Intelligent control strategy in the islanded network of a solar PV microgrid
Introduction
MG provides secure and reliable energy supply to the facilities of critical load and promotes energy independence for the community. MG can be operated in either grid connected or islanded mode of operation [1], [2]. In either mode of operation, it is required to maintain power balance and regulation of frequency level in the MG network [3]. As compared to the grid connected mode of MG operation, maintaining power balance and regulation of frequency level is a big challenge in the islanded mode [4], [5]. A coordinated control operation with equal sharing of power among DG sources and power balance with regulation of frequency level in islanded MG network can be ensured by means of implementing appropriate power sharing and power management control strategies in the MG power system [3]. Similarly as compared to the grid connected mode, variation in power and frequency level can be expected more in the islanded MG network. This is due to the lack of inertia and under performance of PI based controller of DG sources during the transient conditions in the islanded MG network [3]. In such a scenario, implementation of PSO based intelligent control strategy can be considered for the MG model to minimize the variation in power and frequency level during the transient conditions in the islanded MG network. PSO control technique is widely used for MG control applications and most of the research works [6], [7], [8], [9], [10] have been focused on optimizing control parameters of the inverter controller to get an optimized power flow, regulation of voltage and frequency level, improving dynamic response and stability of the MG system. Also some of the research works have been done by using multi objective PSO (MOPSO) [11] and advanced or modified PSO (MPSO) algorithm [12] applied for solving the problem constraints in relation to the energy and operation management of MG.
In most of the research works [13], [14], [15], [16], [17], for power sharing among the DG sources in low voltage (LV) MG network, the reverse droop with virtual impedance control was implemented with either grid feeding (P–Q or current control) or grid forming type (voltage control) VSI of DG sources. However in this study, a reverse droop with virtual impedance (resistor) control has been implemented with GsGfm type VSI for the DG sources in MG model (built in the MATLAB-SIMULINK), since this type of VSI is more flexible and has the ability to be operated in either mode of MG operation without modifying its control configuration. Another novelty in this work is that the PSO algorithm with proposed cost function was also implemented to optimize the power flow and power variation from the DG units and minimize the frequency variation during the transient conditions. The PQ factors in the islanded MG network has been analyzed with the transient conditions like change in load and change in solar irradiance level of the PV units, since both the scenarios are the important factors that could be frequently encountered in a real time MG network and they could cause negative impact over the PQ factors in the islanded MG network.
This paper is organized as follows; Section 2 describes the detail of MG model configuration, control configuration of the VSI, proposed power management control strategies for the MG model, and detail of allowable operating frequency range as per the Network standard, Section 3 outlines the method of optimization which includes the formulation of cost function, setting parameters and process steps of the proposed PSO algorithm, Section 4 demonstrates the optimization analysis, results and discussion, and Section 5 concludes the outcome of this study.
Section snippets
MG model configuration
The configuration of a typical AC MG network model which is operating at voltage and frequency level of 400 V and 50 Hz is shown in Fig. 1. The specification details of each element which are used in the proposed MG model is given in Table 1, Table 2 respectively.
Method of optimization
The PSO algorithm with the assigned cost function was implemented through MATLAB-m-file programme codes in the MATLAB-SIMULINK software environment. The PSO algorithm process was executed through the MATLAB command codes and control gains of the inverter controller in each DG unit was optimized during several run of the MG model simulation.
The PSO control algorithm, applied to the inverter control of DG unit is shown in Fig. 5. Factors like simulation period (per iteration), number of
Optimization, analysis and results discussion
This section includes the detail of results and discussion before and after the optimization for the proposed MG model analysis in island mode with different stage of load change conditions and sudden change in the solar irradiance for the PV units. The analysis results includes the performance factors (overshoot in power level during initial state of simulation, steady state response during the initial state and different stage of load conditions) of the DG inverter controller, optimized power
Conclusion
In this study, a PV based MG model was developed in MATLAB-SIMULINK software environment with implementation of power management and power sharing control strategies. By implementing proposed control strategies, an equal power sharing and better coordinated operation between PV units and battery storage, power balance and regulation of frequency level were ensured during the different stages of load change conditions in the islanded MG network. As compared to the results of PI based inverter
References (27)
- et al.
Voltage and frequency regulation based DG unit in an autonomous microgrid operation using Particle Swarm Optimization
Int. J. Electr. Power Energy Syst.
(2013) - et al.
Power flow control in grid-connected microgrid operation using Particle Swarm Optimization under variable load conditions
Int. J. Electr. Power Energy Syst.
(2013) - et al.
Optimization of micro-grid system using MOPSO
Renew. Energy
(2014) Microgrids: Architectures and Control
(2013)- et al.
Microgrids and Active Distribution Networks
(2009) - et al.
Trends in microgrid control
IEEE Trans. Smart Grid
(2014) - et al.
Accurate reactive power sharing in an islanded microgrid using adaptive virtual impedances
IEEE Trans. Power Electron.
(2015) - et al.
Cooperative control of distributed energy storage systems in a microgrid
IEEE Trans. Smart Grid
(2015) - et al.
Optimal design of microgrids in autonomous and grid-connected modes using particle swarm optimization
IEEE Trans. Power Electron.
(2011) - et al.
Control parameters optimization of a three-phase grid-connected inverter using particle swarm optimisation
Development of optimal PI controllers for a grid-tied photovoltaic inverter
IEEE Symposium Series on Computational Intelligence
Energy and operation management of a microgrid using particle swarm optimization
Eng. Optim.
Efficient power sharing approach for photovoltaic generation based microgrids
IET Renew. Power Gener.
Cited by (50)
A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation
2024, Energy Strategy ReviewsFuzzy assisted optimal tilt control approach for LFC of renewable dominated micro-grid: A step towards grid decarbonization
2023, Sustainable Energy Technologies and AssessmentsMicrogrids: A review, outstanding issues and future trends
2023, Energy Strategy ReviewsA comprehensive review of power quality mitigation in the scenario of solar PV integration into utility grid
2023, e-Prime - Advances in Electrical Engineering, Electronics and EnergyOptimal energy management of distributed generation in micro-grid to control the voltage and frequency based on PSO-adaptive virtual impedance method
2022, Electric Power Systems ResearchCitation Excerpt :The PSO algorithm has many advantages over other meta-heuristic optimization methods that distinguish this algorithm from other algorithms. Features of the PSO algorithm include [6, 7]: The PSO algorithm is a population-based algorithm.