Stochastic DC Optimal Power Flow With Reserve Saturation

TL;DR

This paper introduces a stochastic optimal power flow model that accounts for generator reserve saturation, improving cost efficiency in power system scheduling under uncertainty.

Contribution

It presents a novel nonconvex, nonsmooth two-stage stochastic program incorporating reserve saturation effects, solved via a stochastic approximation method.

Findings

Lower expected generation costs with saturation modeling

Effective handling of wind power uncertainty

Improved solutions over affine policy enforcement

Abstract

We propose an optimization framework for stochastic optimal power flow with uncertain loads and renewable generator capacity. Our model follows previous work in assuming that generator outputs respond to load imbalances according to an affine control policy, but introduces a model of saturation of generator reserves by assuming that when a generator's target level hits its limit, it abandons the affine policy and produces at that limit. This is a particularly interesting feature in models where wind power plants, which have uncertain upper generation limits, are scheduled to provide reserves to balance load fluctuations. The resulting model is a nonsmooth nonconvex two-stage stochastic program, and we use a stochastic approximation method to find stationary solutions to a smooth approximation. Computational results on 6-bus and 118-bus test instances demonstrates that by considering the effects of saturation, our model can yield solutions with lower expected generation costs (at the same target line violation probability level) than those obtained from a model that enforces the affine policy to stay within generator limits with high probability.