Dynamic Modelling and Control of Grid-Level Energy Storage Systems

The focus of this work is on two energy storage technologies, namely pumped storage hydroelectricity (PHS) and secondary batteries. Under secondary battery technologies, two potential technologies for grid-scale storage, namely high-temperature sodium-sulfur (NaS) battery and vanadium redox flow battery (VRFB), are investigated. PHS is a largescale (\u3e100 MW) technology that stores and generates energy by transporting water between two reservoirs at different elevations. The goal is to develop a detailed dynamic model of PHS and then design the controllers to follow the desired load trajectory accurately with high efficiency. The NaS battery and VRFB are advanced secondary batteries which can be charged and discharged rapidly. Since temperature excursion of high temperature NaS batteries especially under fast cycling conditions is a safety hazard and the temperature excursion can take place at some location within the cell where measurement is not feasible, the focus is on a model-based approach for transient analysis and development of novel thermal management techniques. A detailed thermo-electrochemical dynamic model of a single NaS has been developed. As a detailed cell model is computationally intractable for simulating large number of cells in the battery, various strategies such as coordinate transformation, orthogonal collocation, and model reformulation have been developed to obtain a reduced order model that solves significantly faster than the full, high-dimensional model but provides an accurate estimate of the key variables such as transient voltage/current/temperature profile in the cell. Sodium sulfur batteries need to be maintained within a temperature range of 300-4000C. Therefore, the focus was on developing thermal management strategies that can not only maintain the cell temperature near the optimum, but can effectively utilize the heat, improving the overall efficiency of the battery system. VRFBs can provide large amount of storage as the electrolytes are stored in separate tanks. However, the self-discharge reactions (due to crossover) along with the undesired side reactions and the dissolved water in the membrane, can significantly reduce the capacity. A dynamic model-based approach is developed for detection, identification, and estimation of capacity fade and SOC as a function of time. A model-based prognostic capability has been developed for estimating the remaining useful cell life

Transient Modeling of a Vanadium Redox Flow Battery and Real-Time Monitoring of Its Capacity and State of Charge

The vanadium redox flow battery (VRFB) is a rechargeable flow battery that is one of the most promising large-scale energy storage systems making it suitable for grid-level energy storage. However, the self-discharge reactions along with the undesired side reactions and water transfer through the membrane cause imbalance in the electrolyte and state of charge, which can significantly reduce the capacity of these batteries. A first-principles, 2D electrochemical dynamic model of all-VRFB is developed. The electrochemical model includes self-discharge reactions caused by diffusion, convection, and migration of the vanadium ions from one half-cell to the other. In addition, side reactions leading to evolution of hydrogen and oxygen are modeled. Water transport through the membrane is modeled by considering transport of water along with vanadium ions due to electro-osmotic drag and diffusion between two electrolyte half-cells. The model is validated with the experimental data. A filtering approach is developed for co-estimation of state of charge and capacity. Using a state-space model identified using the data generated by the pseudo-random binary sequence in the current and electrolyte flowrates, the filtering approach is found to yield satisfactory estimation of the state of charge and capacity by using voltage as the only measured output variable

Dynamic Optimal Dispatch of Energy Systems with Intermittent Renewables and Damage Model

With the increasing penetration of intermittent renewable energy sources into the grid, there is a growing need for process systems-based strategies that integrate dispatchable and variable energy systems for supplying the demand while maintaining grid reliability. The proposed framework corresponds to a dynamic mixed-integer linear programming optimization approach that integrates coal-fired and natural gas-fired power plants, NaS batteries for energy storage, and solar/wind energy to supply the demand. This optimization approach considers an economic goal and constraints to provide power balance while maintaining the overall damage of the natural gas combined cycle (NGCC) power plant drum under a maximum stress as well as avoiding the overheating of the NGCC superheater and reheater. Renewable curtailment levels are also retained at minimum levels. Case studies are analyzed considering different loads and renewable penetration levels. The results show that the demand was met for all cases. Grid flexibility was mostly provided by the NGCC, while the batteries were used sparingly. In addition, considering a CO2 equivalent analysis, the environmental performance was intrinsically connected to grid flexibility and the level of renewable penetration. Stress analysis results reinforced the necessity for an equipment health-related constraint