Three‐phase AC/DC power‐flow for balanced/unbalanced microgrids including wind/solar, droop‐controlled and electronically‐coupled distributed energy resources using radial basis function neural networks

Abstract

This study presents a novel approach for robust, balanced and unbalanced power-flow analysis of microgrids including wind/solar, droop-controlled and electronically-coupled distributed energy resources. This method is based on using radial basis function neural networks that can be applied to a wide range of non-linear equation sets. Unlike conventional Newton-Raphson, the presented method does not need to calculate partial derivatives and inverse Jacobian matrix and so, has less computation time, can solve all the equation sets for the power grid and distributed energy resources exactly and simultaneously, and has enough robustness with respect to the R/X ratio and load changes. Also, because the power electronic interface provides some degrees of freedom in the steady-state and dynamic models, a new approach is required to solve the non-linear set of the power grid and distributed energy resource equations even with unequal number of equations and variables. The proposed method is a general method applicable to all types of power networks, including radial, meshed, and open-loop, and includes all types of buses, i.e. PQ, photovoltaic and slack buses. This method is tested on different microgrid test systems, and the comparative results validate its efficiency and accuracy.

Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques

“…The proposed prediction solution can be further exploited using other supervised learning algorithms and advanced error correction techniques as well as extensively validated based on more field measurements. The hybrid machine learning techniques can be further extended and incorporated into control and management strategies of renewable energy systems (e.g., [42][43][44][45]) to improve the system operational performance. Funding: This research was funded by the Natural Science Foundation of China (51777183), the Natural Science Foundation of Zhejiang Province (LZ15E070001) and the Natural Science Foundation of Jiangsu Province (BK20161142).…”

Distribution system state estimation-A step towards smart grid

“…Efficient and quickly convergent power flow algorithms, for instance [131][132][133], that considers both radial and meshed network models and integration of multi-DER, may be adopted. With the development of smart grid, which promises many features such as DA, demand responsive loads and increased integration of DERs, the distribution grid is evolving and is turning into active network.…”

Sizing and Allocation of Distributed Energy Resources for Loss Reduction using Heuristic Algorithms

“…In addition to power losses, some other factors, such as the costs of DG investment, total power operation, power supply quality [18], and reliability [32]- [33], have been considered. In this kind of algorithm, the most critical point is employing a robust and accurate power flow solution method, such as one of the efficient approaches discussed in [35], for DNs and micro-grid systems based on nonlinear mapping and the parallel processing capability of radial basis function neural networks (RBFNNs). This ability has been extended to application in a micro-grid hierarchical control structure in [35]- [36].…”