Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow

TL;DR

This paper presents a new analytical stochastic optimization model for multi-period power flow that efficiently manages renewable uncertainty and energy storage, improving grid stability and reducing costs.

Contribution

It introduces a compact, analytically exact reformulation of a stochastic chance-constrained optimal power flow model incorporating distributed energy storage.

Findings

Model is computationally efficient across various network sizes.

Distributed storage stabilizes operation and flattens generation profiles.

Model effectively absorbs renewable uncertainty and reduces costs.

Abstract

The increase in renewable energy sources (RESs), like wind or solar power, results in growinguncertainty also in transmission grids. This affects grid stability through fluctuating energy supplyand an increased probability of overloaded lines. One key strategy to cope with this uncertainty isthe use of distributed energy storage systems (ESSs). In order to securely operate power systemscontaining renewables and use storage, optimization models are needed that both handle uncertaintyand apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained DC optimalpower flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. AssumingGaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation asa second-order cone problem (SOCP). We test the new model on five different IEEE networks withvarying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results showthat the model is computationally efficient and robust with respect to constraint violation risk. Thedistributed energy storage system leads to more stable operation with flattened generation profiles.Storage absorbed RES uncertainty, and reduced generation cost.