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India is endowed with a lot of solar radiation as a result of its location. The Indian government therefore intends to maximize the usage of its solar energy resources through the development of solar power plants across the country. The concentrated solar power plant (CSP) is one of the technologies that rely on solar energy for its electricity generation. The type of condenser model in the CSP technology has the potential to affect its techno-economic viability. In this paper, a 100 MW solar tower power plant (STPP) with two different condenser models, i.e., the dry-cooled STPP and wet-cooled STPP models, are studied using the System Advisor Model (SAM) at six different geographical areas in India. The study employed the optimization of the thermal energy storage and the solar field size to identify the minimum levelized cost of electricity (LCOE) for all six locations. Results from the simulation show that the LCOE will range between 13 and 17 cents/kWh under the optimization conditions for the STPP dry-cooled condenser model, while that of the wet-cooled condenser model will range between 12.40 and 12.96 USD cents/kWh for the study locations. It was also observed that the optimized solar multiple (SM) for the dry-cooled STPP model ranges between 1.4 and 1.8, whereas that of the wet-cooled model ranges between 1 and 1.8. The study identified Bhopal as the best location for installing the STPP plant for both condenser models. In addition, this paper also discusses major potential barriers and government policies that are needed to develop CSP technologies in India. The outcome of the study is expected to help both government and other stakeholders in decision making and policy formulation for the sector. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) power is emerging as one of the most viable renewable energy sources. The recent enhancements in the integration of renewable energy sources into the power grid create a dire need for reliable solar power forecasting techniques. In this paper, a new long‐term solar PV power forecasting approach using long short‐term memory (LSTM) model with Nadam optimizer is presented. The LSTM model performs better with the time‐series data as it persists information of more time steps. The experimental models are realized on a 250.25 kW installed capacity solar PV power system located at MANIT Bhopal, Madhya Pradesh, India. The proposed model is compared with two time‐series models and eight neural network models using LSTM with different optimizers. The obtained results using LSTM with Nadam optimizer present a significant improvement in the forecasting accuracy of 30.56% over autoregressive integrated moving average, 47.48% over seasonal autoregressive integrated moving average, and 1.35%, 1.43%, 3.51%, 4.88%, 11.84%, 50.69%, and 58.29% over models using RMSprop, Adam, Adamax, SGD, Adagrad, Adadelta, and Ftrl optimizer, respectively. The experimental results prove that the proposed methodology is more conclusive for solar PV power forecasting and can be employed for enhanced system planning and management. [ABSTRACT FROM AUTHOR]