NETE A probabilistic economic/CO2eq emissions dispatch model: Real applications

Abstract

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 involve 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.