The surging demand for electricity, propelled by the widespread adoption of intelligent grids and heightened consumer interaction with electricity demand and pricing, underscores the imperative for precise prognostication of optimal power plant utilization. To confront this challenge, a dataset centered on issue-centric power plans is meticulously crafted. This dataset encapsulates pivotal facets indispensable for attaining sustainable power generation, including meager gas emissions, installation cost, low maintenance cost, elevated power generation, and copious resource availability. The selection of an optimal power plant entails a multifaceted decision-making process, demanding a systematic approach. Our research advocates the amalgamation of multiple-criteria decision-making (MCDM) models with self-organizing maps to gauge the efficacy of diverse sustainable energy systems. The examination discerns solar energy as the preeminent MCDM criterion, securing the apex position with a score of 83.4%, attributable to its ample resource availability, considerable energy generation, nil greenhouse gas emissions, and commendable efficiency. Wind and hydroelectric power closely trail, registering scores of 75.3% and 74.5%, respectively, along with other energy sources. The analysis underscores the supremacy of the renewable energy sources, particularly solar and wind, in fulfilling sustainability objectives and scrutinizing factors such as cost, resource availability, and the environmental impact. The proposed methodology empowers stakeholders to make judicious decisions, accentuating facets that are required for more sustainable and resilient power infrastructure.

In improving PV system performance, the parameters associated with electrical photovoltaic equivalent models play a pivotal role. However, due to the increased mathematical complexities and non-linear traits of PV cells, the precise prediction of these parameters is a challenging task. To estimate the parameters associated with PV models, a reliable, robust, and accurate optimization technique is needed. This paper introduces a new algorithm, Rat Swarm Optimizer (RSO), for obtaining the optimum PV cell and module parameters. The proposed method maintains an adequate balance between the exploration and exploitation phases to overcome premature particle issues. The results obtained using RSO are compared with those of other algorithms, i.e., Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), and Grasshopper Optimization (GOA), in this work. The modified one-diode model (MODM) and modified two-diode model (MTDM) are used to analyze the parameters of the mono-crystalline PV cell using the suggested RSO. The obtained findings imply that the parameters estimated by the suggested RSO are more accurate than those calculated by the other algorithms taken into consideration in the paper. The statistical results are compared, and it is clear that RSO is a very accurate, fast, and dependable approach for the parameter estimation of PV cells.

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.