Identification of dynamic equivalents based on heuristic optimization for smart grid applications

Abstract

Abstract-Vulnerability assessment is one of the main tasks in a Self-Healing Grid structure, since it has the function of detecting the necessity of performing global control actions in real time. Due to the short-time requirements of real time applications, the eligible vulnerability assessment methods have to consider the improvement of calculation time. Although there are several methods capable of performing quick assessment, these techniques are not fast enough to analyze real large power systems in real time. Based on the fact that vulnerability begins to develop in specific regions of the system exhibiting coherent dynamics, large interconnected power systems can be reduced through dynamic equivalence in order to reduce the calculation time. A dynamic equivalent should provide simplicity and accuracy sufficient for system dynamic simulation studies. Since the parameters of the dynamic equivalent cannot be easily derived from the mathematical models of generators and their control systems, numerical identification methods are needed. Such an identification task can be tackled as an optimization problem. This paper introduces a novel heuristic optimization algorithm, namely, the Mean-Variance Mapping Optimization (MVMO), which provides excellent performance in terms of convergence behavior and accuracy of the identified parameters. The identification procedure and the level of accuracy that can be reached are demonstrated using the Ecuadorian-Colombian interconnected system in order to obtain a dynamic equivalent representing the Colombian grid.

Optimal dispatch of reactive power sources by using MVMO^s optimization

“…Interested readers can find the basic theoretical background, and reference values for the algorithm's settings in [6]- [8] and [13]. Before introducing the swarm approach, this subsection is intended to highlight further modifications of the singleparticle implementation aiming at improved mapping performance and constraint handling.…”

“…max ≤ s s (8) where P loss denotes the total active power losses of the transmission network, g k and θ ij are, respectively, the line conductance and the difference between the voltage angle between buses i and j, and N k is the total number of network branches. p g and q g are the nodal active and reactive power generation vectors whereas p d and q d are the nodal active and reactive power demand vectors.…”

Identification of Gaussian mixture model using Mean Variance Mapping Optimization: Venezuelan case

“…Hence, MVMO is able to search around the local best-so-far solution with a small chance of being trapped into one of the local optimums. This feature is enhanced with a strategy for handling the zerovariance [12]. So far, MVMO has successfully been applied for the solution of different power system optimization problems such as optimal reactive power dispatch [10], identification of dynamic equivalents [12], optimal location and coordinated tuning of damping controllers [13], and optimal control in wind farms [14], [15].…”

“…Interested readers are referred to [12] for further details on how to set the two different shape factors and the scaling factor as well.…”

Application of mean-variance mapping optimization for parameter identification in real-time digital simulation

“…For these reasons, only the specific region of interest (internal network) is usually modelled in detail while the rest of the system (external network) is reduced to equivalent models that provide similar responses [6]. Dynamic equivalent (DE) models are simple aggregated representation of large networks, able to provide similar dynamic responses and behaviours as the actual network for stability analysis.…”