{'fraction_non_alphanumeric': 0.013322231473771857, 'fraction_numerical': 0.0, 'mean_word_length': 5.790960451977401, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A stochastic generation capacity expansion planning methodology using linear distribution factors and hourly load modeling

This study proposes a formulation for the stochastic generation capacity expansion planning (GCEP) problem under load uncertainty using an improved method to directly compute the generalized generation distribution factors to model transmission network constraints. Load uncertainty in the optimization problem is addressed using a two-stage multi-period stochastic programming model and a clustering technique to reduce the number of scenarios. Traditionally, GCEP has been formulated decomposing the yearly load duration curve in blocks. However, this approach is not suitable for the inclusion of many operational constraints of generators, leading to a distorted estimation of operational costs. Instead, we propose the use of representative daily load patterns. Computational experiments are conducted to validate the methodology, with results showing that the proposed formulation can lead to a more accurate estimation of costs and a more suitable expansion plan. The paper also discusses how the number of load scenarios may impact the computational complexity of GCEP.