Abstract

A short-term Economic Dispatch (ED) model with risk constraint for wind penetrated power systems was built to face the challenge of scheduling spinning reserves brought from wind energies. The proposed model utilizes the probability of spinning reserve shortage as measurement of system risk and evaluates the risk by an Unequal Probabilities Sampling based Monte Carlo (UPSMC) method. A Genetic Algorithm (GA) improved with Simulated Annealing (SA) strategy is presented as SAGA to solve the problem. By comparing simulation results under different wind penetrations and risk constraints, coal consumptions will not always decrease with wind penetration and risk constraint but for most times. In addition, unit risk benefit has a trend to increase with wind penetration and decrease with risk constraint while contribution of unit wind generation has the contrary character. Simulation results also show that the proposed sampling method could improve the sampling efficiency and the SAGA method had better performance than traditional GA.

Keywords:

Economic dispatch, genetic algorithm, monte carlo method, stochastic constraint, unequal probability sampling, wind energy,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.