charge error relative to relaxed solution.
• Figure 4: Performance of cost functions with lowering (L) and raising (R) solar output.