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The present paper utilizes entropy theory and Google earth engine cloud computing technique to investigate system state and river recovery potential in two large sub-basins of the Mahanadi River, India. The cross-sectional intensity entropy (CIE) is computed for the post-monsoon season (October–March) along the selected reaches. Further, a normalized river recovery indicator (NRRI) is formulated to assess the temporal changes in river health. Finally, NRRI is related to a process-based variable-LFE (low flow exceedance) to comprehend the dominating system dynamics and evolutionary adjustments. The results highlight the existence of both threshold-modulated and filter-dominated systems based on CIE and NRRI variabilities. In addition, the gradual decline in CIE and subsequent stabilization of vegetated landforms can develop an 'event-driven' state, where floods exceeding the low-flow channel possess a direct impact on the river recovery trajectory. Finally, this study emphasizes the presence of instream vegetation as an additional degree of freedom, which further controls the hierarchy of energy dissipation and morphological continuum in the macrochannel settings. [ABSTRACT FROM AUTHOR]

Motivated by the growing significance of solar energy, the paper presents the findings of the maiden study on the long-term seasonal performance assessment of three PV module technologies—monocrystalline (m-Si), polycrystalline (p-Si), and amorphous silicon (a-Si) housed in a 1 MW solar plant installed at a local steppe climate in Gandhinagar, Gujarat, India using the key performance indicators (KPIs), daily power generation, final yield (Y f), reference yield (Y r), total energy loss (T EL) and performance ratio (PR). Notably, the am-Si PV modules were found to be the best performing technology, with an average PR of 71.26% and the lowest average total energy loss of 631 h during the monitored period. Moreover, PV systems have become more popular as a source of green, clean energy; yet, they have low base-load energy sustainability. Explainable AI (XAI) aids in understanding predictions made by the machine learning models. The importance of XAI lies in the comprehension and trust which is embedded into the results of machine learning algorithms. Therefore, the present work builds a comprehensive XAI model for predicting KPIs and assesses the performance of several learning algorithms using R-value, MAE, the number of iterations, and execution time. The Levenberg-Marquardt algorithm predicts the KPIs for p-Si, am-Si, and m-Si with prediction accuracies of 98.63%, 98.58%, and 90.09%, respectively. In summary, this study not only advances our understanding of PV module performance in a steppe climate context but also contributes to the broader field by integrating an XAI model for transparent and reliable predictions of the KPIs. [Display omitted] • Comparison of m-Si, p-Si, and a-Si PV modules in a 1 MW solar plant, Gandhinagar, India, showed long-term performance. • a-Si PV modules outperformed with a 71.26% PR and the lowest 631 hours energy loss, surpassing m-Si and p-Si technologies. • Study underscores XAI's role in ML predictions, developing a robust model to predict KPIs and evaluate learning algorithms. • Levenberg-Marquardt showed high accuracy: 98.63% for p-Si, 98.58% for a-Si, and 90.09% for m-Si PV modules. [ABSTRACT FROM AUTHOR]

The present paper utilizes entropy theory and Google earth engine cloud computing technique to investigate system state and river recovery potential in two large sub-basins of the Mahanadi River, India. The cross-sectional intensity entropy (CIE) is computed for the post-monsoon season (October–March) along the selected reaches. Further, a normalized river recovery indicator (NRRI) is formulated to assess the temporal changes in river health. Finally, NRRI is related to a process-based variable-LFE (low flow exceedance) to comprehend the dominating system dynamics and evolutionary adjustments. The results highlight the existence of both threshold-modulated and filter-dominated systems based on CIE and NRRI variabilities. In addition, the gradual decline in CIE and subsequent stabilization of vegetated landforms can develop an 'event-driven' state, where floods exceeding the low-flow channel possess a direct impact on the river recovery trajectory. Finally, this study emphasizes the presence of instream vegetation as an additional degree of freedom, which further controls the hierarchy of energy dissipation and morphological continuum in the macrochannel settings. [ABSTRACT FROM AUTHOR]

The probable availability of renewable power sources is unexceptionable, and the government of India is setting high goals for the use of renewable energy. Renewable distributed generation (DG) reduces the need for fossil fuels, relieve environment change, and decrease radiations of CO2 and other perfluorocarbons. DGs and capacitors are more desirable choices to balance power demand closer to the load centres than centralized power generation. Optimal position and capacity of DGs play an essential role in enhancing the performance of distribution systems in terms of system loss mitigation, voltage profile enhancement and stability concerns. This paper introduces Harris Hawk Optimization (HHO) and Teaching Learning-Based Optimization (TLBO) approaches for efficient distribution of different types of DGs in the radial distribution system (RDS) to enhance system loss minimization, voltage profile, yearly energy savings and decreasing the greenhouse gas emissions. The aim is to depreciate system energy losses, cost of energy losses and more reliable voltage regulation within the frame-work of RDS planning. Four different cases are considered to assess the suggested algorithms. Simulations are carried out on IEEE 33-bus and 69-bus test RDSs. The preponderance of the recommended approaches has been shown by analysing the results with techniques available in the literature. The comparison is made based on the power losses and voltage profile of RDS. The outcomes reveal that a significant decrease in power loss, enrichment of the voltage profile across the network and exactness of the suggested methods. [ABSTRACT FROM AUTHOR]

Abstract: Attractive economic performance of energy systems is important for their long term sustainability. In this paper, a cost benefit analysis (CBA), standard thermoeconomic analysis, and a new method that combines the two was applied to seven modelled candidate decentralised energy systems for small Indian rural villages. By including elements of CBA in standard thermoeconomics, economic inefficiencies at the component level and revenue adjusted costs of exergy for each stream were ascertained. The analysed systems all produced household and irrigation electricity as well as a secondary product or service, which in some cases utilised waste heat from the electricity generation process. The system that produced distilled water as a secondary product was the most economically sustainable out of those modelled. While the standard thermoeconomic electricity cost remained around 0.5 USD/kWh for all analysed systems, the benefit to cost ratios varied from 0.29 to 0.97, demonstrating differing appeal to outside investment. By incorporating CBA into standard thermoeconomics the accounted revenue produced a larger spread in exergetic costs than standard thermoeconomics (for example, an electricity cost range of 0.217–0.507 USD/kWh), thus providing a better comparison of systems with different energy end uses. [Copyright &y& Elsevier]

This study provides experimental validation of a previously published optimal design theory for photovoltaic (PV)-powered electrodialysis reversal (EDR) desalination systems. The prior work describes the co-optimization of PV and EDR subsystems, and flexible operation to accommodate daily and annual solar irradiance variability, significantly reducing water cost. This study presents the fabrication of a PV-EDR pilot system designed using the co-optimization theory and field testing results from the rural village of Chelluru, India. Testing in the field enabled observation and evaluation of real-world factors on system performance, resulting in updates to the previous theory to include unaccounted factors that affect costs, including: filling and draining of water tanks, salt and water accumulation in tanks from prior batches, unexpected energy losses due to locally purchased converters, and scaling in the ED stack. Therefore, water cost in the PV-EDR pilot system was updated from previous estimates based on field performance. The estimated capital cost and lifetime cost of the Chelluru system are 34% and 45% lower, respectively, than the corresponding costs if the PV-EDR system was designed using conventional design practice. The theory and experimental insights presented in this paper will enable desalination engineers to better design and optimize PV-EDR systems. • This study validated previous published optimal design theory of PV-EDR systems. • A pilot-scale co-optimized PV-EDR system was built and tested in rural India. • Testing in the field enabled accounting real-world factors in future PV-EDR design. • 34% and 45% reductions of LCOW and capital cost are achieved by the pilot system. [ABSTRACT FROM AUTHOR]