Background:
As more and more renewable energy such as wind energy is connected to
the power grid, the static economic dispatch in the past cannot meet its needs, so the dynamic economic
dispatch of the power grid is imperative.
Methods:
Hence, in this paper, we proposed an Improved Differential Evolution algorithm (IDE)
based on Differential Evolution algorithm (DE) and Artificial Bee Colony algorithm (ABC). Firstly,
establish the dynamic economic dispatch model of wind integrated power system, in which we consider
the power balance constraints as well as the generation limits of thermal units and wind farm.
The minimum power generation costs are taken as the objectives of the model and the wind speed is
considered to obey the Weibull distribution. After sampling from the probability distribution, the
wind speed sample is converted into wind power. Secondly, we proposed the IDE algorithm which
adds the local search and global search thoughts of ABC algorithm. The algorithm provides more
local search opportunities for individuals with better evolution performance according to the thought
of artificial bee colony algorithm to reduce the population size and improve the search performance.
Results:
Finally, simulations are performed by the IEEE-30 bus example containing 6 generations.
By comparing the IDE with the other optimization model like ABC, DE, Particle Swarm Optimization
(PSO), the experimental results show that obtained optimal objective function value and power
loss are smaller than the other algorithms while the time-consuming difference is minor. The validity
of the proposed method and model is also demonstrated.
Conclusion:
The validity of the proposed method and the proposed dispatch model is also demonstrated.
The paper also provides a reference for economic dispatch integrated with wind power at the
same time.
Conductance estimation of a conductance-based neuron model by the differential evolution algorithm
