Evaluation of Temporal Complexity Reduction Techniques Applied to Storage Expansion Planning in Power System Models

TL;DR

This paper evaluates two temporal complexity reduction techniques for power system models with high renewable integration, aiming to accelerate optimal power flow computations by selecting representative time periods through hierarchical clustering.

Contribution

It introduces and compares two clustering-based methods for reducing temporal complexity in power system optimization models with renewable energy scenarios.

Findings

Hierarchical clustering effectively reduces computation time.

Aggregation errors are within acceptable limits.

Parallel computation enhances efficiency.

Abstract

The growing share of renewable energy makes the optimization of power flows in power system models computationally more complicated, due to the widely distributed weather-dependent electricity generation. This article evaluates two methods to reduce the temporal complexity of a power transmission grid model with storage expansion planning. The goal of the reduction techniques is to accelerate the computation of the linear optimal power flow of the grid model. This is achieved by choosing a small number of representative time periods to represent one whole year. To select representative time periods, a hierarchical clustering is used to aggregate either adjacent hours chronologically or independently distributed coupling days into clusters of time series. The aggregation efficiency is evaluated by means of the error of the objective value and the computational time reduction. Further, both the influence of the network size and the efficiency of parallel computation in the optimization process are analysed. As a test case, the transmission grid of the northernmost German federal state of Schleswig-Holstein with a scenario corresponding to the year 2035 is considered. The considered scenario is characterized by a high share of installed renewables.