Implementation of energy management and demand side management of a solar microgrid using a hybrid platform

Abstract

This paper presents the implementation of the advanced distributed energy management and demand side management of a solar microgrid by a multiagent system (MAS) coordination approach. The proposed approach is built upon a hybrid platform in which a solar microgrid, modelled in MATLAB/Simulink, is controlled by a MAS implemented in Java Agent Development Framework (JADE), bringing the MAS closer to the real-time application. Novel control strategies are designed to implement all the smart grid features in the microgrid. The Simulink model is controlled by strategic action of agents in JADE, a multithreaded computing platform, through a middleware, multiagent control using Simulink with JADE extension (MACSimJX). Environment values are fed to JADE through Simulink and the decisions are given back to the Simulink model for validation and also for real-time deployment. JADE leverages the advantages of the MAS and autonomously manages all the environmental dynamics and challenges introduced by the penetration of intermittent renewable energy resources in a short time, improving the stability, reliability, and fault tolerance of the solar microgrid.

Maximum wind speed forecasting using historical data and artificial neural networks modeling

“…In the study conducted, the determination of the most suitable ANN model has been found by trial and error, and the hidden layer parameters have been changed many times. A formula has not been developed in determining the best hidden layer parameters in ANN, but it can be found by trial and error [22][23]. A model that provides faster and more reliable results was determined both in the training phase of the ANN and in the testing phase.…”

Development of a Mas Based Distributed Intelligent Control and Fault Control Strategy for Microgrid

“…This is a JAVA frame used to create multi-agent systems in agreement with the foundation for intelligent physical agent (FIPA) features [11,12]. JADE provides an appropriate distributed platform to the designers to concentration on developing agents to control and monitor the operation of the microgrid [13].…”

Global maximum operating point tracking for PV system using fast convergence Firefly algorithm

“…The above problems are solved by considering the nature of P-V curves under partial shading and considering advantages of FA properties as described in Section 3. Removing the random third term, αrand 1 2 , the second term may be modified for MPPT application by taking advantage of firefly characteristics, transforming the conventional FA into a fast convergence structure. The modified firefly equation is given in Eq.…”

“…The deployment of photovoltaic (PV) generation with additional capacity of 50 GW, 10 GW, and 8 GW in China, the USA, and India, respectively, was an extraordinary record in 2017. The decreasing cost of PV panels and the availability of controllable power electronic converters have led to huge worldwide growth in off-grid and grid-connected PV systems for reliable power utilization [1,2]. In PV systems, maximum power point tracking (MPPT) methods such as perturb and observe (P&O) and incremental conductance [3] can be implemented to track the optimal operating point.…”