Browsing by Author "Amoako-Yirenkyi, Peter"

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    Seemingly unrelated time series model for forecasting the peak and short-term electricity demand: Evidence from the Kalman filtered Monte Carlo method
    (Heliyon, 2023-08) MARTIN, HENRY; Owusu, Frank Kofi; Amoako-Yirenkyi, Peter; Frempong, Nana Kena; Omari-Sasu, Akoto Yaw; Mensah, Isaac Adjei; Sakyi, Adu; 0000-0003-0173-1238
    In this extant paper, a multivariate time series model using the seemingly unrelated times series equation (SUTSE) framework is proposed to forecast the peak and short-term electricity demand using time series data from February 2, 2014, to August 2, 2018. Further the Markov Chain Monte Carlo (MCMC) method, Gibbs Sampler, together with the Kalman Filter were applied to the SUTSE model to simulate the variances to predict the next day’s peak and electricity demand. Relying on the study results, the running ergodic mean showed the convergence of the MCMC process. Before forecasting the peak and short-term electricity demand, a week’s prediction from the 28th to the 2nd of August of 2018 was analyzed and it found that there is a possible decrease in the daily energy over time. Further, the forecast for the next day (August 3, 2018) was about 2187 MW and 44090 MWh for the peak and electricity demands respectively. Finally, the robustness of the SUTSE model was assessed in comparison to the SUTSE model without MCMC. Evidently, SUTSE with the MCMC method had recorded an accuracy of about 96% and 95.8% for Peak demand and daily energy respectively
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    Stochastic Optimal Selection and Analysis of Allowable Photovoltaic Penetration Level for Grid-Connected Systems Using a Hybrid NSGAII-MOPSO and Monte Carlo Method
    (International Journal of Photoenergy, 2023) Abubakar, Ali; Borkor, Reindorf Nartey; Amoako-Yirenkyi, Peter; 0000-0002-5721-4638
    Generally, the main focus of the grid-linked photovoltaic systems is to scale up the photovoltaic penetration level to ensure full electricity consumption coverage. However, due to the stochasticity and nondispatchable nature of its generation, significant adverse impacts such as power overloading, voltage, harmonics, current, and frequency instabilities on the utility grid arise. These impacts vary in severity as a function of the degree of penetration level of the photovoltaic system. Thus, the design problem involves optimizing the two conflicting objectives in the presence of uncertainty without violating the grid’s operational limitations. Nevertheless, existing studies avoid the technical impact and scalarize the conflicting stochastic objectives into a single stochastic objective to lessen the degree of complexity of the problem. This study proposes a stochastic multiobjective methodology to decide on the optimum allowable photovoltaic penetration level for an electricity grid system at an optimum cost without violating the system’s operational constraints. Five cutting-edge multiobjective optimization algorithms were implemented and compared using hypervolume metric, execution time, and nonparametric statistical analysis to obtain a quality solution. The results indicated that a Hybrid NSGAII-MOPSO had better convergence, diversity, and execution time capacity to handle the complex problem. The analysis of the obtained optimal solution shows that a practical design methodology could accurately decide the maximum allowable photovoltaic penetration level to match up the energy demand of any grid-linked system at a minimum cost without collapsing the grid’s operational limitations even under fluctuating weather conditions. Comparatively, the stochastic approach enables the development of a more sustainable and affordable grid-connected system.