NUMERICAL MODELLING FOR ESTIMATING WIND ENERGY POTENTIAL FOR THE SOUTHERN REGION OF ANDHRA PRADESH, INDIA

Abstract

There is a strong relationship between economic activity and the availability of energy sources. Fossil fuels have played a role in the development of world economies. However, the production and utilisation of such energy have largely contributed to environmental issues such as greenhouse gas emissions, global warming, acid rain, and climate change. Renewable energy In a nutshell, this study provides valuable insights into the evaluation of the model's performance at different heights with an absolute percentage error ranging from 3.63 – 8.31%. Further, a Techno-Economic assessment and sensitivity study results inform cost considerations for the project development at the selected site. The above findings contribute to the improvement and understanding of investors, wind farm developers, designers, policymakers, and researchers to foster the continued growth and sustainability of the wind energy sector. technologies have recently gained recognition as zero-emission, environmentally friendly, and sustainable energy sources to address these issues. Renewable energy sources like solar, wind, hydro, biomass, geothermal, waste, tidal, and wave ocean energy have substantially contributed to power generation, heating, and transportation globally. Of all the renewable energy, wind energy is likely to play a significant role in the global electricity generation portfolio. However, the power output of wind turbines is highly sensitive to variations in wind speed. Therefore, accurate assessment of wind speed data is crucial for operating electrical systems, ensuring grid network reliability, and supporting associated markets. Traditional methods of collecting wind data from meteorological towers and airports have limitations in terms of cost, data quality, availability, and representative duration. These limitations contribute to the challenges and barriers faced by the current wind resource assessment process, including poor consistency, accuracy, lack of verifiable standardized procedures, and poor understanding of on-site data collection methods. To overcome these limitations, numerical weather prediction (NWP) models have emerged as promising alternatives for obtaining reliable wind data to improve wind resource assessment practices and more reliable wind farm development decisions. The work reported in the present thesis aimed to develop suitable methodologies for improving the assessment of wind resources in complex terrain in the southern region of Andhra Pradesh using the NWP model. The present thesis provides a comprehensive literature analysis on the use of NWP models for wind energy assessment. The most commonly used NWP models are discussed, considering local characteristics such as orography, surface roughness, terrain, vegetation, and local data. Despite the positive outcomes of NWP models for atmospheric research and applications, challenges remain in accurately simulating wind variables. One crucial aspect of research in wind variable simulation is the planetary boundary layer (PBL) parameterisation. With a wide range of configurations available, the main challenge lies in determining the most suitable scheme for a specific region. Nine different PBL parameterization setups are investigated with the QNSE PBL scheme and are found to perform the best in terms of wind speed prediction. The station and PBL parameterization selection are found to substantially impact the bias estimation of wind speed. Depending on the location and experiment, the bias values ranged from 0.32 - 1.91 m/s. With reliable predictions of average wind speed, QNSE methodology is recommended as the best approach for simulating the wind resources in the study area. The simulated wind fields have been validated using in-situ data from the National Institute of Wind Energy, Chennai, TN, India. The model replicates the winds for the majority of the time, although it tends to produce lower wind speeds compared to measured winds by 1 - 2 m/s. The characteristics of the simulated wind fluctuations and their sources in the model are studied. The error metrics, mean absolute error values are found as 0.64 m/s at 50 m height, 0.51 m/s at 80 m height and 0.57 m/s at 100 m height, the root mean square error values are found as 0.23 m/s, 0.20 m/s, and 0.22 m/s, indicating the model's accuracy compared to measured wind speeds. In diurnal analysis, the developed model performs well from 9am to 4 pm and poor during 5pm to 10pm. The study also showed height-dependent annual percentage errors ranging from 3.63 – 8.31 %, indicating improved model performance with height. The site's monthly averaged wind power density ranged from 47.37 - 921.61 W/m2, corresponding to average wind speeds of 4.3 - 11.46 m/s. A feasibility analysis is conducted to evaluate the financial sustainability of wind projects. Net present value (NPV), internal rate of return (IRR) and levelized cost of energy (LCOE) measures are considered, taking into account installation, operation, and financing costs over a 25-year lifespan. Wind potential is found to be a crucial factor in the project's financial viability, ranging from 5635 - 6919 MWh/year, considering regulated wholesale prices and relatively uniform investment costs. The selected sites in the study with NPV of ₹ 3,12,810 and estimated IRR of 15.67 % turned out to be profitable based on assumed conditions. The sensitivity analysis showed that at all the locations, a 5 % variation in investment cost had the most impact on IRR in the range of 14.2 - 17.9 %, LCOE in the range of 5.01 - 5.04 % and a 5% variation in discount rate had the most impact on NPV in the range of 10.7 - 11.4 % respectively.In a nutshell, this study provides valuable insights into the evaluation of the model's performance at different heights with an absolute percentage error ranging from 3.63 – 8.31%. Further, a Techno-Economic assessment and sensitivity study results inform cost considerations for the project development at the selected site. The above findings contribute to the improvement and understanding of investors, wind farm developers, designers, policymakers, and researchers to foster the continued growth and sustainability of the wind energy sector.