Optimization of BESS placement and control parameters for frequency stability in renewable-intensive power systems

Abstract

The increasing integration of renewable energy resources into power systems has heightened the need for advanced optimization methods to maintain the grid's stability and efficiency. This thesis presents a framework that integrates Electromagnetic Transient (EMT) simulations in PSCAD/ EMTDC with a genetic algorithm (GA) optimizer, implemented through Python’s PyGAD open-source library, to optimize the placement and sizing of battery energy storage systems (BESS). The primary objective is to enhance frequency regulation in response to disturbances caused by the variability of solar power generation. A 12-bus power system, incorporating both synchronous machines and a solar power plant, is modeled and simulated. Candidate BESS locations are selected with the goal of optimization being to determine the most suited location as well as the size of the required BESS. The objective function considers both the frequency nadir and a measure of the cost associated with the number of BESS units deployed. Simulation results show that the GA successfully identifies optimal BESS configurations, demonstrating that without a cost constraint, the optimizer favors the deployment of a large number of units with low droop settings to minimize frequency deviation. Including the cost terms results in a more balanced trade-off between performance and asset cost. The study further highlights the significant impact of BESS placement on system frequency response, underscoring the importance of strategic siting in future grid planning. The proposed EMT-based optimization framework provides a robust and accurate approach for addressing complex, non-linear optimization problems in modern, renewable-rich power systems.