(226a) Techno-Economic Optimization of a Compressed Air Energy Storage System Integrated with a Natural Gas Combined Cycle Plant Considering Time-Varying Electricity Price

Global electricity demand is increasing. However, it is desired that the greenhouse gas emissions due to electricity production be reduced. These challenges are being addressed by increasing use of renewable energy sources [1,2]. However, renewable energy production is intermittent affected by diurnal and seasonal variability that can adversely affect the reliability and resiliency of the electric grid [2]. One potential way to facilitate higher penetration of renewables without compromising the reliability and resiliency of the electric grid is to develop energy storage systems [3-5]. When electricity is in excess, energy can be stored, while stored energy can be used when there is high demand. While various energy storage technologies are being developed and investigated [1], compressed air energy system (CAES) is one of the technologies that is matured, has fast transients, and can enable large amount of energy storage especially when an appropriate natural formation such as cavern is readily available. While there are several works in the literature investigating the design and optimization of the CAES technology, those studies have been conducted for stand-alone storage systems. In this work, we have investigated CAES integrated with a natural gas combined cycle (NGCC) power plant. For storage, air can be extracted from the gas turbine (GT) compressor in an NGCC plant while injecting the air back to the GT combustor through expanders when needed. This approach utilizes the existing infrastructure in the NGCC plant, especially the GT compressor and turbine, thus reducing the capital costs and improving efficiency due to the use of the highly efficient components of the GT. For optimal design and operation of such systems, variabilities in the electricity demand supply over a long time horizon needs to be considered thus optimal operation of the power plant also needs to be considered simultaneously. Due to the transient characteristics of the storage system as well as that of the NGCC plant, the underlying optimization problem for optimal design and operation of the CAES system and optimal operation of the retrofitted power plant leads to a challenging large-scale dynamic optimization problem.

A dynamic model of the CAES is developed and validated with the literature data [3]. Storage in caverns and above-ground vessels is considered to be alternative options. As the first-principles dynamic model of the NGCC plant is highly nonlinear and computationally expensive, it is difficult to use that model for dynamic optimization. A linear reduced order model of the NGCC plant is developed with air extraction/injection included by linearizing the nonlinear model around steady-state conditions and with further reduction in orders through balanced truncation.

Maximization of the net present value (NPV) is considered to be the objective function for the optimization problem. Optimizations are done in the Python/Pyomo platform considering the whole year locational marginal price (LMP) of electricity for 14 regions [6] with varying carbon taxes. The integrated CAES system is found to be economically superior compared to the standalone NGCC system for some regions. For those regions where the integrated system is not superior, a study is done to evaluate the impact of the reduction in the capital and operating costs of the CAES technology and to understand what minimum reduction of costs will be needed for a specific region for the CAES technology to be economically viable. Levelized cost of storage for cavern and vessel storage is computed for all regions. The study shows interesting optimal dynamic profiles of air injection and extraction that are not only affected by the instantaneous LMP but also the LMP profile before and after of a given time instant showing that consideration of long time windows will be critical for optimal design of the energy storage systems.

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