Method for wind power curtailment optimization for day-ahead operational planning of an electrical power network comprising a set of wind power plants, said method comprising: calculating the power flow for the power network for the day-ahead, comprising forecasted power generation, power consumption and topology of said power network; calculating a contingency analysis for the power network for the day-ahead, comprising forecasted power generation, power consumption and topology of said power network, together with a set of contingency scenarios; calculating a sensitivity factor of a power flow for each power network branch to a power injection by each wind power plant, wherein the calculating of the sensitivity factor is carried out only for the periods of time of the day-ahead for which overloaded branches have been detected during the solving of the power flow or during the solving of the contingency analysis; selecting a subset of the wind power plants having at least one sensitivity factor above a predetermined threshold as curtailment candidates; and calculating the wind power curtailment restricted to the selected subset of wind power plants; wherein the calculating of the wind power curtailment is obtained with Evolutionary Particle Swarm Optimization, EPSO, wherein each EPSO particle is a possible solution to the wind power curtailment calculation; wherein the EPSO particles are evaluated using the fitness function: min J = ∑ i = 1 N α i + β i × Pc i wherein N is the number of selected wind power plants; α i is the cost of curtailed power at wind power plant i; β i is the cost of LVRT capability absence at wind power plant i; Pc i is the amount of power curtailed at wind power plant i; wherein the sensitivity factor s ji of power flow in a power network branch to a power injection by a wind power plant, is: s ji = ∂ F j ∂ PGen i wherein ∂ F j is an increment in power flow in the power network branch j due to an increment ∂PGen i in power injected by wind power plant i . calculating the power flow for the power network for the day-ahead, comprising forecasted power generation, power consumption and topology of said power network; calculating a contingency analysis for the power network for the day-ahead, comprising forecasted power generation, power consumption and topology of said power network, together with a set of contingency scenarios; calculating a sensitivity factor of a power flow for each power network branch to a power injection by each wind power plant, wherein the calculating of the sensitivity factor is carried out only for the periods of time of the day-ahead for which overloaded branches have been detected during the solving of the power flow or during the solving of the contingency analysis; selecting a subset of the wind power plants having at least one sensitivity factor above a predetermined threshold as curtailment candidates; and calculating the wind power curtailment restricted to the selected subset of wind power plants; wherein the calculating of the wind power curtailment is obtained with Evolutionary Particle Swarm Optimization, EPSO, wherein each EPSO particle is a possible solution to the wind power curtailment calculation; wherein the EPSO particles are evaluated using the fitness function: min J = ∑ i = 1 N α i + β i × Pc i wherein N is the number of selected wind power plants; α i is the cost of curtailed power at wind power plant i; β i is the cost of LVRT capability absence at wind power plant i; Pc i is the amount of power curtailed at wind power plant i; wherein the sensitivity factor s ji of power flow in a power network branch to a power injection by a wind power plant, is: s ji = ∂ F j ∂ PGen i wherein ∂ F j is an increment in power flow in the power network branch j due to an increment ∂PGen i in power injected by wind power plant i . <EOS>