Decentralized Stochastic Optimal Power Flow in Radial Networks with Distributed Generation

TL;DR

This paper presents a decentralized stochastic optimization method for power flow management in radial distribution networks with distributed PV generation, accounting for solar variability and reactive power control.

Contribution

It introduces a convex stochastic programming framework with a decentralized algorithm using ADMM for efficient power management in PV-enabled networks.

Findings

Algorithm achieves global optimality in decentralized setting

Numerical tests confirm efficiency and scalability

Outperforms deterministic approaches in handling uncertainty

Abstract

This paper develops a power management scheme that jointly optimizes the real power consumption of programmable loads and reactive power outputs of photovoltaic (PV) inverters in distribution networks. The premise is to determine the optimal demand response schedule that accounts for the stochastic availability of solar power, as well as to control the reactive power generation or consumption of PV inverters adaptively to the real power injections of all PV units. These uncertain real power injections by PV units are modeled as random variables taking values from a finite number of possible scenarios. Through the use of second order cone relaxation of the power flow equations, a convex stochastic program is formulated. The objectives are to minimize the negative user utility, cost of power provision, and thermal losses, while constraining voltages to remain within specified levels. To find the global optimum point, a decentralized algorithm is developed via the alternating direction method of multipliers that results in closed-form updates per node and per scenario, rendering it suitable to implement in distribution networks with large number of scenarios. Numerical tests and comparisons with an alternative deterministic approach are provided for typical residential distribution networks that confirm the efficiency of the algorithm.