Optimal Operation of Cogeneration Power Plant Integrated with Solar Photovoltaics Using DLS-WMA and ANN

• Published in: International journal of energy research 2024-04, Vol.2024, p.1-16
• Main Authors: Reddy, Butukuri Koti, Giri, Nimay Chandra, Yemula, Pradeep Kumar, Agyekum, Ephraim Bonah, Arya, Yogendra
• Format: Article
• Language: English
• Subjects: Adaptability; Algorithms; Alternative energy sources; Artificial neural networks; Atmospheric conditions; Batteries; Case studies; Cogeneration; Cost analysis; Cost control; Electricity; Energy; Energy distribution; Energy efficiency; Energy management; Energy resources; Energy storage; Environmental conditions; Fossil fuels; Heat; Integrated approach; Irradiance; Neural networks; Optimization; Optimization algorithms; Performance evaluation; Photovoltaic cells; Photovoltaics; Power flow; Power plants; Rechargeable batteries; Renewable resources; Reproductive behaviour; Solar radiation; Storage batteries; Sustainable energy; Sustainable yield; System effectiveness; Systems design; Thermal energy
• ISSN: 0363-907X; 1099-114X
• DOI: 10.1155/2024/5562804

Focusing on mitigating global challenges arising from hydrocarbon-based sources, the integration of cogeneration power plants with solar photovoltaics offers a viable solution. The intermittent nature of renewable resources presents a challenge to the consistent performance of cogeneration systems. To address these issues, this work introduces a novel framework for integrating cogeneration power plants (CGPPs) with solar photovoltaic systems. The key innovation of this research lies in its dual-algorithm approach that seamlessly blends cogeneration power plants with solar photovoltaic. This study proposed an integrated approach, employing the Derivative Log Sigmoid-Woodpecker Mating Algorithm (DLS-WMA) and Optimized Artificial Neural Networks (O-ANN), to combine cogeneration power plants with solar photovoltaics in industrial distribution systems. The methodology is aimed at achieving a cost-effective, efficient system design, enhancing the efficiency of cogeneration power plants, and introducing energy storage batteries for uninterrupted power generation under diverse atmospheric conditions and loads. Additionally, the proposed system includes rechargeable batteries for energy storage to support critical services when the solar plant is offline and the CGPP cannot meet the power demand. The industrial system’s photovoltaic component is tuned using the DLS-WMA for cost minimization and O-ANN for solar irradiance prediction, ensuring continuous power flow by optimizing both the photovoltaic system and the cogeneration power plant (CGPP) system. Real-time datasets are used to compare the results obtained by this new approach with those of the previous state-of-the-art algorithms. The error with O-ANN prediction is 1.2%, compared to 4.1% with the existing WMA-ANN technique, while the cost-benefit with DLS-WMA shows a 9% improvement over the WMA-ANN technique. The experimental outcomes demonstrate the efficiency of this new approach. Collaboration with industry stakeholders and policymakers is crucial for the large-scale deployment of this system, facilitating the adoption of sustainable energy practices in industrial distribution systems.