Model Reduction in Capacity Expansion Planning Problems via Renewable Generation Site Selection

TL;DR

This paper introduces a two-stage method for reducing the spatial complexity of capacity expansion planning problems by selecting relevant renewable energy sites, significantly decreasing computational costs while maintaining accuracy.

Contribution

A novel two-stage site selection approach that efficiently reduces the number of candidate renewable sites in capacity expansion planning, improving computational performance.

Findings

Identifies 90% of optimal RES sites with up to 54% fewer locations.

Achieves up to 41% memory reduction in computations.

Reduces solver runtime by 31% to 46% depending on conditions.

Abstract

The accurate representation of variable renewable generation (RES, e.g., wind, solar PV) assets in capacity expansion planning (CEP) studies is paramount to capture spatial and temporal correlations that may exist between sites and impact both power system design and operation. However, it typically has a high computational cost. This paper proposes a method to reduce the spatial dimension of CEP problems while preserving an accurate representation of renewable energy sources. A two-stage approach is proposed to this end. In the first stage, relevant sites are identified via a screening routine that discards the locations with little impact on system design. In the second stage, the subset of relevant RES sites previously identified is used in a CEP problem to determine the optimal configuration of the power system. The proposed method is tested on a realistic EU case study and its performance is benchmarked against a CEP set-up in which the entire set of candidate RES sites is available. The method shows great promise, with the screening stage consistently identifying 90% of the optimal RES sites while discarding up to 54% of the total number of candidate locations. This leads to a peak memory reduction of up to 41% and solver runtime gains between 31% and 46%, depending on the weather year considered.