Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation

Abstract

With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimize allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, and total voltage deviation, and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022a was used for the simulation of meta-heuristic techniques. The proposed techniques were implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction in active power loss, reactive power loss, and an improvement in the voltage profile with optimal allocation across all considered scenarios.

Fast and Robust State Estimation for Active Distribution Networks Considering Measurement Data Fusion and Network Topology Changes

“…As shown in Figure 1, the model is divided into two stages: offline training and online estimation. (1) Offline Training: 1 Real load data is used as the base data, combined with random factors such as load fluctuations, DG outputs, and topological changes to generate load data. The load flow values are then calculated, and noise is added to generate a measurement dataset for training.…”

“…The input to the network is measurement data, and the output includes node voltage magnitude, phase angle, and the system's topological structure. 3 The network is trained using the dataset; (2) Online Estimation: 1 The measurement data is transmitted to the trained network for estimation. The voltage magnitude and phase angle estimated by the network are converted, and the node admittance matrix is adjusted based on the topological output.…”

“…With the rapid integration of DGs [1][2][3][4][5], distribution networks are being transformed into active distribution networks (ADNs) which have more complex structures and put forward higher requirements for monitoring and control [6,7]. To ensure the secure and stable operation of the ADN, it is necessary to efficiently and accurately monitor the system states in real time.…”