Date of Award
Campus Access Dissertation
Roger A Dougal
Energy storage is essential to future power systems. Energy storage systems can be used to follow load, stabilize voltage and frequency, manage peak loads, improve power quality, defer infrastructure upgrade investments, and support renewables. Currently, there is no tool that can support the planning, design, and operation of energy storage systems, and maximize their operational benefits. This thesis presents a methodology for the planning and deployment of energy storage systems, an approach to optimizing the operation of energy storage systems across different markets, and the coordination control of various storage technologies with conventional units. This dissertation research is expected to help to expedite the application of energy storage systems in supporting renewable integration in power systems and improve power system operation efficiency. The main content of this thesis is as follows. Chapter 1 provides background on the need for energy storage, the various energy storage technologies, and the functionalities of these technologies in the power system. The first outcome of this dissertation research is the development of a methodology for energy storage planning analysis. The developed resource-planning model is advanced in the following two aspects. First, the methodology considers balancing requirements in resource planning. The wind and solar generation imposes challenges for intra-hour balancing services. The traditional resource-planning model only pays attention to hourly generation schedule, which is not sufficient for power systems with high penetration of wind and solar. Second, energy storage technologies have been considered as options for resource planning, and operational features of energy storage technologies are included in the model. The results of the study shown in Chapter 2 clearly indicate that energy storage, in particular electro-chemical storage including NaS battery and Li-ion battery, is likely to compete with conventional combustion turbine technology whether or not one accounts for the emission externalities for short-cycling (intra-hour) balancing services. Second contribution of this dissertation research is definition of an approach for cross-market optimization of energy storage systems that relies on genetic optimization algorithms. This approach applies a nonlinear penalty curve to permit flexible control of the state of charge of an energy storage system. The optimization approach is shown in Chapter 3, to increase the total revenue of the hybrid energy storage system by 4% compared to an operation approach that optimizes only in the energy market. Third contribution of this dissertation work is the development of a control algorithm for the joint operation of hybrid energy storage systems. The hybrid energy storage systems include a conventional regulating unit or a slow-regulating energy storage unit and a fast-regulating energy storage device (FR-ESD). The control algorithm dispatches highly variable regulation signals to the FR-ESD. The slow varying regulation signal is dispatched to the slower unit to minimize its movements around the preferred most efficient operating point and reduce wear and tear. When the FR-ESD operates outside its desired stored energy limits (state-of-charge [SOC] range), the algorithm dispatches the slower unit to help the FR-ESD return to its desired SOC range. Simulation results using real system regulation signals demonstrate that, by using the proposed dispatch algorithm: (1) the hybrid regulating system provides regulation services with the same fast response rate as that of the faster unit; (2) the slower regulating unit moves significantly less than it would if it were providing the regulation service by itself; and (3) the SOC of the fast unit stays within the desired SOC range most of the time. Therefore, the hybrid energy storage system can be a cost-effective alternative for providing high quality regulation service that helps the grid to integrate more variable renewable energy resources. Chapter 5 concludes the thesis and suggests some future research directions.
Jin, C.(2011). Energy Storage Planning and Operations For Power Systems. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/2186