Jatin Nathwani

Geological restrictions and the low energy density of compressed air energy storage (CAES) plants constitute a technical and economic barrier to the enablement of variable and intermittent sustainable sources of energy production. Liquid air energy storage (LAES) and pumped thermal energy storage (PTES) systems offer a promising pathway for increasing the share of renewable energy in the supply mix. PTES remains under development while LAES suffers from low liquefaction unit efficiency, although it is at a higher technology readiness level (TRL) than PTES. The most significant element of large-scale EES is related to the discharge features of the power plants, especially the energy storage unit. Here, a novel multi-aspect equation, based on established codes and thermodynamic principles, is developed to quantify the required storage capacity to meet demand consistent with the design parameters and operational limitations of the system. An important conclusion of the application of the multi-aspect equation shows that liquid air storage systems instead of compressed air would reduce the space required for storage by 35 times. Finally, a cost equation was introduced as a function of the required storage volume. Calculations have demonstrated that the use of the novel cost equation, in lieu of the old one-aspect cost equation, for an LAES power plant with a production capacity of about 50 MW makes the costs of installing liquid air storage tanks against the total expenditure of the power plant about six times higher than what was reported in earlier research.

This paper reports on how the trade-off between the incident solar irradiance and conversion efficiency of a photovoltaic panel affects its power production. A neural network was developed through statistical analysis and a data-driven approach to accurately calculate the photovoltaic panel’s power output. Although the incident beam irradiance at a specified location directly relates to the tilt angle, the diffusion irradiance and energy conversion efficiency are nonlinearly dependent on a number of operating parameters, including cell temperature, wind speed, humidity, etc. A mathematical model was implemented to examine and cross-validate the physics of the neural network. Through simulation and comparison of the optimized results for different time horizons, it was found that hourly optimization can increase the energy generated from the photovoltaic panel by up to 42.07%. Additionally, compared to the base scenario, annually, monthly, and hourly optimization can result in 9.7%, 12.74%, and 24.78% more power, respectively. This study confirms the data-driven approach is an effective tool for optimizing solar power. It recommends adjusting the tilt angle of photovoltaic panels hourly, during the daily operation of maximizing the energy output and reducing solar costs.