Optimization of microenergy grid including adiabatic compressed air energy storage by considering uncertainty of intermittent parameters

Abstract

Abstract In recent years, zero‐carbon energy resources such as adiabatic compressed air energy storage with thermal energy storage have been interested due to growing concerns over global warming. This study proposes a microenergy grid including heat and power networks connected through adiabatic compressed air energy storage with thermal energy storage, which can be considered hybrid energy storage supplying power for both networks. The power network is supplied with the main grid and wind turbine systems, and the heat network is provided with heat pumps. The objective function minimizes power purchased from the main grid and power demand of heat pumps in the heat network. Since uncertainty plays a key role in the operation of integrated energy systems, the uncertainty of intermittent parameters such as active and reactive load and wind speed data has been considered in this study. Therefore, predicted values are used in the optimization problem instead of using deterministic values for such uncertain parameters. To do this, an efficient 2‐level corrective forecasting algorithm is proposed to have an accurate prediction for the day‐ahead operation of the microenergy grid. Different scenarios are presented to show the importance of the forecasting method and the utilization of adiabatic compressed air energy storage with thermal energy storage in the system’s structure. The results indicate that corrective actions on the predicted load and wind speed data decrease the operation cost of the microenergy grid from 57.13% to 13.21%. Also, it is found that neglecting adiabatic compressed air energy storage with thermal energy storage in the structure of the microenergy grid increases operation cost to 3423 US$. Other obtained results also indicate the importance of coutilization of compressed air energy storage with thermal energy storage and 2‐level corrective forecasting method leading to optimal operation of the microenergy grid.

Keywords

• adiabatic compressed air energy storage
• energy storage
• integrated energy systems
• uncertainty prediction
• wind turbine