Mohamed Moustafa

Modern power systems are shifting toward decarbonization and incorporation of distributed energy resources (DERs) to replace fossil fuel generators. Although promising, DERs introduce uncertainty because of their intermittent nature. This study provides a comprehensive survey of current approaches for modeling system uncertainties and methods of analysis, particularly in the context of static voltage stability studies within modern power systems. Emphasis is placed on evaluating various models applied to different system random variables (RVs), focusing on their suitability for those particular RVs. Additionally, the study examines the characteristics and frameworks of prominent probabilistic methods (PM), evaluates their efficacy, and discusses static voltage stability analysis approaches, emphasizing solution structures and appropriate applications. It concludes by thoroughly reviewing both numerical and analytical PM methods and offering insights into their strengths and limitations. The provided comprehensive survey reveals that, considering system uncertainties, voltage stability studies have gained the most share, followed by small-signal stability studies, whereas the frequency stability studies have gained the least share.

The integration of distributed energy resources (DERs) and unpredictable loads has increased uncertainty in power systems. Traditional methods struggle to assess performance under these uncertainties, and existing probabilistic methods face challenges with complexity and accuracy. This paper introduces a new combined analytical-numerical probabilistic method to assess the impact of DERs on voltage stability. Using Bayesian Parameter Estimation (BPE), the method derives the analytical properties of random variables (RVs) associated with DERs and loads, obtaining posterior distributions. The Metropolis–Hastings sampling technique then estimates these posteriors numerically, enabling accurate predictions of DERs and load outputs. Voltage stability analysis was performed using the continuation power flow method and validated on the IEEE 59-bus test system in MATLAB/Simulink. The results show that integrating DERs significantly improves voltage stability. The proposed method outperforms the Monte Carlo simulation (MCS)-based method in accuracy and computational speed, increasing DERs penetration and voltage stability limits by 3%. It closely matches MCS voltage estimates but requires fewer iterations (500 per loading increment) compared to MCS’s 1000, leading to faster computation times (a few hours to one day versus up to three days for MCS). This method provides an efficient solution for managing uncertainties in power systems.

This paper presents a novel robust current droop controller (RCDC) using a single droop loop. This scheme is unified supporting dual mode of operation for micro-grids (MGs), including grid connected mode (GCM) and islanded mode (ISM) while ensuring seamless transition between the two modes with proportional power sharing maintained. The proposed controller is further incorporated with an improved maximum power point tracking (MPPT) technique presented for the parallel operation of single-stage inverters fed by multi-string PV array topology. In addition, an improved phase-locked-loop-less (PLL-less) method is presented supporting self-synchronization strategy of the parallel operation of PV-inverters with the main grid while maintaining the full capabilities of the unified control architecture. This obviates the usage of conventional PLLs, which are widely used with active synchronization techniques. The performance of the proposed control scheme is validated using real time simulations (RTS) developed by dSPACE MicroLabBox.

The overcurrent protection relays are a main component of the power system protection. The design of the protection scheme has to ensure the capability of relays to detect the faulty condition and trip the suitable circuit breaker to disconnect only the faulty part of the network without any effect on the healthy loads in the network. The coordination between OCRs may be affected by the penetration of the distributed generation units to the conventional network. This is due to the variable topology nature of networks with multiple feeding sources, which leads to variable network impedances, variable power flow and variable fault current levels. In this paper, a design of an optimized adaptive overcurrent protection scheme is done for an industrial microgrid with distributed generation. The setting of the relays are selected according to a study of the network operation topologies using ETAP software, and the protection setting of relays is optimized by genetic algorithm using MATLAB optimization toolbox to obtain the optimum coordination scheme.

Micro-grid (MG) operation using voltage control methods (VCMs) has been widely recommended for parallel operation of three phase voltage source inverters, especially during islanded mode to maintain the voltage level in the MG. In contrast, this paper presents a new unified control strategy for MG parallel operation using a current control method (CCM). A hybrid control scheme which combines a universal robust droop controller (URDC) and quasi-proportional resonant (QPR) regulator is introduced. It ensures equal power sharing among parallel operated inverters during all MG's operating modes. Moreover, an improved adaptive estimator for the reference current magnitude is integrated to meet dynamic load variations. Furthermore, the scheme is enhanced with a self-synchronized capability using a simple synchronous reference frame phase locked loop (SRF-PLL). This obviates the necessity of communication links and external synchronous clocks required for synchronization process. In addition, a decentralized control action is locally managed to restore the inverter's frequency upon any reactive load change. Modeling and simulation results using MATLAB/SIMULINK software are presented to show the effectiveness of the proposed strategy. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

This paper introduces a probabilistic approach (PA) for the optimum selection of various photovoltaic (PV) modules manufactured by different suppliers to select the most suited module at a specific site location. This approach is based on fitting the probability distributions of the irradiance data measured at a specific hour of a typical day over a long term period (7 years). The goodness of fit test is employed to determine the best fitted probability density function which is then used in the calculation of the average output power and capacity factor (CF) of each module. Thus, the module with the highest average CF over the year is therefore considered as the best suited module for the given site location. The source files of solar irradiance database are approved by the Egyptian Meteorological Authority. Detailed modeling of the PV system characteristics using MATLAB/Simulink has been introduced, which shows complete stepping procedures and respective results. The combination of the detailed PV modeling along with the PA facilitates precise prediction of the best module in terms of output power under varying operating conditions and partial shading for the specified site.