Date of Publication :15th August 2024

Abstract:The research provides a novel model for active distribution network expansion planning that takes into account microgrid integration and numerous possibilities for boosting capacity, modifying feeders, and determining microgrid topology in islanded operating mode. The model considers the present value of network and microgrid installation, maintenance, and operating expenses, as well as distributed generating unit costs [1]. The suggested mixed integer nonlinear model has been successfully simulated and evaluated on a test network. The research describes a deep-learning-based clustering strategy for reliably classifying energy users based on usage patterns in unlabelled data, which employs an autoencoder for dimensionality reduction and clustering [2]. The suggested technique improves load profiling in smart grid analytics, giving useful information for utilities and stakeholders seeking to optimise power network operations. The study proposes HEDGE, an open-source tool for producing realistic domestic energy statistics such as PV generation, household electric loads, and electric car consumption and availability characteristics. HEDGE tackles a data gap in home distributed energy resource characterisation and coordination research, particularly for data-driven approaches such as machine learning-based forecasting and reinforcement learning-based control. The technology provides data sequences that correspond to real-life behavioural clusters and profile magnitudes, allowing researchers to efficiently investigate the energy profiles of specific residences [3]. Localised Demand Control (LDC) is a control system strategy for managing the integration of distributed energy resources into the traditional electrical grid, addressing issues such as demand fluctuation and temporal differences between generation and consumption schedules. The LDC system uses a power line carrier frequency signal to coordinate flexible energy resources so that they follow a chosen demand curve at the distribution transformer level. It has been successfully deployed in a 5-house microgrid, resulting in improved system load factor and enhanced hosting capacity for electric cars and solar photovoltaic panels [4]. The research paper describes a control approach for reactive compensation and power demand control in distribution circuits powered by renewable energy sources, intending to reach an agreement on compensation requirements for the whole circuit. The suggested method is accompanied by a mathematical demonstration of the closed-loop system's convergence and has been shown to efficiently manage reactive power and power demand in distribution circuits with significant renewable energy penetration [5].

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