Ergodic Energy Management Leveraging Resource Variability in Distribution Grids

TL;DR

This paper introduces an ergodic energy management framework that leverages resource variability and stochastic optimization to improve distribution grid efficiency amid renewable energy variability.

Contribution

It develops a novel stochastic optimization approach for energy management that enforces operational constraints in an average sense, enabling better integration of renewables.

Findings

Numerical tests show improved efficiency over deterministic methods.

The framework effectively manages grid flexibilities and resource stochasticity.

Real-world and IEEE test results validate the approach.

Abstract

Contemporary electricity distribution systems are being challenged by the variability of renewable energy sources. Slow response times and long energy management periods cannot efficiently integrate intermittent renewable generation and demand. Yet stochasticity can be judiciously coupled with system flexibilities to enhance grid operation efficiency. Voltage magnitudes for instance can transiently exceed regulation limits, while smart inverters can be overloaded over short time intervals. To implement such a mode of operation, an ergodic energy management framework is developed here. Considering a distribution grid with distributed energy sources and a feed-in tariff program, active power curtailment and reactive power compensation are formulated as a stochastic optimization problem. Tighter operational constraints are enforced in an average sense, while looser margins are enforced to be satisfied at all times. Stochastic dual subgradient solvers are developed based on exact and approximate grid models of varying complexity. Numerical tests on a real-world 56-bus distribution grid and the IEEE 123-bus test feeder relying on both grid models corroborate the advantages of the novel schemes over their deterministic alternatives.