Multi-purpose applications of energy storage systems in a power system network - Okafor, Chukwuemeka Emmanuel (Department of Electrical Engineering, 2025)
Battery energy storage systems (BESSs) are acknowledged by many researchers as the principal technology required to achieve a high penetration of renewable energy into the electricity network. However, the utilization of a BESS for a single application can lead to under-utilization and may not be economical. This thesis focuses on the multi-purpose applications of a BESS in an electricity grid. A novel methodology for the optimal sizing of a BESS for the multiple functions of providing support for the frequency nadir (during contingency events), reduction of real power losses, and mitigation of voltage deviations using the deep sleep optimizer (DSO) algorithm was developed. The proposed method establishes a relationship between frequency nadir, voltage variations, power losses, and the BESS multiplier by utilizing regression analysis techniques in multi-objective mathematical formulations. This approach simplifies and enhances the optimization of the multi-objective functions without relying on weighting factors, which are commonly used in many multi-objective optimizations. Additionally, to ensure that BESS capacity meets the acceptable frequency limits during a contingency event in a grid with a high renewable energy penetration, the suggested approach incorporates the rate of change of frequency (RoCoF) for varying degrees of renewable energy penetration. This is extremely important because as the use of renewable energy resources grows, RoCoF values will increase, causing the frequency to fluctuate rapidly during significant grid emergencies. Consequently, in a grid network with a high renewable energy penetration, a suitable RoCoF selection is necessary for sizing the BESS to satisfy the frequency regulation requirements. When comparing the proposed optimal sizing approach with existing methods, the suggested methodology performed better due to its ability to eliminate the weighting factors, thereby ensuring that none of the objective functions dominate the other. This resulted in a fair optimization process with enhanced results. Additionally, by deploying the new metaheuristic algorithm, the DSO, the computational time was reduced compared with other metaheuristic algorithms such as the particle swarm optimization (PSO) and genetic algorithm (GA). The optimal placement of a BESS in a power grid network was achieved using a novel placement algorithm that leveraged active power injections from mixed sources of power generation during a power system contingency, along with their corresponding inertia contributions. To control a BESS for the desired multiple functions, four sub-units of the control system were designed through the use of suitable controllers. These controllers ensured adequate frequency support during contingency, regulated the charging and discharging actions of the battery, and guaranteed the supply of active and reactive power. Simulation results showed that through the proposed methodologies, the BESS was able to: (a) sustain the frequency nadir within acceptable limits during an outage of the single largest generating unit; (b) reduce real power losses by about 50%; and (c) improve the voltage profile for all buses in an electricity grid network with a high renewable energy penetration.