Energy Management Method of Hybrid AC/DC Microgrid Using Artificial Neural Network

Abstract

This paper proposes an artificial neural network (ANN)-based energy management system (EMS) for controlling power in AC–DC hybrid distribution networks. The proposed ANN-based EMS selects an optimal operating mode by collecting data such as the power provided by distributed generation (DG), the load demand, and state of charge (SOC). For training the ANN, profile data on the charging and discharging amount of ESS for various distribution network power situations were prepared, and the ANN was trained with an error rate within 10%. The proposed EMS controls each power converter in the optimal operation mode through the already trained ANN in the grid-connected mode. For the experimental verification of the proposed EMS, a small-scale hybrid AD/DC microgrid was fabricated, and simulations and experiments were performed for each operation mode.

Artificial Deep Neural Network in Hybrid PV System for Controlling the Power Management

“…In a grid-connected hybrid manner, the suggested EMS uses an already taught ANN to operate every voltage regulator in the best mode of operation. A modest hybrid AD/DC microgrid was built for model validation of the recommended EMS, and calculations and tests were conducted for each operating condition [17].…”

RECLAIM: Renewable Energy Based Demand-Side Management Using Machine Learning Models

“…In commercial and industrial buildings DGs and BSS acts as a backup supply during the outages of main supply [5] [6]. Now Artificial intelligence (AI) and ML based algorithms are used to predict models in energy consumption [7], uncertainties in renewable energies [8] and Demand-side management (DSM) based heating system in buildings [9].An EMS based MG using ANN is developed in [10].However it did not consider any DSM technique . The comparative analysis of Machine learning (ML) techniques for shortterm demand prediction in MG is reported in [11].…”

Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks

“…Thus, several optimization algorithms have been established by researchers recently, such as the genetic algorithm [7], gravitational search algorithm [9][10][11], butterfly algorithm [12], herd-related optimization approaches [13], whale optimization algorithm [14], cat swarm optimization [15], practical swarm optimization (PSO) [16], etc. The energy management duties are to ensure security; use a mixture of energy, generation, transmission, and distribution resources; and minimize losses and increase profit [17][18][19][20][21]. However, VPP has more crucial problems than the conventional power grid concerning inherent inertia, uncertainty, and random penetration of distributed generation.…”