This paper presents the power flow modeling of droop-controlled distributed generation units with secondary frequency and voltage restoration control for hierarchically controlled islanded microgrids. These models are incorporated in the conventional Newton-Raphson power flow method as a new bus, without the necessity of a slack bus, and include the gains of the control systems that influence the steady-state solution. Two case studies are addressed. In the first case study, comparisons of the proposed models against the steady-state solutions obtained with PSCAD and Simscape Power System of Simulink, where the closed-loop controls are explicitly modeled, are presented. In the second case study, the proposed method is contrasted against the droop-based approach. The results obtained exhibit low computing effort, reliability, and effectiveness of the proposed models since quadratic convergence behavior is maintained independently of the size and topology of the microgrid. Besides, it is demonstrated that hierarchical and droop controllers lead to different solutions, which confirms the necessity of including the hierarchical control in the power flow model.
This paper presents the application of a hierarchical control scheme for islanded AC microgrids with a primary droop control and a centralized extended optimal power flow control. The centralized control is responsible for computing and sending, in an online manner, the control references to the primary controls in order to achieve three operational goals, i.e., improvement of the global efficiency, voltage regulation through reactive power management and compliance of the restrictions regarding the generation units capacities. Two case studies are defined and online tested in a laboratory-scaled microgrid implemented in the Microgrid Laboratory at Aalborg University. The primary controllers are included in a real-time simulation platform (dSPACE 1006), while the extended optimal power flow is conducted in a central controller by using a Smart Meter and LabVIEW for data acquisition and MATLAB for its implementation, taking into account load and capacity profiles. The obtained results show the reliability of the proposed scheme in a real system and its advantages over the conventional droop control.
Abstract-This paper presents the application of Newton-based methods in the time-domain for the computation of the periodic steady state solutions of microgrids with multiple distributed generation units, harmonic stability and power quality analysis. Explicit representation of the commutation process of the power electronic converters and closed-loop power management strategies are fully considered. Case studies under different operating scenarios are presented: grid-connected mode, islanded mode, variations in the Thevenin equivalent of the grid and the loads. Besides, the close relation between the harmonic distortion, steady state performance of the control systems, asymptotic stability and power quality is analyzed in order to evaluate the importance and necessity of using full models in stressed and harmonic distorted scenarios.
The virtual synchronous generator (VSG) control has been extensively applied for realizing grid-friendly interconnections of power converters. Nevertheless, due to the direct shaft dynamics emulation, low-frequency power oscillations may be introduced in the case of limited damping effects. To solve this issue, power control law modifications have been proposed based on the feedback control theory. Although power oscillations can be suppressed to an acceptable level, the original inertial response of VSG will be significantly degraded. This paper proposes a new damping method based on the reference feed forward (RFF) control. The proposed method is able to effectively attenuate poorly-damped power oscillations in VSG-controlled converters without affecting the original inertial response. Theoretical analysis and effectiveness of the proposed damping method have been validated by both EMT simulations and experiments.
Due to the high penetration of renewable energy resources in microgrids (MGs), the grid inertia becomes low which leads to the grid to be vulnerable to large disturbances. The energy storage devices can play an important role to enhance the inertia of MGs. However, due to the high investment cost of storages or their dp/dt limitation, the installed energy storages cannot cover the challenge of high df/dt. A prominent solution to solve the problem is to use the inertia response of the wind generators. However, relatively high second frequency nadir is the main drawback of using the inertia response of the wind generators which may impose an extensive disturbance to MGs. Accordingly, a coordinated operation strategy for MGs between wind generator and hybrid energy storage (HES) system is proposed in this paper. In addition, to improve the inertia response of the MG; providing high-quality communication infrastructures with low delay and increasing the Ultracapacitor capacity have been paid attention. In this paper, the costs of the installed Ultracapacitor and quality of communication services are defined as the operation cost. Guaranteeing enough frequency damping for the MG with low operation cost are two conflict objectives. Therefore, a multi-objective optimization method is used to set the controllers' values and reduce the operation cost. The results confirmed that the effectiveness of the proposed strategy to control hybrid power storage in coordination with the wind generator and the frequency recovery process is improved. Also, employing the optimum values guaranteed the frequency damping effectively with low operation cost. The Integral Absolute Error (IAE) value and operation cost are reduced by 13.6% and 32%, respectively. Also, the simulation results show that the maximum MG frequency deviation and maximum df/dt is well compatible with different standards in the presence of load perturbations and different wind speeds.INDEX TERMS Microgrid (MG), Frequency control, Wind generator, Hybrid energy storage (HES), Ultra-capacitor, Inertia response, Multi-objective optimization.
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