(171a) Optimization-Based Process Synthesis of Processes with Seasonal and Daily Variability: Application on Concentrating Solar Power Plants with Thermochemical Energy Storage
The system we use as a testbed is concentrating solar power plants with thermochemical energy storage (TCES). Energy storage is crucial for solar power to avert the grid instability caused by the periodic nature and uncertainty of the solar insolation. Concentrating solar power (CSP) is a technology that converts concentrated sunlight to heat, which then drives a turbine to generate electricity. Using heat as an intermediate provides CSP the opportunity to incorporate efficient and low cost thermal energy storage (TES) . Among all TES technologies, thermochemical energy storage (TCES), that is, the reversible conversion of solar-thermal energy to chemical energy, has high energy density and low heat loss over long periods. Although many reaction types have been studied as TCES candidates , past research has mostly focused on experimental studies and reactor design. The limited system-level studies are for specific reactions  and consider TCES in isolation, neglecting its interactions with the solar field and power generation system . In addition, a common assumption for previous design model is that the process operates at a single steady state. However, CSP plants have daily and seasonal variations in operation due to the periodic nature of solar irradiation.
In this work, we develop a general optimization process model for CSP plants integrated with TCES systems  which allows us to synthesize a process under daily and seasonal solar variations, while considering the interactions between TCES and the power generation system. The model is built upon unit mass and energy balances in TCES process and performance models of the solar field, receiver and turbine. It includes a reactor and heat exchanger network which enables heat integration (the ability to design the coupling between exothermic reactor and steam generation is crucial to target high reaction conversion and turbine efficiency). In addition, we impose constraints tailored to prevent violation of the second law of thermodynamics during steam generation.
To model the âdayâ and ânighâ modes, a physical unit is represented as two units in the model to refer to its day and night operational modes. Special considerations are taken for unit sizing and cost calculations. To address the daily variation in storage compression, we derive the analytic relation between compression work and pressure variables by integrating the daily storage tank pressure profile. Furthermore, to account for the seasonal variation in solar resource, we calculate annual capacity factors for plant subsystems from the annual probability distribution of daily irradiation. These factors then serve as correction factors for the actual plant power output.
The objective function is to minimize the plant levelized cost of electricity (LCOE) by manipulating stream and unit operational variables, as well as plant sizing variables. The model can be applied to different TCES systems given their corresponding reaction properties (e.g., reaction enthalpy and entropy) and unit technologies.
To enhance the solution of the resulting model, we (1) develop algorithms for variable bound generation; (2) we propose case specific model simplifications based on physical insights; and (3) we employ scaling methods for modeling reaction equilibrium.
We apply the process model to a 100 MW CSP plant with methane TCES system, which shows an LCOE reduction potential over current CSP plants using two-tank molten salt storage.
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