Design and Operation of Stationary Distributed Battery Micro-storage Systems

Due to some technical and environmental constraints, expanding the current electric power generation and transmission system is being challenged by even increasing the deployment of distributed renewable generation and storage systems. Energy storage can be used to store energy from utility during low-demand (off-peak) hours and deliver this energy back to the utility during high-demand (on-peak) hours. Furthermore, energy storage can be used with renewable sources to overcome some of their limitations such as their strong dependence on the weather conditions, which cannot be perfectly predicted, and their unmatched or out-of-synchronization generation peaks with the demand peaks. Generally, energy storage enhances the performance of distributed renewable sources and increases the efficiency of the entire power system. Moreover, energy storage allows for leveling the load, shaving peak demands, and furthermore, transacting power with the utility grid. This research proposes an energy management system (EMS) to manage the operation of distributed grid-tied battery micro-storage systems for stationary applications when operated with and without renewable sources. The term micro refers to the capacity of the energy storage compared to the grid capacity. The proposed management system employs four dynamic models; economic model, battery model, and load and weather forecasting models. These models, which are the main contribution of this research, are used in order to optimally control the operation of the micro-storage system (MSS) to maximize the economic return for the end-user when operated in an electricity spot market system. Chapter 1 presents an introduction to the drawbacks of the current power system, the role of energy storage in deregulated electricity markets, limitations of renewable sources, ways for participating in spot electricity markets, and an outline of the main contributions in this dissertation. In Chapter 2, some hardware design considerations for distributed micro-storage systems as well as some economic analyses are presented. Chapters 3 and 4 propose a battery management system (BMS) that handles three main functions: battery charging, state-of-charge (SOC) estimation and state-of-health (SOH) estimation. Chapter 5 proposes load and weather forecasting models using artificial neural networks (ANNs) to develop an energy management strategy to control the operation of the MSS in a spot market system when incorporated with other renewable energy sources. Finally, conclusions and future work are presented in Chapter 6

Adaptive Techniques for Estimation and Online Monitoring of Battery Energy Storage Devices

The battery management system (BMS) plays a defining role in the safety and proper operation of any battery energy storage system (BESS). Without significant advances in the state-of-the-art of BMS algorithms, the future uptake of high power/energy density battery chemistries by consumers in safety-critical applications, is not feasible. Therefore, this thesis aims to provide a coherent body of work on the enhancement of the most important tasks performed by a modern BMS, that is, the estimation and monitoring of various battery states, e.g. state-of-charge (SOC), state-of-health (SOH) and state-of-power (SOP). The Kalman Filter is an elegant set of robust equations that is often utilised by designers in modern BMS, to estimate the battery states and parameters in real time. A nonlinear version of the KF technique, namely the Extended Kalman Filter (EKF) is applied throughout this thesis to estimate the battery’s states including SOC, as well as the battery’s impedance parameters. To this end, a suitable model structure for online battery modelling and identification is selected through a comparative study of the most popular electrical equivalent-circuit battery models for real-time applications. Then, a novel improvement to the EKF-based battery parameters identification technique is made through a deterministic initialisation of the battery model parameters through a broadband system identification technique, namely the pseudorandom binary sequences (PRBS). In addition, a novel decentralised framework for the enhancement of the EKF-based SOC estimation for those lithium-ion batteries with an inherently flat open-circuit voltage (OCV) response is formulated. By combining these techniques, it is possible to develop a more reliable battery states monitoring system, which can achieve estimation errors of less than 1%. Finally, the proposed BMS algorithms in this thesis are embedded on a low-cost microprocessor hardware platform to demonstrated the usefulness of the developed EKF-based battery states estimator in a practical setting. This a significant achievement when compared to those costly BMS development platforms, such as those based on FPGAs (field-programmable gate arrays)