• Energy Research

The stable operation of conventional power systems greatly depends on coherent impedances of the bulk power networks' elements. However, penetration of inverter interfaced distributed generation (IIDG) units put the stability of modern power systems into a risk due the vague and arbitrary output impedance of IIDG units. Besides, the impedance specification of IIDGs can only be established by means of a virtual impedance loop, which needs extra control efforts also imposes voltage drops. Especially, the virtual impedance depends on the output current and cannot be thus freely adjusted. To this end, an optimal voltage regulator (OVR) is proposed for controlling IIDG units to achieve a free/wide range of impedance shaping. The OVR facilitates the optimal impedance shaping based on the control requirement and grid's impedance characteristics, which makes the IIDG units consistent with the power network thus contributing to stabilizing modern power systems. The OVR's control system is based on the state feedback control and the impedance shaping is achieved through an appropriate feedback gain adjustment process. Simulation results prove the effectiveness of the method to achieve the desired impedance shaping.

Fault ride-through (FRT) is essential for inverter-interfaced distributed generation (IIDG) units to protect semiconductor switches from being imposed to overcurrent conditions while the transients are securely passed. To this end, a current limiting strategy is adopted for IIDG units, mostly embedded in control loops, to limit the current within the withstand-able band and to make the IIDG units stay connected to the (micro) grid during the transient. However, the FRT/current limiting of grid-forming inverters affects the transient stability of the autonomous droop-based microgrids and may make them unstable, which yet has not been well-explored in the literature. This issue is considered in this work through a scrupulous observation of the second-order nonlinear differential equation describing the frequency-phase angle dynamics and investigating the problem through the Lyapunov theory. The Chetaev’s instability theorem, which is developed based on the Lyapunov direct method, is used to explore the instability conditions due to the FRT. It is revealed that the phase angle variation, as a consequence of the current limiting, and the arbitrary/resistive transient impedance of grid-forming inverters make the system unstable. Numerical and time-domain results through MATLAB/Simulink platform prove the validity of the models.

A two-stage adaptive robust optimization is developed for pre-disturbance scheduling in microgrids (MGs) for handling uncertainties associated with electricity market prices, renewable generation, demand forecasts, and islanding events. The objective is to produce a reliable and optimal solution for MG operation that minimizes operational costs and the risk/failure in islanding events. In the literature, the uncertainty sets associated with islanding events cover a full scheduling period which results in a sub-optimal solution. In this paper, uncertainty sets corresponding to islanding events are modeled based on reliability/resiliency-oriented indexes of the MG/grid to achieve a more accurate/reliable solution. Besides, the Benders decomposition algorithm which is used to handle uncertainties in solving the optimization problem is time-consuming. Therefore, the column-and-constraint generation (C&CG) decomposition strategy is adopted to make the problem computationally tractable. Further, the uncertainty budget parameters are clarified to balance the conservatism and optimality (cost minimization) of the robust solution in uncertainty sets. The effectiveness of the proposed framework is evaluated and discussed by using a set of numerical studies with different scenarios in an MG. The simulations show that the proposed framework reduces operational costs by using the precise analysis of uncertainty budgets and a change in scheduling periods.

Microgrid (MG) concept is considered as the best solution for future power systems, which are expected to receive a considerable amount of power through renewable energy resources and distributed generation units. Droop control systems are widely adopted in conventional power systems and recently in MGs for power sharing among generation units. However, droop control causes frequency fluctuations, which leads to poor power quality. This paper deals with frequency fluctuation and stability concerns of f-P droop control loop in MGs. Inspired from conventional synchronous generators, virtual damping is proposed to diminish frequency fluctuation in MGs. Then, it is demonstrated that the conventional frequency restoration method inserts an offset to the phase angle, which is in conflict with accurate power sharing. To this end, a novel control method, based on phase angle feedback, is proposed for frequency restoration in conjunction with a novel method for adaptively tuning the feedback gains to preserve precise active power sharing. Nonlinear stability analysis is conducted by drawing the phase variations of the nonlinear second-order equation of the δ-P droop loop and it is proved that the proposed method improves the stability margin of f-P control loop. Simulation results demonstrate the effectiveness of the proposed method.

Abstract As the requirement of expensive and unreliable high band-width communication infrastructure is obviated, decentralized droop-like control method has been considered for power sharing implementation in autonomous microgrids (MGs). To this end, the power network is regarded as a communication link and voltage variables (magnitude and frequency) as control signals. This, however, reduces the stability margin of islanded MGs due to the interaction of droop controllers through the power network. Lack of inertia of droop-controlled power converters and low X/R ratio of interconnecting power lines intensify this interaction which may lead to the instability of Networked MGs (NMG). On the other hand, the existing parallel-based small signal model of MGs is inadequate to represent this interaction, as the adopted common-based reference frame (RF) is not applicable in islanded NMGs. This issue is investigated in this work in details and, inspired from power flow equations, a local RF is proposed to improve the small-signal model accuracy. Droop controllers are also correlated through the power flow equations to properly model their interaction through the power network. Moreover, the state-space model is developed in a fully decentralized approach which does not rely on any converter for any specific role. Eigenvalue analysis and simulation in MATLAB\SIMULINK platforms are executed to evaluate the effectiveness and accuracy of the proposed model.

Conservation voltage reduction (CVR) is a potentially effective and efficient technique for inertia synthesis and frequency support in modern grids comprising power electronics (PE)-based components, aiming to improve dynamic stability. However, due to the complexities of PE-based grids, implementing the CVR methods cannot be performed using traditional techniques as in conventional power systems. Further, quantifying the CVR impacts in modern grids, while focusing on dynamic time scales, is critical, consequently making the traditional methods deficient. This is an important issue as CVR utilization/quantification depends on grid conditions and CVR applications. Considering these concerns, this work offers a thorough analysis of CVR applications, implementation, and quantification strategies, including data-driven AI-based methods in PE-based modern grids. To assess the CVR applications from a new perspective, aiming to choose the proper implementation and quantification techniques, they are divided into categories depending on various time scales. CVR implementation methods are categorized into techniques applied to PE-based grids and islanded microgrids (MGs) where different control systems are adopted. Additionally, to address the evaluation issues in modern grids, CVR quantification techniques, including machine learning- and deep learning-based techniques and online perturbation-based methods are evaluated and divided based on the CVR application. Concerns with the further utilizing and measuring of CVR impacts in modern power systems are discussed in the future trends section, where new research areas are suggested.