Energy Storage Design Using Physics-Based Battery Models in Hybrid Power Systems with Uncertainties

Abstract

Renewable energy resources (RERs) have the potential to satisfy the continuously growing energy demand and provide power to un-electrified remote locations. The power generated by RERs is not always reliable due to its dependence on atmospheric conditions like solar radiation, wind speed, and temperature. To provide uninterrupted power throughout day and night, and to ensure the power stability in the system, a battery energy storage (BES) system may be coupled with a standalone RER system [1]. Proper sizing of the BES system is crucial to make such hybrid power systems technically and economically feasible. Generally, empirical or equivalent circuit models (ECM) are used to calculate different battery parameters which are easy to solve, but not accurate enough to accommodate rapid changes in operating conditions and their effects on the battery’s transport and kinetic parameters which might lead to under-utilization and over-stacking of batteries, increasing the cost of the BES systems. Physics-based battery models can offer better prediction of battery parameters like the state of charge, cell degradation, and amount of energy remaining in the battery thereby enabling us to get the best energy storage design results. This presentation will illustrate the use of physics-based battery models to design the BES system for a standalone PV-BES hybrid power system. The graphical and numerical Pinch Analysis techniques help in finding the minimum resource targets and the detailed design of the standalone RER system. A Power Pinch Analysis (PoPA) framework [2] is used for determining the minimum PV area, and the battery capacity is calculated using physics-based battery models. A single particle model (SPM) [3] and a pseudo-two-dimensional (P2D) model [4] are used for simulating the dynamic behavior of the lithium-ion battery. A framework is developed to evaluate the performance of a PV-BES hybrid power system using SPM [5], and LIONSIMBA [6] is used for the simulation of a P2D model. The chance constrained programming [7] is incorporated to consider renewable resource uncertainty. The minimum PV area and its extreme limits are calculated with a specified system reliability level using the concept of PoPA. An illustrative example of PV-BES hybrid power system is provided to demonstrate the effectiveness of the proposed strategy using real-world data. The results of the proposed approach are compared with the literature [2], and improvements in predicting battery storage capacity are shown in Figure 1. Also, load demand uncertainty is an essential issue in the design of RER based hybrid power system to satisfy the specified reliability [8]. One of the objectives is to incorporate uncertainty associated with the load demand to estimate BES system capacity. Available PoPA tools in the literature have been mainly focused on calculating minimum RER area, however incorporating changes in battery chemistry is also equally crucial to predict battery life. The developed PoPA framework would be extended further by considering the capacity fade that occurs during battery operation under varying climatic conditions and ambient temperature to accurately design the BES system. The number of battery modules and their performance can also be optimized using these models. This work combines the simplicity of PoPA to calculate the RER area and physics-based battery models to represent complex battery chemistry. However, this framework is generic in nature and can be easily extended further to include a combination of several RERs and different storage technologies for hybrid power systems.