Improving power sharing in islanded networked microgrids using fuzzy-based consensus control

Abstract

The rising worldwide trend toward developing clean energy resources has caused dispersed installation of renewable energy resources (RESs) in distribution grids. Microgrid (MG) concept is proposed as a key factor in optimal and secure integration of, mostly converter-based, RESs into power systems. One of the major challenges related to MG control is ineffectiveness of droop control in accurate power sharing which is affected by the feeder impedance. In this paper, a fuzzy-based consensus control protocol is developed to address this issue in multi-bus MGs (MBMGs). Consensus signals are inserted into the conventional droop controller as complementary part to overcome the drawback of the droop control in power sharing in MBMGs. Dynamic fuzzy coefficients of consensus signals are designed to model X/R ratio of the grid impedance in the control system. In addition, a novel small signal model of MBMG is developed, by considering the conventional droop control, MBMG power network and power lines impedance to design and assess performance of the control system. Consensus control is also incorporated into the proposed control system of MBMG to analyze the stability. Simulation results are presented to assess effectiveness of the control strategy in MATLAB\Simulink.

Fuzzy-Based Distributed Cooperative Secondary Control with Stability Analysis for Microgrids

“…The traditional droop control technique considered as the most commonly used control at the primary control level because of its decentralized nature, which lessens the communication requirements [2]. However, the primary droop control shows some other shortcomings, including its frequency/voltage deviations according to load characteristics, inaccurate reactive power-sharing, and poor performance with nonlinear loads [3,4]. The secondary control level is thus introduced as a complement of primary control [2,5].…”

“…To resolve this issue, suitable and efficient tuning is a prerequisite is significant to accomplish the system stability and have reliable operation against load change and large disturbance. Computational intelligence algorithms such as fuzzy logic (FL) [4,38,39], genetic algorithm (GA) [40,41] and particle swarm optimization (PSO) [13,42] have been commonly used to resolve difficulty of the power system. However, some deficiencies in GA performance such as the premature convergence have been recorded.…”

“…Dynamics of the scheme, i.e., plug-and-play capability, and fault occurrence, are not included. Authors in [4] proposed a novel scheme to control the reactive power nominal value of distributed generation (DG) components consistent with their involvement in reactive power sharing in islanded MG. According to the recommended method, FL is implemented to design the X/R ratio of the connecting power lines into the consensus signals in order for proper participation of DG units for precise active and reactive power sharing irrespective of the nature of feeder impedance.…”

“…Recently, numerous DSC strategies have been stated in the literature. Most methods are based on consensus practice [4,8,9,[50][51][52][53], where the agents achieve consensus by means of exchanging information over a sparse communication system. The control method described in [51] reports simply frequency restoration through a distributed averaging proportional-integral controller but requires a centralized communication configuration regardless of its decentralized execution.…”

Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control Architectures

“…An MG can operate in either grid-connected [5][6][7][8][9][10][11][12] or isolated mode [13][14][15][16][17][18][19][20][21] or both [22]. The presence of more than one DER requires energy flow control from various sources to ensure reliable energy supply, safety, and a minimum-cost operation.…”