{'fraction_non_alphanumeric': 0.026829268292682926, 'fraction_numerical': 0.0008130081300813008, 'mean_word_length': 5.726775956284153, '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 Probabilistic Economic/CO2eq Emissions Dispatch Model: Real Applications

The worldwide evolution of the electric systems requires, i. a.: a) fossil-fuel generators with carbon capture and b) clean technologies based on renewable energies. For this reason, dispatch centers are in constant search for solutions in order to improve decision-making that involve the generation matrix. Consequently, in this paper a probabilistic economic dispatch model is proposed. The proposed methodology considers uncertainties, affecting the short–term control, the emission factors and the load dispatching. Wind speed, solar radiation and power demand are treated as random variables. Unavailability factors are also taken into account. The solution strategy is based on the Monte Carlo method and a bi-objective linear optimization constrained procedure. The approach involves multidimensional probabilities, descriptive statistics, clusters studies and bimodal analysis. The optimal solution yields the probability distributions of system marginal prices, dual costs, load-shedding, thermal and renewable power generation and emission factors. The proposed model and methodology are applied to the electric power system of northern Chile.