(544d) Design and Integration of Thermal Energy Storage Systems for Power Plants | AIChE

(544d) Design and Integration of Thermal Energy Storage Systems for Power Plants


Li, M. - Presenter, Texas A&M University
Zantye, M. S., Texas A&M University
Hasan, F., Texas A&M University
Integration of intermittently available renewable energy with electricity grids leads to increased variability and uncertainty in power generation, thereby pushing many conventional power plants to speedily ramp their loads to maintain grid reliability. Considerable loss of power plant energy is incurred due to rapid cycling and time delay during energy transmission. Frequent cycling can also induce low overall efficiency and high failure rates of high-temperature units in the long run [1]. Energy storage can resolve these challenges. Among the several existing energy storage alternatives, thermal energy storage is particularly promising because of cost-effective design, operational flexibility, high efficiency, stability and long storage lifetime. Thermal storage has been widely employed in the cooling and heating system of buildings, chemical processes as well as solar plants in the past [2-5]. However, the optimal design and integration of thermal energy storage with large-scale fossil-fueled power plants have not been investigated rigorously. There are two types of thermal energy storage, namely high-temperature thermal storage (HTTS) and cryogenic energy storage (CES). HTTS employs molten salt or phase change materials (PCM) as storage medium. The operating temperature of molten salt ranges from 70 to 800 °C. Conversely, the operating temperatures of PCM are between 130-580 °C. Depending on the storage medium, process configuration and operation, the costs range from $0.5–60 per kg of the material used. CES provides a highly efficient mechanism to store excess energy by liquefying air to obtain cryogen [6-8]. The stored cryogen is further used to generate energy during high energy demand scenarios. To increase efficiency of the CES process, cryogenic turbines and internal heat transfer loops with intermediary thermic fluids are used for energy retention. There also exists a large range of options for thermal fluids, storage pressures, trains for compressing and cooling, and expansion cycles for energy generation, which affect the capital and operational cost of a CES system.

We propose a model-based process design and synthesis approach for thermal energy storage systems that store energy from high-/medium-/low-pressure steam extracted from the power plant and discharge energy on demand by regenerating steam from stored energy which is sent back to the power plant. The designs are complicated as the heat transfer involves both sensible and latent heat, different types of storage media, various techno-economic and energetic trade-offs, and numerous possible configurations and operational stages for heat integration. To this end, we have developed a superstructure configuration of the storage systems, thereby allowing the various alternative configurations to adapt different charging, discharging, and integration strategies effectively. The superstructure optimization problem is formulated as a mixed-integer nonlinear program (MINLP), which is then solved to identify the optimal process configurations, number of cycles, charging/discharging strategies, and storage medium. The objective function and the constraints consider various trade-offs between ramp rates, load balances, lifespan, and capital costs, and leading to optimal energy storage operations. Our results indicate that the performance and the cost of energy storage depend strongly on charging steam conditions and the qualities of both charging and discharging steam. Using realistic power plant conditions and charging/discharging demands, we illustrate how the MINLP-based synthesis of storage configurations provides the target process configurations, capacities, and operational decisions with an efficiency of 85%-90%, thereby reducing the overall cost of energy storage for a conventional power plant. The insights obtained from the process synthesis study is further incorporated in developing surrogate models for dynamic integration of storage technologies for distributed energy storage for different scenarios. The development of these technology models will pave way for solving an overall storage downselection problem for reducing the cycling of conventional power plants in face of intermittent renewable energy integration.


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