Photovoltaic Power Prediction Using Analytical Models and Homer-Pro: Investigation of Results Reliability

This paper aims to develop an analytical model for the prediction of the electricity produced in a Photovoltaic Power Station (PVS). In this context, the developed mathematical model is implemented in a Simulink Model. The obtained simulation results are compared to the experimental data, the results obtained from the software Homer-Pro model, and the results given by the online PV calculator (Photovoltaic Geographical Information System), developed by the European commission. The comparison results show the reliability of the developed analytical model for specific months of the year. However, an error of 10% between simulations and experimental results is observed for July and August. This error is mainly due to the effects of humidity and dust that were not considered in the analytical model. Nevertheless, the monthly and yearly produced electricity values show the robustness of the proposed model to predict the PVS generated power. The developed model will be used as a powerful tool for data prediction and the optimization of electricity generation. This permits us to reduce the losses in power generation by optimizing the connected generating power stations to the power grid.

[1]  A. Olabi,et al.  Solar Energy: Applications, Trends Analysis, Bibliometric Analysis and Research Contribution to Sustainable Development Goals (SDGs) , 2023, Sustainability.

[2]  T. Lie,et al.  Energy Business Initiatives for Grid-Connected Solar Photovoltaic Systems: An Overview , 2022, Sustainability.

[3]  M. Romero,et al.  Soiling effect in solar energy conversion systems: A review , 2022, Renewable and Sustainable Energy Reviews.

[4]  S. Agha,et al.  Global Maximum Power Point Tracking of Partially Shaded PV System Using Advanced Optimization Techniques , 2022, Energies.

[5]  Asem Alzoubi.  MACHINE LEARNING FOR INTELLIGENT ENERGY CONSUMPTION IN SMART HOMES , 2022, International Journal of Computations, Information and Manufacturing (IJCIM).

[6]  E. M. Barhoumi,et al.  Optimization of PV-Grid Connected System Based Hydrogen Refueling Station , 2022, 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT).

[7]  K. Alameh,et al.  Powering agriculture: Present status, future potential, and challenges of renewable energy applications , 2022, Renewable Energy.

[8]  Jing Jiang,et al.  Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties , 2021, Applied Energy.

[9]  D. Sailor,et al.  Photovoltaics in the built environment: A critical review , 2021, Energy and Buildings.

[10]  E. M. Barhoumi,et al.  Economic evaluation of hybrid electrical systems for rural electrification: A case study of a rural community in Nigeria , 2021, International Journal of Green Energy.

[11]  Xia Chen,et al.  Pathway toward carbon-neutral electrical systems in China by mid-century with negative CO2 abatement costs informed by high-resolution modeling , 2021, Joule.

[12]  Anuradha M. Annaswamy,et al.  One-shot battery degradation trajectory prediction with deep learning , 2021 .

[13]  Yonghua Song,et al.  Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities , 2021, Journal of Cleaner Production.

[14]  Huiling Chen,et al.  Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: A comprehensive review , 2021, Renewable and Sustainable Energy Reviews.

[15]  Eric O’Shaughnessy,et al.  Too much of a good thing? Global trends in the curtailment of solar PV , 2020, Solar Energy.

[16]  Mostafa Safdari Shadloo,et al.  Applications of nanofluids containing carbon nanotubes in solar energy systems: A review , 2020 .

[17]  M. Alhuyi Nazari,et al.  Numerical analysis of photovoltaic solar panel cooling by a flat plate closed-loop pulsating heat pipe , 2020, Solar Energy.

[18]  M. Bettayeb,et al.  Optimization and performance analysis of a solar concentrated photovoltaic-thermoelectric (CPV-TE) hybrid system , 2020, Renewable Energy.

[19]  Robert S. Balog,et al.  Time series forecasting of solar power generation for large-scale photovoltaic plants , 2020 .

[20]  Yating Zhang,et al.  Experimental Investigation of the Shading and Mismatch Effects on the Performance of Bifacial Photovoltaic Modules , 2020, IEEE Journal of Photovoltaics.

[21]  Iskander Tlili,et al.  A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications , 2018, Energies.

[22]  Irfan Jamil,et al.  Evaluation of Energy Production and Energy Yield Assessment Based on Feasibility, Design, and Execution of 3 × 50 MW Grid-Connected Solar PV Pilot Project in Nooriabad , 2017 .

[23]  Anand Singh,et al.  Techno-economic feasibility analysis of hydrogen fuel cell and solar photovoltaic hybrid renewable energy system for academic research building , 2017 .

[24]  Anjali Awasthi,et al.  Techno-economic feasibility analysis of a solar PV grid-connected system with different tracking using HOMER software , 2017, 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE).

[25]  Tomas Cebecauer,et al.  Typical Meteorological Year Data: SolarGIS Approach☆ , 2015 .

[26]  Kenneth E. Okedu,et al.  Optimization of Renewable Energy Efficiency using HOMER , 2014 .

[27]  Elieser Tarigan,et al.  Assessment of PV Power Generation for Household in Surabaya Using SolarGIS–pvPlanner Simulation☆ , 2014 .

[28]  Hussein A. Kazem,et al.  Renewable energy in Oman: Status and future prospects , 2011 .

[29]  L. Kazmerski,et al.  Optimization and life-cycle cost of health clinic PV system for a rural area in southern Iraq using HOMER software , 2010 .