Dynamic modeling and validation of a micro-combined heat and power system with integrated thermal energy storage. (1st August 2020)

• Record Type: Journal Article
• Authors: Bird, Trevor J.; Jain, Neera
• Is Part Of: Applied energy. Volume 271(2020)
• Publication Date: 2020-08-01
• Subjects: Dynamic modeling -- Reduced order modeling -- Combined heat and power -- Sensible thermal energy storage -- Thermal stratification -- Proton exchange membrane fuel cell; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2020.114955
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Dynamic system model predicts electrical power and waste heat outputs. Model of integrated hot water tank predicts thermal stratification dynamics. Algebraic coupling between fuel cell and thermal storage is proposed and analyzed. Model is experimentally validated across its entire operating range (30–60°C). Model serves as a faster than real time simulation testbed for advanced research. Abstract: The operation of combined heat and power systems is inherently challenging due to the fact that they synchronously generate power and heat while being tasked to meet asynchronous loads. By integrating thermal storage into the system, these loads can be temporally decoupled, albeit at the cost of a more complex dynamical system. In this paper we present a nonlinear, reduced order dynamic model of a small-scale, proton exchange membrane fuel cell combined heat and power system with sensible thermal energy storage in the form of a stratified hot water tank. By modeling the thermal stratification dynamics within the thermal storage, its storage capacity, and the temperature of water that will be discharged from the tank, can be accurately quantified. A quasi-static approach is used to couple the dynamic components without increasing the model order, thereby minimizing computational burden. The resulting system model is fully parameterized by the system temperatures and model inputs, making it well suited for use as a faster than real-time, simulated testbed. The model's empirical parameters are identified from experimental data, and the resulting system model is validated against an experimental testbed over a wide operating range. The results of the experimental validation show that the model is able to predict the electrical power and temperature of hot water provided to the end user within a normalized root mean square error of 1.58 % and 8.34 %, respectively. The presented model is available for download from a public repository through the URL provided at the end of the manuscript.