Abstract
This paper proposes the models to solve the optimal generator dispatching problem in an islanded Micro grid with different uncertainties in the constraint and in the objective coefficient. The optimal problem with interval in the power balance constraint is considered as a linear parametric optimization problem, focusing on optimal solutions based on the lower and upper ends of this interval. When the coefficients of cost per power unit caused by load shedding are imprecise and expressed as intervals, the proposed model will be based on the two ends of interval and the problem is converted to a two-objective problem. With uncertainties in both constraint and objective coefficient, the problem will be treated as a four-objective one, considering the lower and upper ends of all intervals. All models are expressed in the linear forms and the linearization is carried out by Max-Affine method. To solve this multi-objective problem, the Bellman-Zadeh approach and Particle Swarm Optimization (PSO) algorithm are applied. The rationality of the proposed models is confirmed in the case study with one low voltage Micro Grid.
Highlights
The penetration of renewable sources is increased in Micro Grid (MG)
The distributed generators based on wind and solar are considered to be non-dispatchable because they depend on natural conditions
The optimality of dispatch and the accuracy of forecasting are concomitant in MG
Summary
The penetration of renewable sources is increased in Micro Grid (MG). The distributed generators based on wind and solar are considered to be non-dispatchable because they depend on natural conditions. B. Phan et al.: Optimal Generator Dispatching With Uncertain Conditions model based on the expected value, and the reserve is expressed in constraint [9], [10]. The [11] used the probability density functions for load, wind and PV power to calculate the expected values, and used the probabilistics sequence of equivalent load for calculating the reserve constraints. It is rather a hard work because it is not easy to deal with probability functions. The smallest middle point value may not reflect the optimality of objective function This paper proposes another approach, based on the ends of interval to improve the problem solution.
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