Connecting Representative Periods in Energy System Optimization Models using Markov-Matrices

TL;DR

This paper introduces a Markov-Matrix-based method to connect representative periods in energy system models, improving accuracy in capturing time-dependent behaviors while maintaining low computational complexity.

Contribution

The novel approach links representative periods using probabilistic transitions, addressing a key challenge in time series aggregation for energy system optimization.

Findings

Reduces error to one fifth of previous methods

Maintains low computational complexity

Validated on NREL-118 bus system

Abstract

Time series aggregation is a common approach to reduce the computational complexity of large-scale energy system optimization models. However, maintaining chronological continuity between the resulting representative periods (RPs) remains a key challenge, as transitions between RPs are typically lost. This leads to inaccuracies in storage behavior, unit commitment, and other time-linked aspects of the model. We propose a novel method that uses Markov-Matrices to link RPs via probabilistic transitions and expected values. The approach is also suitable for constraints that connect multiple time steps, and can be adjusted to work with binary variables. Benefits are shown on an illustrative case study and validated using the NREL-118 bus system, where it reduces the error to one fifth of the current state-of-the-art while retaining low computational complexity.