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Impact of Land-Use Changes on the Runoff of Nandigama, Andhra Pradesh, India

Sainath Reddy Poli, Bhavani R.

Ecological Engineering & Environmental Technology

2024

Vol. 25, iss. 2

121--129

Present study investigated the effect of land-use variations on the excess flow for a Nandigama, Andhra Pradesh, India by using HEC-HMS model. The model was calibrated and validated using observed rainfall and runoff data. The R2 and NSE values were both greater than 0.65 after calibration, indicating a reasonable fit of the model. An analysis was conducted to understand how the land-use changes in a basin have affected the runoff. The analysis revealed that the stream flow increased due to variations in land use, and a reduction in the timing of peak flow at the outlet was observed. Additionally, the study analysed the trend of maximum rainfall time series and found that the months of June, July, and August show a decreasing trend in maximum rainfall over the study period, while other months show an increasing trend. The results of the analysis can be used to implement informed policies and management practices aimed at mitigating the negative impact of land-use changes and climate changes in Nandigama.

An efficient microgrid model based on Markov fuzzy demand-side management

Jabash Samuel G. K., Sivagama Sundari M. S., Bhavani R., Jasmine Gnanamalar A.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2023

Vol. 71, nr 3

art. no. e145569

Today’s electricity management mainly focuses on smart grid implementation for better power utilization. Supply-demand balancing, and high operating costs are still considered the most challenging factors in the smart grid. To overcome this drawback, a Markov fuzzy real-time demand-side manager (MARKOV FRDSM) is proposed to reduce the operating cost of the smart grid system and maintain a supply-demand balance in an uncertain environment. In addition, a non-linear model predictive controller (NMPC) is designed to give a global solution to the non-linear optimization problem with real-time requirements based on the uncertainties over the forecasted load demands and current load status. The proposed MARKOV FRDSM provides a faster scale power allocation concerning fuzzy optimization and deals with uncertainties and imprecision. The implemented results show the proposed MARKOV FRDSM model reduces the cost of operation of the microgrid by 1.95%, 1.16%, and 1.09% than the existing method such as differential evolution and real coded genetic algorithm and maintains the supply-demand balance in the microgrid.