Part of the book: Bio-Inspired Computational Algorithms and Their Applications
This chapter is concerned with the stability enhancement of a power system using power system stabilizers (PSSs) designed based on four evolutionary algorithms (EAs), namely, genetic algorithms (GAs), breeder genetic algorithm (BGA), population-based incremental learning (PBIL), and differential evolution (DE). GAs have been widely applied in many fields of engineering and science and have shown to be a robust and powerful adaptive search algorithm. However, GAs are known to have several limitations. To deal with these limitations, many variant forms of GAs have been suggested often tailored to specific problems. In this research, we investigated the performances of GA-PSS and three other EAs-based PSSs (i.e., BGA-PSS and PBIL-PSS and DE-PSS) in improving the small-signal stability of a power system. These EAs have been selected on the basis of their simplicity, efficiency, and effectiveness in solving the optimization problem at hand. Frequency domain and time-domain simulation results show that DE-PSS, PBIL-PSS, and BGA-PSS performed better than GA-PSS. Time domain simulations suggest that overall, DE-PSS performs better than PBIL-PSS and BGA-PSS in terms of undershoot and subsequent swings, albeit with a relatively large first swing overshoot. The performances of BGA-PSS and PBIL-PSS are similar. On the other hand, GA-PSS gives a better response than the conventional PSS (CPSS).
Part of the book: Genetic Algorithms
The negative environmental impacts of conventional power generation have resulted in increased interest in the use of renewable energy sources to produce electricity. However, the main problem associated with these non-conventional sources of energy generation (wind and solar photovoltaic) is that they are highly intermittent and thereby result in very high fluctuations in power generated. Hence, mechanical energy storage systems can be deployed as a solution to this problem by ensuring that electrical energy is stored during times of high generation and supplied in time of high demand. This work presents a thorough study of mechanical energy storage systems. It examines the classification, development of output power equations, performance metrics, advantages and drawbacks of each of the mechanical energy storage types and their various applications in the grid networks. The key findings in this work are the strategies for the management of the high costs of these mechanical storage devices. These include deployment of hybrid energy storage technologies, multi-functional applications of mechanical energy storage systems through appropriate control methodologies and proper sizing strategies for cost effectiveness and increased penetrations of renewable energy sources in the power grid.
Part of the book: Energy Storage Applications in Power Systems