Browsing by Author "Wanjoli, Paul"

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    A new combined analytical–numerical probabilistic method for assessing the impact of DERs on the voltage stability of bulk power systems
    (Energy reports, 2024) Wanjoli, Paul; Moustafa, Mohamed M. Zakaria; Abbasy, Nabil H.
    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.
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    A state-of-the-art review on the modeling and probabilistic approaches to analysis of power systems integrated with distributed energy resources.
    (Ain Shams Engineering Journal, 2024) Wanjoli, Paul; Abbasya, Nabil H.; Moustafa, Mohamed M. Zakaria
    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.
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    Probabilistic Power Flow Analysis of DERs Integrated Power System From a Bayesian Parameter Estimation Perspective
    (IEEE Access, 2024-11) Wanjoli, Paul; Moustafa, Mohamed M.Zakaria; Abbasy, Nabil H.
    The rise of distributed energy resources (DERs) in power systems demands efficient models for power flow analysis. Existing models often face challenges in balancing computation speed and accuracy. This paper presents a probabilistic power flow (PPF) method for systems with DERs, using Bayesian parameter estimation (BPE) to handle uncertainties in wind speed, solar irradiance, and loads. By applying Bayes’ theorem, BPE estimates posterior distributions, refined by the Metropolis-Hastings algorithm. Validated on IEEE 39-bus and 59-bus test systems in MATLAB/Simulink, BPE outperformed the 2m+1 point estimate method (PEM) in terms of accuracy, computation speed and scalability. Simulation results demonstrate superior accuracy of BPE, yielding voltage profiles and congestion indices closely matching those of Monte Carlo Simulation (MCS) but with lower computation time. For instance, under heavy loading, BPE achieved a congestion index of 0.0617 (compared to PEM’s 0.2228 and MCS’s 0.0611). BPE also provided faster results as system size increased, averaging 3.477 hours, versus PEM’s 6.837 hours and MCS’s 11.524 hours. Statistical analyses confirmed BPE’s consistent performance and minimal error, making it more efficient for large systems. Thus, BPE-based PPF is recommended for power system planning and operation under uncertain conditions, owing to its robustness, accuracy, and computational efficiency.