Feedback Enhancement of Time Series Aggregation for Power System Expansion Planning

TL;DR

This paper introduces a feedback-driven adaptive clustering method for time series aggregation in power system planning, significantly improving accuracy and reliability over traditional mean-based approaches by focusing on critical operational periods.

Contribution

It proposes an iterative feedback enhancement strategy that identifies and re-clusters high-error representatives, improving TSA accuracy and providing a diagnostic for quality assessment.

Findings

Feedback enhancement reduces operational error in TSA.

Improved clustering tightens bounds on solution quality.

Method balances computational effort with accuracy.

Abstract

As a consequence of the high variability of load demand and renewable generation, long-term and high-resolution inputs are required for power system expansion planning, making the problem intractable in real-world applications. Time series aggregation (TSA), which captures representative patterns, reduces temporal complexity while providing similar planning outputs. However, purely statistical clustering, even when enhanced with predefined ``extremes'', can overlook system-specific critical operating conditions, making it unreliable across real-world systems. Therefore, this paper links TSA accuracy on specific system operation and final solution quality, which becomes a practical bound with mean-based TSA approaches. It is observed that the distribution of operational errors is highly imbalanced, such that a few representatives dominate the total error. This paper proposes an adaptive clustering strategy based on feedback enhancement of TSA that iteratively identifies poor-performing representatives with high operational error and re-clusters only their associated periods. A study shows that the feedback enhancement improves the decision error and tighten the bound significantly compared with the plain mean-based clustering method, offering a diagnostic for TSA quality, while balancing the computational effort with solution accuracy.