A novel modification approach based on MTLBO algorithm for optimal management of renewable micro-grids in power systems

Abstract

The consumer's demand of more reliable and economic sources has made an impact on the new competitive electricity markets. In this regard, the accurate operation management of Micro-Grid (MG) with various types of renewable power sources (RES) can be an effective approach. This paper presents a novel solution methodology based on Teacher-Learning-Based Optimization (TLBO) algorithm to solve the optimal energy management of MG including several RESs with the back-up of Fuel Cell (FC), Wind Turbine (WT), photovoltaic (PV) and Micro Turbine (MT). Moreover, storage devices have been considered to meet the energy mismatch. The solution of this nonlinear constraint optimization problem minimizes the total cost of the grid and RESs, concurrently. Nevertheless, in finding the optimal solution, the interactive effects of MG and utility in a 24 hour time interval are taken into consideration which would increase the complexity of the problem intensely. In order to explore the total search space globally, a modification method is proposed which is compromised of two modification methods based on TLBO. In the end, the suggested algorithm is tested through a typical renewable MG as the test system to demonstrate the superiority of the proposed method over the other well-known algorithms.which is defined as the aggregation of DGs, electrical loads and generation interconnected among themselves and with the distribution network as well [1]. On the other side, lower cost, cleaner power generation mechanism, more reliability, better power quality and high flexibility are some of the main advantages of using RESs [2]. Recently, several researches have been used to investigate the MG problem under different loading and distinctive targets. The developing solution methods are experiencing continuous replacements in order to keep these net-works at the optimal operating point and control with different targets in the MG operation management domain.

Energy management system optimization in islanded microgrids: An overview and future trends

“…Several modifications to improve the TLBO algorithm have been proposed. In [97], the authors propose to modify the learning phase in TLBO for avoiding to get trapped in a local minimum and to search wider in the global search space. The ED-UC optimization is carried out in a grid-connected MG for operation cost minimization.…”

“…[2,47,53,55,56,60,62,65,71,75,81,88,89,95,96,97] Minimizing generation cost [4,54,58,64,66,73,74,77,79,90,98,99,100,101]. …”

Performance Analysis of Micro-Grid Based On Measurement and Processing Different Electrical Quantities

“…When it comes to the promotion of the effectiveness with which micro-grid systems performs, to keep the system operating at the optimal point controller has to. Investigate the system under different power flow load and different type of load [13], while the issue of optimal capacitor placement also serves as a notable aspect of emphasis. In essence, there is the need to accentuate the fact that there are various ways in which the nexus between the performances of micro-grid systems is impacted by the optimal placement of capacitors.…”

Multi-objective day-ahead scheduling of microgrids using modified grey wolf optimizer algorithm

“…are selected from the repository as follows: If the number of non-dominated solutions in the repository is more than three, after being sorted according to one of the objective functions the repository is divided into three sections. 1 k is selected randomly from the first section which is from the first non-dominated solution in the repository to k is chosen from the last section which is from the (…”

“…Recent increases in energy prices and environmental concerns have led to the penetration of renewable distribution generators (DGs) in distribution systems [1]. Microgrid (MG) is a concept resulted from the need of reliable power systems with clean energy sources which can make it easy to have a satisfactory communication and optimal energy management of the power system [2].…”