A. O. Issa, A. I. Abdullateef, A. Sulaiman, A. Y. Issa, M. J. E. Salami, M. A. Onasanya ,
Volume 19, Issue 3 (September 2023)
Abstract
Grid-connected photovoltaic (PV) system is often needed whenever utilities fail to provide consumers with a reliable, sufficient and quality power supply. It provides more effective utilization of power, however, there are technical requirements to ensure the safety of the PV installation and utility grid reliability. In solar systems there is often excessive use of components, resulting in high installation costs. Consequently, appropriate measures must be taken to develop a cost-effective grid-connected PV system. An optimally sized PV system incorporated into an existing unreliable grid-connected commercial load for Mount Olive food processing is presented in this paper. The study focused on providing a reliable electricity supply which is cost-effective and environment-friendly. The techno-economic analysis of grid-connected PV/Diesel/Battery Storage systems was carried out using HOMER Pro software. Results showed that Grid/PV/BSS are technically, economically and environmentally feasible with the cost of energy at 0.136$/kWh and net present cost at $254,469. Also, the excess electricity produced by this combination is 13,264kWh/year, which generates income for the company by selling excess generated energy back to the grid if net metering were to be implemented. Furthermore, the CO2 emissions for these combinations decreased to 10,081.6 kg/year as compared to the existing systems (Grid/Diesel Generator) with emissions of 124,480 kg/year. This is an additional advantage in that it improves the greenhouse effect. A sensitivity analysis was carried out on the variation of load change, grid power price and schedule outages for the optimal system.
Aboubakeur Hadjaissa, Mohammed Benmiloud, Khaled Ameur, Halima Bouchenak, Maria Dimeh,
Volume 20, Issue 4 (Special Issue on ADLEEE - December 2024)
Abstract
As solar photovoltaic power generation becomes increasingly widespread, the need for photovoltaic emulators (PVEs) for testing and comparing control strategies, such as Maximum Power Point Tracking (MPPT), is growing. PVEs allow for consistent testing by accurately simulating the behavior of PV panels, free from external influences like irradiance and temperature variations. This study focuses on developing a PVE model using deep learning techniques, specifically a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) with backpropagation as the learning algorithm. The ANN is integrated with a DC-DC push-pull converter controlled via a Linear Quadratic Regulator (LQR) strategy. The ANN emulates the nonlinear characteristics of PV panels, generating precise reference currents. Additionally, the use of a single voltage sensor paired with a current observer enhances control signal accuracy and reduces the PVE system's hardware requirements. Comparative analysis demonstrates that the proposed LQR-based controller significantly outperforms conventional PID controllers in both steady-state error and response time.
