Abstract
To mitigate the effects of the intermittent generation of renewable energy sources, reliable and efficient energy storage is critical. Since nearly 80% of households energy consumption is destined to water and space heating, thermal energy storage is particularly important. In this context, we propose and validate a new model for one of the most efficient heat storage systems: stratified thermal storage tanks. The novelty of the model is twofold: first, unlike the non-smooth models from the literature, it identifies the mixing and buoyancy dynamics using a smooth and continuous function. This smoothness property is critical to efficiently integrate thermal storage vessels in optimization and control problems. Second, unlike models from literature, it considers two types of buoyancy: slow, linked to naturally occurring buoyancy, and fast, associated with charging/discharging effects. As we show, this distinction is paramount to identify accurate models. To show the relevance of the model, we consider a real tank that can satisfy heat demands up to 100 kW. Using real data from this vessel, we validate the proposed model and show that the estimated parameters correctly identify the physical properties of the vessel. Then, we employ the model in a control problem where the vessel is operated to minimize the cost of providing a given heat demand and we compare the model performance against that of a non-smooth model from literature. We show that: (1) the smooth model obtains the best optimal solutions; (2) its computation costs are 100 times cheaper; (3) it is the best alternative for use in real-time model- based control strategies, e.g. model predictive control.
Highlights
In the last decade, as the integration of renewable energy sources into the electrical grid has steadily increased, energy storage has emerged as one of the key components in this change
We show that: (1) the smooth model obtains the best optimal solutions; (2) its computation costs are 100 times cheaper; (3) it is the best alternative for use in real-time model- based control strategies, e.g. model predictive control
As motivated in the introduction, the goal of the proposed model is to provide a smooth representation of the dynamics of heat storage vessels so that the model can be employed in derivative-based optimization problems
Summary
As the integration of renewable energy sources into the electrical grid has steadily increased, energy storage has emerged as one of the key components in this change. As the amount of variable renewable electricity is expected to increase in future electrical systems [1], these problems will become worse In this context, energy storage is paramount to tackle these imbalances as it shifts consumption and generation and keeps the grid stable. The new class of systems that exploit the interaction between different energy carriers are usually called multi-energy systems, and as they try integrate diverse energy systems to achieve a higher energy utilization efficiency [6], they have become in a central point of research [7,8] Another application where TES systems are a key component is concentrated solar power plants; there, they help to smooth the production, to maximize earnings from the electricity market fluctuation, and to increase the lifespan of the power block [9]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call